Exploring the primary magnetic parameters affecting chemical fractions of heavy metal(loid)s in lake sediment through an interpretable workflow.

The magnetic properties of lake sediments account for close relationships with heavy metal(loid)s (HMs), but little is known about their relationships with chemical fractions (CFs) of HMs. Establishing an effective workflow to predict HMs risk among various machine learning (ML) methods in conjunction with magnetic measurement remains challenging. This study evaluated the simulation efficiency of nine ML methods in predicting the risk assessment code (RAC) and ratio of the secondary and primary phases (RSP) of HMs with magnetic parameters in sediment cores of a shallow lake. The sediment cores were collected and sliced, and the total amount and CFs of HMs, as well as magnetic parameters, were determined. Support vector machine (SVM) outperformed other models, as evidenced by coefficient of determination (R2) > 0.8. Interpretable machine learning (IML) methods were employed to identify key indicators of RAC and RSP among the magnetic parameters. Values of χARM, HIRM, χARM/χ, and χARM/SIRM of sediments ranging in 220-500 × 10-8 m3/kg, 30-40 × 10-5Am2/kg, 15-25, and 0.5-1, respectively, indicated the potential ecological risks of Cd, Hg, and Sb. This study offers new perspectives on the risk assessment of HMs in lake sediments by combining magnetic measurement with IML workflow.

Revisiting the Ultraviolet Absorption Cross Section of Gaseous Nitrous Acid (HONO): New Insights for Atmospheric HONO Budget.

Nitrous acid (HONO) is an important source of hydroxyl radicals (OH) in the atmosphere. Precise determination of the absolute ultraviolet (UV) absorption cross section of gaseous HONO lays the basis for the accurate measurement of its concentration by optical methods and the estimation of HONO loss rate through photolysis. In this study, we performed a series of laboratory and field intercomparison experiments for HONO measurement between striping coil-liquid waveguide capillary cell (SC-LWCC) photometry and incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS). Specified HONO concentrations prepared by an ultrapure standard HONO source were utilized for laboratory intercomparisons. Results show a consistent ∼22% negative bias in measurements of the IBBCEAS compared with a SC-LWCC photometer. It is confirmed that the discrepancies occurring between these techniques are associated with the overestimation of the absolute UV absorption cross sections through careful analysis of possible uncertainties. We quantified the absorption cross section of gaseous HONO (360-390 nm) utilizing a custom-built IBBCEAS instrument, and the results were found to be 22-34% lower than the previously published absorption cross sections widely used in HONO concentration retrieval and atmospheric chemical transport models (CTMs). This suggests that the HONO concentrations retrieved by optical methods based on absolute absorption cross sections may have been underestimated by over 20%. Plus, the daytime loss rate and unidentified sources of HONO may also have evidently been overestimated in pre-existing studies. In summary, our findings underscore the significance of revisiting the absolute absorption cross section of HONO and the re-evaluation of the previously reported HONO budgets.

Reactive aldehyde chemistry explains the missing source of hydroxyl radicals.

Hydroxyl radicals (OH) determine the tropospheric self-cleansing capacity, thus regulating air quality and climate. However, the state-of-the-art mechanisms still underestimate OH at low nitrogen oxide and high volatile organic compound regimes even considering the latest isoprene chemistry. Here we propose that the reactive aldehyde chemistry, especially the autoxidation of carbonyl organic peroxy radicals (R(CO)O2) derived from higher aldehydes, is a noteworthy OH regeneration mechanism that overwhelms the contribution of the isoprene autoxidation, the latter has been proved to largely contribute to the missing OH source under high isoprene condition. As diagnosed by the quantum chemical calculations, the R(CO)O2 radicals undergo fast H-migration to produce unsaturated hydroperoxyl-carbonyls that generate OH through rapid photolysis. This chemistry could explain almost all unknown OH sources in areas rich in both natural and anthropogenic emissions in the warm seasons, and may increasingly impact the global self-cleansing capacity in a future low nitrogen oxide society under carbon neutrality scenarios.

Innovative explorations: unveiling the potential of organoids for investigating environmental pollutant exposure.

As the economy rapidly develops, chemicals are widely produced and used. This has exacerbated the problems associated with environmental pollution, raising the need for efficient toxicological evaluation techniques to investigate the toxic effects and mechanisms of toxicity of environmental pollutants. The progress in the techniques of cell culture in three dimensions has resulted in the creation of models that are more relevant in terms of biology and physiology. This enables researchers to study organ development, toxicology, and drug screening. Adult stem cells (ASCs) and induced pluripotent stem cells (iPSCs) can be obtained from various mammalian tissues, including cancerous and healthy tissues. Such stem cells exhibit a significant level of tissue memory and ability to self-assemble. When cultivated in 3D in vitro environments, the resulting organoids demonstrate a remarkable capacity to recapitulate the cellular composition and function of organs in vivo. Recently, many tumors' tissue-derived organoids have been widely used in research on tumor pathogenesis, drug development, precision medicine, and other fields, including those derived from colon cancer, cholangiocarcinoma, liver cancer, and gastric cancer. However, the application of organoid models for evaluating the toxicity of environmental pollutants is still in its infancy. This review introduces the characteristics of the toxicity responses of organoid models upon exposure to pollutants from the perspectives of organoid characteristics, tissue types, and their applications in toxicology; discusses the feasibility of using organoid models in evaluating the toxicity of pollutants; and provides a reference for future toxicological studies on environmental pollutants based on organoid models.

Pan-cancer scRNA-seq analysis reveals immunological and diagnostic significance of the peripheral blood mononuclear cells.

Peripheral blood mononuclear cells (PBMCs) reflect systemic immune response during cancer progression. However, a comprehensive understanding of the composition and function of PBMCs in cancer patients is lacking, and the potential of these features to assist cancer diagnosis is also unclear. Here, the compositional and status differences between cancer patients and healthy donors in PBMCs were investigated by single-cell RNA sequencing (scRNA-seq), involving 262,025 PBMCs from 68 cancer samples and 14 healthy samples. We observed an enhanced activation and differentiation of most immune subsets in cancer patients, along with reduction of naïve T cells, expansion of macrophages, impairment of NK cells and myeloid cells, as well as tumor promotion and immunosuppression. Based on characteristics including differential cell type abundances and/or hub genes identified from weight gene co-expression network analysis (WGCNA) modules of each major cell type, we applied logistic regression to construct cancer diagnosis models. Furthermore, we found that the above models can distinguish cancer patients and healthy donors with high sensitivity. Our study provided new insights into using the features of PBMCs in non-invasive cancer diagnosis.

Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing.

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called "thermal neuristors", which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. This study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

Analysis of China's PM2.5 and ozone coordinated control strategy based on the observation data from 2015 to 2020.

The coordinated control of PM2.5 and ozone has become the strategic goal of national air pollution control. Considering the gradual decline in PM2.5 concentration and the aggravation of ozone pollution, a better understanding of the coordinated control of PM2.5 and ozone is urgently needed. Here, we collected and sorted air pollutant data for 337 cities from 2015 to 2020 to explore the characteristics of PM2.5 and ozone pollution based on China's five major air pollution regions. The results show that it is necessary to continue to strengthen the emission reduction in PM2.5 and ozone precursors, and control NOx and VOCs while promoting a dramatic emission reduction in PM2.5. The primary method of curbing ozone pollution is to strengthen the emission control of VOCs, with a long-term strategy of achieving substantial emission reductions in NOx, because VOCs and NOx are also precursors to PM2.5; hence, their reductions also contribute to the reduction in PM2.5. Therefore, the implementation of a multipollutant emission reduction control strategy aimed at the prevention and control of PM2.5 and ozone pollution is the only means to realize the coordinated control of PM2.5 and ozone.

Inflammatory response in mouse lungs to haze episodes under different backgrounds of particulate matter exposure.

Particulate matter (PM) toxicity has mostly been investigated through in vitro exposure or tracheal infusion in animal models. However, given the complexity of ambient conditions, most animal studies do not mimic real-life PM exposure. In this work, we established a novel integrated exposure model to study the dynamic inflammatory response and defense strategies in ambient PM-exposed mice. Three groups of male C57BL/6 mice were kept in three chambers with pre-exposure to filtered air (FA), unfiltered air (UFA), or the air with a low PM concentration (PM2.5 ≤ 75 µg/m3) (LPM), respectively, for 37 days. Then all three groups of mice were exposed to haze challenge for 3 days, followed by exposure in filtered air for 7 days to allow recovery. Our results suggest that following a haze challenge, the defense strategies of mice of filtered air (FA) and low PM (LPM) groups comprised a form of "counterattack", whereas the response of the unfiltered air (UFA) group could be viewed as a "silence". While the latter strategy protected the lung tissues of mice from acute inflammatory damage, it also foreshadowed the development of chronic inflammatory diseases. These findings contribute to explaining previously documented PM-associated pathogenic mechanisms.

Nanoscale Insights into the Mechanical Behavior of Interfacial Composite Structures between Calcium Silicate Hydrate/Calcium Hydroxide and Silica.

The failure of the interfacial transition zone has been identified as the primary cause of damage and deterioration in cement-based materials. To further understand the interfacial failure mechanism, interfacial composite structures between the main hydration products of ordinary Portland cement (OPC), calcium silicate hydrate (CSH) and calcium hydroxide (Ca(OH)2), and silica (SiO2) were constructed while considering their anisotropy. Afterwards, uniaxial tensile tests were conducted using molecular dynamics (MD) simulations. Our results showed that the interfacial zones (IZs) of interfacial composite structures tended to have relatively lower densities than those of the bulk, and the anisotropy of the hydration products had almost no effect on the IZ being a low-density zone. Interfacial composite structures with different configurations exhibited diverse nanomechanical behaviors in terms of their ultimate strength, stress-strain relationship and fracture evaluation. A higher strain rate contributed to a higher ultimate strength and a more prolonged decline in the residual strength. In the interfacial composite structures, both CSH and Ca(OH)2 exhibited ruptures of the Ca-O bond as the primary atomic pair during the tensile process. The plastic damage characteristics of the interfacial composite structures during the tensile process were assessed by analyzing the normalized number of broken Ca-O bonds, which also aligned with the atomic chain break characteristics evident in the per-atom stress map.

Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines.

Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.

Insights into the Peroxide-Bicyclic Intermediate Pathway of Aromatic Photooxidation: Experimental Yields and NOx-Dependency of Ring-Opening and Ring-Retaining Products.

Aromatic hydrocarbons are important contributors to the formation of ozone and secondary organic aerosols in urban environments. The different parallel pathways in aromatic oxidation, however, remain inadequately understood. Here, we investigated the production yields and chemical distributions of gas-phase tracer products during the photooxidation of alkylbenzenes at atmospheric OH levels with NOx present using high-resolution mass spectrometers. The peroxide-bicyclic intermediate pathway emerged as the major pathway in aromatic oxidation, accounting for 52.1 ± 12.6%, 66.1 ± 16.6%, and 81.4 ± 24.3% of the total OH oxidation of toluene, m-xylene, and 1,3,5-trimethylbenzene, respectively. Notably, the yields of bicyclic nitrates produced from the reactions of bicyclic peroxy radicals (BPRs) with NO were considerably lower (3-5 times) than what the current mechanism predicted. Alongside traditional ring-opening products formed through the bicyclic pathway (dicarbonyls and furanones), we identified a significant proportion of carbonyl olefinic acids generated via the 1,5-aldehydic H-shift occurring in subsequent reactions of BPRs + NO, contributing 4-7% of the carbon flow in aromatic oxidation. Moreover, the observed NOx-dependencies of ring-opening and ring-retaining product yields provide insights into the competitive nature of reactions involving BPRs with NO, HO2, and RO2, which determine the refined product distributions and offer an explanation for the discrepancies between the experimental and model-based results.

Self-averaging of digital memcomputing machines.

Digital memcomputing machines (DMMs) are a new class of computing machines that employ nonquantum dynamical systems with memory to solve combinatorial optimization problems. Here, we show that the time to solution (TTS) of DMMs follows an inverse Gaussian distribution, with the TTS self-averaging with increasing problem size, irrespective of the problem they solve. We provide both an analytical understanding of this phenomenon and numerical evidence by solving instances of the 3-SAT (satisfiability) problem. The self-averaging property of DMMs with problem size implies that they are increasingly insensitive to the detailed features of the instances they solve. This is in sharp contrast to traditional algorithms applied to the same problems, illustrating another advantage of this physics-based approach to computation.

High-Power Femtosecond Laser Processing of SiC Ceramics with Optimized Material Removal Rate.

Silicon carbide (SiC) ceramics are widely used as structural materials for various applications. However, the extraordinarily high hardness, brittleness, low material removal rate, and severe tool wear of these materials significantly impact the performance of conventional mechanical processing techniques. In this study, we investigated the influence of different parameters on the material removal rate, surface quality, and surface oxidation during the laser processing of SiC ceramic samples using a high-repetition-frequency femtosecond laser at a wavelength of 1030 nm. Additionally, an experimental investigation was conducted to analyze the effects of a burst mode on the material removal rate. Our results demonstrate that the surface oxidation, which significantly affects the material removal rate, can be effectively reduced by increasing the laser scanning speed and decreasing the laser scanning pitch. The material removal rate and surface quality are mainly affected by laser fluence. The optimal material removal rate is obtained with a laser fluence of 0.4 J/cm2 at a pulse width of 470 fs.

Gas-particle partitioning of organophosphate esters in indoor and outdoor air and its implications for individual exposure.

The extensive utilization of organophosphate esters (OPEs) has resulted in their widespread presence in the environment, raising concerns about potential human health risks. In this study, 13 OPEs were analyzed in both gas and particle phases as well as in indoor and outdoor atmospheric environments. Moreover, human exposure to OPEs were investigated within a university environment, focusing on forehead contact and individual PM2.5 inhalation. The results showed similar distribution patterns of OPEs indoors and outdoors, although higher concentrations were found indoors. The average atmospheric concentration of ∑OPEs (combining particle and gaseous OPEs) was 1575 pg/m3 in the outdoor environment and 6574 pg/m3 ∑OPEs in the indoor microenvironments. The overwhelming majority of OPEs exhibit a pronounced propensity to adsorb onto PM2.5 particles. Notably, the concentration of OPEs on the forehead differed significantly from that in the atmospheric environment, whereas individual PM2.5 exposure was consistent with the concentration of indoor PM2.5. Intriguingly, some OPEs with high octanol-water partition coefficient (log Kow) were not detected in the environment but found on human foreheads. Gas-particle partitioning was predicted using the Harner-Bidleman and Li-Ma-Yang models and the results were in agreement with the monitoring data for approximately half of the OPE monomers. Correlations between OPEs exposure and gas-particle partitioning were found to be more significant for novel OPEs. No non-cancer risk to humans through individual exposure to OPEs was identified via forehead exposure or inhalation. The previously unreported relationship between individual exposure and the environmental occurrence of traditional and novel OPEs demonstrated in this study highlights the importance of evaluating the potential health risks associated with actual OPE exposure.

Time series prediction of the chemical components of PM2.5 based on a deep learning model.

Modeling-based prediction methods enable rapid, reagent-free air pollution detection based on inexpensive multi-source data than traditional chemical reaction-based detection methods in order to quickly understand the air pollution situation. In this study, a convolutional neural network (CNN) and long and short-term memory (LSTM) neural networks are integrated to create a CNN-LSTM time series prediction model to predict the concentration of PM2.5 and its chemical components (i.e., heavy metals, carbon component, and water-soluble ions) using meteorological data and air pollutants (PM2.5, SO2, NO2, CO, and O3). In the integrated CNN-LSTM model, the CNN uses convolutional and pooling layers to extract features from the data, whereas the powerful nonlinear mapping and learning capabilities of LSTM enable the time series prediction of air pollution. The experimental results showed that the CNN-LSTM exhibited good generalization ability in the prediction of As, Cd, Cr, Cu, Ni, and Zn, with a mean R2 above 0.9. Mean R2 predicted for PM2.5, Pb, Ti, EC, OC, SO42-, and NO3- ranged from 0.85 to 0.9. Shapley value showed that PM2.5, NO2, SO2, and CO had a greater influence on the predicted heavy metal results of the model. Regarding water-soluble ions, the predicted results were dominantly influenced by PM2.5, CO, and humidity. The prediction of the carbon fraction was affected mainly by the PM2.5 concentration. Additionally, several input variables for various components were eliminated without affecting the prediction accuracy of the model, with R2 between 0.70 and 0.84, thereby maximizing modeling efficiency and lowering operational costs. The fully trained model prediction results showed that most predicted components of PM2.5 were lower during January to March 2020 than those in 2018 and 2019. This study provides insight into improving the accuracy of modeling-based detection methods and promotes the development of integrated air pollution monitoring toward a more sustainable direction.

Base-Promoted Tandem Synthesis of 2-Substituted Indoles and N-Fused Polycyclic Indoles.

Herein is developed a base-promoted approach for the synthesis of C2-substituted indoles and N-fused polycyclic indoles via 5-endo-dig cyclization of 2-alkynyl anilines, followed by a 1,3'-acyl migration or a dearomatizing Michael addition process. A range of N-H free indoles and 8,9-dihydropyrido[1,2-a]indol-6(7H)-one scaffolds were synthesized in good to excellent yields with broad scope.

Laser2: A two-domain photon-phonon laser.

The laser is one of the greatest inventions in history. Because of its ubiquitous applications and profound societal impact, the concept of the laser has been extended to other physical domains including phonon lasers and atom lasers. Quite often, a laser in one physical domain is pumped by energy in another. However, all lasers demonstrated so far have only lased in one physical domain. We have experimentally demonstrated simultaneous photon and phonon lasing in a two-mode silica fiber ring cavity via forward intermodal stimulated Brillouin scattering (SBS) mediated by long-lived flexural acoustic waves. This two-domain laser may find potential applications in optical/acoustic tweezers, optomechanical sensing, microwave generation, and quantum information processing. Furthermore, we believe that this demonstration will usher in other multidomain lasers and related applications.

Molecular detection and genetic characteristics of a novel porcine circovirus (porcine circovirus 4) and porcine reproductive and respiratory syndrome virus in Shaanxi and Henan Provinces of China.

Porcine circovirus 4 (PCV4) is a recently discovered circovirus that was first reported in 2019 in several pigs with severe clinical disease in Hunan province of China, and also identified in pigs infected with porcine reproductive and respiratory syndrome virus (PRRSV). To further investigate the epidemic profile and genetic characteristics of the two viruses, 150 clinical samples were collected from 9 swine farms in Shaanxi and Henan provinces of China, and a SYBR Green I-based duplex quantitative real-time polymerase chain reaction (qPCR) was developed for detecting PCV4 and PRRSV simultaneously. The results showed the limits of detection were 41.1 copies/µL and 81.5 copies/µL for PCV4 and PRRSV, respectively. The detection rates of PCV4 and PRRSV were 8.00% (12/150) and 12.00% (18/150) respectively, and a case of co-infection with PCV4 and PRRSV was found in the lung tissue of a suckling pig with respiratory symptom. Subsequently, the complete genomic sequences of five PCV4 strains were obtained, of which one PCV4 strain (SX-ZX) was from Shaanxi province, and these strains were 1770 nucleotides in length and had 97.7%-99.4% genomic identity with 59 PCV4 reference strains. The genome characteristic of the SX-ZX strain was evaluated from three aspects, a "stem-loop" structure, ORF1 and ORF2. As essential elements for the replication, the 17-bp iterative sequence was predicted as the stem structure, in which three non-tandem hexamers were found at downstream with H1/H2 (12-CGGCACACTTCGGCAC-27) as the minimal binding site. Three of the five PCV4 strains were clustered into PCV4b, which was composed of Suidae, fox, dairy cow, dog and raccoon dog. Phylogenetic analysis revealed that seven PRRSV strains from the present study were clustered into the PRRSV-2 genotype. Collectively, these data extend our understanding of the genome characteristic of PCV4 as well as the molecular epidemiology and the genetic profile of PCV4 and PRRSV.