Salicylic acid attenuates brassinosteroid signaling via protein de-S-acylation.

Brassinosteroids (BRs) are important plant hormones involved in many aspects of development. Here, we show that BRASSINOSTEROID SIGNALING KINASEs (BSKs), key components of the BR pathway, are precisely controlled via de-S-acylation mediated by the defense hormone salicylic acid (SA). Most Arabidopsis BSK members are substrates of S-acylation, a reversible protein lipidation that is essential for their membrane localization and physiological function. We establish that SA interferes with the plasma membrane localization and function of BSKs by decreasing their S-acylation levels, identifying ABAPT11 (ALPHA/BETA HYDROLASE DOMAIN-CONTAINING PROTEIN 17-LIKE ACYL PROTEIN THIOESTERASE 11) as an enzyme whose expression is quickly induced by SA. ABAPT11 de-S-acylates most BSK family members, thus integrating BR and SA signaling for the control of plant development. In summary, we show that BSK-mediated BR signaling is regulated by SA-induced protein de-S-acylation, which improves our understanding of the function of protein modifications in plant hormone cross talk.

S-acylation of a non-secreted peptide controls plant immunity via secreted-peptide signal activation.

Small peptides modulate multiple processes in plant cells, but their regulation by post-translational modification remains unclear. ROT4 (ROTUNDIFOLIA4) belongs to a family of Arabidopsis non-secreted small peptides, but knowledge on its molecular function and how it is regulated is limited. Here, we find that ROT4 is S-acylated in plant cells. S-acylation is an important form of protein lipidation, yet so far it has not been reported to regulate small peptides in plants. We show that this modification is essential for the plasma membrane association of ROT4. Overexpression of S-acylated ROT4 results in a dramatic increase in immune gene expression. S-acylation of ROT4 enhances its interaction with BSK5 (BRASSINOSTEROID-SIGNALING KINASE 5) to block the association between BSK5 and PEPR1 (PEP RECEPTOR1), a receptor kinase for secreted plant elicitor peptides (PEPs), thereby activating immune signaling. Phenotype analysis indicates that S-acylation is necessary for ROT4 functions in pathogen resistance, PEP response, and the regulation of development. Collectively, our work reveals an important role for S-acylation in the cross-talk of non-secreted and secreted peptide signaling in plant immunity.

The genetic algorithm-aided three-stage ensemble learning method identified a robust survival risk score in patients with glioma.

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P < 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients.

A parylene/graphene UV photodetector with ultrahigh responsivity and long term stability.

Long term stability, high responsivity, and fast response speed are essential for the commercialization of graphene photodetectors (GPDs). In this work, a parylene/graphene UV photodetector with long term stability, ultrahigh responsivity and fast response speed, is demonstrated. Parylene as a stable physical and chemical insulating layer reduces the environmental sensitivity of graphene, and enhances the performances of GPDs. In addition, utilizing bilayer electrodes reduces the buckling and damage of graphene after transferring. The parylene/graphene UV photodetector exhibits an ultrahigh responsivity of 5.82 × 105AW-1under 325 nm light irradiation at 1 V bias. Additionally, it shows a fast response speed with a rise time of 80µs and a fall time of 17µs, and a long term stability at 405 nm wavelength which is absent in the device without parylene. The parylene/graphene UV photodetector possesses superior performances. This paves the way for the commercial application of the high-performance graphene hybrid photodetectors, and provides a practical method for maintaining the long term stability of two dimensional (2D) materials.

Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer.

In the realm of colon carcinoma, significant genetic and epigenetic diversity is observed, underscoring the necessity for tailored prognostic features that can guide personalized therapeutic strategies. In this study, we explored the association between the type 2 bitter taste receptor (TAS2Rs) family-related genes and colon cancer using RNA-sequencing and clinical datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Our preliminary analysis identified seven TAS2Rs genes associated with survival using univariate Cox regression analysis, all of which were observed to be overexpressed in colon cancer. Subsequently, based on these seven TAS2Rs prognostic genes, two colon cancer molecular subtypes (Cluster A and Cluster B) were defined. These subtypes exhibited distinct prognostic and immune characteristics, with Cluster A characterized by low immune cell infiltration and less favorable outcomes, while Cluster B was associated with high immune cell infiltration and better prognosis. Finally, we developed a robust scoring system using a gradient boosting machine (GBM) approach, integrated with the gene-pairing method, to predict the prognosis of colon cancer patients. This machine learning model could improve our predictive accuracy for colon cancer outcomes, underscoring its value in the precision oncology framework.

Narrative enhancement and cognitive therapy for perceived stigma of chronic schizophrenia: A multicenter randomized controlled trial study.

This study explored the effects of NECT on self-stigma among people with schizophrenia. Eighty-six participants were recruited and assigned to two groups. The NECT group received 20-session group meetings, while the control group received routine care. Self-stigma was measured by Internalized Stigma of Mental Illness Scale (ISMIS) and Discrimination and Stigma Scale (DISC). Generalized estimating equations were employed to explore the intervention's effectiveness. The NECT group showed a significant reduction in ISMIS total scores after 20 sessions and Stopping Self subscale scores of DISC decreased over time. The intervention is effective for improving self-stigma in people with schizophrenia.

Automated Stanford classification of aortic dissection using a 2-step hierarchical neural network at computed tomography angiography.

OBJECTIVES: This study aimed to evaluate the feasibility of automatic Stanford classification of classic aortic dissection (AD) using a 2-step hierarchical neural network. METHODS: Between 2015 and 2019, 130 arterial phase series (57 type A, 43 type B, and 30 negative cases) in aortic CTA were collected for the training and validation. A 2-step hierarchical model was built including the first step detecting AD and the second step predicting the probability (0-1) of Stanford types. The model's performance was evaluated with an off-line prospective test in 2020. The sensitivity and specificity for Stanford type A, type B, and no AD (Sens A, B, N and Spec A, B, N, respectively) and Cohen's kappa were reported. RESULTS: Of 298 cases (22 with type A, 29 with type B, and 247 without AD) in the off-line prospective test, the Sens A, Sens B, and Sens N were 95.45% (95% confidence interval [CI], 77.16-99.88%), 79.31% (95% CI, 60.28-92.01%), and 93.52% (95% CI, 89.69-96.25%), respectively. The Spec A, Spec B, and Spec N were 98.55% (95% CI, 96.33-99.60%), 94.05% (95% CI, 90.52-96.56%), and 94.12% (95% CI, 83.76-98.77%), respectively. The classification rate achieved 92.28% (95% CI, 88.64-95.04%). The Cohen's kappa was 0.766 (95% CI, 0.68-0.85; p < 0.001). CONCLUSIONS: Stanford classification of classic AD can be determined by a 2-step hierarchical neural network with high sensitivity and specificity of type A and high specificity in type B and no AD. KEY POINTS: • The Stanford classification for aortic dissection is widely adopted and divides it into Stanford type A and type B based on the ascending thoracic aorta dissected or not. • The 2-step hierarchical neural network for Stanford classification of classic aortic dissection achieved high sensitivity (95.45%) and specificity (98.55%) of type A and high specificity in type B and no aortic dissection (94.05% and 94.12%, respectively) in 298 test cases. • The 2-step hierarchical neural network demonstrated moderate agreement (Cohen's kappa: 0.766, p < 0.001) with cardiovascular radiologists in detection and Stanford classification of classic aortic dissection in 298 test cases.

High sensitivity UV photodetectors based on low-cost TiO2P25-graphene hybrids.

Photodetectors (PDs) are the core component of multiple commercial optical sensing systems. Currently, the detection of ultra-weak ultraviolet (UV) optical signals is becoming increasingly important for wide range of applications in civil and military industries. Due to its wide band gap, low cost, and long-term stability, titanium dioxide (TiO2) is an attractive material for UV photodetection. A kind of low-cost TiO2nanomaterial (named as P25) manufactured by flame hydrolysis is an easily available commercial material. However, a low-cost and high-sensitivity UV PD based on P25 has not been achieved until now. Here, a hybrid UV PD with monolayer CVD graphene covered by a thin film of P25 quantum dots was prepared for the first time, and its responsivity was approximately 105A W-1at 365 nm wavelength. The response time and recovery time of the UV PD were 32.6 s and 34 s, respectively. Strong light absorption and photocontrolled oxygen adsorption of the P25 layer resulted in high UV sensitivity. The UV PDs proposed in this work have great potential for commercialization due to their low cost and high sensitivity.

Bullous Pemphigoid and Diabetes medications: A disproportionality analysis based on the FDA Adverse Event Reporting System.

Background: The world's first Diabetes Medications (Insulin) was marketed in October 1923. Some studies suggested the association of diabetes medications with Bullous Pemphigoid (BP), especially the Dipeptidyl Peptidase 4 (DPP-4) inhibitors. The study aims to detect an association between diabetes medications (focusing on DPP-4 inhibitors) and bullous pemphigoid based on FDA Adverse Event Reporting System (FAERS). Methods: All spontaneous reports of diabetes medications inhibitors-related BP recorded in the FAERS between March 2004 and August 2020 were included in the present study. Disproportionality analysis was performed to find the signal between diabetes medications and BP. The Chi-Squared with Yates' correction (χ2 Yates), proportional reporting ratio (PRR) and the lower limit of the 95% confidence interval of the Reporting Odds Ratio (ROR025) were calculated as a measure. A signal was detected when ROR025 > 1, PRR > 2, χ2 Yates > 4 and at least 3 cases. Results: There were 3770 reports for BP in FAERS. The strongest signal for diabetes medications-BP association were DDP-4 inhibitors (ROR025: 13.700, PRR: 15.408), followed by Meglitinides (ROR025: 12.708, PRR: 16.777), Non-sulfonylureas (ROR025: 6.434, PRR: 7.016), Alpha-glucosidase inhibitors (ROR025: 6.105, PRR: 10.738), Sulfonylureas (ROR025:2.655, PRR: 3.200). Conclusions: This study detected a strong signal between BP and DDP-4 inhibitors, alpha-glucosidase inhibitors, meglitinides, non-sulfonylureas, and sulfonylureas in FAERS. The signal was significantly higher with alogliptin than with the other DPP-4 inhibitors. The study doesn't suggest the association between the incretin mimetics, insulin, SGLT-2 inhibitors, thiazolidinediones and BP in FAERS.

Association between the pattern of mobile phone use and sleep quality in Northeast China college students.

OBJECTIVES: Currently, mobile penetration is high amongst college students. The aims of this study were to investigate the characteristics of mobile phone use and to explore the influence of mobile phone use characteristics on sleep quality amongst college students. METHODS: From December 2016 to January 2017, we collected mobile phone use characteristics and sleep quality data using the Pittsburgh Sleep Quality Index (PSQI) and standardised questionnaires that were answered by 4500 medical university students in Liaoning Province (actual response rate of 94%, n = 4234 college students). This study used the SPSS 21.0 software to establish the database and perform the statistical analysis. RESULTS: One hundred percent of the college students had mobile phones and used mobile phones for entertainment (91%), work (51%), obtaining information (61%), and other purposes (23%). Additionally, there was a statistically significant difference in the PSQI score between students who held the phone at a distance of more than 10 cm from their eyes and those who held it a distance of less than 10 cm (P = 0.002). Multiple logistic regression analysis showed that the risk of poor sleep quality was 1.21-1.53 times higher for those who spent more than 5 h a day using their phones and 1.41-1.59 times higher for those who used their phones for more than half an hour before going to bed when the lights were off. CONCLUSIONS: Daily cumulative mobile phone use and use with the lights off before sleep are associated with poorer sleep quality.

Investigating the interpretability of fetal status assessment using antepartum cardiotocographic records.

BACKGROUND: Cardiotocography (CTG) interpretation plays a critical role in prenatal fetal monitoring. However, the interpretation of fetal status assessment using CTG is mainly confined to clinical research. To the best of our knowledge, there is no study on data analysis of CTG records to explore the causal relationships between the important CTG features and fetal status evaluation. METHODS: For analyses, 2126 cardiotocograms were automatically processed and the respective diagnostic features measured by the Sisporto program. In this paper, we aim to explore the causal relationships between the important CTG features and fetal status evaluation. First, we utilized data visualization and Spearman correlation analysis to explore the relationship among CTG features and their importance on fetal status assessment. Second, we proposed a forward-stepwise-selection association rule analysis (ARA) to supplement the fetal status assessment rules based on sparse pathological cases. Third, we established structural equation models (SEMs) to investigate the latent causal factors and their causal coefficients to fetal status assessment. RESULTS: Data visualization and the Spearman correlation analysis found that thirteen CTG features were relevant to the fetal state evaluation. The forward-stepwise-selection ARA further validated and complemented the CTG interpretation rules in the fetal monitoring guidelines. The measurement models validated the five latent variables, which were baseline category (BCat), variability category (VCat), acceleration category (ACat), deceleration category (DCat) and uterine contraction category (UCat) based on fetal monitoring knowledge and the above analyses. Furthermore, the interpretable models discovered the cause factors of fetal status assessment and their causal coefficients to fetal status assessment. For instance, VCat could predict BCat, and UCat could predict DCat as well. ACat, BCat and DCat directly affected fetal status assessment, where ACat was the important causal factor. CONCLUSIONS: The analyses revealed the interpretation rules and discovered the causal factors and their causal coefficients for fetal status assessment. Moreover, the results are consistent with the computerized fetal monitoring and clinical knowledge. Our approaches are conducive to evidence-based medical research and realizing intelligent fetal monitoring.

Expression and Prognostic Characteristics of m6A RNA Methylation Regulators in Colon Cancer.

Colon cancer is a common and leading cause of death and malignancy worldwide. N6-methylation of adenosine (m6A) is the most common reversible mRNA modification in eukaryotes, and it plays a crucial role in various biological functions in vivo. Dysregulated expression and genetic changes of m6A regulators have been correlated with tumorigenesis, cancer cell proliferation, tumor microenvironment, and prognosis in cancers. This study used RNA-seq and colon cancer clinical data to explore the relationship between N6-methylation and colon cancer. Based on the seven m6A regulators related to prognosis, three molecular subgroups of colon cancer were identified. Surprisingly, we found that each subgroup had unique survival characteristics. We then identified three subtypes of tumors based on 299 m6A phenotype-related genes, and one subtype was characterized as an immunosuppressive tumor and patients in this subtype may be more suitable for immunotherapy than other subtypes. Finally, using m6A-related genes and clinical information from The Cancer Genome Atlas cohort, we constructed a prognosis model, and this model could be used to predict the prognosis of patients in clinics.

Pet ownership and risk of dying from cancer: observation from a nationally representative cohort.

We longitudinally examined the relationship between pet ownership and risk of dying from cancer in a nationally representative cohort of 13,725 adults in the Third National Health and Nutrition Examination Survey, 1988-1994. The vital status was followed through 31 December 2010. Women who owned pets (any type) presented one-year shorter survival time (15.88 years) than non-pet owner (16.83 years). A larger difference of survival time was particularly seen in bird owners (13.01 years) compared to non-bird owners (16.82 years). After adjusting for potential confounders, hazard ratio (HR) of dying from cancer associated with any type of pets was 1.08 (95% CI = 0.77-1.50) for men and 1.40 (1.01-1.93) for women. The association in women was presumably driven by owning birds [HR 2.41 (1.34-4.31)] or cats [HR 1.48 (0.97-2.24)]. Keeping birds and cats in the household was associated with an increased risk of dying from cancer, especially in women.

Measurements of Nonsinglet Moments of the Nucleon Structure Functions and Comparison to Predictions from Lattice QCD for Q

We present extractions of the nucleon nonsinglet moments utilizing new precision data on the deuteron F_{2} structure function at large Bjorken-x determined via the Rosenbluth separation technique at Jefferson Lab Experimental Hall C. These new data are combined with a complementary set of data on the proton previously measured in Hall C at similar kinematics and world datasets on the proton and deuteron at lower x measured at SLAC and CERN. The new Jefferson Lab data provide coverage of the upper third of the x range, crucial for precision determination of the higher moments. In contrast to previous extractions, these moments have been corrected for nuclear effects in the deuteron using a new global fit to the deuteron and proton data. The obtained experimental moments represent an order of magnitude improvement in precision over previous extractions using high x data. Moreover, recent exciting developments in lattice QCD calculations provide a first ever comparison of these new experimental results with calculations of moments carried out at the physical pion mass, as well as a new approach that first calculates the quark distributions directly before determining moments.

Dihydrotanshinone exhibits an anti-inflammatory effect in vitro and in vivo through blocking TLR4 dimerization.

Dihydrotanshinone (DHT), one of the major ingredients of Salvia miltiorrhiza Bunge (Danshen), displays many bioactivities. However, the activity and underlying mechanism of DHT in anti-inflammation have not yet been elucidated. In this study, we investigated the anti-inflammatory activity and molecular mechanism of action of DHT both in vitro and in vivo. Our data showed that DHT significantly decreased the release of inflammatory cytokines tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and IL-1ß in lipopolysaccharide (LPS)-stimulated RAW264.7 cells, THP-1 cells, and bone marrow-derived macrophages (BMDMs), and altered the expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS). In addition, flow cytometry results indicated that DHT reduced the calcium influx, and generation of reactive oxygen species (ROS), and nitric oxide (NO) generation in LPS-stimulated RAW264.7 cells. Moreover, DHT suppressed the transcription of nuclear factor-κB (NF-κB), the expressions of NF-κB proteins, and nuclear translocation of NF-κB/p65, thereby suggesting that the NF-κB pathway played a role in the anti-inflammatory action of DHT. In addition, DHT attenuated LPS-challenged activator protein-1 (AP-1) activity, resulting from interference of the mitogen-activated protein kinase (MAPK) pathway. The molecular docking simulation of DHT to toll-like receptor 4 (TLR4) suggested that DHT binds to the active sites of TLR4 to block TLR4 dimerization, which was further corroborated by cellular thermal shift assay and co-immunoprecipitation (Co-IP) experiments. Furthermore, the recruitment of myeloid differentiation primary response gene 88 (MyD88) and the expression of transforming growth factor-b (TGF-b)-activated kinase 1 (p-TAK1) were disturbed by the inhibition of TLR4 dimerization. Thus, investigating the molecular mechanism of DHT indicated that TLR4-MyD88-NF-κB/MAPK signaling cascades were involved in the anti-inflammatory activity of DHT in vitro. In in vivo mouse models, DHT significantly ameliorated LPS-challenged acute kidney injury, inhibited dimethylbenzene-induced mouse ear oedema, and rescued LPS-induced sepsis in mice. Taken together, our results indicated that DHT exhibited significant anti-inflammatory activity both in vitro and in vivo, suggesting that DHT may be a potential therapeutic agent for inflammatory diseases.

Diethyl Blechnic Exhibits Anti-Inflammatory and Antioxidative Activity via the TLR4/MyD88 Signaling Pathway in LPS-Stimulated RAW264.7 Cells.

Inflammation is a common pathogenesis in many diseases. Salvia miltiorrhiza Bunge (Danshen), a traditional Chinese medicine, has been considered to have good anti-inflammatory effects. In the present study, we investigated the anti-inflammatory effect of diethyl blechnic (DB), a novel compound isolated from Danshen, and its possible mechanisms in lipopolysaccharide (LPS)-induced RAW264.7 macrophages. The results showed that DB can inhibit the LPS-induced pro-inflammatory cytokines release of prostaglandin E2 (PGE2) and mRNA expression of TNF-α, IL-6, and IL-1ß. In addition, the results of the flow cytometry assay and the fluorometric intracellular ROS kit assay indicated that DB reduced the generation of ROS in LPS-stimualted RAW264.7 cells. DB reversed the LPS-induced loss of the mitochondrial membrane potential (MMP). Furthermore, DB suppressed the LPS-stimulated increased expression of Toll-like receptor 4 (TLR4), myeloid differential protein-88 (MyD88) and phosphorylation of TAK1, PI3K, and AKT. DB promoted NF-E2-related factor 2 (Nrf2) into the nucleus, increased the expression of heme oxygenase-1 (HO-1) and NAD(P)H dehydrogenase [quinone] 1 (NQO1) and reduced the expression of Keap1. In summary, DB may inhibit LPS-induced inflammation, which mainly occurs through TLR4/MyD88 and oxidative stress signaling pathways in RAW264.7 cells.

Licochalcone A Prevents Platelet Activation and Thrombus Formation through the Inhibition of PLCγ2-PKC, Akt, and MAPK Pathways.

Platelet activation is involved in cardiovascular diseases, such as atherosclerosis and ischemic stroke. Licochalcone A (LA), an active ingredient of licorice, exhibits multiple biological activities such as anti-oxidation and anti-inflammation. However, its role in platelet activation remains unclear. Therefore, the study investigated the antiplatelet mechanism of LA. Our data revealed that LA (2-10 µM) concentration dependently inhibited platelet aggregation induced by collagen, but not thrombin and U46619. LA markedly attenuated collagen-stimulated ATP release, P-selectin secretion, calcium mobilization, and GPIIbIIIa activation, but did not interfere with the collagen binding to platelets. Moreover, LA significantly reduced the activation of PLCγ2, PKC, Akt and MAPKs. Thus, LA attenuates platelet activation, possibly by inhibiting collagen receptor downstream signaling but not by blocking the collagen receptors. In addition, LA prevented adenosine diphosphate (ADP)-induced acute pulmonary thrombosis, fluorescein sodium-induced platelet thrombus formation, and middle cerebral artery occlusion/reperfusion-induced brain injury in mice, but did not affect normal hemostasis. This study demonstrated that LA effectively reduced platelet activation and thrombus formation, in part, through the inhibition of PLCγ2-PKC, Akt, and MAPK pathways, without the side effect of bleeding. These findings also indicate that LA may provide a safe and alternative therapeutic approach for preventing thromboembolic disorders such as stroke.

Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges.

Aerobic composting stands as a widely-adopted method for treating organic solid waste (OSW), simultaneously producing organic fertilizers and soil amendments. This biologically-driven biochemical reaction process, however, presents challenges due to its complex non-linear metabolism and the heterogeneous nature of the solid medium. These characteristics inherently limit the simulation accuracy and efficiency optimization in aerobic composting. Recently, significant efforts have been made to simulate and control composting process parameters, as well as predicting and optimizing composting product quality. Notably, the integration of machine learning (ML) in aerobic composting of organic waste has garnered considerable attention for its applicability and predictive capability in exploring the complex non-linear relationships of organic waste composting parameters. Despite numerous studies on ML applications in OSW composting, a systematic review of research findings in this field is lacking. This study offers a systematic overview of the application level, current status, and versatility of ML in OSW composting. It spans various aspects, such as compost maturity, environmental pollutants, nutrients, moisture, heat loss, and microbial metabolism. The survey reveals that ML-intervention predominantly focuses on compost maturity and environmental pollutants, followed by nutrients, moisture, heat loss, and microbial activity. The most commonly employed predictive models and optimization algorithms are artificial neural networks (47%) and genetic algorithms (10%). These demonstrate high prediction accuracy and maximize composting efficiency in the simulation and prediction of organic waste composting, alongside regulation of key parameters. Deep neural networks and ensemble learning models prove effective in achieving superior predictive performance by selecting feature variables in compost maturity and pollutant residue prediction of organic waste composting in a simpler and more objective manner.

Determining the abundance, composition and spatial distribution of organohalogens in marine sediments using combustion-ion chromatography.

Understanding the distribution of halogenated organic compounds (HOCs) in marine sediments is essential for understanding the marine carbon and halogen cycling, and also important for assessing the ecosystem health. In this study, a method based on combustion-ion chromatography was developed for determination of the composition and abundance of HOCs in marine sediments. The method showed high accuracy, precision and reproducibility in determining the content of adsorbable organic halogens (AOX), including fluorine, chlorine and bromine (AOF, AOCl, AOBr) and the corresponding insoluble organic halogens (IOF, IOCl, IOBr, IOX), as well as total organic halogen contents (TOX). Application of the method in coastal and deep-sea sediments revealed high ratios of organic halogens in the organic carbon pool of marine sediments, suggesting that organic halogen compounds represent an important yet previously overlooked stock of carbon and energy in marine sediments. Both the TOX and the proportion of organohalogens in organic carbon (X:C ratio) showed an increasing trend from the coast to the deep-sea sediments, indicating an increased significance of HOCs in deep-sea environments. The developed method and the findings of this study lay the foundation for further studies on biogeochemical cycling of HOCs in the ocean.