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Abstract Natural herbs, which contain pharmacologically active compounds, have been used historically as medicines. Conventionally, the analysis of chemical components in herbal medicines requires time-consuming sample separation and state-of-the-art analytical instruments. Nanopore, a versatile single molecule sensor, might be suitable to identify bioactive compounds in natural herbs. Here, a phenylboronic acid appended Mycobacterium smegmatis porin A (MspA) nanopore is used as a sensor for herbal medicines. A variety of bioactive compounds based on salvianolic acids, including caffeic acid, protocatechuic acid, protocatechualdehyde, salvianic acid A, rosmarinic acid, lithospermic acid, salvianolic acid A and salvianolic acid B are identified. Using a custom machine learning algorithm, analyte identification is performed with an accuracy of 99.0%. This sensing principle is further used with natural herbs such as Salvia miltiorrhiza, Rosemary and Prunella vulgaris. No complex sample separation or purification is required and the sensing device is highly portable.

Abstract Natural fruits contain a large variety of cis-diols. However, due to the lack of a high-resolution sensor that can simultaneously identify all cis-diols without a need of complex sample pretreatment, direct and rapid analysis of fruits in a hand-held device has never been previously reported. Nanopore, a versatile single molecule sensor, can be specially engineered to perform this task. A hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore modified with a sole phenylboronic acid (PBA) adapter is prepared. This engineered MspA accurately recognizes 1,2-diphenols, alditols, α-hydroxy acids and saccharides in prune, grape, lemon, different varieties of kiwifruits and commercial juice products. Assisted with a custom machine learning program, an accuracy of 99.3% is reported and the sample pretreatment is significantly simplified. Enantiomers such as DL-malic acids can also be directly identified, enabling sensing of synthetic food additives. Though demonstrated with fruits, these results suggest wide applications of nanopore in food and drug administration uses.

Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0–50 cm (the upper layer), 50–100 cm (the middle layer), and 100–160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR.

Soil carbon content prediction based on hyperspectral images can achieve large-scale spatial measurement, which has the advantages of wide coverage and fast information collection, is more suitable for field data collection. However, the research on soil carbon content prediction based on hyperspectral images mainly focuses on feature extraction of spectral information, ignoring the spatial information, and cannot well reveal the intrinsic structural characteristics of data. Aiming at the lack of spatial features consideration in hyperspectral images, soil carbon content prediction methods based on multi-scale feature fusion are proposed by hyperspectral image. At the same time of extracting spectral features from hyperspectral images, the spatial information is used for the first time and a multi-scale spectral and spatial feature network (SpeSpaMN) is designed. In the SpeSpaMN, the multi-scale spectral feature network (SpeMN) is constructed to extract spectral features, the multi-scale spatial feature network (SpaMN) is constructed to extract spatial features. The two networks are fused by using the complementary relationship between different scale features to achieve soil carbon content prediction based on multi-scale feature fusion. The results showed that SpeSpaMN had the best results compared to other methods, followed by the method of SpeMN. The RPD of Inland, Aoshan Bay and Jiaozhou Bay samples based on SpeSpaMN were increased by 47.36%, 37.96% and 4.30% respectively. This paper can effectively solve the problem of the deep fusion of spatial and spectral features in the soil carbon content prediction by hyperspectral image, so as to improve the accuracy and stability of soil carbon content prediction.

Nonsteroidal anti-inflammatory drugs (NSAIDs) have been widely detected in wastewater and surface water, indicating that the removal of NSAIDs by wastewater treatment plants was not efficient. Electrochemical advanced oxidation technology is considered to be an effective process. This study presents an investigation of the kinetics, mechanism, and influencing factors of diclofenac (DCF) degradation by an electrochemical process with boron-doped diamond anodes. Relative operating parameters and water quality parameters are examined. It appears that the degradation follows the pseudo-first-order degradation kinetics. DCF degradation was accelerated with the increase of pH from 6 to 10. The degradation was promoted by the addition of electrolyte concentrations and current density. Humic acid and bicarbonate significantly inhibited the degradation, whereas chloride accelerated it. According to the quenching tests, hydroxyl radicals (•OH) and sulfate radicals contributed 76.5% and 6.5%, respectively, to the degradation. Sodium sulfate remains a more effective electrolyte, compared to sodium nitrate and sodium phosphate, suggesting the quenching effect of nitrate and phosphate on •OH. Major DCF transformation products were identified. According to the degradation products detected by liquid chromatography–mass spectrometry, hydroxylation and decarboxylation are the main pathways of DCF degradation; while dechlorination, chlorination, and nitro substitution are also included in this electrochemical degradation process. Highlights The electrochemical degradation of DCF at BDD anodes follows pseudo-first-order kinetics.; •OH contributes more than SO4•–, but the effect of SO4•– cannot be ignored.; The presence of Cl− significantly accelerated the degradation of DCF.; Hydroxylation and decarboxylation are main pathways in DCF degradation.;

Nanopores have been used for direct observation of RNA structure in native environments but have limited RNA differentiation capabilities. Here, the authors report on the use of Mycobacterium smegmatis porin A nanopores for the trapping and translocation identification of microRNA, siRNA, tRNA and ribosomal RNA.

Marine sediments record much information of the ecological process, which highly correlated with global and local environmental change. Particle size and its distributions of sediments indicate different ecological functions, thus are the key questions in marine ecology. The analysis method is tedious and laborious, which is not conducive for in-situ monitoring. Here, a spectral analysis was explored using surface sediments sampled in the intertidal zone of Dongdayang village, Qingdao, China. These samples were dried and sieved to pass through the mesh size of 0.3 mm, 0.2 mm, 0.1 mm, and 0.075 mm, respectively. Then, four types of subsamples were collected with the particle size of 0.3-0.2 mm, 0.2-0.1 mm, 0.1-0.075 mm, and

ObjectiveTo validate the reliability and efficiency of clinical diagnosis in practice based on a well-established system for the automatic segmentation of cerebral microbleeds (CMBs).MethodThis is a retrospective study based on Magnetic Resonance Imaging-Susceptibility Weighted Imaging (MRI-SWI) datasets from 1,615 patients (median age, 56 years; 1,115 males, 500 females) obtained between September 2018 and September 2019. All patients had been diagnosed with cerebral small vessel disease (CSVD) with clear cerebral microbleeds (CMBs) on MRI-SWI. The patients were divided into training and validation cohorts of 1,285 and 330 patients, respectively, and another 30 patients were used for internal testing. The model training and validation data were labeled layer by layer and rechecked by two neuroradiologists with 15 years of work experience. Afterward, a three-dimensional convolutional neural network (CNN) was applied to the MRI data from the training and validation cohorts to construct a deep learning system (DLS) that was tested with the 72 patients, independent of the aforementioned MRI cohort. The DLS tool was used as a segmentation program for these 72 patients. These results were evaluated and revised by five neuroradiologists and subjected to an output analysis divided into the missed label, incorrect label, and correct label. The interneuroradiologists DLS agreement rate, which was assessed using the interrater agreement kappas test, was used for the quality analysis.ResultsIn the detection and segmentation of the CMBs, the DLS achieved a Dice coefficient of 0.72. In the evaluation of the independent clinical data, the neuroradiologists reported that more than 90% of the lesions were directly detected and less than 10% of lesions were incorrectly labeled or the label was missed by our DLS. The kappa value for interneuroradiologist DLS agreement reached 0.79 on average.ConclusionBased on the results, the automatic detection and segmentation of CMBs are feasible. The proposed well-trained DLS system might represent a trusted tool for the segmentation and detection of CMB lesions.

The difference in the nutrient content and harmful heavy metal content of different regions and types of soils can have a certain impact on the crops grown on them. Different crop varieties have their own ecologically suitable areas for planting. Identification of the types of agricultural soil from different regions, can quickly providing references for the selection and cultivation of crops on different types/regions of soil, which is conducive to ensuring the safety of agricultural products. This study is carried out using a laser-induced breakdown spectra (LIBS) device with a frequency-doubled 532 nm excitation wavelength, a laser pulse width of 8 ns, a repetition frequency of 5 Hz, and a pulsed laser energy of 15 mJ. The LIBS data of nine standard agricultural soils collected from different regions. Each sample is collected 60 sets of spectra and a total of 540 sets of data are obtained. At first, the all obtained spectral data are normalized to compensate for the spectral changes in the measurement process. Then the spectral data in the 200–800 nm band are analyzed. Randomly select 30 %, 50 %, and 70 % of the spectral data according to the Kennard-Stone (KS) classification as the training set, and the remaining spectral data as the test set. The Support Vector Machine (SVM) model is used to classify and identify the soil samples of agricultural soil in different areas. The results show that when 30 % of the spectral data are selected as the training set, the accuracy of identifying different agricultural soil types is 88.6 %, and the accuracy of identifying all nine types of agricultural soils is above 76.7 %. The accuracy of soil identification for the two types of agricultural soil is 100 %, and the identification accuracy for the seven types of soil is above 86 %. When 50 % spectral data are selected as training set, the overall recognition accuracy can reach 95.9 %, the recognition accuracy of 8 types of agricultural soil samples is above 90.5 %, and the recognition accuracy of 4 types of soils can reach 100 %, The recognition accuracy of only one type of soil is less than 90 %, which is 86.7 %. When the selected training set contains 70 % of spectral data, the overall recognition accuracy can reach 96.3 %, the recognition accuracy of 8 types of agricultural soil samples is above 90 %, and the accuracy of 1 type of agricultural soil recognition is less than 90 %, which is 89.19 %. The results show that LIBS technology can be applied to the rapid identification of soil types in agricultural soils.

Visible and near infrared spectroscopy has been widely used to develop a method for rapidly determining organic carbon in soils or sediments (SOC). Most of these studies concentrated on how to establish a good spectral model but ignored how to evaluate the method, such as the use of detection range (max and min), resolution and error for SOC spectral analysis. Here, we proposed a method to evaluate the spectral analysis of SOC. Using 96 sediments sampled in the Yellow Sea and Bohai Sea, China, we established three spectral models of SOC after collecting their spectral reflectance by Agilent Cary 5000, ASD FieldSpec 4 and Ocean Optics QEPro, respectively. For both the calibration set and validation set in each spectrometer, the predicted SOC concentrations followed a distribution curve (function), in which the x-axis was the SOC concentrations. Using these curves, we developed these four technical parameters. The detection ranges were the SOC concentrations where the curve was near to or crossing with the lateral axis, while the detection resolution was the average difference between the two neighboring SOC concentrations. The detection errors were the differences between the predicted SOC and the measured SOC. Results showed that these technical parameters were better in the bench-top spectrometer (Cary 5000) than those in the portable spectrometers when analyzing the same samples. For the portable spectrometers, QEPro had a broader detection range and more consistent detection error than FieldSpec 4, suggesting that the low-cost QEPro performed as well as the high-cost FieldSpec 4. This study provides a good example for evaluating spectral analysis by spectroscopy, which can support the development of the spectral method.

Visible and near infrared spectroscopy (VNIR) has been successfully used to determine many soil parameters rapidly. However, total phosphorous (P) cannot be well quantified by VNIR, mainly because its absorption spectrum or reflectance spectrum was too weak to be studied. Here, we explored a new way to reveal the reflectance spectrum of total P in soils. Firstly, six types of soil residues were prepared after the original soil was sequentially extracted Ca2-P, Ca8-P, Al-P, Fe-P, O-P, and Ca10-P, respectively. Secondly, the reflectance spectra of these P fractions were obtained using these soil residues by QE65000 spectrometer (Ocean Optics). Specifically, if we intended to get the spectrum of a certain P fraction, we would measure the reflectance spectrum of the soil residues without its neighboring former P fraction, using the soil residues after extracting this P fraction as the reference spectrum. Results showed that these P fractions had different characteristics of reflectance spectrum and can be well analyzed qualitatively and quantitatively. After summarizing the diagnostic spectra of these P fractions, the total P diagnostic spectrum was found to be distributed in 250–750 nm. Using these diagnostic bands, the spectral model of total P was established and showed a better result. This study provided an important evidence for understanding the characteristics of total P reflectance spectrum in soils and predicting total P concentrations.

Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.

The available training samples for hyperspectral image (HSI) are limited, which will lead to the failure to improve the classification accuracy. Aiming at increasing the number of available training samples, a superpixel pair (SSP) method is proposed to solve the problem of small samples. Combining the advantages of pixel pair (PP) and superpixel, the number of original training samples are augmented multiple times. The SSP method mainly includes three steps: first, according to the regional clustering-based spatial preprocessing (RCSPP) algorithm and its improved algorithms, superpixel segmentation is carried out to realize the first sample enlargement; second, the first expanded training samples are paired to form SSP; last, SVM and deep CNN are used to train and learn the SSP, and the testing pixels obtain the final result through the voting mechanism. Experimental results based on three HSI data sets demonstrate that the proposed method can achieve better classification performance than some existing algorithms to solve the problem of small samples and very small samples classification. [ABSTRACT FROM AUTHOR]

Fruit colour is a crucial factor influencing both the marketability and quality of pepper (Capsicum annuum), particularly ornamental varieties. Fruit colour is a complex multigenic trait in plant species. Previously, the Arabidopsis pseudo-response regulator 2 (APRR2) gene, one of the regulators that control fruit chlorophyll content and chloroplast development, thereby influencing fruit colour at the green immature stage in tomato (Solanum lycopersicum), cucumber (Cucumis sativus) and pepper, was reported. Functional molecular markers associated with this homologous gene in pepper could be employed to discern fruit colour at the seedling stage, thus improving the efficiency of green/white-fruited pepper breeding. In this study, a derived cleaved amplified polymorphic sequences (dCAPS) marker was developed based on the mutation site in the sequence of a CaAPRR2-like gene that was cloned from the inbred line B1-2, which exhibits milky white fruit at the green immature stage. The marker was subsequently validated in a CaAPRR2-like progeny segregation population. After digestion with the restriction endonuclease TaqI, the amplification product exhibited evident polymorphic bands, enabling it to distinguish between peppers with milky white and green fruit colours. The developed molecular marker displayed remarkable stability and repeatability, thus offering a straightforward and effective tool for enhancing fruit colour in pepper breeding programs.

AimsThe aim of this study was to conduct a bibliometric analysis of the relevant literature on Duchenne muscular dystrophy (DMD) to ascertain its current status, identify key areas of research and demonstrate the evolution of the field.MethodsThe analysis sourced documents from the Science Citation Index Expanded in the Web of Science core collection, utilizing CiteSpace software and an online bibliometric platform to analyze collaborative networks among authors, institutions and countries, and to map out the research landscape through journal and reference evaluations. Keyword analyses, including clustering and emergent term identification, were conducted, alongside the development of knowledge maps.ResultsThe study included 9,277 documents, indicating a rising publication trend in the field. The Institut National de la Santé et de la Recherche Médicale emerged as the top publishing institution, with Francesco Muntoni as the most prolific author. The United States dominated in publication output, showcasing significant leadership. The keyword analysis highlighted 786 key emergent terms, primarily focusing on the mechanisms, diagnostics and treatment approaches in DMD.ConclusionThe field of DMD research is experiencing robust growth, drawing keen interest globally. A thorough analysis of current research and trends is essential for advancing knowledge and therapeutic strategies in this domain.

Abstract Background Satellite repeats are one of the most rapidly evolving components in eukaryotic genomes and play vital roles in genome regulation, genome evolution, and speciation. As a consequence, the composition, abundance and chromosome distribution of satellite repeats often exhibit variability across various species, genome, and even individual chromosomes. However, we know little about the satellite repeat evolution in allopolyploid genomes. Results In this study, we investigated the satellite repeat signature in five okra (Abelmoschus esculentus) accessions using genomic and cytogenetic methods. In each of the five accessions, we identified eight satellite repeats, which exhibited a significant level of intraspecific conservation. Through fluorescence in situ hybridization (FISH) experiments, we observed that the satellite repeats generated multiple signals and exhibited variations in copy number across chromosomes. Intriguingly, we found that five satellite repeats were interspersed with centromeric retrotransposons, signifying their involvement in centromeric satellite repeat identity. We confirmed subgenome-biased amplification patterns of these satellite repeats through existing genome assemblies or dual-color FISH, indicating their distinct dynamic evolution in the allotetraploid okra subgenome. Moreover, we observed the presence of multiple chromosomes harboring the 35 S rDNA loci, alongside another chromosomal pair carrying the 5 S rDNA loci in okra using FISH assay. Remarkably, the intensity of 35 S rDNA hybridization signals varied among chromosomes, with the signals predominantly localized within regions of relatively weak DAPI staining, associated with GC-rich heterochromatin regions. Finally, we observed a similar localization pattern between 35 S rDNA and three satellite repeats with high GC content and confirmed their origin in the intergenic spacer region of the 35 S rDNA. Conclusions Our findings uncover a unique satellite repeat signature in the allotetraploid okra, contributing to our understanding of the composition, abundance, and chromosomal distribution of satellite repeats in allopolyploid genomes, further enriching our understanding of their evolutionary dynamics in complex allopolyploid genomes.

The shape of fruits is a critical trait affecting the commercial value and consumer acceptance of horticultural crops. Genetic regulation of fruit shape involves complex interactions among multiple genes and environmental factors. This review summarizes recent advances in understanding the genetic mechanisms controlling fruit shape in several key horticultural crops, including tomato, pepper, cucumber, peach, and grape. We present the identification and characterization of genes and quantitative trait loci (QTLs) that influence fruit shape, focusing on the roles of genes such as OVATE, SUN, FAS, LC, ENO, GLOBE, CsSUN, CsFUL1, CsCRC, PpCAD1, PpOFP1, and VvSUN. This review highlights the importance of hormonal pathways, particularly those involving synthesis and concentration of cytokinins and brassinosteroids in shaping fruit morphology, and explores how these genes interact and form regulatory networks that collectively determine the final fruit shape. This knowledge provides a foundation for developing strategies to improve fruit quality and yield through genetic modification and breeding programs.

Understanding urban consumers’ preferences for rice attributes is crucial for rice breeders, producers, and retailers to meet diverse and evolving market demands. Based on the sample data of 629 rice consumers in Shanghai, China, obtained through the choice experiment (CE) approach, this study uses the mixed logit (ML) model to analyze consumers’ preferences and willingness to pay (WTP) for food safety labels, brands, nutritional quality, and taste quality. Furthermore, the latent class (LC) model examines the heterogeneity in consumer group preferences. The research findings highlight that consumers prioritize taste quality as the most crucial attribute, followed by nutritional quality, food safety labels, and brand attributes. The premium rates for superior taste quality, organic certification labels, and green certification labels exceeded 100%. Interestingly, while combining organic certification with well-known international or domestic brands does not uniformly boost consumer preferences, incorporating green certification alongside well-known international or domestic brands significantly elevates those preference levels. Factors such as the external environment, consumption habits, and personal characteristics significantly influence individuals’ preferences for rice attributes. Based on these insights, the study puts forth policy recommendations for rice breeders, producers, and retailers.

Abstract Background Duchenne muscular dystrophy (DMD) is an X-linked, incurable, degenerative neuromuscular disease that is exacerbated by secondary inflammation. N6-methyladenosine (m6A), the most common base modification of RNA, has pleiotropic immunomodulatory effects in many diseases. However, the role of m6A modification in the immune microenvironment of DMD remains elusive. Methods Our study retrospectively analyzed the expression data of 56 muscle tissues from DMD patients and 26 from non-muscular dystrophy individuals. Based on single sample gene set enrichment analysis, immune cells infiltration was identified and the result was validated by flow cytometry analysis and immunohistochemical staining. Then, we described the features of genetic variation in 26 m6A regulators and explored their relationship with the immune mircoenvironment of DMD patients through a series of bioinformatical analysis. At last, we determined subtypes of DMD patients by unsupervised clustering analysis and characterized the molecular and immune characteristics in different subgroups. Results DMD patients have a sophisticated immune microenvironment that is significantly different from non-DMD controls. Numerous m6A regulators were aberrantly expressed in the muscle tissues of DMD and inversely related to most muscle-infiltrating immune cell types and immune response-related signaling pathways. A diagnostic model involving seven m6A regulators was established using LASSO. Furthermore, we determined three m6A modification patterns (cluster A/B/C) with distinct immune microenvironmental characteristics. Conclusion In summary, our study demonstrated that m6A regulators are intimately linked to the immune microenvironment of muscle tissues in DMD. These findings may facilitate a better understanding of the immunomodulatory mechanisms in DMD and provide novel strategies for the treatment.

Abstract Background Jute is considered one of the most important crops for fiber production and multipurpose usages. Caffeoyl-CoA 3-O-methyltransferase (CCoAOMT) is a crucial enzyme involved in lignin biosynthesis in plants. The potential functions of CCoAOMT in lignin biosynthesis of jute have been reported in several studies. However, little is known about the evolution of the CCoAOMT gene family, and either their expression level at different developing stages in different jute cultivars, as well as under abiotic stresses including salt and drought stress. Results In the present study, 66 CCoAOMT genes from 12 species including 12 and eight CCoAOMTs in Corchorus olitorius and C. capsularis were identified. Phylogenetic analysis revealed that CCoAOMTs could be divided into six groups, and gene expansion was observed in C. olitorius. Furthermore, gene expression analysis of developing jute fibers was conducted at different developmental stages (15, 30, 45, 60, and 90 days after sowing [DAS]) in six varieties (Jute-179 [J179], Lubinyuanguo [LB], and Qiongyueqing [QY] for C. capsularis; Funong No.5 [F5], Kuanyechangguo [KY], and Cvlv [CL] for C. olitorius). The results showed that CCoAOMT1 and CCoAOMT2 were the dominant genes in the CCoAOMT family. Of these two dominant CCoAOMTs, CCoAOMT2 showed a constitutive expression level during the entire growth stages, while CCoAOMT1 exhibited differential expression patterns. These two genes showed higher expression levels in C. olitorius than in C. capsularis. The correlation between lignin content and CCoAOMT gene expression levels indicated that this gene family influences the lignin content of jute. Using real-time quantitative reverse transcription PCR (qRT-PCR), a substantial up-regulation of CCoAOMTs was detected in stem tissues of jute 24 h after drought treatment, with an up to 17-fold increase in expression compared to that of untreated plants. Conclusions This study provides a basis for comprehensive genomic studies of the entire CCoAOMT gene family in C. capsularis and C. olitorius. Comparative genomics analysis among the CCoAOMT gene families of 12 species revealed the close evolutionary relationship among Corchorus, Theobroma cacao and Gossypium raimondii. This study also shows that CCoAOMTs are not only involved in lignin biosynthesis, but also are associated with the abiotic stress response in jute, and suggests the potential use of these lignin-related genes to genetically improve the fiber quality of jute.

Sleep quality is an important issue of public concern. This study, combined with sensor application, aims to explore the determinants of perceived comfort when using smart bedding to provide empirical evidence for improving sleep quality. This study was conducted in a standard sleep laboratory in Quanzhou, China, from March to April of 2023. Perceived comfort was evaluated using the Subjective Lying Comfort Evaluation on a seven-point rating scale, and body pressure distribution was measured using a pressure sensor. Correlation analysis was employed to analyze the relationship between perceived comfort and body pressure, and multiple linear regression was used to identify the factors of perceived comfort. The results showed that body pressure was partially correlated with perceived comfort, and sleep posture significantly influenced perceived comfort. In addition, height, weight, and body mass index are common factors that influence comfort. The findings highlight the importance of optimizing the angular range of boards based on their comfort performance to adjust sleeping posture and equalize pressure distribution. Future research should consider aspects related to the special needs of different populations (such as height and weight), as well as whether users are elderly and whether they have particular diseases. The design optimization of the bed board division and mattress softness, based on traditional smart bedding, can improve comfort and its effectiveness in reducing health risks and enhancing health status.

Abstract Background G-quadruplexes (G4s) are unique noncanonical nucleic acid secondary structures, which have been proposed to physically interact with transcription factors and chromatin remodelers to regulate cell type-specific transcriptome and shape chromatin landscapes. Results Based on the direct interaction between G4 and natural porphyrins, we establish genome-wide approaches to profile where the iron-liganded porphyrin hemin can bind in the chromatin. Hemin promotes genome-wide G4 formation, impairs transcription initiation, and alters chromatin landscapes, including decreased H3K27ac and H3K4me3 modifications at promoters. Interestingly, G4 status is not involved in the canonical hemin-BACH1-NRF2-mediated enhancer activation process, highlighting an unprecedented G4-dependent mechanism for metabolic regulation of transcription. Furthermore, hemin treatment induces specific gene expression profiles in hepatocytes, underscoring the in vivo potential for metabolic control of gene transcription by porphyrins. Conclusions These studies demonstrate that G4 functions as a sensor for natural porphyrin metabolites in cells, revealing a G4-dependent mechanism for metabolic regulation of gene transcription and chromatin landscapes, which will deepen our knowledge of G4 biology and the contribution of cellular metabolites to gene regulation.

The aging of the agricultural labor force is an irreversible trend that has become an important issue in China’s economic transformation. Previous studies on the effects of an aging population in developing countries on agriculture mainly focused on food crops, and the conclusions were mixed. Using data for apple growers in Shaanxi Province, China, we used ordinary least squares (OLS), stochastic frontier production function (SFA), and truncated regression to investigate how rural aging affects apple production under different landform conditions. We provided evidence that (i) aging leads apple growers to use hired labor to replace family labor in the flatlands, but not in mountainous and hilly areas, due to landform constraints on the factor substitution; (ii) aging has no significant impact on mechanical inputs in either the plains or the mountains, indicating that machinery cannot effectively replace the labor force; (iii) limited by a shortage of labor quantity and quality, apple growers respond to aging by reducing agricultural inputs in mountainous and hilly areas; (iv) changes in input structure cause aging to have little influence on yield and technical efficiency in flatlands, while aging significantly reduces yield in mountainous and hilly areas; (v) there is a nonlinear relationship between aging and technical efficiency and yield; and (vi) because the overall mechanization level of China’s apple industry is low, mechanical substitution for labor is not common in apple production.