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The automatic extraction of inspection points for pylons is essential for intelligent Unmanned Aerial Vehicle (UAV) power inspections, especially in generating inspection route. However, current researches primarily focus on power scene perception and power component fault detection, with little attention paid to the inspection point detection. The primary reason are the lack of public pylon datasets for keypoint detection and the neglect of distinguish the inspection points from the keypoints used in feature extraction. Regarding that, this paper proposes a two-stage unsupervised pylon keypoint detection (UPKD) method to improve the efficiency of power inspections. In the first stage, the UPKD Network processes the point cloud to generate candidate keypoints, which comprises two main components: a data normalization module and an unsupervised keypoint detection network (UKD-Net). The data normalization module compresses information based on the symmetric structure of pylons, thereby reducing instability in inspection point detection. The UKD-Net incorporates a Point Transformer layer that uses self-attention mechanisms to extract features from the point cloud. In the second stage, a convex optimization strategy is applied to filter and acquire inspection points. These inspection points are then interconnected using a shortest-path strategy to generate the UAV inspection route. Our dataset is obtained using the Riegl VUX-1 laser measurement system and comprises 3,296 pylons of 10 types. Each pylon’s point cloud contains up to 25,000 points, with a high point density of 100 pts/m2. Extensive experiments show that the UPKD Network achieved state-of-the-art performance on our dataset, with repeatability achieving 90.39%, effectiveness (the ratio of effective keypoints to annotation points) achieving 69.95%, and completeness (the ratio of detected annotation points to keypoints) achieving 79.55%.

Abstract Background RNA secondary structure (RSS) can influence the regulation of transcription, RNA processing, and protein synthesis, among other processes. 3′ untranslated regions (3′ UTRs) of mRNA also hold the key for many aspects of gene regulation. However, there are often contradictory results regarding the roles of RSS in 3′ UTRs in gene expression in different organisms and/or contexts. Results Here, we incidentally observe that the primary substrate of miR159a (pri-miR159a), when embedded in a 3′ UTR, could promote mRNA accumulation. The enhanced expression is attributed to the earlier polyadenylation of the transcript within the hybrid pri-miR159a-3′ UTR and, resultantly, a poorly structured 3′ UTR. RNA decay assays indicate that poorly structured 3′ UTRs could promote mRNA stability, whereas highly structured 3′ UTRs destabilize mRNA in vivo. Genome-wide DMS-MaPseq also reveals the prevailing inverse relationship between 3′ UTRs’ RSS and transcript accumulation in the transcriptomes of Arabidopsis, rice, and even human. Mechanistically, transcripts with highly structured 3′ UTRs are preferentially degraded by 3′–5′ exoribonuclease SOV and 5′–3′ exoribonuclease XRN4, leading to decreased expression in Arabidopsis. Finally, we engineer different structured 3′ UTRs to an endogenous FT gene and alter the FT-regulated flowering time in Arabidopsis. Conclusions We conclude that highly structured 3′ UTRs typically cause reduced accumulation of the harbored transcripts in Arabidopsis. This pattern extends to rice and even mammals. Furthermore, our study provides a new strategy of engineering the 3′ UTRs’ RSS to modify plant traits in agricultural production and mRNA stability in biotechnology.

ObjectiveThe risk of suicide can be decreased by accurately identifying high-risk suicide groups and implementing the right interventions. The aim of this study was to develop a nomogram for suicide attempts (SA) in patients with first-episode drug-naïve (FEDN) major depressive disorder (MDD).MethodsThis study undertook a cross-sectional analysis of 1,718 patients diagnosed with FEDN MDD, providing comprehensive clinical data from September 2016 to December 2018. Data on anthropometric and sociodemographic factors were gathered, and the severity of depression and anxiety was evaluated using the 17-item Hamilton Depression Scale (HAMD-17) and the Hamilton Anxiety Scale (HAMA), respectively. Additionally, thyroid hormone levels, lipid profile parameters, and fasting blood glucose (FBG) were measured. Suicide attempt (SA) history was verified based on an amalgamation of medical records, patient interviews, and family interviews. Participants were randomly divided into a training group (70%, n = 1,204) and a validation group (30%, n = 514). In the training group, LASSO analysis and multivariate regression were used to identify variables associated with SA. A nomogram was then constructed using the identified risk factors to estimate the likelihood of SA within the training group. To assess the accuracy, the area under the receiver operating characteristic curve (AUC) was utilized, and calibration plots were employed to evaluate calibration. Additionally, decision curve analysis (DCA) was performed to assess the precision of the model. Finally, internal validation was carried out using the validation group.ResultsA practical nomogram has been successfully constructed, incorporating HAMD, HAMA, thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), and systolic blood pressure (SBP) parameters, to estimate the probability of SA in Chinese patients diagnosed with FEDN MDD. The pooled area under the ROC for SA risk in both the training and validation groups was found to be 0.802 (95% CI: 0.771 to 0.832) and 0.821 (95% CI: 0.774 to 0.868), respectively. Calibration analysis revealed a satisfactory correlation between the nomogram probabilities and the actual observed probabilities. The clinical applicability of the nomogram was confirmed through decision curve analysis. To enhance accessibility for clinicians and researchers, an online version of the nomogram can be accessed at https://doctorjunjunliu.shinyapps.io/dynnomapp/.ConclusionsWe constructed and validated a nomogram for the early detection of FEDN MDD patients with a high risk of SA, thereby contributing to the implementation of effective suicide prevention programs.

Abstract Background The rapid devolvement of single cell RNA sequencing (scRNA-seq) technology leads to huge amounts of scRNA-seq data, which greatly advance the research of many biomedical fields involving tissue heterogeneity, pathogenesis of disease and drug resistance etc. One major task in scRNA-seq data analysis is to cluster cells in terms of their expression characteristics. Up to now, a number of methods have been proposed to infer cell clusters, yet there is still much space to improve their performance. Results In this paper, we develop a new two-step clustering approach to effectively cluster scRNA-seq data, which is called TSC — the abbreviation of Two-Step Clustering. Particularly, by dividing all cells into two types: core cells (those possibly lying around the centers of clusters) and non-core cells (those locating in the boundary areas of clusters), we first clusters the core cells by hierarchical clustering (the first step) and then assigns the non-core cells to the corresponding nearest clusters (the second step). Extensive experiments on 12 real scRNA-seq datasets show that TSC outperforms the state of the art methods. Conclusion TSC is an effective clustering method due to its two-steps clustering strategy, and it is a useful tool for scRNA-seq data analysis.

The global rise in electricity demand necessitates extensive transmission infrastructure, where insulators play a critical role in ensuring the safe operation of power transmission systems. However, insulators are susceptible to burst defects, which can compromise system safety. To address this issue, we propose an insulator defect detection framework, ID-Det, which comprises two main components, i.e., the Insulator Segmentation Network (ISNet) and the Insulator Burst Detector (IBD). (1) ISNet incorporates a novel Insulator Clipping Module (ICM), enhancing insulator segmentation performance. (2) IBD leverages corner extraction methods and the periodic distribution characteristics of corners, facilitating the extraction of key corners on the insulator mask and accurate localization of burst defects. Additionally, we construct an Insulator Defect Dataset (ID Dataset) consisting of 1614 insulator images. Experiments on this dataset demonstrate that ID-Det achieves an accuracy of 97.38%, a precision of 97.38%, and a recall rate of 94.56%, outperforming general defect detection methods with a 4.33% increase in accuracy, a 5.26% increase in precision, and a 2.364% increase in recall. ISNet also shows a 27.2% improvement in Average Precision (AP) compared to the baseline. These results indicate that ID-Det has significant potential for practical application in power inspection.

Abstract Viral RNA-host protein interactions are indispensable during RNA virus transcription and replication, but their detailed structural and dynamical features remain largely elusive. Here, we characterize the binding interface for the SARS-CoV-2 stem-loop 3 (SL3) cis-acting element to human TIA1 protein with a combined theoretical and experimental approaches. The highly structured SARS-CoV-2 SL3 has a high binding affinity to TIA1 protein, in which the aromatic stacking, hydrogen bonds, and hydrophobic interactions collectively direct this specific binding. Further mutagenesis studies validate our proposed 3D binding model and reveal two SL3 variants have enhanced binding affinities to TIA1. And disruptions of the identified RNA-protein interactions with designed antisense oligonucleotides dramatically reduce SARS-CoV-2 infection in cells. Finally, TIA1 protein could interact with conserved SL3 RNA elements within other betacoronavirus lineages. These findings open an avenue to explore the viral RNA-host protein interactions and provide a pioneering structural basis for RNA-targeting antiviral drug design.

Expanding urbanization has led to an increased risk of urban flooding, which poses a hazard to humans. Scientific assessment of urban flooding vulnerability (UFV) is essential to protect human health and reduce losses. In this study, UFV was assessed using the Analytic Hierarchy Process-Press-State-Response (AHP-PSR) model in Jining City, China. The urban flooding index (UFI) was calculated from 13 indicators. Based on the magnitude of UFI, the vulnerability was classified into five classes. Among them, the areas of very high, high, medium, low and very low vulnerability are 897.21 km2 (8.02%), 3192.14 km2 (28.53%), 2063.22 km2 (18.44%), 39773.96 km2 (35.52%) and 1060.47 km2 (9.48%), respectively. Finally, suggestions were proposed for further urban flooding management based on the results. The results of the study can provide an important reference for the government to prevent and mitigate urban flooding. Meanwhile, the modeling framework can be easily transferred to other cities, providing new ideas for UFV assessment.

AbstractHigh-resolution remote sensing images provide complete and detailed ground scenes. However, these complex and diverse details make it difficult to interpret large area objects and their small area details at a single granularity. Therefore, the method proposed in this paper refers to the principles of visual working memory, visual attention and visual reasoning, independently analyse the ground objects of different granularities, and integrates the recognition results of the different granularity ground objects to form accurate classification results. This multi-granularity classification strategy suppresses noise interference, protects typical edges, and highlights clear details. Experimental results with dataset of real land use survey projects, including images of different time, multiple perspectives, and different altitudes, The results show that the idea of combining unsupervised edge detection with neural network classification can greatly enhance the generalization ability and edge cognitive ability, making the classification accuracy reach 80% on average, with an improvement of about 5%.

Most of the Pythium species are pathogenic to a wide range of economically important crops and, sometimes, can even cause diseases in animals and humans. An exception is that the soil-inhabiting P. oligandrum is an effective biocontrol agent against a diverse suite of pathogens and promotes plant growth. In this work, we sequenced the whole genome of P. oligandrum PO-1, isolated from rhizosphere soils of Chinese Angelica sinensis, using a combination of long-read single-molecule real-time sequencing technology (Pacific Biosciences [PacBio]) and Illumina sequencing. The 2.5-Gb and 5.2-Gb bases were generated respectively. The sequencing depths were 93× with PacBio and 145× with Illumina sequencing. With the PacBio sequencing results further corrected by Illumina sequencing, the genome was assembled into 71 scaffolds with a total size of 39.10 Mb (N50 = 1.45 Mb; L50 = 9)and the longest scaffold is 3.49 Mb. Genome annotation identifies 15,632 protein-coding genes and 0.47 Mb of transposable elements. Our genomic assembly and annotation have been greatly improved compared with the already released three genomes of P. oligandrum. This genomic data will provide valuable information to understand the mechanism underlying its biocontrol potentials and will also facilitate the dissection of genome evolution and environmental adaptation within the genus Pythium. [Graphic: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 “No Rights Reserved” license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2022.

Abstract Anaerobic fungus–methanogen co-cultures from rumen liquids and faeces can degrade lignocellulose efficiently. In this study, 31 fungus–methanogen co-cultures were first obtained from the rumen of yaks grazing in Qinghai Province, China, using the Hungate roll-tube technique. The fungi were identified according to morphological characteristics and internal transcribed spacer (ITS) sequences. The methanogens associated with each fungus were identified by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and 16S rRNA gene sequencing. They were five co-culture types: Neocallimastix frontalis + Methanobrevibacter ruminantium, Neocallimastix frontalis + Methanobrevibacter gottschalkii, Orpinomyces joyonii + Methanobrevibacter ruminantium, Caecomyces communis + Methanobrevibacter ruminantium, and Caecomyces communis + Methanobrevibacter millerae. Among the 31 co-cultures, during the 5-day incubation, the N. frontalis + M. gottschalkii co-culture YakQH5 degraded 59.0%–68.1% of the dry matter (DM) and 49.5%–59.7% of the neutral detergent fiber (NDF) of wheat straw, corn stalk, rice straw, oat straw and sorghum straw to produce CH4 (3.0–4.6 mmol/g DM) and acetate (7.3–8.6 mmol/g DM) as end-products. Ferulic acid (FA) released at 4.8 mg/g DM on corn stalk and p-coumaric acid (PCA) released at 11.7 mg/g DM on sorghum straw showed the highest values, with the following peak values of enzyme activities: xylanase at 12,910 mU/mL on wheat straw, ferulic acid esterase (FAE) at 10.5 mU/mL on corn stalk, and p-coumaric acid esterase (CAE) at 20.5 mU/mL on sorghum straw. The N. frontalis + M. gottschalkii co-culture YakQH5 from Qinghai yaks represents a new efficient combination for lignocellulose biodegradation, performing better than previously reported fungus–methanogen co-cultures from the digestive tract of ruminants.

Abstract Purpose Continuous monoculture leads to deterioration of soil microenvironment, which can severely threat the quality and efficiency of Angelica sinensis (A. sinensis), especially for seedlings production. However, little attention has been paid to investigate how continuous monocropping affects the growth of A. sinensis seedling, rhizosphere microbial populations, and nutrient status. Methods A field experiment consisting of two different planting patterns — raising A. sinensis seedlings in reclamation alpine uncultivated meadow (RW) and 1-year continuous monoculture (CC), was carried out at Min County, Gansu province, China. Result The results showed that compared with RW, the growth rate and valid quantity of A. sinensis seedlings were significantly reduced by 195.4% and 36.7% in CC, respectively. Continuous monocropping significantly increased the rhizosphere soil pH value during the growing season, ranging from 6.18 to 7.10, while reducing the content of SOM, total N, and available P and K. Glomalin, AMF spore densities, and the number of actinomycetes, ammonifiers, and azotobacter were also decreased by CC. The CC treatment significantly increased the abundance of fungi. The diversity and richness of bacteria in CC were lower than RW. Furthermore, the composition and structure of bacterial and fungal flora also changed and that the abundance of beneficial bacteria decreased, while the abundance of pathogens increased in CC. Thus, CC appeared to completely upend the relationship between soil nutrient availability and microbial activity. Conclusion The results illustrated that continuous monoculture led the flora of bacteria and fungi to changed dramatically, with the abundance of beneficial bacteria decreased and the abundance of harmful microbes, such as Lasiosphaeriaceae, Vishniacozyma, Myrmecridium, and Hypocreales, increased. The function of microbial population has changed from “beneficial bacteria dominated” to “harmful microbes dominant.” We concluded that continuous monoculture significantly reduced the growth and the efficiency of A. sinensis seedlings and deteriorated the rhizosphere soil microenvironment by increasing pH and decreasing nutrient availability, as well as altering the function of interactions between soil nutrients and microbial populations, thereby resulting in an unsuitable microenvironment for A. sinensis seedlings growth.

As the most common primitives, line segments play an essential role in the vectorized reconstruction of artificial scenes. In this paper, a geometric feature enhanced line extraction method with Hierarchical Topological Optimization is proposed to extract line segments from large-scale point clouds. Firstly, 3D projection plane regions are extracted from point clouds using region growing and merging; Secondly, the point clouds in the detected planar region are projected onto 2D grids for efficiently geometry features enhanced line detection. Specifically, the edge feature index of each point inside each projection grid is calculated and fused to represent the edge-geometry enhanced feature. The likelihood of edge presenting on the projection grids is produced using a pre-trained convolutional neural network that combines multi-scale edge locating outputs. Then, line segments on the projected edge maps are extracted by the MCMLSD algorithm and further back-projected into 3D space to form the 3D line segment candidates. Thirdly, the hierarchical topological relationships between the contour and line segments are used to optimize the candidate 3D line segments. The optimization process consists of merging perceptually accurate 3D line segments, closing the contour line based on the contour and merging plane intersection lines based on line segments. The point clouds of seven large-scale outdoor scenes from the Semantic 3D and WHU-TLS public datasets are selected for qualitative and quantitative evaluations. Experiments show that the proposed approach can efficiently extract line segments that represent the geometric characteristics of the scenes comprehensively and accurately. Compared with the SOTA line segments extraction algorithms (i.e., Lu et al. (2019), Zhang et al. (2020a)), the proposed method filters out major miscellaneous/broken lines and extract more complete line segments. The Completeness and Correctness of the line segments extraction results reach 86% on average compared with manual labeled ground truth (dl, ds: 0.5,0.5), at an average processing speed of 25,000 points per second.

The stable and reliable supply of electric power is strongly related to the normal social production. In recent years, power transmission lines inspection based on remote sensing methods has made great progress, especially using the UAV-borne LiDAR system. The extraction and identification of power transmission lines (i.e., conductors) from 3D point clouds are the basis of LiDAR data-based power grid risk management. However, existing rule-based/traditional machine learning extraction approaches have exposed some limitations, such as the lack of timeliness and generalization. Moreover, the potential of deep learning is seriously overlooked in RoW (Right of Way) LiDAR inspection tasks. Thus, we proposed DCPLD-Net: a diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-borne LiDAR data. To implement efficient 2D convolution on 3D point clouds, we proposed a novel point cloud representation, named Cross Section View (CSV), which transforms the discrete point clouds into 3D tensors constructed by voxels with a deformed geometric shape along the flight trajectory. After the CSV feature generation, the encoded features of each voxel are treated as energy (i.e., heat) signals and diffused in space to generate diffusion feature maps. The feature of the power line points are thus enhanced through this simulated physical process (diffusion). Finally, a single-stage detector named PLDNet is proposed for the multiscale detection of conductors on the diffused CSV representations. The experimental results show that the DCPLD-Net achieves an average F1 score of 97.14 % at 8 Hz detection frequency on RoWs inspection LiDAR datasets collected by both mini-UAV LiDAR and large-scale fully autonomous UAV power lines inspection robots, and surpasses compared methods (i.e. PointNet ++, RandLA-Net) in terms of F1 scores and IoU.

As an ornamental flower crop, the long-term continuous monocropping of cut chrysanthemum causes frequent occurrence of diseases, seriously affecting the quality of cut chrysanthemum. The rhizosphere microbial community plays an important role in maintaining the healthy growth of plants, whereas the composition and dynamics of rhizosphere microbial community under continuous monocropping of cut chrysanthemum have not been fully revealed. In this study, the Illumina MiSeq high-throughput sequencing platform was used to monitor the dynamic changes of rhizosphere microbial communities in four varieties of cut chrysanthemum during 0–3 years of monocropping, and the soil physicochemical properties were also determined. Results showed that continuous monocropping significantly increased the fungal community richness and altered the profiles of the bacterial and fungal communities, leading to variation of community beta-diversity. With the increase of continuous cropping time, biocontrol bacteria decreased, while some plant pathogenic fungi were enriched in the rhizosphere of cut chrysanthemum. FAPROTAX-based functional prediction showed that the abundance of gene related to nitrogen and sulfur metabolism and chitin lysis was reduced in the rhizosphere of cut chrysanthemum. FUNGuild-based fungal function prediction showed that plant pathogenic fungal taxa were increasing in the rhizosphere of cut chrysanthemum, mainly Acremonium, Plectosphaerellaceae, Fusarium, and Cladosporium. Continuous cropping also reduced the content of ammonium nitrogen and increased soil salinity, resulting in deterioration of soil physical and chemical properties, which, together with the transformation of rhizosphere microbial community, became part of the reasons for the continuous cropping obstacle of cut chrysanthemum.

RNA transcripts form various secondary and tertiary structures that have a wide range of regulatory functions. Several methods have been developed to profile in vivo RNA secondary structure in prokaryotes and eukaryotes. These methods, such as dimethyl sulfate (DMS) mutational profiling with high-throughput sequencing (DMS-MaPseq), couple small chemical-mediated RNA modifications with next-generation sequencing. DMS-MaPseq, a powerful method for genome-wide and target-specific RNA secondary structure profiling, has been applied in yeast, mammals, Drosophila, and Arabidopsis thaliana, but not in crops. Here, we used DMS-MaPseq to conduct a target-specific and genome-wide profile of in vivo RNA secondary structure in rice (Oryza sativa). The DMS treatment conditions were optimized for rice leaf and root tissues. To increase the sequencing depth and coverage of low-abundance transcripts in genome-wide DMS-MaPseq, we used streptavidin-biotin depletion to reduce the abundance of highly expressed chloroplast transcripts during library construction. The resulting target-specific and genome-wide rice DMS-MaPseq data were of high quality and reproducibility. Furthermore, we used DMS-MaPseq to profile the in vivo RNA secondary structure of an OsmiR399 target region located at 5′UTR of OsPHO2, which participates in rice phosphate homeostasis. An unfolded RNA structure downstream of miRNA target site was observed in predicted in vivo RNA secondary structure, reminiscence of the TAM (Target Adjacent nucleotide Motif) involved in mRNA structure-mediated regulation in miRNA cleavage. Our study optimized DMS-MaPseq for probing in vivo RNA secondary structure in rice, facilitating the study of RNA structure-mediated regulations in crops.

Single-cell RNA sequencing(scRNA-seq) technology has boomed in the past decade which makes it possible to study biological problems at the resolution of cellular-level. Currently, the research mainly focuses on exploring the cellular heterogeneity, involving studies about identifying cell type identification, cell lineage tracing, spatial model reconstruction of complex organizations, etc. Clustering analysis is always the most effective way in grouping single cells in previous studies. However, existing scRNA-seq clustering methods separate pre-processing and clustering tasks that complicated the problem. In addition, the emergence of big data further limits the traditional clustering algorithms' application on scRNA-seq data. Therefore, developing novel clustering methods and improving clustering accuracy for growing scRNA-seq data is a continuous task. In this paper, we propose a highly integrated Deep Subspace Clustering Denoise Network named DSCD, which integrates denoise, dimension reduction and clustering in a unified framework. Based on the neural network architecture of autoencoder, DSCD discovers the low dimensional latent structure within scRNA-seq data from the compressed representation. Furthermore, we add a novel self-expressive denoise layer to learning the global relationships between single cells, which is the main innovation of DSCD. Experimental results on the synthetic data demonstrate the effectiveness of the novel denoise layer. From the clustering results on 5 real scRNA-seq datasets, we find that DSCD outperforms the related subspace clustering algorithms and state of the art methods. In conclusion, DSCD responds well to the rapidly increasing scRNA-seq data scale, greatly reduces human interference in dimension reduction and handles the noisy scRNA-seq data in proper way thus obtain a higher clustering accuracy.

The majority of cellular proteins are degraded by the 26S proteasome in eukaryotes. However, intrinsically disordered proteins (IDPs), which contain large portions of unstructured regions and are inherently unstable, are degraded via the ubiquitin-independent 20S proteasome. Emerging evidence indicates that plant IDP homeostasis may also be controlled by the 20S proteasome. Relatively little is known about the specific functions of the 20S proteasome and the regulatory mechanisms of IDP degradation in plants compared to other species because there is a lack of systematic protocols for in vitro assembly of this complex to perform in vitro degradation assays. Here, we present a detailed protocol of in vitro reconstitution assay of the 20S proteasome in Arabidopsis by modifying previously reported methods. The main strategy to obtain the 20S core proteasome here is to strip away the 19S regulatory subunits from the 26S proteasome. The protocol has two major parts: 1) Affinity purification of 20S proteasomes from stable transgenic lines expressing epitope-tagged PAG1, an essential component of the 20S proteasome (Procedures A-D) and 2) an in vitro 20S proteasome degradation assay (Procedure E). We anticipate that these protocols will provide simple and effective approaches to study in vitro degradation by the 20S proteasome and advance the study of protein metabolism in plants.

In this study, the Cutaneotrichosporon dermatis strain M503 was isolated and could efficiently degrade tetracycline, doxycycline, and chlorotetracyline. The characteristics of tetracycline degradation were investigated under a broad range of cultural conditions. Response surface methodology (RSM) predicted that the highest degradation rate of tetracycline could be obtained under the following conditions: 39.69 °C, pH of 8.79, and inoculum dose of 4.0% (v/v, ~3.5 × 106 cells/mL in the medium). In accordance with the five identified degradation products of tetracycline, two putative degradation pathways, which included the shedding of methyl and amino groups, were proposed. Moreover, the well diffusion method showed that the strain of M503 decreases the antibacterial potency of tetracycline, doxycycline, and chlorotetracycline. These findings proposed a putative mechanism of tetracycline degradation by a fungus strain and contributed to the estimation of the fate of tetracycline in the aquatic environment.

To clarify the relationship between neutral lipid content and cordycepin accumulation in Cordyceps militaris, mutants were generated from mixed spores of two C. militaris strains with varying cordycepin-producing capacities. Fifteen stable mutants producing from 0.001 to 2.363 mg/mL cordycepin were finally selected. The relative fluorescence intensities of the 15 mutants, two C. militaris strains and an Aspergillus nidulans strain at different concentrations of lyophilized mycelium powder were then investigated using the Nile red method. The mutant CM1-1-1 with the highest relative fluorescence intensity among the eighteen strains was selected for optimizing the Nile red method. Relative fluorescence intensity was linearly correlated with cordycepin concentration in liquid broth (R2 = 0.9514) and in lyophilized mycelium powder (R2 = 0.9378) for the 18 cordycepin-producing strains under identical culture conditions and with cordycepin concentration in liquid broth (R2 = 0.9727) and in lyophilized mycelium powder (R2 = 0.9613) for CM1-1-1 under eight different sets of conditions. In addition, the cordycepin content in lyophilized mycelium powder measured by the Nile red method was linearly correlated with that determined by an HPLC method (R2 = 0.9627). In conclusion, neutral lipids in lipid droplets are required during cordycepin accumulation; these neutral lipids are potential biomarkers of cordycepin biosynthesis.