Associations between long-term night shift work and incidence of chronic obstructive pulmonary disease: a prospective cohort study of 277,059 UK Biobank participants.

BACKGROUND: Little is known about the effects of night shifts and their interactions with genetic factors on chronic obstructive pulmonary disease (COPD). In this study, we aim to investigate relationships between long-term night shift work exposure and COPD risk, and assess modification effects of genetic predisposition. METHODS: A total of 277,059 subjects who were in paid employment or self-employed were included in the UK Biobank. Information on current and lifetime employment was obtained, and a weighted COPD-specific genetic risk score (GRS) was constructed. We used Cox proportional hazard models to investigate associations between night shift work and COPD risk, and their interaction with COPD-specific GRS. RESULTS: The cohort study included 277,059 participants (133,063 men [48.03%]; mean [SD] age, 52.71 [7.08] years). During a median follow-up of 12.87 years, we documented 6558 incidents of COPD. From day work, irregular night shifts to regular night shifts, there was an increased trend in COPD incidence (P for trend < 0.001). Compared with day workers, the hazard ratio (HR) and 95% confidence interval (CI) of COPD was 1.28 (1.20, 1.37) for subjects with rarely/sometimes night shifts and 1.49 (1.35, 1.66) for those with permanent night shifts. Besides, the longer durations (especially in subjects with night shifts ≥ 10 years) and increasing monthly frequency of night shifts (in workers with > 8 nights/month) were associated with a higher COPD risk. Additionally, there was an additive interaction between night shifts and genetic susceptibility on the COPD risk. Subjects with permanent night shifts and high genetic risk had the highest risk of COPD (HR: 1.90 [95% CI: 1.63, 2.22]), with day workers with low genetic risk as a reference. CONCLUSIONS: Long-term night shift exposure is associated with a higher risk of COPD. Our findings suggest that decreasing the frequency and duration of night shifts may offer a promising approach to mitigating respiratory disease incidence in night shift workers, particularly in light of individual susceptibility.

Exploring the Enhanced Zinc Storage Performance of α-MnSe Polyhedral Microspheres and H+/Zn2+ Co-Insertion Mechanism.

The newly emerged Mn-based selenides as cathodes for aqueous Zn-ion batteries (ZIBs) have drawn researchers' interest because of their lower electronegativity and better electronic conductivity compared with the corresponding Mn-based oxides. Nevertheless, the energy storage mechanism of Mn-based selenides still needs to be further clarified. Herein, the MnSe/Se and MnSe polyhedral microspheres are reported as cathodes for ZIBs, and the MnSe cathode achieves significantly enhanced specific capacity, rate performance, and cycling stability. In-depth kinetic analysis confirms that the MnSe cathode presents better kinetic behavior and density functional theory (DFT) calculations verify the fast diffusion kinetics of the MnSe cathode. More importantly, systematic ex situ characterizations reveal that the microstructured MnSe can exist stably during the charge-discharge process and store energy with H+/Zn2+ co-insertion mechanism, which is greatly different from the phase transformation of the nanostructured α-MnSe reported in the literature. Additionally, it is verified that the different types of separators exhibit remarkably different zinc storage performance of the MnSe cathode. This study not only offers a good guidance for developing high-performance ZIBs Mn-based cathode materials and explores the effect of separators on the zinc storage performance, but also provides new insights into the energy storage mechanism of the MnSe cathode.

ViMRT: a text-mining tool and search engine for automated virus mutation recognition.

MOTIVATION: Virus mutation is one of the most important research issues which plays a critical role in disease progression and has prompted substantial scientific publications. Mutation extraction from published literature has become an increasingly important task, benefiting many downstream applications such as vaccine design and drug usage. However, most existing approaches have low performances in extracting virus mutation due to both lack of precise virus mutation information and their development based on human gene mutations. RESULTS: We developed ViMRT, a text-mining tool and search engine for automated virus mutation recognition using natural language processing. ViMRT mainly developed 8 optimized rules and 12 regular expressions based on a development dataset comprising 830 papers of 5 human severe disease-related viruses. It achieved higher performance than other tools in a test dataset (1662 papers, 99.17% in F1-score) and has been applied well to two other viruses, influenza virus and severe acute respiratory syndrome coronavirus-2 (212 papers, 96.99% in F1-score). These results indicate that ViMRT is a high-performance method for the extraction of virus mutation from the biomedical literature. Besides, we present a search engine for researchers to quickly find and accurately search virus mutation-related information including virus genes and related diseases. AVAILABILITY AND IMPLEMENTATION: ViMRT software is freely available at http://bmtongji.cn:1225/mutation/index.

ViMIC: a database of human disease-related virus mutations, integration sites and cis-effects.

Molecular mechanisms of virus-related diseases involve multiple factors, including viral mutation accumulation and integration of a viral genome into the host DNA. With increasing attention being paid to virus-mediated pathogenesis and the development of many useful technologies to identify virus mutations (VMs) and viral integration sites (VISs), much research on these topics is available in PubMed. However, knowledge of VMs and VISs is widely scattered in numerous published papers which lack standardization, integration and curation. To address these challenges, we built a pilot database of human disease-related Virus Mutations, Integration sites and Cis-effects (ViMIC), which specializes in three features: virus mutation sites, viral integration sites and target genes. In total, the ViMIC provides information on 31 712 VMs entries, 105 624 VISs, 16 310 viral target genes and 1 110 015 virus sequences of eight viruses in 77 human diseases obtained from the public domain. Furthermore, in ViMIC users are allowed to explore the cis-effects of virus-host interactions by surveying 78 histone modifications, binding of 1358 transcription regulators and chromatin accessibility on these VISs. We believe ViMIC will become a valuable resource for the virus research community. The database is available at http://bmtongji.cn/ViMIC/index.php.

Ambient air pollution, low-grade inflammation, and lung function: Evidences from the UK Biobank.

The associations of ambient air pollution exposure and low-grade inflammation with lung function remain uncertain. In this study, 276,289 subjects were enrolled in the UK Biobank. Individual exposure to ambient air pollution (including nitrogen dioxide [NO2], nitrogen oxides [NOx]), and particulate matter [PM2.5, PM10, PMcoarse]) were estimated by using the land-use regression model. Forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) were tested, and low-grade inflammation score (INFLA score) was calculated for each subject. In this cross-sectional study, the median concentrations of air pollution were 9.89 µg/m3 for PM2.5, 15.98 µg/m3 for PM10, 6.09 µg/m3 for PMcoarse, 25.60 µg/m3 for NO2, and 41.46 µg/m3 for NOx, respectively. We observed that PM2.5, PM10, PMcoarse, NO2, NOx was negatively associated with lung function. Besides, significant positive associations between PM exposure and low-grade inflammation were noted. Per interquartile range (IQR) increase in PM2.5, PM10, and PMcoarse was related to higher INFLA score, and the ß (95 % CI) was 0.06 (0.03, 0.08), 0.03 (0.02, 0.05), and 0.03 (0.01, 0.04), respectively. Additionally, we found significant negative associations between INFLA scores and lung function. One-unit increase in INFLA score was linked with 12.41- and 11.31-ml decreases in FVC and FEV1, respectively. Compared with individuals with low air pollution exposure and low INFLA scores, participants with high air pollution and high INFLA scores had the lowest FVC and FEV1. Additionally, we observed that INFLA scores could modify the relationships of PM2.5, NO2, and NOx with FVC and FEV1 (Pinteraction <0.05). The negative impact of air pollutants on lung function was more pronounced in subjects with high INFLA scores in comparison to those with low INFLA scores. In conclusion, we demonstrated negative associations between ambient air pollution and lung function, and the observed associations were strengthened and modified by low-grade inflammation.

Glycolytic reprogramming governs crystalline silica-induced pyroptosis and inflammation through promoting lactylation modification.

Prolonged inhalation of environmental crystalline silica (CS) can cause silicosis, characterized by persistent pulmonary inflammation and irreversible fibrosis, but the mechanism has not been elucidated. To uncover the role and underlying mechanism of glycolytic reprogramming in CS-induced pulmonary inflammation, the mouse silicosis models and glycolysis inhibition models were established in vivo. And the CS-induced macrophage activation models were utilized to further explore the underlying mechanism in vitro. The results showed that CS induced lung inflammation accompanied by glycolytic reprogramming and pyroptosis. The application of glycolysis inhibitor (2-DG) suppressed CS-induced pyroptosis and alleviated lung inflammation. In vitro, 2-DG effectively impeded CS-induced macrophage pyroptosis and inflammatory response. Mechanistically, 2-DG suppressed pyroptosis by inhibiting NLRP3 inflammasome activation both in vivo and in vitro. Furtherly, metabolite lactate facilitated NLRP3-dependent pyroptosis synergistically with CS particles, while blocking the source of lactate largely alleviated NLRP3 inflammasome activation and subsequent pyroptosis triggered by CS. More profoundly, the increment of lactate induced by CS might drive NLRP3-dependent pyroptosis by increasing histone lactylation levels. In conclusion, our findings demonstrated inhibiting glycolytic reprogramming could alleviate CS-induced inflammatory response through suppressing NLRP3 -dependent pyroptosis. Increased glycolytic metabolite lactate and protein lactylation modifications might represent significant mechanisms during CS-induced NLRP3 activation and macrophage pyroptosis.

Morphological and Molecular Characterization, Including Parasitic and Pathogenic Studies of a New Spherical Cyst Nematode Species, Globodera vulgaris n. sp. (Nematoda: Heteroderidae), Associated with Potatoes in China.

In this study, a new spherical cyst nematode belonging to the genus Globodera, herein described as Globodera vulgaris n. sp., was extracted from the roots and rhizosphere soil of potato and circumjacent weeds belonging to different families in three provinces in southwest China. The new species was characterized by 8 to 24 ridges between the anus and fenestra and an average Granek's ratio of 2.8 to 3.8 in cysts, a head with three to four annules, a dorsal knob anteriorly projected, ventral knobs round or anteriorly projected in second stage juveniles, a head with three to five annules, a short spicule with an average length of less than 30.0 µm, and a developed velum in males. Pathogenicity tests showed that G. vulgaris n. sp. infected potato but did not damage or affect the potato yield compared with the control, and it parasitized tomato with a low reproduction rate (RF < 1) while it did not parasitize tobacco (RF = 0). The new species was closely related to G. rostochiensis, based on molecular diagnostic marker sequences and constructed phylogenetic analysis, based on internal transcribed spacers of ribosomal DNA, large-subunit rDNA, and small-subunit rDNA. However, the new species exhibited differences from G. rostochiensis in terms of morphological characteristics, a wide host preference, lack of damage to hosts, and an egg-hatching rate induced by hatching factors.

Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19.

BACKGROUND: Biomedical relation extraction (RE) is of great importance for researchers to conduct systematic biomedical studies. It not only helps knowledge mining, such as knowledge graphs and novel knowledge discovery, but also promotes translational applications, such as clinical diagnosis, decision-making, and precision medicine. However, the relations between biomedical entities are complex and diverse, and comprehensive biomedical RE is not yet well established. OBJECTIVE: We aimed to investigate and improve large-scale RE with diverse relation types and conduct usability studies with application scenarios to optimize biomedical text mining. METHODS: Data sets containing 125 relation types with different entity semantic levels were constructed to evaluate the impact of entity semantic information on RE, and performance analysis was conducted on different model architectures and domain models. This study also proposed a continued pretraining strategy and integrated models with scripts into a tool. Furthermore, this study applied RE to the COVID-19 corpus with article topics and application scenarios of clinical interest to assess and demonstrate its biological interpretability and usability. RESULTS: The performance analysis revealed that RE achieves the best performance when the detailed semantic type is provided. For a single model, PubMedBERT with continued pretraining performed the best, with an F1-score of 0.8998. Usability studies on COVID-19 demonstrated the interpretability and usability of RE, and a relation graph database was constructed, which was used to reveal existing and novel drug paths with edge explanations. The models (including pretrained and fine-tuned models), integrated tool (Docker), and generated data (including the COVID-19 relation graph database and drug paths) have been made publicly available to the biomedical text mining community and clinical researchers. CONCLUSIONS: This study provided a comprehensive analysis of RE with diverse relation types. Optimized RE models and tools for diverse relation types were developed, which can be widely used in biomedical text mining. Our usability studies provided a proof-of-concept demonstration of how large-scale RE can be leveraged to facilitate novel research.

Hierarchical Clustering Algorithm for Multi-Camera Vehicle Trajectories Based on Spatio-Temporal Grouping under Intelligent Transportation and Smart City.

With the emergence of intelligent transportation and smart city system, the issue of how to perform an efficient and reasonable clustering analysis of the mass vehicle trajectories on multi-camera monitoring videos through computer vision has become a significant area of research. The traditional trajectory clustering algorithm does not consider camera position and field of view and neglects the hierarchical relation of the video object motion between the camera and the scenario, leading to poor multi-camera video object trajectory clustering. To address this challenge, this paper proposed a hierarchical clustering algorithm for multi-camera vehicle trajectories based on spatio-temporal grouping. First, we supervised clustered vehicle trajectories in the camera group according to the optimal point correspondence rule for unequal-length trajectories. Then, we extracted the starting and ending points of the video object under each group, hierarchized the trajectory according to the number of cross-camera groups, and supervised clustered the subsegment sets of different hierarchies. This method takes into account the spatial relationship between the camera and video scenario, which is not considered by traditional algorithms. The effectiveness of this approach has been proved through experiments comparing silhouette coefficient and CPU time.

Neutralization of interleukin-11 attenuates silica particles-induced pulmonary inflammation and fibrosis in vivo.

Environmental exposure to crystalline silica particles can lead to silicosis, which is one of the most serious pulmonary interstitial fibrosis around the world. Unfortunately, the exact mechanism on silicosis is unclear, and the effective treatments are lacking to date. In this study, we aim to explore the molecular mechanism by which interleukin-11 (IL-11) affects silica particles-induced lung inflammation and fibrosis. We observed that IL-11 expressions in mouse lungs were significantly increased after silica exposure, and maintained at high levels across both inflammation and fibrosis phase. Immunofluorescent dual staining further revealed that the overexpression of IL-11 mainly located in mouse lung epithelial cells and fibroblasts. Using neutralizing anti-IL-11 antibody could effectively alleviate the overexpression of pro-inflammatory cytokines (i.e., interleukin-6 and tumor necrosis factor-α) and fibrotic proteins (i.e., collagen type I and matrix metalloproteinase-2) induced by silica particles. Most importantly, the expressions of IL-11 receptor subunit α (IL-11Rα), Glycoprotein 130 (GP130), and phosphorylated extracellular signal-regulated kinase (p-ERK) were significantly increased in response to silica, whereas blocking of IL-11 markedly reduced their levels. All findings suggested that the overexpression of IL-11 was involved in the pathological of silicosis, while neutralizing IL-11 antibody could effectively alleviate the silica-induced lung inflammation and fibrosis by inhibiting the IL-11Rα/GP130/ERK signaling pathway. IL-11 might be a promising therapeutic target for lung inflammation and fibrosis caused by silica particles exposure.

Insights into the mechanism underlying crystalline silica-induced pulmonary fibrosis via transcriptome-wide m6A methylation profile.

Silicosis is one of the most severe interstitial lung fibrosis diseases worldwide, caused by crystalline silica exposure. While the mechanisms and pathogenesis underlying silicosis remained unknown. N6-methyladenosine (m6A) methylation has received significant attention in a variety of human diseases. However, whether m6A methylation is involved in silicosis has not been clarified. In this study, we conducted methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and transcriptome sequencing (RNA-Seq) to profile the m6A modification in normal and silicosis mouse models (n = 3 pairs). The global levels of m6A methylation were further assessed by m6A RNA methylation quantification kits, and the major regulators of m6A RNA methylation were verified by qRT-PCR. Our results showed that long-term exposure to crystalline silica led to silicosis, accompanied by increasing levels of m6A methylation. Upregulation of METTL3 and downregulation of ALKBH5, FTO, YTHDF1, and YTHDF3 might contribute to aberrant m6A modification. Compared with controls, 359 genes showed differential m6A methylation peaks in silicosis (P < 0.05 and FC ≥ 2). Among them, 307 genes were hypermethylated, and 52 genes were hypomethylated. RNA-Seq analysis revealed 1091 differentially expressed genes between the two groups, 789 genes were upregulated and 302 genes were downregulated in the lungs of silicosis mice (P < 0.05 and FC ≥ 2). In the conjoint analysis of MeRIP-Seq and RNA-Seq, we identified that 18 genes showed significant changes in both m6A modification and mRNA expression. The functional analysis further noted that these 18 m6A-mediated mRNAs regulated pathways that were closely related to "phagosome", "antigen processing and presentation", and "apoptosis". All findings suggested that m6A methylation played an essential role in the formation of silicosis. Our discovery with multi-omics approaches not only gives clues for the epigenetic mechanisms underlying the pathogenesis of silicosis but also provides novel and viable strategies for the prevention and treatment of silicosis.

Inhibition of Gas6 promotes crystalline silica-induced inflammatory response of macrophages via blocking autophagy flux.

Inhalation of crystalline silica (CS) can cause silicosis, which is one of the most serious interstitial lung diseases worldwide. Autophagy dysfunction is an essential step in silicosis progression. In this study, we aim to identify the effect of growth arrest-specific protein 6 (Gas6) during autophagy induction and macrophage inflammatory response caused by CS. After RAW 264.7 macrophages exposed to CS, the levels of Gas6 and autophagy markers (p62, Beclin1, and LC3-II/LC3-I) were increased, accompanied with enhanced inflammatory cytokines secretion. Using autophagy activator (rapamycin) repressed, whereas autophagy inhibitor (3-methyladenine) promoted inflammatory cytokines release. Besides, inhibition of Gas6 aggravated CS-induced inflammatory response, and autophagy inhibition facilitated the promoted effect of Gas6 silencing, resulting in elevated expression of inflammatory cytokines. These findings reveal the protective effects of Gas6 and autophagy in macrophages in response to CS exposure, and highlight the autophagy regulated by Gas6 may be a potential prevention target for CS-induced lung inflammatory response.

Parallel Subgradient Algorithm with Block Dual Decomposition for Large-scale Optimization.

This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel sub-gradient methods. Since there are many possible reformulations for a given problem, an important question is: Which reformulation leads to the fastest solution time? One approach is to detect a block diagonal structure in the constraint matrix, and reformulate the problem by dualizing the constraints outside of the blocks; the approach is defined herein as block dual decomposition. Main advantage of such a reformulation is that the Lagrangian relaxation has a block diagonal constraint matrix, thus decomposable into smaller sub-problems that can solved in parallel. We show that the block decomposition can critically affect convergence rate of the sub-gradient method. We propose various decomposition methods that use domain knowledge or apply algorithms using knowledge about the structure in the constraint matrix or the dependence in the decision variables, towards reducing the computational effort to solve large-scale optimization problems. In particular, we introduce a block decomposition approach that reduces the number of dualized constraints by utilizing a community detection algorithm. We present empirical experiments on an extensive set of problem instances including a real application. We illustrate that if the number of the dualized constraints in the decomposition increases, the computational effort within each iteration of the sub-gradient method decreases while the number of iterations required for convergence increases. The key message is that it is crucial to employ prior knowledge about the structure of the problem when solving large scale optimization problems using dual decomposition.

Construction of Stretching-Bending Sequential Pattern to Recognize Work Cycles for Earthmoving Excavator from Long Video Sequences.

Counting the number of work cycles per unit of time of earthmoving excavators is essential in order to calculate their productivity in earthmoving projects. The existing methods based on computer vision (CV) find it difficult to recognize the work cycles of earthmoving excavators effectively in long video sequences. Even the most advanced sequential pattern-based approach finds recognition difficult because it has to discern many atomic actions with a similar visual appearance. In this paper, we combine atomic actions with a similar visual appearance to build a stretching-bending sequential pattern (SBSP) containing only "Stretching" and "Bending" atomic actions. These two atomic actions are recognized using a deep learning-based single-shot detector (SSD). The intersection over union (IOU) is used to associate atomic actions to recognize the work cycle. In addition, we consider the impact of reality factors (such as driver misoperation) on work cycle recognition, which has been neglected in existing studies. We propose to use the time required to transform "Stretching" to "Bending" in the work cycle to filter out abnormal work cycles caused by driver misoperation. A case study is used to evaluate the proposed method. The results show that SBSP can effectively recognize the work cycles of earthmoving excavators in real time in long video sequences and has the ability to calculate the productivity of earthmoving excavators accurately.

Epidemiological Characteristics and Incubation Period of 7015 Confirmed Cases With Coronavirus Disease 2019 Outside Hubei Province in China.

BACKGROUND: Disease caused by SARS-CoV-2 broke out in Wuhan in December 2019. We utilized confirmed cases outside Hubei Province to analyze epidemiologic characteristics and evaluate the effect of traffic restrictions implemented in Hubei beginning on 23 January 2020. METHODS: Information on 7015 confirmed cases from 19 January to 8 February 2020 in all provinces outside Hubei was collected from the national and local health commissions in China. Incubation period and interval times were calculated using dates of the following events: contact with an infected person, onset, first visit, and diagnosis. We evaluated changes in incubation period and interval times. RESULTS: The average age of all cases was 44.24 years. The median incubation period was 5 days and extended from 2 days on 23 January to 15 days on 8 February. The proportion of imported cases decreased from 85.71% to 33.19% after 23 January. In addition, lengths of intervals between onset and diagnosis, onset and first visit, and first visit and diagnosis decreased over time. CONCLUSIONS: Rapidly transmitting COVID-19 has a short incubation period. The onset mainly occurred among young to middle-aged adults. Traffic restrictions played an important role in the decreased number of imported cases outside Hubei.

Cell phone addiction and sleep disturbance among medical students in Jiangsu Province, China: the mediating role of psychological resilience and the moderating role of gender.

Background: Cell phone addiction presents a widespread and severe physical and mental health concern, now recognized as a global public health issue. Among medical college students, the issue of poor sleep quality has become particularly prevalent. This study aimed to investigate the relationship between cell phone addiction and sleep disturbance in a population of medical college students, exploring the potential mediating role of psychological resilience and the moderating impact of gender. Methods: A random cluster sampling method was employed to survey 5,048 students from four medical colleges in Jiangsu Province, China, utilizing the Mobile Phone Addiction Index (MPAI), Connor-Davidson Resilience Scale (CD-RISC), and Pittsburgh Sleep Quality Index (PSQI) for data collection. Statistical analyses were conducted using SPSS 26.0 and the PROCESS macro version 4.1. To assess mediation, Model 4 of the PROCESS macro was utilized, while Model 15 was employed to investigate the moderating effect of gender. Results: The results revealed a significant positive correlation between cell phone addiction and sleep disturbance, with psychological resilience found to partially mediate this relationship. Moreover, gender was observed to significantly moderate the impact of cell phone addiction on sleep disturbance. Specifically, bootstrap analysis indicated a significant interaction between cell phone addiction and gender (Coeff. = -0.0215, P< 0.001), with a stronger relationship found in males (simple slope = 0.0616, t = 16.66, P< 0.001) compared to females (simple slope = 0.0401, t = 9.51, P< 0.001). Conclusion: Ultimately, psychological resilience was identified as a partial mediator between cell phone addiction and sleep disturbance in medical students from Jiangsu Province, with gender playing a significant moderating role in this association.

Rice volatile compound (E)-ß-caryophyllene induced by rice dwarf virus (RDV) attracts the natural enemy Cyrtorhinus lividipennis to prey on RDV insect vectors.

BACKGROUND: Rice dwarf virus (RDV)-induced rice plant volatiles (E)-ß-caryophyllene and 2-heptanol modulate the olfactory behavior of RDV insect vectors that promote viral acquisition and transmission. However, it remains elusive whether these two volatiles could influence the behaviors of the natural enemies of RDV insect vectors. Herein, we determined the effects of these two volatiles on the olfactory and predatory behaviors of Cyrtorhinus lividipennis (Hemiptera: Miridae), an important predator of RDV insect vectors in rice paddies. RESULTS: The results showed that C. lividipennis preferred RDV-infected rice plant odors over RDV-free rice plant odors. C. lividipennis was attracted by (E)-ß-caryophyllene, but showed no behavioral responses to 2-heptanol. The attraction of (E)-ß-caryophyllene towards C. lividipennis was further confirmed using oscas1 rice plants, which do not release (E)-ß-caryophyllene in response to RDV infection, through a series of complementary assays. The oviposition preference of the RDV vector insect Nephotettix cincticeps (Hemiptera: Cicadellidae) showed no significant difference between RDV-infected and RDV-free wild-type plants, nor between oscas1-RDV and oscas1 plants. However, the predation rate of C. lividipennis for N. cincticeps eggs on RDV-infected plants was higher than that on RDV-free plants, whereas there was no significant difference between oscas1-RDV and oscas1 plants. CONCLUSION: (E)-ß-caryophyllene induced by RDV attracted more C. lividipennis to prey on N. cincticeps eggs and played a crucial role in plant-virus-vector-enemy interactions. These novel findings will promote the design of new strategies for disease control by controlling the populations of insect vectors, for example recruiting more natural enemies by virus-induced plant volatiles. © 2023 Society of Chemical Industry.

Long-term night shift work, genetic predisposition, and risk of incident asthma: a prospective cohort study.

BACKGROUND: Evidence about a potential link between current and lifetime night shift work and risk of incident asthma is insufficient. AIM: To investigate the association of current and lifetime night shift work with risk of incident asthma, and the modified effect of genetic susceptibility on this association. DESIGN AND METHODS: We included 253,773 individuals with complete night shift work information in the UK biobank. We calculated the standard polygenetic risk score (PRS) for asthma. The Cox proportional hazard models were conducted to estimate hazard ratios (HRs) and 95% CIs. RESULTS: After multivariable adjustments, we found that current night shift work was associated with an increased risk of incident asthma in a dose-response fashion (P for trend<0.001). Compared with day workers, those working usual/permanent night shifts had a 17% (95% CI: 1.04-1.33) higher risk of asthma incidence. In addition, we observed significant dose-dependent relationships of longer lifetime duration or frequency of night shift work with elevated risk of asthma incidence (all P for trend<0.05). Compared with never night shift workers, those with a duration (≥5 years) or frequency (≥8 nights/month) of night shift work exhibited a 20% (95% CI: 1.03-1.39) or 22% (95% CI: 1.03-1.44) higher risk of incident asthma, respectively. Moreover, the elevated risk of incident asthma related to current and lifetime night shift work exposure was strengthened by high PRS, although no significant shift work-PRS interactions were detected. CONCLUSION: Both current and lifetime night shift work may increase the risk of incident asthma, regardless of genetic predisposition to asthma.

Circulating metabolomics revealed novel associations between multiple ambient air pollutants exposure and chronic obstructive pulmonary disease incidence: Evidence from a prospective cohort study.

The mechanisms underlying relationships between ambient air pollution and chronic obstructive pulmonary disease (COPD) risk remained largely uncertain. In this study, we aim to evaluate whether metabolic signature comprising multiple circulating metabolites can characterize metabolic response to the multiple air pollution; and to assess whether the identified metabolic signature contribute to COPD risk. A total of 227,962 participants with complete data were included from the UK biobank study. Concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), and particulate matter (PM2.5 and PM10) were evaluated by land-use regression models. We newly computed an air pollution score to reflect joint exposure to multiple air pollutants. Circulating metabolome was quantified by nuclear magnetic resonance (NMR) spectroscopy. During a median of 12.78 years of follow-up, a total of 8685 incident COPD cases were documented. After multiple correction, the Cox regression models showed that 102 of 143 metabolites were significantly associated with COPD risk. Utilizing elastic net regularized regressions, we identified a metabolic signature comprising 106 metabolites (including lipid, fatty acids, glycolysis and amino acids et al.) were robustly related to air pollution score. In the multivariate-adjusted Cox regression models, the derived metabolic signature showed a positive correlation with incident COPD [HR per SD = 1.20 (95% CI: 1.17-1.22)]. Casual mediation analysis further noted that the constructed metabolic signature mediated 10.5 % (8.3%-13.1%) of the air pollution-COPD associations. Taken together, our findings identified a metabolic signature that captured metabolic response to various air pollutants exposure jointly, and predicted future COPD risk independent of known risk factors.

Process, advances, and perspectives of graphene oxide-SELEX for the development of aptamer molecular probes: A comprehensive review.

BACKGROUND: Aptamers are screened via the systematic evolution of ligands by exponential enrichment (SELEX) and are widely used in molecular diagnostics and targeted therapies. The development of efficient and convenient SELEX technology has facilitated rapid access to high-performance aptamers, thereby advancing the aptamer industry. Graphene oxide (GO) serves as an immobilization matrix for libraries in GO-SELEX, making it suitable for screening aptamers against diverse targets. RESULTS: This review summarizes the detailed steps involved in GO-SELEX, including monitoring methods, various sublibrary acquisition methods, and practical applications from its inception to the present day. In addition, the potential of GO-SELEX in the development of broad-spectrum aptamers is explored, and its current limitations for future development are emphasized. This review effectively promotes the application of the GO-SELEX technique by providing valuable insights and assisting researchers interested in conducting related studies. SIGNIFICANCE AND NOVELTY: To date, no review on the topic of GO-SELEX has been published, making it challenging for researchers to initiate studies in this area. We believe that this review will broaden the SELEX options available to researchers, ensuring that they can meet the growing demand for molecular probes in the scientific domain.