PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures

Phosphorylation is indispensable in comprehending biological processes, while biological experimental methods for identifying phosphorylation sites are tedious and arduous. With the rapid growth of biotechnology, deep learning methods have made significant progress in site prediction tasks. Nevertheless, most existing predictors only consider protein sequence information, that limits the capture of protein spatial information. Building upon the latest advancement in protein structure prediction by AlphaFold2, a novel integrated deep learning architecture PhosAF is developed to predict phosphorylation sites in human proteins by integrating CMA-Net and MFC-Net, which considers sequence and structure information predicted by AlphaFold2. Here, CMA-Net module is composed of multiple convolutional neural network layers and multi-head attention is appended to obtaining the local and long-term dependencies of sequence features. Meanwhile, the MFC-Net module composed of deep neural network layers is used to capture the complex representations of evolutionary and structure features. Furthermore, different features are combined to predict the final phosphorylation sites. In addition, we put forward a new strategy to construct reliable negative samples via protein secondary structures. Experimental results on independent test data and case study indicate that our model PhosAF surpasses the current most advanced methods in phosphorylation site prediction.

Semi-supervised learning methods for weed detection in turf

BACKGROUND

Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi-supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub-images containing weeds from those containing only turfgrass.

RESULTS

Compared with ResNet50, the evaluated SSL methods, Π-model, Mean Teacher, and FixMatch, increased the classification accuracy by 2.8%, 0.7%, and 3.9%, respectively, when only 100 labeled images per class were utilized. FixMatch was the most efficient and reliable model, as it exhibited higher accuracy (≥0.9530) and F1 scores (≥0.951) with fewer labeled data (50 per class) in the validation and testing data sets than the other neural networks evaluated.

CONCLUSION

These results reveal that the SSL deep neural networks are capable of being highly accurate while requiring fewer labeled training images, thus being more time- and labor-efficient than the FSL method. © 2024 Society of Chemical Industry.

Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage

BACKGROUND

Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds.

RESULTS

The precision, recall, F1-score, mAP0.5, mAP0.5:0.95 of You Only Look Once (YOLO) v5 for detecting cabbage were 0.986, 0.979, 0.982, 0.995, and 0.851, respectively, while these metrics were 0.973, 0.985, 0.979, 0.993, and 0.906 for YOLOv8, respectively. However, none of these metrics exceeded 0.891 when detecting weeds. The reduced performances for directly detecting weeds could be attributed to the diverse weed species at varying densities and growth stages with different plant morphologies. A segmentation procedure demonstrated its effectiveness for extracting weeds outside the bounding boxes covering the crops, and thereby realizing effective indirect weed detection.

CONCLUSION

The indirect weed detection approach demands less manpower as the need for constructing a large training dataset containing a variety of weed species is unnecessary. However, in a certain case, weeds are likely to remain undetected due to their growth in close proximity with crops and being situated within the predicted bounding boxes that encompass the crops. The models generated in this research can be used in conjunction with the machine vision subsystem of a smart sprayer or mechanical weeder. © 2024 Society of Chemical Industry.

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa

BACKGROUND

Accurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy for broadleaf weeds growing in alfalfa.

RESULTS

A novel neural network, ResNet-101-segmentation, was developed by fusing an image classification and segmentation module with the backbone selected from ResNet-101. Compared with existing neural networks (AlexNet, GoogLeNet, VGG16, and ResNet-101), ResNet-101-segmentation improved the detection of Carolina geranium, catchweed bedstraw, mugwort and speedwell from 78.27% to 98.17%, from 79.49% to 98.28%, from 67.03% to 96.23%, and from 75.95% to 98.06%, respectively. The novel network exhibited high values of confusion matrices (>90%) when trained with sufficient data sets.

CONCLUSION

ResNet-101-segmentation demonstrated excellent performance compared with existing models (AlexNet, GoogLeNet, VGG16, and ResNet-101) for detecting broadleaf weeds growing in alfalfa. This approach offers a promising solution to increase the accuracy of weed detection, especially in cases where weeds and crops have similar plant morphology. © 2024 Society of Chemical Industry.

First Results from a Broadband Search for Dark Photon Dark Matter in the 44 to 52 μeV Range with a Coaxial Dish Antenna

We present first results from a dark photon dark matter search in the mass range from 44 to 52  μeV (10.7-12.5 GHz) using a room-temperature dish antenna setup called GigaBREAD. Dark photon dark matter converts to ordinary photons on a cylindrical metallic emission surface with area 0.5  m^{2} and is focused by a novel parabolic reflector onto a horn antenna. Signals are read out with a low-noise receiver system. A first data taking run with 24 days of data does not show evidence for dark photon dark matter in this mass range, excluding dark photon photon mixing parameters χ≳10^{-12} in this range at 90% confidence level. This surpasses existing constraints by about 2 orders of magnitude and is the most stringent bound on dark photons in this range below 49  μeV.

Assessment of the implementation of accelerated drug marketing registration procedures for antineoplastic and immunomodulating agents in China: based on 2016-2022 review data

Objective

Since 2016, China has successively implemented Accelerated Drug Marketing Registration Procedures (ADMRPs) for drugs, including Breakthrough Therapy Drug (BTD), Conditional Approval (CA), and Priority Review and Approval (PRA), which have played an important role in promoting the development and review of clinically urgently needed drugs. In this study, we focused on the antineoplastic and immunomodulating agents approved for marketing through ADMRPs, to provide a reference for promoting the formation of a stable and mature regulatory system for the review and approval of antineoplastic drugs and immunomodulating agents in China.

Methods

Reviewed the National Medical Products Administration (NMPA) drug review reports for the years 2016-2022 and screened the antineoplastic and immunomodulating agents approved through ADMRPs. Then, with the help of the NMPA website and the Yaozhi Database, two researchers independently queried and entered the detailed information of the selected drugs, and checked with each other. The attribute classification and main characteristics of the drugs were then analyzed with descriptive statistics to obtain the trend of drug types, drug review and approval status, and timeliness.

Results

A total of 206 antineoplastic and immunomodulating agents were approved for marketing through five accelerated marketing registration procedures (or procedure combinations), with the average review time shortened by about 81 days. Among them, imported drugs accounted for a larger proportion, the most drugs for treating non-small cell lung cancer and lymphoma, and the largest number of PD-1/PDL-1 inhibitors, but pediatric drugs and rare disease drugs accounted for a smaller proportion.

Conclusion

ADMRPs can promote the accessibility of antineoplastic and immunomodulating agents in China and safeguard the life and health rights of more patients. Nevertheless, it is necessary to pay attention to the expansion of the types of indications for medicines and to increase the development of drugs that are urgently needed by a small number of patients.

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.

Pharmacokinetics and safety evaluation of intravenously administered Pseudomonas phage PA_LZ7 in a mouse model

IMPORTANCE

Phage therapy is gaining traction as an alternative to antibiotics due to the rise of multi-drug-resistant (MDR) bacteria. This study assessed the pharmacokinetics and safety of PA_LZ7, a phage targeting MDR Pseudomonas aeruginosa, in mice. After intravenous administration, the phage showed an exponential decay in plasma and its concentration dropped significantly within 24 h for all dosage groups. Although there was a temporary increase in certain plasma cytokines and spleen weight at higher dosages, no significant toxicity was observed. Therefore, PA_LZ7 shows potential as an effective and safe candidate for future phage therapy against MDR P. aeruginosa infections.

Efficacy and Safety of Huangqi Guizhi Wuwu Decoction for Oxaliplatin-Induced Peripheral Neurotoxicity: A Systematic Review and Meta-Analysis

Objective

Oxaliplatin is a first-line chemotherapy drug for the treatment of colorectal cancer, but its induced oxaliplatin-induced peripheral neurotoxicity (OIPN) affect the chemotherapy process and quality of life of tumor patients. OIPN is a serious and potentially permanent side effect of cancer treatment. Currently, no unified standard has been established for preventing and treating OIPN in Western medicine. Therefore, it is very important to seek effective prevention and treatment measures. Many clinical trials have reported that Huangqi Guizhi Wuwu decoction can effectively prevent OIPN, but substantial evidence base to support this treatment is lacking. We collected existing literature and evaluated the clinical efficacy and safety of Huangqi Guizhi Wuwu decoction for OIPN by performing a meta-analysis.

Methods

We systematically searched China National Knowledge Internet (CNKI), VIP, Wan Fang Database, Pubmed, EMBASE, and Cochrane Library from inception through to Oct 2022 to identify only randomized controlled trials examining the prevention of OIPN using Huangqi Guizhi Wuwu decoction. This search was supplemented by manual retrieval, including dissertations and conference papers. All data were analyzed using RevMan 5.3 software.

Results

A total of 18 papers involving 564 patients in the treatment group and 523 patients in the control group were included. A total of 17 articles reported the overall incidence of peripheral neurotoxicity (I² = 0%), and the overall incidence of peripheral neurotoxicity in the treatment group was 0.27 times higher than in the control group (95% CI: 0.20-0.36). A total of 16 articles reported the incidence of level III-IV severe peripheral neurotoxicity (I² = 0%), which was 0.16 times higher in the treatment group than in the control group (95% CI: 0.09-0.32). In the Huangqi Guizhi Wuwu VS no-interference subgroup, it showed that the incidence of severe peripheral neurotoxicity in the treat group was significantly lower than in the control group (OR:0.13, 95% CI:0.06-0.28). But in the Huangqi Guizhi Wuwu VS west medicine therapy subgroup, no significant difference between Huangqi Quizhi Wuwu and conventional Western medicine was observed for the prevention and treatment of severe OIPN (OR:0.37, 95% CI:0.09-1.53). A total of 2 articles were reported median nerve conduction velocity (I² = 51.2%); and no significant difference was found between the treatment and control groups (SMD: 1.43; 95% CI: 0.80-2.08); 4 studies showed Huangqi Guizhi Wuwu decoction did not increase the incidence of chemotherapy-related adverse reactions and was safe.

Conclusions

Our current findings support the application of Huangqi Guizhi Wuwu decoction for the clinical prevention and treatment of patients with OIPN. However, high-quality RCT research is still needed to further exploration. The potential impact of Huangqi Guizhi Wuwu decoction on the quality of life or treatment compliance of cancer patients needs further research.

The key determinant for forming pollen density and using crop barriers to mitigate wind-blown pollen dispersal in alfalfa (Medicago sativa L.)

The potential for commercial cultivation of genetically engineered (GE) alfalfa has raised ecological concerns due to the possibility of introgression of GE alleles into conventional populations. The main objectives of this study were to determine the key affecting factors (i.e. size of pollen source, number of pollinating bees) on forming alfalfa pollen cloud density and test the mitigating effect using maize barrier rows on alfalfa pollen dispersal. The results showed that the mean pollen densities of alfalfa pollen source (Ø = 10 or 20 m) were statistically similar when treated with the same number of worker bumblebees and increased accordingly with increasing the worker bees (887 and 853 pollens m-3 h-1 for Ø = 10 and 20 m with 100-150 worker bees, respectively; 1040 and 1070 pollens m-3 h-1 for the two plots with 200-300 worker bees, respectively), demonstrating that the number of worker bees but not the size of the pollen source was the key determinant for forming alfalfa pollen density. A maize barrier row established at 0.5 m from the alfalfa edge consistently decreased downwind pollen densities (percent pollen density of pollen source) to 0.2-4.4 % at 1-9 m compared to 3.4-25.4 % and 7.5-37.8 % at the same distance range for the upwind and downwind sites without maize barrier rows, respectively. Based on the pods formed on the emasculated alfalfa flowers (due to pollen dispersal) located at various distances from the pollen source and subsequent prediction model, the pollen density threshold value for fertilizing alfalfa recipient under the wind-blown condition was determined of 65.8 pollens m-3 h-1 at 14.7 m from the pollen source edge. The results would help in understanding the pollination biology (minimum pollen density for fertilizing alfalfa recipient ovule) and the process of pollen-mediated gene flow and helpful in developing management strategies to reduce the pollen density and thus mitigate the gene flow in alfalfa.

Detection mechanism and the outlook of metal-organic frameworks for the detection of hazardous substances in milk

Milk has a high nutritional value. However, milk is easily contaminated in the production, processing, and storage processes, which harms consumers' health. Therefore, the harmful substances' detection in milk is important. Metal-organic frameworks (MOFs) have proven high potential in food safety detection due to their unique porous structure, large effective surface area, large porosity, and structural tunability. This article systematically describes the detection mechanism of fluorescence, electrochemical, colorimetric, and enzyme-linked immunosorbent assay based on MOFs. The progress of the application of MOFs in the detection of antibiotics, harmful microorganisms and their toxins, harmful ions, and other harmful substances in milk in recent years is reviewed. The structural tunability of MOFs enables them to be functionalized, giving the ability to be applied to different detection methods or substances. Therefore, MOFs can be used as an advantageous sensing material for detecting harmful substances in the complex environment of milk.

Corrigendum to "Photocatalytic oxygen evolution and antibacterial biomimetic repair membrane for diabetes wound repair via HIF1-α pathway" [Mater. Today Bio 20 (2023) 100616]

[This corrects the article DOI: 10.1016/j.mtbio.2023.100616.].

STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks

With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training process may be unstable, such as gradient disappearance and model collapse, which may lead to the generation of molecules that do not conform to chemical rules or a single style. In this paper, a novel method called STAGAN was proposed to solve the difficulty of model training, by adding a new gradient penalty term in the discriminator and designing a parallel layer of batch normalization used in generator. As an illustration of method, STAGAN generated higher valid and unique molecules than previous models in training datasets from QM9 and ZINC-250K. This indicates that the proposed method can effectively solve the instability problem in the model training process, and can provide more instructive guidance for the further study of molecular graph generation.

TRAF6 triggers Mycobacterium-infected host autophagy through Rab7 ubiquitination

Tumor necrosis factor receptor-associated factor 6 (TRAF6) is an E3 ubiquitin ligase that is extensively involved in the autophagy process by interacting with diverse autophagy initiation and autophagosome maturation molecules. However, whether TRAF6 interacts with lysosomal proteins to regulate Mycobacterium-induced autophagy has not been completely characterized. Herein, the present study showed that TRAF6 interacted with lysosomal key proteins Rab7 through RING domain which caused Rab7 ubiquitination and subsequently ubiquitinated Rab7 binds to STX17 (syntaxin 17, a SNARE protein that is essential for mature autophagosome), and thus promoted the fusion of autophagosomes and lysosomes. Furthermore, TRAF6 enhanced the initiation and formation of autophagosomes in Mycobacterium-induced autophagy in both BMDMs and RAW264.7 cells, as evidenced by autophagic flux, colocalization of LC3 and BCG, autophagy rates, and autophagy-associated protein expression. Noteworthy to mention, TRAF6 deficiency exacerbated lung injury and promoted BCG survival. Taken together, these results identify novel molecular and cellular mechanisms by which TRAF6 positively regulates Mycobacterium-induced autophagy.

Engineered phage with cell-penetrating peptides for intracellular bacterial infections

IMPORTANCE

Salmonella infection is a significant threat to global public health, and the increasing prevalence of antibiotic resistance exacerbates the situation. Therefore, finding new and effective ways to combat this pathogen is essential. Phages are natural predators of bacteria and can be used as an alternative to antibiotics to kill specific bacteria, including drug-resistant strains. One significant limitation of using phages as antimicrobial agents is their low cellular uptake, which limits their effectiveness against intracellular bacterial infections. Therefore, finding ways to enhance phage uptake is crucial. Our study provides a straightforward strategy for displaying cell-penetrating peptides on non-model phages, offering a promising novel and effective therapeutic approach for treating intracellular and drug-resistant bacteria. This approach has the potential to address the global challenge of antibiotic resistance and improve public health outcomes.

[Epidemiological characteristics and analysis of related infection risk factors for influenza in Anhui Province from 2013 to 2021]

Objective: Analysis of the characteristics of influenza epidemic in Anhui Province and quantification of the impact of different factors on influenza occurrence, providing scientific basis for better influenza prevention and control. Methods: Descriptive analysis and factor analysis were conducted on influenza-like illness (ILI) cases and RT-PCR results in Anhui Province from 2013 to 2021 using data from China's Influenza Monitoring Information System. Results: The percentage of influenza-like illness (ILI%) of sentinel hospitals in Anhui Province from April 1, 2013 to March 31, 2021 was 3.80% (1 209 142/31 779 987), showing an overall increasing trend, with a relatively high proportion in 2017-2018 at 4.30% (191 148/4 448 211). The proportion of ILI cases in infants and young children aged 0-4 years was a relatively high at 54.14% (654 676/1 209 142), and the highest ILI% was observed in Fuyang City, Anhui Province (6.25%, 236 863/3 788 863). Laboratory monitoring results showed that the positive rate of ILI cases in sentinel hospitals in 8 influenza monitoring years was 16.38% (34 868/212 912), showing an increasing trend year by year, with a relatively proportion in 2017-2018 at 26.19% (6 936/26 488). The detection rate of school-age children aged 5-14 years was a relativelyhigh at 28.81% (13 869/48 144), and the positive rate was a relatively high in Wuhu City among the 16 cities, reaching 22.01% (2 693/122 237). Influenza activity showed a single peak in winter-spring and alternating double peaks in winter-spring and summer, with different subtypes alternating, and A (H3N2) was the dominant subtype in summer. The results of a multiple logistic regression model showed that the positive rate was higher in 2017-2018, among children aged 5-14 years, in winter, and in southern Anhui. Conclusions: Influenza epidemic in Anhui Province has a clear seasonal pattern, and the ILI% and detection rate have shown an upward trend from 2013 to 2021. Therefore, it is suggested to ensure vaccine supply before the winter-spring influenza season arrives, and to strengthen vaccine uptake and health education to avoid the risk of infection during the peak period of influenza.

Preoperative Radiomics Nomogram Based on CT Image Predicts Recurrence-Free Survival After Surgical Resection of Hepatocellular Carcinoma

RATIONALE AND OBJECTIVES

To construct preoperative models based on CT radiomics, radiologic and clinical features to predict recurrence-free survival (RFS) after liver resection (LR) of BCLC 0 to B stage hepatocellular carcinoma (HCC) and to classify the prognosis.

MATERIALS AND METHODS

This study retrospectively analyzed 161 HCC patients who underwent radical LR. Two methods, the least absolute shrinkage and selection operator and random survival forest analysis, were performed for radiomics signature (RS) construction. Univariate and multivariate stepwise Cox regression analyses were performed to establish a combined nomogram (RCN) of RS and clinical parameters and a clinical nomogram (CN). The performance of the models was assessed comprehensively using Harrell's concordance index (C-index), the calibration curve, and decision curve analysis. The discrimination accuracy of the models was compared using integrated discrimination improvement index (IDI). The risk stratification effect was assessed with Kaplan-Meier survival analysis and subgroup analysis.

RESULTS

The RCN achieved a C-index of 0.792/0.758 in the training/validation set, which was higher than the CN, RS, and BCLC stage system. The discriminatory accuracy of the RCN was improved when compared to the CN, RS, and BCLC staging systems (IDI > 0). Decision curve analysis reflected the clinical net benefit of the RCN. The RCN allows risk stratification of patients in different clinical subgroups.

CONCLUSION

The integrated model combining RS and clinical factors can more effectively predict RFS after LR of BCLC 0 to B stage HCC patients and can effectively stratify the prognostic risk.

Integrating host transcriptomic signatures for distinguishing autoimmune encephalitis in cerebrospinal fluid by metagenomic sequencing

BACKGROUND

The early accurate diagnoses for autoimmune encephalitis (AE) and infectious encephalitis (IE) are essential since the treatments for them are different. This study aims to discover some specific and sensitive biomarkers to distinguish AE from IE at early stage to give specific treatments for good outcomes.

RESULTS

We compared the host gene expression profiles and microbial diversities of cerebrospinal fluid (CSF) from 41 patients with IE and 18 patients with AE through meta-transcriptomic sequencing. Significant differences were found in host gene expression profiles and microbial diversities in CSF between patients with AE and patients with IE. The most significantly upregulated genes in patients with IE were enriched in pathways related with immune response such as neutrophil degranulation, antigen processing and presentation and adaptive immune system. In contrast, those upregulated genes in patients with AE were mainly involved in sensory organ development such as olfactory transduction, as well as synaptic transmission and signaling. Based on the differentially expressed genes, a classifier consisting of 5 host genes showed outstanding performance with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95.

CONCLUSIONS

This study provides a promising classifier and is the first to investigate transcriptomic signatures for differentiating AE from IE by using meta-transcriptomic next-generation sequencing technology.

Photocatalytic oxygen evolution and antibacterial biomimetic repair membrane for diabetes wound repair via HIF1-α pathway

Diabetic wounds always have puzzled patients and caused serious social problems. Due to the lack of local blood vessels, severe hypoxia is generated in the defect area, which is an essential reason for the difficulty of wound healing. We have constructed a photocatalytic oxygen evolution and antibacterial biomimetic repair membrane to solve the problems of wound repair. A scanning electron microscope and transmission electron microscope characterized the biomimetic repair membrane. The oxygen evolution of the biomimetic membrane was tested by an oxygen meter. The excellent antibacterial performance of the biomimetic repair membrane was also verified by co-culture with Staphylococcus aureus and Escherichia coli. It was confirmed that the expression of collagen and HIF1-α in fibroblasts was significantly increased in vitro. And the mitochondrial activity of the vascular and nerve was increased considerably. In vivo, the healing time of diabetes wounds treated with the biomimetic repair membrane was significantly reduced, the collagen and the number of pores were increased considerably, and vascular regeneration was enhanced. The biomimetic repair membrane has an excellent performance in photocatalytic oxygen evolution and antibacterial and can significantly promote the repair of diabetes wounds. This will provide a promising treatment for diabetes wound repair.

An easy and straightforward synthesized nano calcium phosphate for highly capture of multiply phosphorylated peptides

Multisite phosphorylation of proteins regulates various cellular life activities, however, the capture of low abundance multi-phosphopeptides from biosamples and identification of phosphorylation sites are largely limited due to the limited enrichment materials and their unclear interactions with multi-phosphopeptides. Here we propose using two cheap raw materials (CaCl₂·2H₂O and Na₂HPO₄·12H₂O) in 10 min at room temperature to synthesize the structurally simple Nanometric Calcium Phosphate (CaP) to resolve this challenge. The current results showed that the "simple" CaP has good selection specificity, high sensitivity and stability for multi-phosphopeptides enrichment and the identification of phosphorylation sites, which facilitate the popularization and application of phosphoproteomics research. Further, the interaction of CaP and multi-phosphopeptides were qualitatively characterized at the molecular/atomic level and the high affinity between them was quantified by the isothermal titration microcalorimeter based on the laws of thermodynamics. The results indicated that the interaction was a spontaneous (ΔG < 0) exothermic reaction with enthalpy reduction (ΔH < 0) and driven mainly by hydrogen bond and electrostatic interaction process.

LncRNA NR_003508 Suppresses Mycobacterium tuberculosis-Induced Programmed Necrosis via Sponging miR-346-3p to Regulate RIPK1

Emerging evidence suggests that long non-coding RNAs (LncRNAs) are involved in Mtb-induced programmed necrosis. Among these LncRNAs, LncRNA NR_003508 is associated with LPS-induced acute respiratory distress syndrome. However, whether LncRNA NR_003508 contributes to Mtb-induced programmed necrosis remains undocumented. Firstly, the expression of LncRNA NR_003508 was determined using RT-qPCR and FISH. The protein expression of RIPK1, p-RIPK1, RIPK3, p-RIPK3, MLKL, and p-MLKL was measured by Western blot in RAW264.7 and mouse lung tissues. Furthermore, luciferase reporter assays and bioinformatics were used to predict specific miRNA (miR-346-3p) and mRNA (RIPK1) regulated by LncRNA NR_003508. In addition, RT-qPCR was used to detect the RIPK1 expression in TB patients and healthy peripheral blood. The flow cytometry assay was performed to detect cell necrosis rates. Here we show that BCG infection-induced cell necrosis and increased LncRNA NR_003508 expression. si-NR_003508 inhibited BCG/H37Rv-induced programmed necrosis in vitro or in vivo. Functionally, LncRNA NR_003508 has been verified as a ceRNA for absorbing miR-346-3p, which targets RIPK1. Moreover, RIPK1 expression was elevated in the peripheral blood of TB patients compared with healthy people. Knockdown of LncRNA NR_003508 or miR-346-3p overexpression suppresses cell necrosis rate and ROS accumulation in RAW264.7 cells. In conclusion, LncRNA NR_003508 functions as a positive regulator of Mtb-induced programmed necrosis via sponging miR-346-3p to regulate RIPK1. Our findings may provide a promising therapeutic target for tuberculosis.

De novo drug design based on Stack-RNN with multi-objective reward-weighted sum and reinforcement learning

CONTEXT

In recent decades, drug development has become extremely important as different new diseases have emerged. However, drug discovery is a long and complex process with a very low success rate, and methods are needed to improve the efficiency of the process and reduce the possibility of failure. Among them, drug design from scratch has become a promising approach. Molecules are generated from scratch, reducing the reliance on trial and error and prefabricated molecular repositories, but the optimization of its molecular properties is still a challenging multi-objective optimization problem.

METHODS

In this study, two stack-augmented recurrent neural networks were used to compose a generative model for generating drug-like molecules, and then reinforcement learning was used for optimization to generate molecules with desirable properties, such as binding affinity and the logarithm of the partition coefficient between octanol and water. In addition, a memory storage network was added to increase the internal diversity of the generated molecules. For multi-objective optimization, we proposed a new approach which utilized the magnitude of different attribute reward values to assign different weights to molecular optimization. The proposed model not only solves the problem that the properties of the generated molecules are extremely biased towards a certain attribute due to the possible conflict between the attributes, but also improves various properties of the generated molecules compared with the traditional weighted sum and alternating weighted sum, among which the molecular validity reaches 97.3%, the internal diversity is 0.8613, and the desirable molecules increases from 55.9 to 92%.

Wnt/β-catenin signaling pathway-a versatile player in apoptosis and autophagy

The Wnt/β-catenin signaling pathway is a highly conserved pathway that is involved in cell development, proliferation, differentiation, apoptosis and autophagy. Among these processes, apoptosis and autophagy occur physiologically during host defense and the maintenance of intracellular homeostasis. Mounting evidence suggests that the crosstalk between Wnt/β-catenin-regulated apoptosis and autophagy has broad functional significance in various diseases. Herein, we summarize the recent studies in understanding the role of the Wnt/β-catenin signaling pathway in apoptosis and autophagy, and draw the following conclusions: a) For apoptosis, the regulation of Wnt/β-catenin is generally positive. However, a small amount of evidence indicates the presence of a negatively regulated relationship between Wnt/β-catenin and apoptosis; b) Wnt/β-catenin influences the occurrence and development of autophagy by regulating autophagy-related factors, and these factors in turn affect Wnt/β-catenin pathway; c) Wnt/β-catenin always balances the molecular damage caused by the crosstalk between autophagy and apoptosis in a compensatory manner. Understanding the specific role of the Wnt/β-catenin signaling pathway during different stages of autophagy and apoptosis may provide new insights into the progression of related diseases regulated by the Wnt/β-catenin signaling pathway.

Integrating metabolomics and network pharmacology to investigate Panax japonicus prevents kidney injury in HFD/STZ-induced diabetic mice

ETHNOPHARMACOLOGICAL RELEVANCE

Panax japonicus C. A. Meye (PJ) has unique effects on diseases by "qi" stagnation and blood stasis in ancient. Modern studies have shown that PJ can treat diabetic kidney disease (DKD) caused by deficiency and blood stasis.

AIM OF THE STUDY

This study evaluated the potential effects of PJ on DKD, a microvascular complication, and investigated its possible mechanisms.

MATERIALS AND METHODS

In this study, the chemical constituents of PJ were analyzed by HPLC. In vivo studies, we constructed a diabetic mice model by HDF combined with STZ, then administered PJ to diabetic mice for 6 weeks. Blood lipid, BUN, 24h urine protein, and renal tissue HE staining were detected to comprehensively evaluate the protective effect of PJ on DKD. Metabolomics investigated the metabolic pathways influenced by PJ in the treatment of DKD. Moreover, the potential targets and signal pathways were investigated using network pharmacology. Finally, molecular docking predicts affinity of active compounds and core targets, and western blotting was used to detect core target expression levels.

RESULTS

In vivo study, PJ can reduce hyperlipidemia, serum BUN, and 24-h urinary protein in diabetic mice, and protect the pathological changes in renal tissue. Metabolomics results showed that PJ had significant regulatory effect on unsaturated fatty acids, glycerophospholipid metabolism, and purine metabolism. Network pharmacology showed that MAPK1, MAPK8, Bcl-2, and Caspase 3 were the core targets in PJ against DKD. Molecular docking revealed that Bcl-2 and Caspase 3 have a strong affinity for Chikusetsusaponin Iva, Ginsenoside Rb1, and Ginsenoside Rg1. Moreover, when compared to the model group, the PJ group had higher levels of anti-apoptosis protein Bcl-2 and lower levels of pro-apoptosis protein Caspase 3.

CONCLUSION

PJ can reduce blood lipids, regulate the biosynthesis of unsaturated fatty acids and purine metabolism, thereby alleviating the renal injury of diabetic mice. Moreover, it can regulate the Bcl-2/caspase 3 apoptosis signaling pathway to prevent the apoptosis of renal cells and protect the renal function of diabetic mice.

Tanshinone IIA Alleviates the Biological Characteristics of Colorectal Cancer via Activating the ROS/JNK Signaling Pathway

BACKGROUND

Tanshinone IIA (Tan IIA) exerts a significant inhibitory effect on various tumor cells since it induces cell apoptosis and affects the proliferation, differentiation, metastasis, and invasion of tumor cells. However, the mechanism underlying the antitumor activity of Tan IIA has not been totally elucidated.

OBJECTIVE

This study aimed to uncover the role of Tan IIA in colorectal cancer (CRC) and its potential mechanism of action.

METHODS

Cell proliferation was assessed using CCK-8 and colony formation assays. Western blot analysis was carried out to detect the expression of related proteins. Cell apoptosis was assessed using flow cytometry. Furthermore, tumor size and tumor weight of CRC xenograft mice were recorded before and after Tan IIA treatment. The production of reactive oxygen species (ROS) was measured by a ROS kit.

RESULTS

The results revealed that Tan IIA induced autophagy and apoptosis via activating the ROS/JNK signaling pathway in CRC cells, thus inhibiting the progression of CRC in vivo.

CONCLUSION

The aforementioned findings indicated that Tan IIA exerted an antiproliferative effect on CRC by inducing cell autophagy and apoptosis via activating the ROS/JNK signaling pathway. Therefore, Tan IIA may be considered a potential therapeutic agent for treating CRC.

Identification of genetic susceptibility in preterm newborns with bronchopulmonary dysplasia by whole-exome sequencing: BIVM gene may play a role

Bronchopulmonary dysplasia (BPD) is a common chronic respiratory disease in preterm infants caused by multifactorial etiology. Genetic factors are involved in the occurrence of BPD, but studies have found that candidate genes have poor reproducibility and are influenced by ethnic heterogeneity; therefore, more exploration is still needed. We performed whole-exon sequencing in 34 preterm infants with BPD and 32 non-BPD control neonates. The data were analyzed and interpreted by Fisher difference comparison, PLINK and eQTL association analysis, KEGG and GO enrichment analysis, STRING tool, Cytoscape software, ProtParam tool, HOPE online software, and GEOR2 analysis on NCBI GEO dataset. BPD has a highly heterogeneity in different populations, and we found 35 genes overlapped with previous whole-exon sequencing studies, such as APOB gene. Arterial and epithelial cell development and energy metabolism pathways affect BPD. In this study, 24 key genes were identified, and BIVM rs3825519 mutation leads to prolonged assisted ventilation in patients with BPD. A novel DDAH1 mutation site (NM_012137: exon1: c.89 T > G: p.L30R) was found in 9 BPD patients.

CONCLUSION

BIVM gene rs3825519 mutation may play a role in the pathogenesis of BPD by affecting cilia movement, and the DDAH1 and APOB genes mutations may have a pathogenic role in BPD.

WHAT IS KNOWN

• Genetic factors are involved in the occurrence of bronchopulmonary dysplasia. • The candidate genes have poor reproducibility and are influenced by ethnic heterogeneity, therefore, more exploration is still needed.

WHAT IS NEW

• We identified the role of susceptible SNPs in BPD in Shenzhen, China, and identified 24 key genes that influence the pathogenesis of BPD, and also found 35 genes overlapped with previous whole exon sequencing studies, such as APOB gene. • We found that BIVM and DDAH1 genes may play a pathogenic role in the pathogenesis of BPD.

RIP3 impedes Mycobacterium tuberculosis survival and promotes p62-mediated autophagy

Macrophage is believed to play a vital role in the fight against Mycobacterium tuberculosis (M.tb) infection by activating autophagy. Recently, receptor-interacting protein kinase-3 (RIP3), an essential kinase for necroptotic cell death signaling, has been demonstrated to be involved in autophagy. However, RIP3's role in fighting against M.tb infection remains elusive. Here we show that a substantial increase in inflammatory cell infiltration and higher bacterial burden are observed in the lungs of RIP3^-/- mice with Mycobacterium bovis Bacillus Calmette-Guerin (BCG) infection. Meanwhile, RIP3 ameliorates lung injury and promote autophagy via induce autophagosome and autophagolysosome formation which indicate that RIP3 is indispensable for host clearance of BCG via autophagy. Mechanically, RIP3 enhances p62 binding to ubiquitylated proteins and LC3 by interacting with p62, and RHIM domain is required for RIP3-p62 interaction. Hence, our results conclusively show that RIP3 impedes M.tb survival and promotes p62-mediated autophagy. The findings provide further insight into understanding the mechanism of M.tb immune escape and pathogenesis of tuberculosis.

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.

Osthole: A potential AMPK agonist that inhibits NLRP3 inflammasome activation by regulating mitochondrial homeostasis for combating rheumatoid arthritis

BACKGROUND

Osthole (OST), a characteristic coumarin compound in Angelicae pubescentis radix (APR), has shown potent efficacy in the treatment of rheumatoid arthritis (RA), but its specific targets and potential mechanism are limited.

PURPOSE

This study aimed to explore the potential targets and molecular mechanisms of OST against RA using computer-assisted techniques in combination with RA fibroblast-like synoviocytes (FLS) inflammation model and CIA rat model.

METHODS

Network pharmacology and molecular docking were applied to initially predict the potential targets of OST for the treatment of RA. Thereafter, TNFα was used to stimulate FLS to build an in vitro model of inflammation, combined with RNA-seq technology and molecular biology such as qPCR to investigate the anti-inflammatory effects and related mechanisms of OST. Finally, the anti-RA effect of OST was demonstrated by establishing a CIA rat model.

RESULTS

The network model results showed that the anti-RA effect of OST was mainly related to its anti-inflammatory effect, and AMPK was identified as a potential target for the potency of OST. In the TNFα-induced FLS cells, OST inhibited the secretion of FLS inflammatory factors, which was attributed to the ability of OST to activate AMPK to inhibit the activation of the NLRP3 inflammasome. Further, it was observed that the activation of AMPK by OST facilitated mitochondrial biogenesis, and corrected abnormal mitochondrial dynamics in FLS, which was favoured to the restoration of mitochondrial homeostasis, and further promoted the occurrence of apoptosis and the decrease of ROS in FLS. Consistent with in vivo studies, administration of OST significantly improved joint deformity and toe erythema, reduced arthritis index scores and inhibited synovial inflammation in CIA rats.

CONCLUSION

Our study proposed for the first time that AMPK, served as a potential target of OST, positively participated in the anti-RA therapeutic effect of OST. By regulating mitochondrial homeostasis and function, OST can effectively inhibit the activation of inflammasome and the secretion of inflammatory factors in vitro and in vivo, and finally achieve beneficial effects in the treatment of RA, which provides support and greater possibility to make further efforts on pharmacological research and clinical application of OST.

Bibliometric analysis of global research on physical activity and sedentary behavior in the context of cancer

Objective

Numerous studies focusing on sedentary behavior (SB) and physical activity (PA) in the context of cancer have been reported in recent years. We analyzed and visualized studies on SB and PA in patients with cancer over the last 20 years using scientometric methods, to provide insights on gaps and deficiencies in the literature, and to inform future research guidelines.

Methods

All relevant studies in the field from 2001 to October 2022 were reviewed using bibliometric tools, including VOSviewer, Bibliometric online analysis platform, and biblioshiny, to determine the most influential countries, institutions, journals, and authors. We explored current research hotpots and potential research trends, based on keyword clustering and dynamic changes. Our research focuses on PA, SB, and cancer across the entire cancer continuum, from primary prevention to treatment to cancer survivorship.

Results

Scientometric analysis identified 4,382 relevant manuscripts on SB and PA in the context of cancer, with a 10-fold increase in articles over the past 20 years. The United States, Canada, and Australia were the most influential countries. The journal, Supportive Care in Cancer, had the highest number of publications, while Clinical Oncology had the highest H-index. K.S. Courneya was the most influential author in this field, with the highest number of publications, total citations, and H-index. Keyword analysis revealed that current research is focused on PA and SB in patients with breast cancer, quality of life, and aerobic exercise. Future frontiers include cancer prehabilitation programs and cardiorespiratory fitness, and remote intervention and social support.

Conclusion

By using bibliometrics, we conducted a comprehensive review of SB and PA in cancer-related studies. The current research focused on exercise and sedentariness in breast cancer patients and the role of PA in improving quality of life in survivorship. Emerging research foci were generally around cancer prehabilitation programs and remote intervention issues for PA. In addition, some publication deficits are noted: studies of PA and SB in less common cancers; the recommended doses and intensities of exercise for cancer; the timing of interventions for prehabilitation and the establishment of individualized exercise protocols. These deficiencies align with the needs for future research topics.

Data mining analysis reveals key acupoints and meridians for the treatment of chemotherapy-induced peripheral neuropathy

OBJECTIVE

To explore effective acupoints and combinations for the treatment of chemotherapy-induced peripheral neuropathy (CIPN) METHODS: Clinical controlled trials and randomized controlled trials of acupuncture for CIPN were sourced from PubMed, Embase, Web of Science, and Chinese databases, including the Wanfang database, VIP Journals database, and China National Knowledge Infrastructure database. The quality of eligible research was evaluated based on CONSORT and STRICTA statements. The common acupoints, meridians, and acupoint combinations were determined from acupuncture prescriptions reporting positive effects and were analyzed using SPSS 23.0 and SPSS Modeler 14.1. Finally, a complex network was constructed using Cytoscape 3.8.2 to explore the core acupoints.

RESULTS

The quality of 24 clinical trials was evaluated, and 20 acupuncture prescriptions that reported positive outcomes were included in subsequent data mining analysis. The most frequently used acupoints are ST36, LI11, LI4, LR3, and SP6. Meanwhile, they are also the core acupoints in acupuncture prescriptions according to the complex network with 28 nodes and 177 edges. The most commonly used meridians were the large intestine, stomach, and spleen. Acupoint combinations of LI11 and ST36, SP6 and ST36 were frequently used.

CONCLUSION

Our study provides a reference for the selection of effective acupoints for CIPN treatment and a basis for the effective use of this form of traditional Chinese medicine. Furthermore, we found limitations in the design and implementation of the available clinical research, which should be minimized in future studies.

Aptamer engineering exosomes loaded on biomimetic periosteum to promote angiogenesis and bone regeneration by targeting injured nerves via JNK3 MAPK pathway

Repairing critical bone defects is a complex problem in the clinic. The periosteum rich in nerve plays a vital role in initiating and regulating bone regeneration. However, current studies have paid little attention to repairing nerves in the periosteum to promote bone regeneration. Thus, it is essential to construct bionic periosteum with the targeted injured nerves in the periosteum. We coupled phosphatidylserine (PS) targeted aptamers with repair Schwann cell exosomes to construct exosome@aptamer (EA). Then through PEI, EA was successfully built on the surface of the electrospun fiber, which was PCL@PEI@exosome@aptamer (PPEA). Through SEM, TEM, and other technologies, PPEA was characterized. Experiments prove in vivo and in vitro that it has an excellent repair effect on damaged nerves and regeneration of vascular and bones. In vivo, we confirmed that biomimetic periosteum has an apparent ability to promote nerve and bone regeneration by using Microcomputer tomography, hematoxylin-eosin, Masson, and Immunofluorescence. In vitro, we used Immunofluorescence, Real-Time Quantitative PCR, Alkaline phosphatase staining, and other tests to confirm that it has central nerve, blood vessel, and bone regeneration ability. The PPEA biomimetic periosteum has apparent neurogenic, angiogenic, and osteogenic effects. The PPEA biomimetic periosteum will provide a promising method for treating bone defects.

•

To construct a biomimetic periosteum that can target injured axons and bone regeneration.

•

PS targeted aptamer is coupled with repair Schwann cell exosomes.

•

PEI self-assembly was used for the PCL electrospun biomimetic membrane loading.

•

It targeted and repaired the injured axons and promoted the secretion of CGRP and SP.

•

Biomimetic periosteum promotes vascular regeneration and bone regeneration.

[A two-stitch continuous suture method for single-lumen ileostomy]

Objective: To explore the value of a two-stitch continuous suture in single- lumen ileostomy. Methods: This was a retrospective cohort study. Data for 98 patients who underwent single-lumen enterostomy were retrospectively collected between 1 January 2021 and 1 May 2022 at Zhujiang Hospital of Southern Medical University. All patients met the indications for prophylactic single-lumen ileostomy. Those older than 80 years of age, with complex underlying diseases, extremely poor systemic conditions who could not tolerate surgery, poor blood supply at the end of the bowel, and severe edema or severe infection at the end of the bowel were excluded. Among the included patients, patients who underwent surgery before 1 October 2021 underwent ileostomy with interrupted suture (control group, n=60), and patients operated on and after 1 October 2021 routinely underwent two-stitch continuous suture ileostomy (two-stitch stoma group, n=38). Two-stitch continuous suture ileostomy is performed as follows: the first continuous suture is used to suture the intestinal seromuscular layer, peritoneum, posterior sheath, and anterior sheath from deep to superficial layers. The bowel wall is then opened. The second continuous suture is used to suture the full thickness of the bowel and the skin. The differences in postoperative ostomy-related complications and operation time were compared between the groups. Results: There were no significant differences in baseline data between the groups (all, P>0.05). The operative time in the two-stitch stoma group was shorter than that of the control group (16.6±2.2 minutes vs. 25.1±2.4 minutes, respectively; t=-17.874;P<0.001). The incidences of mucocutaneous separation, dermatitis, and stoma rebound in the two-stitch stoma group were lower than those of the control group [5.3% (2/38) vs. 31.7% (19/60), χ²=9.633, P=0.002;5.3% (2/38) vs. 28.3% (17/60), χ²=7.923, P=0.005; and 2.6% (1/38) vs. 18.3% (11/60), P=0.026, respectively], while the incidences of parastomal hernia and stoma prolapse, and the postoperative visual analog scale scores in the two groups were similar (all P>0.05). Conclusion: Compared with traditional single-lumen ileostomy, two-stitch continuous suture ileostomy has the advantages of short operation time, simplicity, esthetic appearance of the stoma, and a significant reduction in the postoperative complications associated with ileostomy.

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.

Assessment of AI-Based Protein Structure Prediction for the NLRP3 Target

The recent successes of AlphaFold and RoseTTAFold have demonstrated the value of AI methods in highly accurate protein structure prediction. Despite these advances, the role of these methods in the context of small-molecule drug discovery still needs to be thoroughly explored. In this study, we evaluated whether the AI-based models can reliably reproduce the three-dimensional structures of protein–ligand complexes. The structure we chose was NLRP3, a challenging protein target in terms of obtaining a three-dimensional model both experimentally and computationally. The conformation of the binding pockets generated by the AI models was carefully characterized and compared with experimental structures. Further molecular docking results indicated that AI-predicted protein structures combined with molecular dynamics simulations offers a promising approach in small-molecule drug discovery.

Agronomic evaluation of shade tolerance of 16 spring Camelina sativa (L.) Crantz genotypes under different artificial shade levels using a modified membership function

Camelina [Camelina sativa (L.) Crantz] is currently gaining considerable attention as a potential oilseed feedstock for biofuel, oil and feed source, and bioproducts. Studies have shown the potential of using camelina in an intercropping system. However, there are no camelina genotypes evaluated or bred for shade tolerance. The objective of this study was to evaluate and determine the shade tolerance of sixteen spring camelina genotypes (growth stage: BBCH 103; the plants with 4-5 leaves) for intercropping systems. In this study, we simulated three different shade levels, including low (LST), medium (MST), and high shade treatments (HST; 15, 25, and 50% reduction of natural light intensity, respectively), and evaluated the photosynthetic and physiological parameters, seed production, and seed quality. The mean chlorophyll pigments, including the total chlorophyll and chlorophyll a and b across the 16 genotypes increased as shade level increased, while the chlorophyll fluorescence parameter F_v/F_m, chlorophyll a/b, leaf area, the number of silicles and branches plant^-1 decreased as shade level increased. The first day of anthesis and days of flowering duration of camelina treated with shade were significantly delayed and shortened, respectively, as shade increased. The shortened lifecycle and altered flowering phenology decreased camelina seed yield. Additionally, the shade under MST and HST reduced the seed oil content and unsaturated fatty acids, but not saturated fatty acids. The dendrograms constructed using the comprehensive tolerance membership values revealed that CamK9, CamC4, and 'SO-40' were the relatively shade-tolerant genotypes among the 16 camelina genotypes. These camelina genotypes can grow under the shade level up to a 25% reduction in natural light intensity producing a similar seed yield and seed oil quality, indicating the potential to intercrop with maize or other small grain crops. The present study provided the baseline information on the response of camelina genotypes to different shade levels, which would help in selecting or breeding shade-tolerant genotypes.

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.

Palmitoylation of γb protein directs a dynamic switch between Barley stripe mosaic virus replication and movement

Viral replication and movement are intimately linked; however, the molecular mechanisms regulating the transition between replication and subsequent movement remain largely unknown. We previously demonstrated that the Barley stripe mosaic virus (BSMV) γb protein promotes viral replication and movement by interacting with the αa replicase and TGB1 movement proteins. Here, we found that γb is palmitoylated at Cys-10, Cys-19, and Cys-60 in Nicotiana benthamiana, which supports BSMV infection. Intriguingly, non-palmitoylated γb is anchored to chloroplast replication sites and enhances BSMV replication, whereas palmitoylated γb protein recruits TGB1 to the chloroplasts and forms viral replication-movement intermediate complexes. At the late stages of replication, γb interacts with NbPAT15 and NbPAT21 and is palmitoylated at the chloroplast periphery, thereby shifting viral replication to intracellular and intercellular movement. We also show that palmitoylated γb promotes virus cell-to-cell movement by interacting with NbREM1 to inhibit callose deposition at the plasmodesmata. Altogether, our experiments reveal a model whereby palmitoylation of γb directs a dynamic switch between BSMV replication and movement events during infection.

Barley stripe mosaic virus γb protein targets thioredoxin h-type 1 to dampen salicylic acid-mediated defenses

Salicylic acid (SA) acts as a signaling molecule to perceive and defend against pathogen infections. Accordingly, pathogens evolve versatile strategies to disrupt the SA-mediated signal transduction, and how plant viruses manipulate the SA-dependent defense responses requires further characterization. Here, we show that barley stripe mosaic virus (BSMV) infection activates the SA-mediated defense signaling pathway and upregulates the expression of Nicotiana benthamiana thioredoxin h-type 1 (NbTRXh1). The γb protein interacts directly with NbTRXh1 in vivo and in vitro. The overexpression of NbTRXh1, but not a reductase-defective mutant, impedes BSMV infection, whereas low NbTRXh1 expression level results in increased viral accumulation. Similar with its orthologs in Arabidopsis (Arabidopsis thaliana), NbTRXh1 also plays an essential role in SA signaling transduction in N. benthamiana. To counteract NbTRXh1-mediated defenses, the BSMV γb protein targets NbTRXh1 to dampen its reductase activity, thereby impairing downstream SA defense gene expression to optimize viral cell-to-cell movement. We also found that NbTRXh1-mediated resistance defends against lychnis ringspot virus, beet black scorch virus, and beet necrotic yellow vein virus. Taken together, our results reveal a role for the multifunctional γb protein in counteracting plant defense responses and an expanded broad-spectrum antibiotic role of the SA signaling pathway.

A novel deep learning-based method for detection of weeds in vegetables

BACKGROUND

Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at various growth stages and densities. This paper presents a novel deep-learning-based method for weed detection that recognizes vegetable crops and classifies all other green objects as weeds.

RESULTS

The optimal confidence threshold values for YOLO-v3, CenterNet, and Faster R-CNN were 0.4, 0.6, and 0.4/0.5, respectively. These deep-learning models had average precision (AP) above 97% in the testing dataset. YOLO-v3 was the most accurate model for detection of vegetables and yielded the highest F 1 score of 0.971, along with high precision and recall values of 0.971 and 0.970, respectively. The inference time of YOLO-v3 was similar to CenterNet, but significantly shorter than that of Faster R-CNN. Overall, YOLO-v3 showed the highest accuracy and computational efficiency among the deep-learning architectures evaluated in this study.

CONCLUSION

These results demonstrate that deep-learning-based methods can reliably detect weeds in vegetable crops. The proposed method avoids dealing with various weed species, and thus greatly reduces the overall complexity of weed detection in vegetable fields. Findings have implications for advancing site-specific robotic weed control in vegetable crops.

Overcoming the inadaptability of sparse group lasso for data with various group structures by stacking

MOTIVATION

Efficiently identifying genes based on gene expression level have been studied to help to classify different cancer types and improve the prediction performance. Logistic regression model based on regularization technique is often one of the effective approaches for simultaneously realizing prediction and feature (gene) selection in genomic data of high dimensionality. However, standard methods ignore biological group structure and generally result in poorer predictive models.

RESULTS

In this article, we develop a classifier named Stacked SGL that satisfies the criteria of prediction, stability and selection based on sparse group lasso penalty by stacking. Sparse group lasso has a mixing parameter representing the ratio of lasso to group lasso, thus providing a compromise between selecting a subset of sparse feature groups and introducing sparsity within each group. We propose to use stacked generalization to combine different ratios rather than choosing one ratio, which could help to overcome the inadaptability of sparse group lasso for some data. Considering that stacking weakens feature selection, we perform a post hoc feature selection which might slightly reduce predictive performance, but it shows superior in feature selection. Experimental results on simulation demonstrate that our approach enjoys competitive and stable classification performance and lower false discovery rate in feature selection for varying sets of data compared with other regularization methods. In addition, our method presents better accuracy in three public cancer datasets and identifies more powerful discriminatory and potential mutation genes for thyroid carcinoma.

AVAILABILITY AND IMPLEMENTATION

The real data underlying this article are available from https://github.com/huanheaha/Stacked_SGL; https://zenodo.org/record/5761577#.YbAUyciEwk2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bibliometric Analysis Reveals a 20-Year Research Trend for Chemotherapy-Induced Peripheral Neuropathy

Objective

A lot of research has focused on the field of chemotherapy-induced peripheral neuropathy (CIPN). In this study, we performed a bibliometric analysis of CIPN-related publications to identify the key research areas and trends over the last 20 years.

Methods

We searched the Web of Science core collection for publications related to CIPN that were published between January 2001 and September 2021. We then performed bibliometric analysis and visualization using Microsoft Excel 2019, VOSviewer, and the Bibliometric online analysis platform (https://bibliometric.com/).

Results

In total, we identified 2,188 eligible publications in the field of CIPN, with an increasing trend in the annual number of publications. The United States and Italy were dominant in the CIPN field. Supportive Care in Cancer was the most productive journal. G. Cavaletti and A.A. Argyriou published the largest number of papers. Of all institutions, the University of Milano-Bicocca, Italy, published the highest number of papers. Analysis of the co-occurrence of keywords revealed the specific characteristics relating to the four main clusters: oxaliplatin, paclitaxel, pain management, and quality of life (QOL). Newly emerging research focusses predominantly on neuroinflammatory mechanisms and non-pharmacological interventions for CIPN.

Conclusion

This bibliometric study reviewed the evolutionary trends in CIPN research and identified current research hotspots and research trends. In addition, we identified journals, institutions, and authors, with the highest levels of impact to enhance the collaboration and learning.

Detecting Fine-Grained Emotions on Social Media during Major Disease Outbreaks: Health and Well-being before and during the COVID-19 Pandemic

The COVID-19 pandemic has affected the whole world in various ways. One type of impact is that communication, work, interaction, a great part of our lives has moved online on various platforms, with some of the most popular being the social media ones. Another, arguably less visible impact, is the emotional impact. Detecting and understanding emotions is important, to better discern the emotional health and well-being of the global population. Thus, in this work, we use a social media platform (Twitter) to analyse emotions in detail. Our contribution is twofold: (1) we propose EmoBERT, a new emotion-based variant of the BERT transformer model, able to learn emotion representations and outperform the state-of-the-art; (2) we provide a fine-grained analysis of the pandemic's effect in a major location, London, comparing specific emotions (annoyed, anxious, empathetic, sad) before and during the epidemic.

Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

BACKGROUND

In-field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat.

RESULTS

The object detection neural networks, including CenterNet, Faster R-CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels.

CONCLUSION

CenterNet, Faster R-CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.

A risk scoring system to predict the individual incidence of early-onset colorectal cancer

BACKGROUND

The incidence of early-onset colorectal cancer (EOCRC) is increasing at an alarming rate and further studies are needed to identify risk factors and to develop prevention strategies.

METHODS

Risk factors significantly associated with EOCRC were identified using meta-analysis. An individual risk appraisal model was constructed using the Rothman-Keller model. Next, a group of random data sets was generated using the binomial distribution function method, to determine nodes of risk assessment levels and to identify low, medium, and high risk populations.

RESULTS

A total of 32,843 EOCRC patients were identified in this study, and nine significant risk factors were identified using meta-analysis, including male sex, Caucasian ethnicity, sedentary lifestyle, inflammatory bowel disease, and high intake of red meat and processed meat. After simulating the risk assessment data of 10,000 subjects, scores of 0 to 0.0018, 0.0018 to 0.0036, and 0.0036 or more were respectively considered as low-, moderate-, and high-risk populations for the EOCRC population based on risk trends from the Rothman-Keller model.

CONCLUSION

This model can be used for screening of young adults to predict high risk of EOCRC and will contribute to the primary prevention strategies and the reduction of risk of developing EOCRC.

P. aeruginosa biofilm activates the NLRP3 inflammasomes in vitro

The ability of P.aeruginosa to form biofilms renders common treatments inefficient, thereby promoting chronic infection. Inflammasomes activate caspase-1, which is important for the maturation of IL-1β and IL-18 and evoke an inflammatory response. We aimed to investigate the activation of inflammasomes induced by P.aeruginosa biofilm. THP-1 cells were mock-infected or infected with PAO1 biofilms. Protein levels of caspase-1 p20, pro-caspase-1, caspase-4 p20, and pro-caspase-4 in THP-1 macrophages were determined by Western blotting. The expression of NLRC4 and NLRP3 was measured by RT-PCR. The production of IL-1β and IL-18 was monitored using ELISA. P. aeruginosa biofilm significantly elevated caspase-1 levels, and decreased NLRC4 levels. Additionally, caspase-4 and NLRP3 levels were significantly increased. P.aeruginosa biofilm significantly enhanced IL-1β and IL-18 production. We concluded that P. aeruginosa biofilm induced the production of IL-1β and IL-18, possibly via NLRP3 inflammasomes, rather than NLRC4 inflammasomes.

Knockdown of GBP1 inhibits BCG-induced apoptosis in macrophage RAW 264.7 cells via p38/JNK pathway

Alveolar macrophage apoptosis induced by Mycobacterium tuberculosis (Mtb) plays a significant role in mediating the pathogenesis of tuberculosis. There is growing evidence that guanylate-binding proteins (GBPs) are associated with different pathological processes such as microbial infection. However, it remains unclear whether GBPs can regulate the apoptosis of macrophages induced by Mtb. In this study, we investigated the potential effect of GBP1 on RAW 264.7 cell apoptosis during Bacillus Calmette-Guerin (BCG) infection. The results demonstrated that BCG could induce macrophage apoptosis and GBP1 upregulation. In addition, we explored the role of GBP1 in regulating BCG-induced RAW 264.7 cell apoptosis using small interfering RNAs targeting GBP1. The results showed that knockdown of GBP1 could attenuate BCG-induced apoptosis in RAW 264.7 cells. Moreover, we found that GBP1 knockdown decreased the levels of cleaved-Caspase 3 and cleaved-PARP-1, while decreased those of cleaved-Caspase 9, BAX, Cytochrome C and APAF1. These findings imply that GBP1 knockdown can prevent BCG-induced apoptosis through an endogenous apoptosis pathway. In addition, the mitochondrial membrane potential of macrophages was significantly increased after BCG infection, and GBP1 knockdown could alleviate this phenomenon. Furthermore, downregulation of GBP1 also attenuated BCG-induced accumulation of reactive oxygen species in macrophages. Mechanistically, GBP1 suppressed the phosphorylation of the target molecules in p38/JNK pathway, thus regulating the apoptosis of BGC-infected macrophages. Collectively, these findings reveal a significant role of GBP1 in mediating cell apoptosis in macrophages infected with BCG, and the molecular mechanism underlying its suppressive effect on BCG-induced apoptosis.

The Airway Microbiome and Metabolome in Preterm Infants: Potential Biomarkers of Bronchopulmonary Dysplasia

OBJECTIVES

We investigated the genomic and metabolic characteristics of the airway microbiome in mild, moderate, severe, and non-bronchopulmonary dysplasia (BPD) preterm infants and explored possible mechanisms underlying BPD.

METHODS

Twenty-eight preterm infants with gestational age ≤34 weeks and intubated within 24 h after birth were enrolled. According to the severity of BPD, the patients were divided into mild, moderate and severe BPD groups, and the non-BPD group was the control group. Tracheal aspirates (TA) were obtained at intubation and on day 7 after birth. The bacterium in the aspirates were sequenced by 16S rRNA, and the metabolomics of the aspirates were identified by high performance liquid chromatography-quadrupole time of flight mass spectrometry (UHPLC-Q-TOF/MS). The correlation between the differential metabolite and differential bacteria was investigated using Pearson's correlation coefficient corrected for gestational age and birth weight and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases.

RESULTS

There were significant differences in the diversity and composition of airway microbiome and metabolome between severe, moderate and mild BPD and non-BPD premature infants. At birth (day 1), the difference was more pronounced than at day 7. The diversity of airway microbial community decreased, the abundance of Stenotrophomonas increased, and the increased level of sn-glycerol 3-phosphoethanolamine was positively correlated with the severity of BPD. There was a significant positive correlation between the abundance of Stenotrophomonas and the level of sn-glycerol 3-phosphoethanolamine.

CONCLUSION

Decreased diversity of the airway microbiome, increased abundance of Stenotrophomonas, and increased level of sn-glycerol 3-phosphoethanolamine may have potential as biomarkers for BPD. The occurrence and severity of BPD are closely related to Stenotrophomonas, which may influence the composition of the lower airway microbiome through its metabolite sn-glycerol 3-phosphoethanolamine, and may be the triggering factor of the disease. The causal relationship needs further study.