Detecting thyroid nodules along with surrounding tissues and tracking nodules using motion prior in ultrasound videos.

Ultrasound examination plays a crucial role in the clinical diagnosis of thyroid nodules. Although deep learning technology has been applied to thyroid nodule examinations, the existing methods all overlook the prior knowledge of nodules moving along a straight line in the video. We propose a new detection model, DiffusionVID-Line, and design a novel tracking algorithm, ByteTrack-Line, both of which fully leverage the prior knowledge of linear motion of nodules in thyroid ultrasound videos. Among them, ByteTrack-Line groups detected nodules, further reducing the workload of doctors and significantly improving their diagnostic speed and accuracy. In DiffusionVID-Line, we propose two new modules: Freq-FPN and Attn-Line. Freq-FPN module is used to extract frequency features, taking advantage of these features to reduce the impact of image blur in ultrasound videos. Based on the standard practice of segmented scanning by doctors, Attn-Line module enhances the attention on targets moving along a straight line, thus improving the accuracy of detection. In ByteTrack-Line, considering the characteristic of linear motion of nodules, we propose the Match-Line association module, which reduces the number of nodule ID switches. In the testing of the detection and tracking datasets, DiffusionVID-Line achieved a mean Average Precision (mAP50) of 74.2 for multiple tissues and 85.6 for nodules, while ByteTrack-Line achieved a Multiple Object Tracking Accuracy (MOTA) of 83.4. Both nodule detection and tracking have achieved state-of-the-art performance.

A new input-output-based framework for measuring the active and passive water use.

•Construct a dual-perspective framework for measuring water use.•Define the water use fluctuating due to macroeconomic condition changes.•Introduce a new indicator to measure the rate of passive water use.•Estimate the key water use channels from a passive perspective through SPA.•Discuss the supply chain's intermediate sector's role for the water use.

Artificial Intelligence-Related Dental Research: Bibliometric and Altmetric Analysis.

BACKGROUND: Recent years have witnessed an explosive surge in dental research related to artificial intelligence (AI). These applications have optimised dental workflows, demonstrating significant clinical importance. Understanding the current landscape and trends of this topic is crucial for both clinicians and researchers to utilise and advance this technology. However, a comprehensive scientometric study regarding this field had yet to be performed. METHODS: A literature search was conducted in the Web of Science Core Collection database to identify eligible "research articles" and "reviews." Literature screening and exclusion were performed by 2 investigators. Thereafter, VOSviewer was utilised in co-occurrence analysis and CiteSpace in co-citation analysis. R package Bibliometrix was employed to automatically calculate scientific impacts, determining the core authors and journals. Altmetric data were described narratively and supplemented with Spearman correlation analysis. RESULTS: A total of 1558 research publications were included. During the past 5 years, AI-related dental publications drastically increased in number, from 36 to 581. Diagnostics and Scientific Reports published the most articles, whereas Journal of Dental Research received the highest number of citations per article. China, the US, and South Korea emerged as the most prolific countries, whilst Germany received the highest number of citations per article (23.29). Charité Universitätsmedizin Berlin was the institution with the highest number of publications and citations per article (29.16). Altmetric Attention Score was correlated with News Mentions (P < .001), and significant associations were observed amongst Dimension Citations, Mendeley Readers, and Web of Science Citations (P < .001). CONCLUSIONS: The publication numbers regarding AI-related dental research have been rising rapidly and may continue their upwards trend. China, the US, South Korea, and Germany had promoted the progress of AI-related dental research. Disease diagnosis, orthodontic applications, and morphology segmentation were current hotspots. Attention mechanism, explainable AI, multimodal data fusion, and AI-generated text assistants necessitate future research and exploration.

Measurements of Atmospheric HO2 Radicals Using Br-CIMS with Elimination of Potential Interferences from Ambient Peroxynitric Acid.

As a promising direct measurement method of atmospheric hydroperoxyl radicals (HO2), bromide chemical ionization mass spectrometry (Br-CIMS) has been first demonstrated by Sanchez et al. (Atmos. Meas. Tech. 2016, 9, 3851-3861). However, field application of this method is currently still sparse, and there is still a gap between measured HO2 concentrations and calculated ones derived from the atmospheric equilibrium between HO2 and peroxynitric acid (HO2NO2). In this work, we constructed an improved Br-CIMS with optimizations of custom-built front-end devices, chamber pressures, and instrumental voltages to achieve a 3σ detection limit of 0.5 ppt at an integration time of 60 s and a sensitivity of 1-3 cps ppt-1 under a total reagent ion signal of 0.2 MHz for HO2 detection. HO2NO2, a product from atmospheric reactions between HO2 and NO2, can also be detected by Br-CIMS, whose interference on the HO2 measurement was found but nearly eliminated by regulating key CIMS voltages to minimize the decomposition of (BrHO2NO2)- ions in the MS. In addition, a 2 week field campaign was carried out in urban Shanghai, demonstrating that the interference of HO2 from ambient HO2NO2 was less than 10% of the true HO2 signal under our optimized CIMS voltage setting. Our study suggests that Br-CIMS is a reliable technique for atmospheric HO2 measurements.

Labor linkages and flow paths of industry in China.

As economic power increases and market patterns adapt, labor becomes an increasingly significant factor of production. However, there is a dearth of discourse regarding the structural changes that have occurred in the correlation of the labor force across industries, as well as a visual representation of the labor force's movement across industries. To quantify and analyze the correlation effect with greater precision, it is necessary to establish an input-output model as the foundation of analysis, comparing the changes in the total output of the economic system prior to and subsequent to the exclusion using the vertical integration algorithm. By decomposing the path structure, the average propagation distance of the labor force population's demand for each industry can be determined. By employing labor force population data from the corresponding years and China's input-output tables published by the National Bureau of Statistics of China (NBS) from 2005 to 2020, this study conducts a quantitative analysis of the correlation effect between labor force population and the trend of its transfer across 19 industries. The findings indicate that the correlation effect between labor force and population is most pronounced in the manufacturing sector. Furthermore, the construction sector faces an especially critical requirement for labor force personnel from other industries. The article culminates with a recommendation that the government enhance its macro-control endeavors to address labor market risk shocks and take an active stance in response to labor market fluctuations.

Catalytic pyrolysis and in situ carbonization of walnut shells: poly-generation and enhanced electrochemical performance of carbons.

In this study, walnut shell (WS) was used as feedstock, incorporating lithium carbonate (LC), sodium carbonate (SC), potassium carbonate (PC), and potassium hydroxide (PH) as pyrolysis catalysts and carbonization activators. A one-step method that allows catalytic pyrolysis and carbonization to be carried out consecutively under their respective optimal conditions is employed, enabling the concurrent production of high-quality pyrolysis oil, pyrolysis gas, and carbon materials from biomass conversion. The effects of LC, SC, PC, and PH on the yield and properties of products derived from WS pyrolysis as well as on the properties and performance of the resulting carbon materials were examined. The results indicated that the addition of LC, SC, PC, and PH enhanced the secondary cracking of tar, leading to increased solid and gas yields from WS. Additionally, it increased the production of phenolic compounds in bio-oil and H2 in syngas, concurrently yielding a walnut shell-based carbon material exhibiting excellent electrochemical performance. Specifically, when PC was used as an additive, the phenolic content in the pyrolysis oil increased by 27.64% compared to that without PC, reaching 74.9%, but the content of ketones, acids, aldehydes, and amines decreased. The hydrogen content increased from 2.5% (without the addition of PC) to 12.75%. The resulting carbon (WSC-PC) displayed a specific surface area of 598.6 m2 g-1 and achieved a specific capacitance of 245.18 F g-1 at a current density of 0.5 A g-1. Even after 5000 charge and discharge cycles at a current density of 2 A g-1, the capacitance retention rate remained at 98.16%. This method effectively enhances the quality of the biomass pyrolysis oil, gas, and char, contributing to the efficient and clean utilization of biomass in industrial applications.

The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.

Distributed cyber attack detection and physical fault diagnosis for a class of interconnected large-scale systems.

In this paper we focus on the distributed cyber attack detection and physical fault diagnosis problem for a class of interconnected large-scale systems (ILSSs). In the proposed scheme, apart from node measurement, edge measurement is also used to construct distributed Kalman filter to estimate the state of each subsystem. The gain matrices of Kalman filter are determined by minimizing the covariance of estimation error in the attack-free and fault-free case, which reduces the false alarm rate of cyber attack detection and physical fault diagnosis. Based on this filter, a bank of adjacent residual generators is constructed to characterize the influence of cyber attack on the edge measurement, and the Chi-square test is used to detect whether the received edge measurements are attacked. At the same time, a local residual generator is constructed for each subsystem to characterize the influence of physical faults on it, and the residual signal is evaluated by variance and directional residual, so as to make distributed fault detection and isolation of each subsystem. It is worth noting that at each step, each subsystem first performs attack detection on the received edge measurements, and then estimates its own state using the attack-free edge measurements and node measurement, which further improves the accuracy of fault detection and isolation. In addition, a sufficient condition that ensuring the mean square exponential boundedness of the estimation error is given. Finally, the proposed scheme is verified by an illustrative example.

Bimetal doped Cu-Fe-ZIF-8/g-C3N4 nanocomposites for the adsorption of tetracycline hydrochloride from water.

Bimetal doped Cu-Fe-zeolitic imidazole framework-8 (ZIF-8)/graphitic carbon nitride (GCN) (Cu-Fe-ZIF-8/GCN) nanocomposites were prepared via one-pot and ion-exchange methods. The main influencing factors, such as adsorbent concentration, TC concentration, initial pH, and coexisting ions, were evaluated in detail. Due to the suitable pore structures and the presence of multiple interactions on the surface, the nanocomposite showed a high adsorption capacity up to 932 mg g-1 for tetracycline hydrochloride (TC), outperforming ZIF-8 by 4.8 times. The adsorption kinetics and adsorption isotherm were depicted in good detail using pseudo-second-order kinetic and Langmuir models, respectively. Thermodynamic calculation revealed that the adsorption of the nanocomposite under experimental conditions was a spontaneous heat absorption process, and was primarily driven by chemisorption. After four cycles of use, the nanocomposite retained 87.2% of its initial adsorption capacity, confirming its high reusability and broad application prospects in removing tetracycline-type pollutants from wastewater.

Melatonin inhibits the formation of intraplaque neovessels in ApoE-/- mice via PPARγ- RhoA-ROCK pathway.

BACKGROUND: According to former research, the atherosclerotic plaque is thought to be aggravated by intraplaque neovessels (IPN) and intraplaque hemorrhage (IPH). Intriguingly, a lower incidence of IPH was found in plaque treated with melatonin. In this study, we attempted to investigate the impact and underlying mechanism regarding the influences of melatonin upon IPN. METHODS: A mouse model was established by subjecting the high fat diet (HFD)-fed ApoE-/- mice to tandem stenosis (TS) surgery with melatonin and GW9662, a PPARγ antagonist, being given by gavage. In vitro experiment was conducted with HUVECs exposing to according treatments of VEGF, melatonin, GW9662, or Y27632. RESULTS: Plaque and IPN were attenuated by treatment with melatonin, which was then reversed by blocking PPARγ. Western blotting results showed that melatonin increased PPARγ and decreased RhoA/ROCK signaling in carotid artery. Elevated RhoA/ROCK signaling was observed in melatonin-treated mice when PPARγ was blocked. In accordance with it, experiments using protein and mRNA from HUVECs revealed that melatonin inhibited the RhoA/ROCK signaling by enhancing PPARγ. According to in vitro study, melatonin was able to inhibit cell migration and angiogenesis, which was aborted by GW9662. Blockage of ROCK using Y27632 was able to cease the effect of GW9662 and restored the suppression on cell migration and angiogenesis by melatonin. CONCLUSIONS: Our study demonstrates that melatonin is able to curb development of plaque and IPN formation by inhibiting the migration of endothelial cells via PPARγ- RhoA-ROCK pathway. That provides a therapeutic potential for both melatonin and PPARγ agonist targeting IPN, IPH, and atherosclerotic plaque.

Lactic acid promotes nucleus pulposus cell senescence and corresponding intervertebral disc degeneration via interacting with Akt.

The accumulation of metabolites in the intervertebral disc is considered an important cause of intervertebral disc degeneration (IVDD). Lactic acid, which is a metabolite that is produced by cellular anaerobic glycolysis, has been proven to be closely associated with IVDD. However, little is known about the role of lactic acid in nucleus pulposus cells (NPCs) senescence and oxidative stress. The aim of this study was to investigate the effect of lactic acid on NPCs senescence and oxidative stress as well as the underlying mechanism. A puncture-induced disc degeneration (PIDD) model was established in rats. Metabolomics analysis revealed that lactic acid levels were significantly increased in degenerated intervertebral discs. Elimination of excessive lactic acid using a lactate oxidase (LOx)-overexpressing lentivirus alleviated the progression of IVDD. In vitro experiments showed that high concentrations of lactic acid could induce senescence and oxidative stress in NPCs. High-throughput RNA sequencing results and bioinformatic analysis demonstrated that the induction of NPCs senescence and oxidative stress by lactic acid may be related to the PI3K/Akt signaling pathway. Further study verified that high concentrations of lactic acid could induce NPCs senescence and oxidative stress by interacting with Akt and regulating its downstream Akt/p21/p27/cyclin D1 and Akt/Nrf2/HO-1 pathways. Utilizing molecular docking, site-directed mutation and microscale thermophoresis assays, we found that lactic acid could regulate Akt kinase activity by binding to the Lys39 and Leu52 residues in the PH domain of Akt. These results highlight the involvement of lactic acid in NPCs senescence and oxidative stress, and lactic acid may become a novel potential therapeutic target for the treatment of IVDD.

RBM10 regulates the tumorigenic potential of human cancer cells by modulating PPM1B and YBX1 activities.

RNA binding protein RBM10 participates in various RNA metabolism, and its decreased expression or loss of function by mutation has been identified in many human cancers. However, how its dysregulation contributes to human cancer pathogenesis remains to be determined. Here, we found that RBM10 expression was decreased in breast tumors, and breast cancer patients with low RBM10 expression presented poorer survival rates. RBM10 depletion in breast cancer cells significantly promotes the cellular proliferation and migration. We further demonstrated that RBM10 forms a triple complex with YBX1 and phosphatase 1B (PPM1B), in which PPM1B serves as the phosphatase of YBX1. RBM10 knock-down markedly attenuated association between YBX1 and PPM1B, leading to elevated levels of YBX1 phosphorylation and its nuclear translocation. Furthermore, cancer cells with RBM10 depletion had a significantly accelerated tumor growth in nude mice. Importantly, these enhanced tumorigenic phenotypes can be reversed by overexpression of PPM1B. Our findings provide the mechanistic bases for functional loss of RBM10 in promoting tumorigenicity, and are potentially useful in the development of combined therapeutic strategies for cancer patients with defective RBM10.

The activation mechanism of peroxymonosulfate and peroxydisulfate by modified hydrochar: Based on the multiple active sites formed by N and Fe.

Modified hydrochar (NHC@Fe), with multiple functional groups and transition metal oxide-containing surface, was successfully synthesized by one-step hydrothermal method. The differences in its catalytic activity for peroxymonosulfate (PMS) and peroxydisulfate (PDS) activation were studied in detail. Experimental and DFT studies showed that abundant active sites, namely, transition metals and functional groups on NHC@Fe provided multiple effective pathways for the activation of persulfate (PS). The NHC@Fe/PMS and NHC@Fe/PDS systems could degrade about 80% of tetracycline hydrochloride (TC) in 120 min and were found to be better than those modified by iron or nitrogen alone. This emphasized the advantage of N-Fe co-modification in persulfate activation. Although the Fe2+/Fe3+ cycle accelerated the activation, the activation of PMS mainly relied on Fe3+, whereas that of PDS mainly relied on Fe2+. Moreover, Fe-N, pyrrolic N, pyridine N, C-O, and O-CO groups also played a key role in the activation process, but the dominant action sites were not the same. Multiple free radicals, such as SO4•-, •OH, O2•-, and 1O2 were generated in PMS and PDS activation systems. 1O2 induced non-free radical pathway was mainly involved in the degradation of TC in both activation systems, but the generation pathway of 1O2 was more direct and rapid in the PDS system. This study provides detailed DFT models of the active sites activated by PMS and PDS and discusses the activation pathways of PMS and PDS along with the similarities and differences in ROS reaction mechanisms.

Prediction of 19F NMR chemical shifts for organic compounds with ORCA.

Accurate assignment of 19F NMR has long been a challenge, and quantum chemical methods are possible solutions. Herein we reported a scaling method for the prediction of 19F NMR chemical shift with freely available ORCA program package. Performance of 31 DFT functionals coupled with 11 basis sets were evaluated and influence of geometry optimization was also studied with five functionals coupled with three basis sets. The significance of geometry was further examined through the execution of relaxed surface scans of seven flexible compounds, and averaged shieldings of obtained conformers yielded notable improvement of the correlation between calculated isotropic shielidings and experimental chemical shifts. Utilization of the best scaling factor obtained successfully assigned of fluorine atoms in multifluorinated molecules with different conformations. The method reported here was computationally inexpensive, easily available with acceptable accuracy.

Real-time 3D shape measurement of dynamic scenes using fringe projection profilometry: lightweight NAS-optimized dual frequency deep learning approach.

Achieving real-time and high-accuracy 3D reconstruction of dynamic scenes is a fundamental challenge in many fields, including online monitoring, augmented reality, and so on. On one hand, traditional methods, such as Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP), are struggling to balance measuring efficiency and accuracy. On the other hand, deep learning-based approaches, which offer the potential for improved accuracy, are hindered by large parameter amounts and complex structures less amenable to real-time requirements. To solve this problem, we proposed a network architecture search (NAS)-based method for real-time processing and 3D measurement of dynamic scenes with rate equivalent to single-shot. A NAS-optimized lightweight neural network was designed for efficient phase demodulation, while an improved dual-frequency strategy was employed coordinately for flexible absolute phase unwrapping. The experiment results demonstrate that our method can effectively perform 3D reconstruction with a reconstruction speed of 58fps, and realize high-accuracy measurement of dynamic scenes based on deep learning for what we believe to be the first time with the average RMS error of about 0.08 mm.

Association Between Temperature During Intensive Care Unit and Mortality in Patients With Acute Respiratory Distress Syndrome.

The relationship between body temperature changes and prognosis in patients with acute respiratory distress syndrome (ARDS) remains inconclusive. Our study aimed to investigate the clinical value of body temperature in the management of ARDS. Data from the Medical Information Mart for Intensive Care III database were collected. Adult patients with ARDS were enrolled and further grouped based on their temperature values in the intensive care unit. Both the maximum (temperaturemax) and minimum (temperaturemin) temperatures were used. The primary outcome was 28-day mortality rate. Polynomial regression, subgroup analysis, and logistic regression analysis were performed in the final analysis. A total of 3922 patients with ARDS were enrolled. There was a U-shaped relationship between 28-day mortality and body temperature. For patients with infection, the elevated temperaturemax (≥37.0°C) was associated with decreased mortality, with an odds ratio ranging from 0.39 to 0.49, using temperaturemax from 36.5°C to 36.9°C as reference. For patients without infection, a similar tendency was observed, but the protective effect was lost at extremely high temperatures (≥38.0°C, p < 0.05). Elevated temperaturemin (≥37.0°C) and decreased temperaturemin (<35.0°C) were associated with increased mortality, using the temperaturemin from 36.0°C to 36.9°C as a reference. Hypothermia was associated with increased mortality in patients with ARDS, while the effect of hyperthermia (≥37.0°C) on the mortality of patients with ARDS was not fully consistent in the infection and noninfection subgroups. Short-term and transient temperatures above 37.0°C would be beneficial to patients with ARDS, but extreme hyperthermia and persistent temperatures above 37.0°C should be avoided.

Screening of Potential Probiotic Lactobacillaceae and Their Improvement of Type 2 Diabetes Mellitus by Promoting PI3K/AKT Signaling Pathway in db/db Mice.

The study aimed to isolate Lactobacillaceae strains with in vitro hypoglycemic activity and probiotic properties and to determine their antidiabetic abilities in vivo. Lactiplantibacillus plantarum 22, L. plantarum 25, Limosilactobacillus fermentum 11, and L. fermentum 305 with high in vitro hypoglycemic activity were screened from 23 strains of Lactobacillaceae isolated from human feces and identified by 16S rDNA sequencing. The fasting blood glucose (FBG) of the mice was recorded weekly. After 12 weeks, liver, kidney, and pancreas tissues were stained with hematoxylin and eosin (H&E) to observe histomorphology; the inflammatory factors were assayed by Quantitative Real-time PCR; PI3K and AKT were measured by Western blot; the short-chain fatty acids (SCFAs) were determined by LC-MS/MS. Inhibitory activities of L. plantarum 22, L. plantarum 25, L. fermentum 11, and L. fermentum 305 against α-amylase were 62.29 ± 0.44%, 51.81 ± 3.65%, 58.40 ± 1.68%, and 57.48 ± 5.04%, respectively. Their inhibitory activities to α-glucosidase were 14.89 ± 0.38%, 15.32 ± 0.89%, 52.63 ± 3.07%, and 51.79 ± 1.13%, respectively. Their survival rate after simulated gastrointestinal test were 12.42 ± 2.84%, 9.10 ± 1.12%, 5.86 ± 0.52%, and 8.82 ± 2.50% and their adhesion rates to Caco-2 cell were 6.09 ± 0.39%, 6.37 ± 0.28%, 6.94 ± 0.27%, and 6.91 ± 0.11%, respectively. The orthogonal tests of bacterial powders of the four strains showed that the maximum inhibitory activities to α-amylase and α-glucosidase were 93.18 ± 1.19% and 75.33 ± 2.89%, respectively. The results showed that the mixture of Lactobacillaceae could lower FBG, reduce inflammation, and liver, kidney, and pancreas damage, promote PI3K/AKT signaling pathway, and increase the content of SCFAs. The combination of L. plantarum 22, L. plantarum 25, L. fermentum 11, and L. fermentum 305 can potentially improve type 2 diabetes mellitus (T2DM).

The Moderating Effects of Trust and Felt Trust on the Nonlinear Relationship Between Compulsory Citizenship Behavior and Counterproductive Work Behavior.

Purpose: This study explored the J-shaped effect of compulsory citizenship behavior on counterproductive work behavior of new generation employees, as well as the separate and joint moderating effects of trust and felt trust on the J-shaped relationship between compulsory citizenship behavior and counterproductive work behavior. Methods: Three waves of data were collected from 659 new generation employees in China. A self-report method was used to measure compulsory citizenship behavior, counterproductive work behavior, trust and felt trust. Then, based on the cognitive appraisal theory of stress and social information processing theory, a nonlinear model was constructed and tested. Results: (1) Compulsory citizenship behavior had a J-shaped effect on job performance. That is, when the compulsory citizenship behavior level was lower, the effect of compulsory citizenship behavior on counterproductive work behavior was not significant; but when it increased to medium and higher levels, the effect was significant and stronger. (2) The moderating effect of trust (employees' perceived trust in leader) or felt trust (employees' perception of being trusted by leader) was significant. That is, when trust or felt trust was lower, the J-shaped effect was stronger; conversely, the J-shaped effect was weak. (3) The joint moderating effect of trust and felt trust was significant. That is, when trust was high, the moderation effect of felt trust was significant; conversely, the moderation effect of felt trust was not significant. Conclusion: The results identify the nonlinear effect of compulsory citizenship behavior through exploring the J-shaped effect of compulsory citizenship behavior on counterproductive work behavior and the boundary conditions in the nonlinear relationship. Meanwhile, the study provide implications for organizations regarding how to manage employees' work behavior.

Volatility Parametrization of Low-Volatile Components of Ambient Organic Aerosols Based on Molecular Formulas.

Evaluating the volatility of organic compounds based solely on their molecular formulas would avoid tough demands in deriving molecular structures. Here, we deployed an iodide-adduct Long Time-of-Flight Chemical Ionization Mass Spectrometry (LToF-CIMS) combined with a Filter Inlet for Gases and AEROsols (FIGAERO) to investigate molecular formulas and thermograms of organic compounds on ambient particulate samples collected in the summer of 2021 in a suburban site of Shanghai and to estimate saturation vapor pressures of low- and semivolatile components of ambient organic aerosols. Then, a hierarchical cluster analysis and a subsequent classification of obtained clusters by similarity calculation were applied to the measured data set of molecular formulas and saturation vapor pressures of organic aerosols with at least a 2/3 appearance frequency, together with a similar data set collected at a rural site in the Beijing-Tianjin-Hebei region during the winter of 2018 (Ren et al., 2018), to classify all compounds into multiple groups. For each group of compounds, parametrizations between volatility and elemental composition were derived, and then relationships between each group of parameters and the mean O:C were established to achieve a volatility-molecular formula parametrization with the O:C as a key input. Statistical comparison of estimated volatilities of low-volatile organic compounds shows a much better performance of our parametrization than previous molecular formula-based ones.