An inverse association of dietary choline with atherosclerotic cardiovascular disease among US adults: a cross-sectional NHANES analysis.

BACKGROUND: The role of diet choline in atherosclerotic cardiovascular disease (ASCVD) is uncertain. Findings from animal experiments are contradictory while there is a lack of clinical investigations. This study aimed to investigate the association between choline intake and ASCVD based on individuals from the National Health and Nutrition Examination Survey (NHANES) database. METHODS: This cross-sectional study was conducted in 5525 individuals from the NHANES between 2011 and 2018. Participants were categorized into the ASCVD (n = 5015) and non-ASCVD (n = 510) groups. Univariable and multivariable-adjusted regression analyses were employed to investigate the relationship between diet choline and pertinent covariates. Logistic regression analysis and restricted cubic spline analysis were used to evaluate the association between choline intake and ASCVD. RESULTS: ASCVD participants had higher choline intake compared to those without ASCVD. In the higher tertiles of choline intake, there was a greater proportion of males, married individuals, highly educated individuals, and those with increased physical activity, but a lower proportion of smokers and drinkers. In the higher tertiles of choline intake, a lower proportion of individuals had a history of congestive heart failure and stroke. After adjusting for age, gender, race, ethnicity, and physical activity, an inverse association between choline intake and heart disease, stroke, and ASCVD was found. A restricted cubic spline analysis showed a mirrored J-shaped relationship between choline and ASCVD, stroke and congestive heart failure in males. There was no association between dietary choline and metabolic syndrome. CONCLUSION: An inverse association was observed between choline intake and ASVCD among U.S. adults. Further large longitudinal studies are needed to test the causal relationship of choline and ASVCD.

Detecting early-warning biomarkers associated with heart-exosome genetic-signature for acute myocardial infarction: A source-tracking study of exosome.

The genetic information of plasma total-exosomes originating from tissues have already proven useful to assess the severity of coronary artery diseases (CAD). However, plasma total-exosomes include multiple sub-populations secreted by various tissues. Only analysing the genetic information of plasma total-exosomes is perturbed by exosomes derived from other organs except the heart. We aim to detect early-warning biomarkers associated with heart-exosome genetic-signatures for acute myocardial infarction (AMI) by a source-tracking analysis of plasma exosome. The source-tracking of AMI plasma total-exosomes was implemented by deconvolution algorithm. The final early-warning biomarkers associated with heart-exosome genetic-signatures for AMI was identified by integration with single-cell sequencing, weighted gene correction network and machine learning analyses. The correlation between biomarkers and clinical indicators was validated in impatient cohort. A nomogram was generated using early-warning biomarkers for predicting the CAD progression. The molecular subtypes landscape of AMI was detected by consensus clustering. A higher fraction of exosomes derived from spleen and blood cells was revealed in plasma exosomes, while a lower fraction of heart-exosomes was detected. The gene ontology revealed that heart-exosomes genetic-signatures was associated with the heart development, cardiac function and cardiac response to stress. We ultimately identified three genes associated with heart-exosomes defining early-warning biomarkers for AMI. The early-warning biomarkers mediated molecular clusters presented heterogeneous metabolism preference in AMI. Our study introduced three early-warning biomarkers associated with heart-exosome genetic-signatures, which reflected the genetic information of heart-exosomes carrying AMI signals and provided new insights for exosomes research in CAD progression and prevention.

Opioids-Induced Long QT Syndrome: A Challenge to Cardiac Health.

The challenge posed by opioid overdose has become a significant concern for health systems due to the complexities associated with drug prohibition, widespread clinical use, and potential abuse. In response, healthcare professionals have primarily concentrated on mitigating the hallucinogenic and respiratory depressant consequences of opioid overdose to minimize associated risks. However, it is crucial to acknowledge that most opioids possess the capacity to prolong the QT interval, particularly in cases of overdose, thereby potentially resulting in severe ventricular arrhythmias and even sudden death if timely intervention is not implemented. Consequently, alongside addressing the typical adverse effects of opioids, it is imperative to consider their cardiotoxicity. To enhance comprehension of the correlation between opioids and arrhythmias, identify potential targets for prompt intervention, and mitigate the hazards associated with clinical utilization, an exploration of the interaction between drugs and ion channels, as well as their underlying mechanisms, becomes indispensable. This review primarily concentrates on elucidating the impact of opioid drugs on diverse ion channels, investigating recent advancements in this domain, and attaining a deeper understanding of the mechanisms underlying the prolongation of the QT interval by opioid drugs, along with potential interventions.

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks.

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image-classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf. RESULTS: Weed-detection NNs, including DenseNet, GoogLeNet and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k-fold cross-validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing data sets for estimating weed coverage. The inference speed of ResNet was similar to that of GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network for weed detection and coverage estimation. CONCLUSION: These results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing calculation of the herbicide requirements for variable-rate herbicide applications. The proposed method can be employed in a machine vision-based autonomous site-specific spraying system of smart sprayers. © 2024 Society of Chemical Industry.

Strategies for decellularization, re-cellularIzation and crosslinking in liver bioengineering.

Liver transplantation is the only definitive treatment for end-stage liver disease and its availability is restricted by organ donor shortages. The development of liver bioengineering provides the probability to create a functional alternative to reduce the gap in organ demand and supply. Decellularized liver scaffolds have been widely applied in bioengineering because they can mimic the native liver microenvironment and retain extracellular matrix (ECM) components. Multiple approaches including chemical, physical and biological methods have been developed for liver decellularization in current studies, but a full set of unified criteria has not yet been established. Each method has its advantages and drawbacks that influence the microstructure and ligand landscape of decellularized liver scaffolds. Optimizing a decellularization method to eliminate cell material while retaining as much of the ECM intact as possible is therefore important for biological scaffold applications. Furthermore, crosslinking strategies can improve the biological performance of scaffolds, including reinforcing biomechanics, delaying degradation in vivo and reducing immune rejection, which can better promote the integration of re-cellularized scaffolds with host tissue and influence the reconstruction process. In this review, we aim to present the different liver decellularization techniques, the crosslinking methods to improve scaffold characteristics with crosslinking and the preparation of soluble ECM.

Comprehensive analysis of clinical prognosis and biological significance of CNIH4 in cervical cancer.

BACKGROUND: Cornichon homolog 4 (CNIH4) belongs to the CNIH family. It functions as an oncogene in many tumors. However, CNIH4's significance in the immune landscape and its predictive potential in cervical cancer (CESC) is unexplored. METHODS: CNIH4 levels and its effect on the survival of patients with CESC were evaluated using data retrieved from The Cancer Genome Atlas (TCGA). The oncogenic effect of CNIH4 in CESC was determined using small interfering RNA-mediated transfected cell lines and tumorigenesis experiments in animal models. RESULTS: Higher expression of CNIH4 was found in advanced tumor and pathological stages, as well as lymph node metastasis. CNIH4 expression correlated positively with the infiltration of macrophages M2 and resting dendritic cells into the affected tissue. Additionally, functional enrichment of RNA-sequencing of CNIH4-knocked down CESC cell lines showed the association of CNIH4 to the PI3K-Akt signaling pathway. Single-sample gene set enrichment analysis highlighted several immune pathways that were elevated in the CESC samples with enhanced levels of CNIH4, including Type-I and Type-II IFN-response pathways. The impact of CNIH4 on drug sensitivity was further assessed using the GDSC database. As CNIH4 is linked to the immune landscape in CESC, this study determined a four-gene risk prediction signature utilizing CNIH4-related immunomodulators. The risk score quantified from the prediction signature was an independent predictive indicator in CESC. Receiver operating characteristic curve analysis verified the good predictive ability of the four-gene signature in TCGA-CESC cohort. Thus, the CNIH4-related model showed potential as an auxiliary TNM staging system tool. CONCLUSION: CNIH4 may be an effective predictive biomarker for patients with cervical cancer, thus providing new ideas and research directions for CESC.

FOXD1 expression-based prognostic model for uveal melanoma.

FOXD1, a new member of the FOX transcription factor family, serves as a mediator and biomarker for cell reprogramming. But its contribution to prognosis of uveal melanoma (UVM) is unclear. This study demonstrated that FOXD1 might promote tumor growth and invasion, because FOXD1 expression was negatively correlated with overall survival, progression-free survival, and disease-specific survival in UVM patients. This conjecture was verified in cell culture with human uveal melanoma cell line (MUM2B) as model cells. Additionally, the biological mechanisms of FOXD1 based on FOXD1-related genomic spectrum, molecular pathways, tumor microenvironment, and drug treatment sensitivity were examined using The Cancer Genome Atlas (TCGA) database, aiming to reasonably explain why FOXD1 leads to poor prognosis of UVM. On these bases, a novel tumor prognostic model was established using the FOXD1-related immunomodulators TMEM173, TNFRSF4, TNFSF13, and ULBP1, which will enable the stratification of disease seriousness and clinical treatment for patients.

Risk factors analysis of surgical site infections in postoperative colorectal cancer: a nine-year retrospective study.

BACKGROUND: Colorectal cancer (CRC) patients undergoing surgery are at a high risk of developing surgical site infections (SSIs), which contribute to increased morbidity, prolonged hospitalization, and escalated healthcare costs. Understanding the incidence, risk factors, and impact of SSIs is crucial for effective preventive strategies and improved patient outcomes. METHODS: This retrospective study analyzed data from 431 CRC patients who underwent surgery at Huangshan Shoukang Hospital between 2014 and 2022. The clinical characteristics and demographic information were collected. The incidence and impact of SSIs were evaluated, and independent risk factors associated with SSIs were identified using multivariable logistic regresison. A nomogram plot was constructed to predict the likelihood of SSIs occurrence. RESULTS: The overall incidence rate of SSIs was 7.65% (33/431). Patients with SSIs had significantly longer hospital stays and higher healthcare costs. Risk factors for SSIs included elevated Body Mass Index (BMI) levels (odds ratio, 1.12; 95% CI, 1.02-1.23; P = 0.017), the presence of diabetes (odds ratio, 3.88; 95% CI, 1.42 - 9.48; P = 0.01), as well as specific surgical factors such as open surgical procedures (odds ratio, 2.39; 95% CI [1.09; 5.02]; P = 0.031), longer surgical duration (odds ratio, 1.36; 95% CI [1.01; 1.84]; P = 0.046), and the presence of a colostomy/ileostomy (odds ratio, 3.17; 95% CI [1.53; 6.62]; P = 0.002). Utilizing multivariable regression analysis, which encompassed factors such as open surgical procedures, the presence of diabetes and colostomy/ileostom, the nomogram plot functions as a visual aid in estimating the individual risk of SSIs for patients. CONCLUSIONS: Risk factors for SSIs included higher BMI levels, the presence of diabetes, open surgical procedures, longer surgical duration, and the presence of colostomy/ileostomy. The nomogram plot serves as a valuable tool for risk assessment and clinical decision-making.

Deciphering three-dimensional bioanode configuration for augmenting power generation and nitrogen removal in air-cathode microbial fuel cells.

In this study, the engineering-oriented three-dimensional (3D) bioanode concept was applied, demonstrating that spiral-stairs-like/rolled carbon felt (SCF/RCF) configurations achieved good performances in air-cathode microbial fuel cells (ACMFCs). With the 3D anodes, ACMFCs generated significantly higher power densities of 1535 mW/m3 (SCF) and 1800 mW/m3 (RCF), compared with that of a traditional flat carbon felt anode (FCF, 315 mW/m3). The coulombic efficiency of 15.39 % at SCF anode and 14.34 % at RCF anode also is higher than the 7.93 % at FCF anode. The 3D anode ACMFCs exhibited favorable removal of chemical oxygen demand (96 % of SCF and RCF) and total nitrogen (97 % of SCF, 99 % of RCF). Further results show that three-dimensional anode structures could enrich more electrode surface biomass and diversify the biofilm microbial communities for promoting bioelectroactivity, denitrification, and nitrification. These results demonstrate that three-dimensional anodes with active biofilm is a promising strategy for creating scalable MFCs-based wastewater treatment system.

Precision Joint RF Measurement of Inter-Satellite Range and Time Difference and Scalable Clock Synchronization for Multi-Microsatellite Formations.

The rapid development of multi-satellite formations requires inter-satellite radio frequency (RF) measurement to be both precise and scalable. The navigation estimation of multi-satellite formations using a unified time reference demands the simultaneous RF measurement of the inter-satellite range and time difference. However, high-precision inter-satellite RF ranging and time difference measurements are investigated separately in existing studies. Different from the conventional two-way ranging (TWR) method, which is limited by its reliance on a high-performance atomic clock and navigation ephemeris, asymmetric double-sided two-way ranging (ADS-TWR)-based inter-satellite measurement schemes can eliminate such reliance while ensuring measurement precision and scalability. However, ADS-TWR was originally proposed for ranging-only applications. In this study, by fully exploiting the time-division non-coherent measurement characteristic of ADS-TWR, a joint RF measurement method is proposed to obtain the inter-satellite range and time difference simultaneously. Moreover, a multi-satellite clock synchronization scheme is proposed based on the joint measurement method. The experimental results show that when inter-satellite ranges are hundreds of kilometers, the joint measurement system has a centimeter-level accuracy for ranging and a hundred-picosecond-level accuracy for time difference measurement, and the maximum clock synchronization error was only about 1 ns.

Evaluation of convolutional neural networks for herbicide susceptibility-based weed detection in turf.

Deep learning methods for weed detection typically focus on distinguishing weed species, but a variety of weed species with comparable plant morphological characteristics may be found in turfgrass. Thus, it is difficult for deep learning models to detect and distinguish every weed species with high accuracy. Training convolutional neural networks for detecting weeds susceptible to herbicides can offer a new strategy for implementing site-specific weed detection in turf. DenseNet, EfficientNet-v2, and ResNet showed high F1 scores (≥0.986) and MCC values (≥0.984) to detect and distinguish the sub-images containing dollarweed, goosegrass, old world diamond-flower, purple nutsedge, or Virginia buttonweed growing in bermudagrass turf. However, they failed to reliably detect crabgrass and tropical signalgrass due to the similarity in plant morphology. When training the convolutional neural networks for detecting and distinguishing the sub-images containing weeds susceptible to ACCase-inhibitors, weeds susceptible to ALS-inhibitors, or weeds susceptible to synthetic auxin herbicides, all neural networks evaluated in this study achieved excellent F1 scores (≥0.995) and MCC values (≥0.994) in the validation and testing datasets. ResNet demonstrated the fastest inference rate and outperformed the other convolutional neural networks on detection efficiency, while the slow inference of EfficientNet-v2 may limit its potential applications. Grouping different weed species growing in turf according to their susceptibility to herbicides and detecting and distinguishing weeds by herbicide categories enables the implementation of herbicide susceptibility-based precision herbicide application. We conclude that the proposed method is an effective strategy for site-specific weed detection in turf, which can be employed in a smart sprayer to achieve precision herbicide spraying.

Complement Heat Tolerance as a Marker of Protein Fragility and Its Clinical Significance.

This study aimed to establish a complement tolerance test (CTT) as a marker of protein fragility and discuss its clinical significance. Total complement activity (TCA) of serum was measured using a self-hemolysis colorimetric method. Human O-erythrocytes and rabbit anti-human O-erythrocyte antibodies were used to replace sheep erythrocytes and the corresponding hemolysin for the hemolysis test, respectively. The antigen-antibody specific binding activated the classical pathway of complement, generating a membrane attack complex, and the red blood cells rupture. A CTT was established to measure complement heat tolerance according to the sensitivity of complement proteins to temperature, which was calculated according to differences in TCA at different temperatures. The smaller the CTT the stronger the complement resistance to heat. The method was applied to the detection of diabetic patients and healthy controls. The mean value of CTT (mean) = 0.063 ± 0.003 with a coefficient of variation of 4.8% for the same specimen tested for complementary thermal resistance on 5 consecutive days, which is a good stability of the assay. Application of CTT on samples from patients with different ages revealed significantly higher mean CTT values for elderly patients (≥60-years old) relative to those for younger patients (20-40-years old) (p < 0.05). In addition, the mean CTT values for diabetic patients were significantly higher than those for healthy patients (p < 0.001). We successfully established a method that uses complement thermal resistance as a marker of protein fragility, with the results demonstrating the ability of the CTT identify age- and disease-related variations in patient samples and its potential efficacy for clinical application.

Coupling mixotrophic denitrification and electroactive anodic nitrification by nitrate addition for promoting current generation and nitrogen removal.

Nitrate promotes anodic denitrification and fasts organic matter removal in microbial fuel cells (MFCs). However, it suffers from poor total nitrogen (TN) removal and current recovery. In this study, some novel electroactive nitrifying/denitrifying bacteria (ENDB) were introduced in a single chambered air-cathode MFC to investigate the performance of this device and the microbial community shift by adding nitrate. Results showed a similar disturbance in current output by adding nitrate during a short-term operation. However, a stable and reproducible current increase was achieved in the continuous experiment. A maximum current of 0.76 A m-3 and a maximum TN removal of >99 % were accomplished. The corresponding corrected coulombic efficiency was approximately 18 %. Under repeatable batches, a sharp decrease in chemical oxygen demand (COD) with feeding nitrate confirmed the temporary competition on electron donors through heterotrophic denitrification. The later current increase and nitrite detection occurring without metabolized COD could be considered evidence of electroactive anodic nitrification. The ENDB biofilm successfully coupled mixotrophic denitrification and electroactive anodic nitrification. It eventually promoted TN removal. In the process, genera Pseudoxanthomonas, Thauera, and Pseudomonas were enriched in the anodic ENDB biofilms. Cyclic voltammetry data confirmed the promotion of the electron transfer process by biofilms. The bacterial function predication revealed that the genes related to nitrogen removal and electron transfer were upregulated. Therefore, mixotrophic denitrification and electroactive anodic nitrification processes facilitated power recovery with the high efficiency of pollutant removal, finally ensuring water body security.

Role played by the physical structure of carbon anode materials in MFC biosensor for BOD measurement.

Microbial fuel cell (MFC) has been extensively studied as a biosensor for determining biochemical oxygen demand (BOD). The method for quantifying BOD by employing coulombic yield (Q) of a bio-electrochemical degradation process obtained from MFC biosensors is referred to as BODQ. The physical structures of anode materials greatly affect the sensitivity and accuracy of the biosensor. In this work, the effects of carbon cloth (CC) and carbon felt (CF) as anode substrate materials on the BODQ determination efficiencies were studied. The CF-MFC biosensor showed higher BODQ response than that of the CC-MFC within 25-400 mg L-1 BOD concentration range, and the test value was very close to the theoretical BOD. The difference is resulting from higher coulombic efficiency (CE) of CF-MFC (64.89-65.38 %) than CC-MFC (55.58-63.51 %). It should be noted that for water samples with low BOD concentrations the physical structures of anode materials play a leading role in CE. For synthetic wastewaters with 25 mg L-1 BOD, the CE of CF-MFC (65.38 %) was 17.63 % higher than that of CC-MFC (55.58 %). In contrast to the densely woven CC coated with thick biofilm, CF with loose carbon fiber and thin biofilm makes it good for organic diffusion and electron transportation, thus contributing to higher and more stable CE. These results indicate that the CF-MFC is more suitable for determining BODQ values over a wide concentration range. This work provides a useful strategy for selecting desirable MFC's anode material as the BOD biosensor. MFC biosensors with high-porosity biological anodes can obtain more accurate BOD test values.

The Advantages, Challenges, and Future of Human-Induced Pluripotent Stem Cell Lines in Type 2 Long QT Syndrome.

Type 2 long QT syndrome (LQT2) is the second most common subtype of long QT syndrome and is caused by mutations in KCHN2 encoding the rapidly activating delayed rectifier potassium channel vital for ventricular repolarization. Sudden cardiac death is a sentinel event of LQT2. Preclinical diagnosis by genetic testing is potentially life-saving.Traditional LQT2 models cannot wholly recapitulate genetic and phenotypic features; therefore, there is a demand for a reliable experimental model. Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) meet this challenge. This review introduces the advantages of the hiPSC-CM model over the traditional model and discusses how hiPSC-CM and gene editing are used to decipher mechanisms of LQT2, screen for cardiotoxicity, and identify therapeutic strategies, thus promoting the realization of precision medicine for LQT2 patients.

Polarization error in resonant micro-optic gyroscope with different waveguide-type ring resonator structures.

The waveguide-type ring resonator (WRR) is the key rotation-sensing element in a resonant micro-optic gyroscope (RMOG). A universal model used to analyze both the polarization characteristics of the WRR and corresponding temperature-related polarization error in the RMOG is presented. It indicates that the polarization problem stems from the excitation of two polarization states within the WRR. Unequal variations of incident lights on the cavity in the two directions can cause bias errors at the RMOG output. With the application of different silica WRRs to the RMOG, the polarization errors are tested and verify the theoretical results. Finally, a segment of tilted waveguide gratings with Brewster's angle is fabricated on the silica waveguide within the cavity. The measured polarization extinction ratio of the output light from the WRR is as high as 35.2 dB. The corresponding temperature dependence of the polarization error is theoretically reduced to 0.0019 (°/s)/°C, which indicates that temperature control is sufficient for a tactical grade RMOG.

Deep learning for detecting herbicide weed control spectrum in turfgrass.

BACKGROUND: Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides. RESULTS: GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F1 scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated. CONCLUSION: These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers.

A deep learning-based method for classification, detection, and localization of weeds in turfgrass.

BACKGROUND: Precision spraying of synthetic herbicides can reduce herbicide input. Previous research demonstrated the effectiveness of using image classification neural networks for detecting weeds growing in turfgrass, but did not attempt to discriminate weed species and locate the weeds on the input images. The objectives of this research were to: (i) investigate the feasibility of training deep learning models using grid cells (subimages) to detect the location of weeds on the image by identifying whether or not the grid cells contain weeds; and (ii) evaluate DenseNet, EfficientNetV2, ResNet, RegNet and VGGNet to detect and discriminate multiple weed species growing in turfgrass (multi-classifier) and detect and discriminate weeds (regardless of weed species) and turfgrass (two-classifier). RESULTS: The VGGNet multi-classifier exhibited an F1 score of 0.950 when used to detect common dandelion and achieved high F1 scores of ≥0.983 to detect and discriminate the subimages containing dallisgrass, purple nutsedge and white clover growing in bermudagrass turf. DenseNet, EfficientNetV2 and RegNet multi-classifiers exhibited high F1 scores of ≥0.984 for detecting dallisgrass and purple nutsedge. Among the evaluated neural networks, EfficientNetV2 two-classifier exhibited the highest F1 scores (≥0.981) for exclusively detecting and discriminating subimages containing weeds and turfgrass. CONCLUSION: The proposed method can accurately identify the grid cells containing weeds and thus precisely locate the weeds on the input images. Overall, we conclude that the proposed method can be used in the machine vision subsystem of smart sprayers to locate weeds and make the decision for precision spraying herbicides onto the individual map cells. © 2022 Society of Chemical Industry.

A Predictive Model for Prognosis and Therapeutic Response in Hepatocellular Carcinoma Based on a Panel of Three MED8-Related Immunomodulators.

The current tumor-node-metastasis (TNM) system is limited in predicting the survival and guiding the treatment of hepatocellular carcinoma (HCC) patients since the TNM system only focuses on the anatomical factors, regardless of the intratumoral molecule heterogeneity. Besides, the landscape of intratumoral immune genes has emerged as a prognostic indicator. The mediator complex subunit 8 (MED8) is a major polymerase regulator and has been described as an oncogene in renal cell carcinoma, but its pathophysiological significance of HCC and its contribution to the prognosis of HCC remain unclear. Here, we aimed to discuss the expression profile and clinical correlation of MED8 in HCC and construct a predictive model based on MED8-related immunomodulators as a supplement to the TNM system. According to our analyses, MED8 was overexpressed in HCC tissues and increased expression of MED8 was an indicator of poor outcome in HCC. The knockdown of MED8 weakened the proliferation, colony forming, and migration of HepG2 and Huh7 cells. Subsequently, a predictive model was identified based on a panel of three MED8-related immunomodulators using The Cancer Genome Atlas (TCGA) database and further validated in International Cancer Genome Consortium (ICGC) database. The combination of the predictive model and the TNM system could improve the performance in predicting the survival of HCC patients. High-risk patients had poor overall survival in TCGA and ICGC databases, as well as in subgroup analysis with early clinicopathology classification. It was also found that high-risk patients had a higher probability of recurrence in TCGA cohort. Furthermore, low-risk score indicated a better response to immunotherapy and drug therapy. This predictive model can be served as a supplement to the TNM system and may have implications in prognosis stratification and therapeutic guidance for HCC.

Membrane penetration of nitrogen and its effects on nitrogen removal in dual-chambered microbial fuel cells.

Owing to membrane penetration, a novel route of nitrogen removal was proposed in a dual-chamber microbial fuel cell with a proton exchange membrane (PEM). The results showed that NH4+-N rapidly migrated across PEM with a mass transfer coefficient (KA) of 1.79 ± 0.51 × 10-4 cm s-1, 50% of which was oxidized to NO3--N in the cathode chamber, then the remainder being eliminated by short-cut nitrification/denitrification. Meanwhile, NO3--N went across the PEM again with a low KA of 5.50 ± 0.24 × 10-6 cm s-1, and was subsequently reduced via anodic denitrification. In the anode, the functional microorganisms were divided into exoelectrogenic bacteria (46.2%) and denitrifying bacteria (37.3%), while the dominated bacteria were mainly affiliated with nitrifying bacteria (19.6%) and aerobic denitrifying bacteria (52.9%) in the cathode. These findings provide a new insight into nitrogen removal during bioelectrochemical treatment of actual wastewater.