Bcwf regulates the white petal color in pak choi [Brassica campestris (syn. Brassica rapa) ssp. chinensis].

Flower color is important in determining the ornamental value of Brassica species. However, our knowledge about the regulation of flower color in pak choi [Brassica campestris (syn. Brassica rapa) ssp. chinensis] is limited. In this study, we investigated the molecular mechanism underlying white flower traits in pak choi by analyzing a genetic population with white and yellow flowers. Our genetic analysis revealed that the white trait is controlled by a single recessive gene called Bcwf. Through BSA-Seq and fine mapping, we identified a candidate gene, BraC02g039450.1, which is similar to Arabidopsis AtPES2 involved in carotenoid ester synthesis. Sequence analysis showed some mutations in the promoter region of Bcwf in white flowers. Tobacco transient assay confirmed that these mutations reduce the promoter's activity, leading to downregulation of Bcwf expression in white flowers. Furthermore, the silencing of Bcwf in pak choi resulted in lighter petal color and reduced carotenoid content. These findings provide new insights into the molecular regulation of white flower traits in pak choi and highlight the importance of Bcwf in petal coloring and carotenoid accumulation.

Genome-Wide Identification and Role of the bHLH Gene Family in Dendrocalamus latiflorus Flowering Regulation.

The basic helix-loop-helix (bHLH) gene family is a crucial regulator in plants, orchestrating various developmental processes, particularly flower formation, and mediating responses to hormonal signals. The molecular mechanism of bamboo flowering regulation remains unresolved, limiting bamboo breeding efforts. In this study, we identified 309 bHLH genes and divided them into 23 subfamilies. Structural analysis revealed that proteins in specific DlbHLH subfamilies are highly conserved. Collinearity analysis indicates that the amplification of the DlbHLH gene family primarily occurs through segmental duplications. The structural diversity of these duplicated genes may account for their functional variability. Many DlbHLHs are expressed during flower development, indicating the bHLH gene's significant role in this process. In the promoter region of DlbHLHs, different homeopathic elements involved in light response and hormone response co-exist, indicating that DlbHLHs are related to the regulation of the flower development of D. latiflorus.

De novo Whole-Genome Assembly of the 10-Gigabase Fokienia Hodginsii Genome to Reveal Differential Epigenetic Events Between Callus and Xylem.

Fokienia hodginsii (F. hodginsii), belonging to the genus Fokienia of the Cupressaceae. F. hodginsii has significant application value due to its wood properties and great research value in evolutionary studies as a gymnosperm. However, the genome of F. hodginsii remains unknown due to the large size of gymnosperms genome. Pacific Bioscience sequencing, Hi-C mapping, whole-genome Bisulfite Sequencing (BS-Seq), long-read isoform sequencing (Iso-Seq), direct RNA sequencing (DRS), quantitative proteomics, and metabonomics analysis are employed to facilitate genome assembly, gene annotation, and investigation into epigenetic mechanisms. In this study, the 10G F. hodginsii genome is assembled into 11 chromosomes. Furthermore, 50 521 protein-coding genes are annotated and determined that 65% of F. hodginsii genome comprises repetitive sequences. It is discovered that transposable element (TE)-including introns is associated with higher expression. The DNA methylome of F. hodginsii reveals that xylem has a higher DNA methylation level compared to callus. Moreover, DRS reveals the significant alterations in RNA full-length ratio, which potentially associated with poly(A) length (PAL) and alternative polyadenylation (APA). Finally, the morphology measurement and metabonomics analysis revealed the difference of 14 cultivars. In summary, the genomes and epigenetics datasets provide a molecular basis for callus formation in the gymnosperm family.

Transcriptomic and Phenotypical Analysis of the Physiological Plasticity of Chamaecyparis hodginsii Roots under Different Nutrient Environments and Adjacent Plant Competition.

Chamaecyparis hodginsii seedlings undergo significant changes during growth due to different nutrient environments and adjacent plant competition, which is evident in the physiological plasticity changes in their roots. Therefore, in this experiment, 20 one-year-old elite C. hodginsii family seedlings were selected as the test objects, and the different nutrient environments and adjacent plant competition environments in nature were artificially simulated. Four nutrient environments (N heterogeneous nutrient environment, P heterogeneous nutrient environment, K heterogeneous nutrient environment, and homogeneous environment) and three planting patterns (single plant, conspecific neighbor, and heterospecific neighbor) were set up to determine the differences in root physiological indexes and plasticity of different family seedlings, and the families and treatment combinations with higher comprehensive evaluation were selected. The transcriptome sequencing of fine roots of C. hodginsii under different treatments was performed to analyze the differentially expressed genes. The results showed that the root activity, antioxidant enzyme activity, and nutrient element content of C. hodginsii seedlings in the N and P heterogeneous environments were higher than those in the homogeneous nutrient environment, while there was no significant difference between the K heterogeneous nutrient environment and the homogeneous environment, but MDA content was higher than that in other nutrient environments. The root activity and antioxidant enzyme activity in the competitive patterns were generally higher than those in the single plant and reached the peak in the heterospecific neighbor. The root physiological plasticity index of line 490 was the highest, but the comprehensive evaluation of root physiological indexes of lines 539 and 535 was better. The pattern with the highest comprehensive evaluation score was P heterogeneous nutrient environment × heterospecific neighbor. The effects of the N and P heterogeneous nutrient environments on root transcriptome genes were similar, which significantly increased DNA transcription and regulatory factor activity, while K heterogeneous nutrient environment focused on the regulation of root enzyme activity. The heterogeneous nutrient environment induces the conduction of hormone signals in the roots of C. hodginsii and induces the synthesis of phenylpropanone. The biosynthesis of phenylpropanone in the roots of C. hodginsii will increase significantly under competitive patterns. In summary, the N and P heterogeneous nutrient environments and the heterospecific neighbor can improve the root physiological indexes of C. hodginsii families, and the root physiological indexes of lines 539 and 535 are the best. The nutrient environment and competition pattern mainly affect the root system to transmit hormone signals to regulate enzyme activity.

Self-Supervised Representation Distribution Learning for Reliable Data Augmentation in Histopathology WSI Classification.

Multiple instance learning (MIL) based whole slide image (WSI) classification is often carried out on the representations of patches extracted from WSI with a pre-trained patch encoder. The performance of classification relies on both patch-level representation learning and MIL classifier training. Most MIL methods utilize a frozen model pre-trained on ImageNet or a model trained with self-supervised learning on histopathology image dataset to extract patch image representations and then fix these representations in the training of the MIL classifiers for efficiency consideration. However, the invariance of representations cannot meet the diversity requirement for training a robust MIL classifier, which has significantly limited the performance of the WSI classification. In this paper, we propose a Self-Supervised Representation Distribution Learning framework (SSRDL) for patch-level representation learning with an online representation sampling strategy (ORS) for both patch feature extraction and WSI-level data augmentation. The proposed method was evaluated on three datasets under three MIL frameworks. The experimental results have demonstrated that the proposed method achieves the best performance in histopathology image representation learning and data augmentation and outperforms state-of-the-art methods under different WSI classification frameworks. The code is available at https://github.com/lazytkm/SSRDL.

Expression of Iron Metabolism Genes Is Potentially Regulated by DOF Transcription Factors in Dendrocalamus latiflorus Leaves.

Transcription factors (TFs) are crucial pre-transcriptional regulatory mechanisms that can modulate the expression of downstream genes by binding to their promoter regions. DOF (DNA binding with One Finger) proteins are a unique class of TFs with extensive roles in plant growth and development. Our previous research indicated that iron content varies among bamboo leaves of different colors. However, to our knowledge, genes related to iron metabolism pathways in bamboo species have not yet been studied. Therefore, in the current study, we identified iron metabolism related (IMR) genes in bamboo and determined the TFs that significantly influence them. Among these, DOFs were found to have widespread effects and potentially significant impacts on their expression. We identified specific DOF members in Dendrocalamus latiflorus with binding abilities through homology with Arabidopsis DOF proteins, and established connections between some of these members and IMR genes using RNA-seq data. Additionally, molecular docking confirmed the binding interactions between these DlDOFs and the DOF binding sites in the promoter regions of IMR genes. The co-expression relationship between the two gene sets was further validated using q-PCR experiments. This study paves the way for research into iron metabolism pathways in bamboo and lays the foundation for understanding the role of DOF TFs in D. latiflorus.

Partial-Label Contrastive Representation Learning for Fine-grained Biomarkers Prediction from Histopathology Whole Slide Images.

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSI) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel partial-label contrastive representation learning approach to enhance the discrimination of histopathology image representations for fine-grained biomarkers prediction. We designed a partial-label contrastive clustering (PLCC) module for partial-label disambiguation and a dynamic clustering algorithm to sample the most representative features of each category to the clustering queue during the contrastive learning process. We conducted comprehensive experiments on three gene mutation prediction datasets, including USTC-EGFR, BRCA-HER2, and TCGA-EGFR. The results show that our method outperforms 9 existing methods in terms of Accuracy, AUC, and F1 Score. Specifically, our method achieved an AUC of 0.950 in EGFR mutation subtyping of TCGA-EGFR and an AUC of 0.853 in HER2 0/1+/2+/3+ grading of BRCA-HER2, which demonstrates its superiority in fine-grained biomarkers prediction from histopathology whole slide images. The source code is available at https://github.com/WkEEn/PLCC.

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning.

Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non-small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence-powered deep learning framework was developed for the weakly supervised prediction of EGFR mutations in non-small cell lung cancer from hematoxylin and eosin-stained histopathology whole-slide images. The study cohort was partitioned into training and validation subsets. Foreground regions containing tumor tissue were extracted from whole-slide images. A convolutional neural network employing a contrastive learning paradigm was implemented to extract patch-level morphologic features. These features were aggregated using a vision transformer-based model to predict EGFR mutation status and classify patient cases. The established prediction model was validated on unseen data sets. In internal validation with a cohort from the University of Science and Technology of China (n = 172), the model achieved patient-level areas under the receiver-operating characteristic curve (AUCs) of 0.927 and 0.907, sensitivities of 81.6% and 83.3%, and specificities of 93.0% and 92.3%, for surgical resection and biopsy specimens, respectively, in EGFR mutation subtype prediction. External validation with cohorts from the Second Affiliated Hospital of Anhui Medical University and the First Affiliated Hospital of Wannan Medical College (n = 193) yielded patient-level AUCs of 0.849 and 0.867, sensitivities of 79.2% and 80.7%, and specificities of 91.7% and 90.7% for surgical and biopsy specimens, respectively. Further validation with The Cancer Genome Atlas data set (n = 81) showed an AUC of 0.861, a sensitivity of 84.6%, and a specificity of 90.5%. Deep learning solutions demonstrate potential advantages for automated, noninvasive, fast, cost-effective, and accurate inference of EGFR alterations from histomorphology. Integration of such artificial intelligence frameworks into routine digital pathology workflows could augment existing molecular testing pipelines.

Variation in Nitrogen Utilization and Nutrient Composition across Various Organs under Different Strip Logging Management Models in Moso Bamboo (Phyllostachys edulis) Forest.

The rapid restoration and renewal of the moso bamboo logging zone after strip logging has emerged as a key research area, particularly regarding whether nutrient accumulation and utilization in reserve zones can aid in the restoration and regeneration of the logging zone. In this study, a dynamic 15N isotope tracking experiment was conducted by injecting labeled urea fertilizer into bamboo culms. Logging zones and reserve zones of 6 m, 8 m, and 10 m widths were established. The conventional selective logging treatment served as a control (Con). Measurements were taken in May and October to assess the differences in nitrogen accumulation ability, utilization rates, and nutrient content across different organs in bamboo forests at different growth stages and under different treatments. Principal component analysis was conducted to evaluate and determine the importance of each indicator and strip logging treatment comprehensively. The results showed that various bamboo organs exhibited higher nitrogen accumulation and utilization rates during the peak growth period compared to the late growth period. Leaves had the highest nitrogen accumulation and utilization rates than the other organs. The average C content in various bamboo organs under different logging treatments exhibited subtle differences, irrespective of variation in logging width treatments. Bamboo culm exhibited the highest carbon accumulation. The C content in various bamboo organs was higher during the peak growth period than in the late growth period. The nitrogen content peaked in the leaves during the two growth stages and was significantly higher compared to the other organs. Most bamboo organs in the logging zones exhibited relatively higher nitrogen content than in the reserve zone and Con group. The P content was highest in bamboo leaves compared with other organs across the different strip logging treatments. Principal component analysis revealed relatively high absolute values of the coefficients for the C content, bamboo stump C content, and culm Ndff%. Log8 and Res10 zones had the highest comprehensive evaluation scores, indicating that Log8 and Res10 had the best effect on the promotion of nitrogen utilization and nutrient accumulation in various organs of moso bamboo.

Masked hypergraph learning for weakly supervised histopathology whole slide image classification.

BACKGROUND AND OBJECTIVES: Graph neural network (GNN) has been extensively used in histopathology whole slide image (WSI) analysis due to the efficiency and flexibility in modelling relationships among entities. However, most existing GNN-based WSI analysis methods only consider the pairwise correlation of patches from one single perspective (e.g. spatial affinity or embedding similarity) yet ignore the intrinsic non-pairwise relationships present in gigapixel WSI, which are likely to contribute to feature learning and downstream tasks. The objective of this study is therefore to explore the non-pairwise relationships in histopathology WSI and exploit them to guide the learning of slide-level representations for better classification performance. METHODS: In this paper, we propose a novel Masked HyperGraph Learning (MaskHGL) framework for weakly supervised histopathology WSI classification. Compared with most GNN-based WSI classification methods, MaskHGL exploits the non-pairwise correlations between patches with hypergraph and global message passing conducted by hypergraph convolution. Concretely, multi-perspective hypergraphs are first built for each WSI, then hypergraph attention is introduced into the jointed hypergraph to propagate the non-pairwise relationships and thus yield more discriminative node representation. More importantly, a masked hypergraph reconstruction module is devised to guide the hypergraph learning which can generate more powerful robustness and generalization than the method only using hypergraph modelling. Additionally, a self-attention-based node aggregator is also applied to explore the global correlation of patches in WSI and produce the slide-level representation for classification. RESULTS: The proposed method is evaluated on two public TCGA benchmark datasets and one in-house dataset. On the public TCGA-LUNG (1494 WSIs) and TCGA-EGFR (696 WSIs) test set, the area under receiver operating characteristic (ROC) curve (AUC) were 0.9752±0.0024 and 0.7421±0.0380, respectively. On the USTC-EGFR (754 WSIs) dataset, MaskHGL achieved significantly better performance with an AUC of 0.8745±0.0100, which surpassed the second-best state-of-the-art method SlideGraph+ 2.64%. CONCLUSIONS: MaskHGL shows a great improvement, brought by considering the intrinsic non-pairwise relationships within WSI, in multiple downstream WSI classification tasks. In particular, the designed masked hypergraph reconstruction module promisingly alleviates the data scarcity and greatly enhances the robustness and classification ability of our MaskHGL. Notably, it has shown great potential in cancer subtyping and fine-grained lung cancer gene mutation prediction from hematoxylin and eosin (H&E) stained WSIs.

Histopathology language-image representation learning for fine-grained digital pathology cross-modal retrieval.

Large-scale digital whole slide image (WSI) datasets analysis have gained significant attention in computer-aided cancer diagnosis. Content-based histopathological image retrieval (CBHIR) is a technique that searches a large database for data samples matching input objects in both details and semantics, offering relevant diagnostic information to pathologists. However, the current methods are limited by the difficulty of gigapixels, the variable size of WSIs, and the dependence on manual annotations. In this work, we propose a novel histopathology language-image representation learning framework for fine-grained digital pathology cross-modal retrieval, which utilizes paired diagnosis reports to learn fine-grained semantics from the WSI. An anchor-based WSI encoder is built to extract hierarchical region features and a prompt-based text encoder is introduced to learn fine-grained semantics from the diagnosis reports. The proposed framework is trained with a multivariate cross-modal loss function to learn semantic information from the diagnosis report at both the instance level and region level. After training, it can perform four types of retrieval tasks based on the multi-modal database to support diagnostic requirements. We conducted experiments on an in-house dataset and a public dataset to evaluate the proposed method. Extensive experiments have demonstrated the effectiveness of the proposed method and its advantages to the present histopathology retrieval methods. The code is available at https://github.com/hudingyi/FGCR.

Variability in Leaf Color Induced by Chlorophyll Deficiency: Transcriptional Changes in Bamboo Leaves.

The diversity of leaf characteristics, particularly leaf color, underscores a pivotal area of inquiry within plant science. The synthesis and functionality of chlorophyll, crucial for photosynthesis, largely dictate leaf coloration, with varying concentrations imparting different shades of green. Complex gene interactions regulate the synthesis and degradation of chlorophyll, and disruptions in these pathways can result in abnormal chlorophyll production, thereby affecting leaf pigmentation. This study focuses on Bambusa multiplex f. silverstripe, a natural variant distinguished by a spectrum of leaf colors, such as green, white, and green-white, attributed to genetic variations influencing gene expression. By examining the physiological and molecular mechanisms underlying chlorophyll anomalies and genetic factors in Silverstripe, this research sheds light on the intricate gene interactions and regulatory networks that contribute to leaf color diversity. The investigation includes the measurement of photosynthetic pigments and nutrient concentrations across different leaf color types, alongside transcriptomic analyses for identifying differentially expressed genes. The role of key genes in pathways such as ALA biosynthesis, chlorophyll synthesis, photosynthesis, and sugar metabolism is explored, offering critical insights for advancing research and plant breeding practices.

A Key-Points Based Anchor-Free Cervical Cell Detector.

Cervical cell detection is crucial to cervical cytology screening at early stage. Currently most cervical cell detection methods use anchor-based pipeline to achieve the localization and classification of cells, e.g. faster R-CNN and YOLOv3. However, the anchors generally need to be pre-defined before training and the detection performance is inevitably sensitive to these pre-defined hyperparameters (e.g. number of anchors, anchor size and aspect ratios). More importantly, these preset anchors fail to conform to the cells with different morphology at inference phase. In this paper, we present a key-points based anchor-free cervical cell detector based on YOLOv3. Compared with the conventional YOLOv3, the proposed method applies a key-points based anchor-free strategy to represent the cells in the initial prediction phase instead of the preset anchors. Therefore, it can generate more desirable cell localization effect through refinement. Furthermore, PAFPN is applied to enhance the feature hierarchy. GIoU loss is also introduced to optimize the small cell localization in addition to focal loss and smooth L1 loss. Experimental results on cervical cytology ROI datasets demonstrate the effectiveness of our method for cervical cell detection and the robustness to different liquid-based preparation styles (i.e. drop-slide, membrane-based and sedimentation).

Effects of Different Heterogeneous Nutrient Environments on the Growth and Activities of Enzymes in the Roots of Fokienia hodginsii Families.

Currently, research on the F. hodginsii asexual lineage primarily focuses on the screening of growth traits and the control of single fertilizer applications. The effects of the heterogeneity of soil nutrients on root growth and activity have not been studied in detail. Therefore, we propose forest management measures to improve the foraging ability of forest trees in conjunction with stand productivity. In this experiment, annual containerized seedlings of 10 free-pollinated F. hodginsii lines from a primary asexual seed orchard were used as test subjects, and three heterogeneous nutrient environments of nitrogen (N), phosphorus (P), and potassium (K) were constructed. In contrast, homogeneous nutrient environments were used as the control to carry out potting experiments, to study the growth of F. hodginsii lines and the differences in the activities of root enzymes under the three heterogeneous nutrient environments, and to carry out the comprehensive evaluation using the principal component and cluster analysis method. The results were as follows: (1) The seedling height of F. hodginsii family lines under a homogeneous nutrient environment was significantly higher than that of all heterogeneous nutrient environments; the diameter of the ground was the highest under N heterogeneous nutrient environment and significantly higher than that of all the other nutrient environments; the biomass of the root system was the highest under P heterogeneous nutrient environment, which was significantly higher than that of homogeneous nutrient environment and K heterogeneous nutrient environment. The catalase (CAT) activity of F. hodginsii roots was higher than that of homogeneous nutrients in all heterogeneous nutrient environments but not significant, and the superoxide dismutase (SOD) activity was slightly higher than that of K heterogeneous and homogeneous nutrient environments in N and P heterogeneous nutrient environments. SOD activity was slightly higher than that of K heterogeneous and homogeneous nutrient environments under N, and P. peroxidase (POD) activity in the F. hodginsii root system was the highest under the P heterogeneous nutrient environment, which was significantly higher than that of the other nutrient environments. Unlike the activities of the enzymes, the content of malondialdehyde (MDA) in the roots of F. hodginsii was higher in the heterogeneous environment than in all the other nutrient environments. (2) Under N and P heterogeneous nutrient environments, lines 552 and 590 had higher seedling height, ground diameter, and root enzyme activity, while root biomass was highest in line 544; and under K heterogeneous nutrient environments, line 591 had higher seedling height, ground diameter, and root enzyme activity while root biomass was highest in line 551. In contrast to the patterns of seedling height, accumulation of root biomass and activities of root enzymes, family No. 590 had the highest ground diameter of all the F. hodginsii families under the heterogeneous nutrient environments. Family No. 547 had the highest MDA content. In conclusion, it can be seen that N heterogeneous and homogeneous nutrient environments can significantly increase the seedling height and diameter of F. hodginsii compared with P and K heterogeneous nutrient environments, and N and P heterogeneous nutrient environments can also increase the root biomass, root enzyme activities and significantly reduce the MDA content of F. hodginsii. According to the principal component analysis and cluster analysis, it can be seen that among the 10 F. hodginsii family lines, family lines 590 and 552 have higher evaluation in growth, root biomass accumulation, and enzyme activity.

Identification and characterization of the cupin_1 domain-containing proteins in ma bamboo (Dendrocalamus latiflorus) and their potential role in rhizome sprouting.

Cupin_1 domain-containing protein (CDP) family, which is a member of the cupin superfamily with the most diverse functions in plants, has been found to be involved in hormone pathways that are closely related to rhizome sprouting (RS), a vital form of asexual reproduction in plants. Ma bamboo is a typical clumping bamboo, which mainly reproduces by RS. In this study, we identified and characterized 53 Dendrocalamus latiflorus CDP genes and divided them into seven subfamilies. Comparing the genetic structures among subfamilies showed a relatively conserved gene structure within each subfamily, and the number of cupin_1 domains affected the conservation among D. latiflorus CDP genes. Gene collinearity results showed that segmental duplication and tandem duplication both contributed to the expansion of D. latiflorus CDP genes, and lineage-specific gene duplication was an important factor influencing the evolution of CDP genes. Expression patterns showed that CDP genes generally had higher expression levels in germinating underground buds, indicating that they might play important roles in promoting shoot sprouting. Transcription factor binding site prediction and co-expression network analysis indicated that D. latiflorus CDPs were regulated by a large number of transcription factors, and collectively participated in rhizome buds and shoot development. This study significantly provided new insights into the evolutionary patterns and molecular functions of CDP genes, and laid a foundation for further studying the regulatory mechanisms of plant rhizome sprouting.

Kernel Attention Transformer for Histopathology Whole Slide Image Analysis and Assistant Cancer Diagnosis.

Transformer has been widely used in histopathology whole slide image analysis. However, the design of token-wise self-attention and positional embedding strategy in the common Transformer limits its effectiveness and efficiency when applied to gigapixel histopathology images. In this paper, we propose a novel kernel attention Transformer (KAT) for histopathology WSI analysis and assistant cancer diagnosis. The information transmission in KAT is achieved by cross-attention between the patch features and a set of kernels related to the spatial relationship of the patches on the whole slide images. Compared to the common Transformer structure, KAT can extract the hierarchical context information of the local regions of the WSI and provide diversified diagnosis information. Meanwhile, the kernel-based cross-attention paradigm significantly reduces the computational amount. The proposed method was evaluated on three large-scale datasets and was compared with 8 state-of-the-art methods. The experimental results have demonstrated the proposed KAT is effective and efficient in the task of histopathology WSI analysis and is superior to the state-of-the-art methods.

Toward ultra-tough and heat-resistant biodegradable polylactide/core-shell rubber blends by regulating the distribution of rubber particles with stereocomplex crystallites.

Ultra-tough and heat-resistant poly(l-lactide)/core-shell rubber (PLLA/CSR) blends were fabricated by utilizing stereocomplex (SC) crystallites to effectively regulate the CSR distribution in PLLA matrix. Linear and 3-11 armed poly(d-lactide)s (PDLAs) were synthesized and then melt-mixed with PLLA/CSR blend. Interestingly, the incorporated PDLA chains could collaborate with PLLA chains to form dense SC crystallites network in PLLA/PDLA/CSR blends, thus inducing the CSR particles to transform from uniform distribution structure to network-like structure. With increasing the PDLA arm numbers, the size of CSR clusters in the network-like structure first increased and then decreased, and the continuity of the network-like structure first remained at a high level and then decreased obviously. The formation of CSR network-like structure could remarkably improve the impact strength of PLLA/PDLA/CSR blends without deteriorating their strength and modulus (compared with PLLA/CSR blend), and the CSR network-like structure with larger-sized CSR clusters and higher continuity could help obtain higher impact strength (78.3 kJ/m2). Moreover, the heat resistance of PLLA/PDLA/CSR blends could also be significantly improved (the highest Vicat softening temperature was 131 °C) by the SC crystallites network and CSR network-like structure. This work provides an effective strategy for controlling the rubber network-like morphology and thereby preparing high-performance PLLA materials.

Autofluorescence imaging-assisted medical thoracoscopy in the diagnosis of malignant pleural disease.

BACKGROUND: Medical thoracoscopy (MT) does not always provide a conclusive diagnosis of pleural diseases because the endoscopic appearance of pleural diseases can be misleading. Autofluorescence imaging (AFI) is an effective assistive diagnostic tool. However, its clinical application for pleural disease remains controversial. OBJECTIVES: This prospective study evaluated the clinical usefulness of AFI-assisted MT for diagnosis of malignant pleural diseases. METHODS: Patients with unexplained pleural effusion admitted to our clinics between December 2018 and September 2021 were enrolled. We performed white-light thoracoscopy (WLT) first, and then AFI, during MT. Images of endoscopic real-time lesions were recorded under both modes. Pleural biopsy specimens were analyzed pathologically. Between-groups differences in diagnostic sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV) were assessed using 95% confidence intervals (CI). Receiver operating characteristic curves and decision curve analyses were employed to analyze the diagnostic efficiency of these two modes. RESULTS: Of 126 eligible patients, 73 cases were diagnosed with malignant pleural disease. A total of 1292 biopsy specimens from 492 pleural sites were examined for pathological changes. The diagnostic sensitivity, PPV, and NPV of AFI were 99.7%, 58.2%, and 99.2%, respectively. AFI was significantly superior to WLT, which had a sensitivity of 79.7%, PPV of 50.7%, and NPV of 62.8%. Subgroup analysis showed that the AFI type III pattern was significantly more specific for pleural malignant disease than that of WLT. CONCLUSIONS: AFI could further improve the diagnostic efficacy of MT by providing better visualization, convenience, and safety.

Response of the root morphological structure of Fokienia hodginsii seedlings to competition from neighboring plants in a heterogeneous nutrient environment.

Introduction: Critical changes often occur in Fokienia hodginsii seedlings during the process of growth owing to differences in the surrounding environment. The most common differences are heterogeneous nutrient environments and competition from neighboring plants. Methods: In this study, we selected one-year-old, high-quality Fokienia hodginsii seedlings as experimental materials. Three planting patterns were established to simulate different competitive treatments, and seedlings were also exposed to three heterogeneous nutrient environments and a homogeneous nutrient environment (control) to determine their effect on the root morphology and structure of F. hodginsii seedlings. Results: Heterogeneous nutrient environments, compared with a homogeneous environment, significantly increased the dry matter accumulation and root morphology indexes of the root system of F. hodginsii, which proliferated in nutrient-rich patches, and the P heterogeneous environment had the most significant enhancement effect, with dry matter accumulation 70.2%, 7.0%, and 27.0% higher than that in homogeneous and N and K heterogeneous environments, respectively. Homogeneous environments significantly increased the specific root length and root area of the root system; the dry matter mass and morphological structure of the root system of F. hodginsii with a heterospecific neighbor were higher than those under conspecific neighbor and single-plant treatments, and the root area of the root system under the conspecific neighbor treatment was higher than that under the heterospecific neighbor treatment, by 20% and 23%, respectively. Moreover, the root system under heterospecific neighbor treatment had high sensitivity; the heterogeneous nutrient environment increased the mean diameter of the fine roots of the seedlings of F. hodginsii and the diameter of the vascular bundle, and the effect was most significant in the P heterogeneous environment, exceeding that in the N and K heterogeneous environments. The effect was most significant in the P heterogeneous environment, which increased fine root diameter by 20.5% and 10.3%, respectively, compared with the homogeneous environment; in contrast, the fine root vascular ratio was highest in the homogeneous environment, and most of the indicators of the fine root anatomical structure in the nutrient-rich patches were of greater values than those in the nutrient-poor patches in the different heterogeneous environments; competition promoted most of the indicators of the fine root anatomical structure of F. hodginsii seedlings. According a principal component analysis (PCA), the N, Pm and K heterogeneous environments with heterospecific neighbors and the P heterogeneous environment with a conspecific neighbor had higher evaluation in the calculation of eigenvalues of the PCA. Discussion: The root dry matter accumulation, root morphology, and anatomical structure of F. hodginsii seedlings in the heterogeneous nutrient environment were more developed than those in the homogeneous nutrient environment. The effect of the P heterogeneous environment was the most significant. The heterospecific neighbor treatment was more conducive to the expansion and development of root morphology of F. hodginsii seedlings than were the conspecific neighbor and single-plant treatments.