NTRK2 Promotes Sheep Granulosa Cells Proliferation and Reproductive Hormone Secretion and Activates the PI3K/AKT Pathway.

Neurotrophin receptor B (NTRK2), also named TRKB, belongs to the neurotrophic factor family. Previous studies have shown that NTRK2 is associated with high fertility in mammals. However, the molecular mechanism and regulatory pathway of this neurotrophic factor remain unclear. In this study, NTRK2 overexpression and NTRK2-siRNA were constructed to detect the effects of NTRK2 on the proliferation and hormone secretion of the ovarian granulosa cells (GCs) of sheep. We successfully isolated follicular phase granulosa cells in vitro from the ovaries of sheep in simultaneous estrus, and the immunofluorescence results confirmed that NTRK2 was expressed in the collected cells. Subsequently, the effect of NTRK2 on the proliferation of sheep granulosa cells was examined via cell transfection experiments. The results showed that the expression of CDK4 and CyclinD2 was significantly increased after NTRK2 overexpression, while the opposite trend was observed after the inhibition of NTRK2 expression (p < 0.05). The EdU and CCK-8 assays showed that the proliferation rate of sheep GCs was significantly increased after NTRK2 overexpression, while the opposite trend was observed after the inhibition of NTRK2 expression (p < 0.05). Moreover, NTRK2 significantly increased the expression of steroidogenesis-related genes, including steroidogenic acute regulatory protein (STAR) and hydroxy-δ-5-steroid dehydrogenase (HSD3B1), and cytochrome P450 family 19 subfamily A member 1 (CYP19A1). The ELISA results showed that the secretion levels of E2 and P4 significantly increased after NTRK2 overexpression, while the opposite trend was observed after the inhibition of NTRK2 expression (p < 0.05). Previous studies had confirmed that NTRK2 gene belongs to the PI3K-AKT signaling pathway and participates in the signaling of this pathway. This was demonstrated by protein-protein interaction analysis and NTRK2 belongs to the PI3K-AKT pathway. The modification of PI3K and AKT, markers of the PI3K-AKT pathway, via phosphorylation was increased after NTRK2 overexpression in the sheep GCs, while the opposite trend was observed after the inhibition of NTRK2 expression (p < 0.05). Overall, these results suggest that the NTRK2 gene regulates the proliferation of GCs and the secretion of steroid hormones in sheep, and that it influences the phosphorylation level of the PI3K/AKT signaling pathway. These findings provided a theoretical basis and new perspectives for exploring the regulation of NTRK2 gene in the development of ovine follicles.

Accurate Annotation for Differentiating and Imbalanced Cell Types in Single-Cell Chromatin Accessibility Data.

Rapid advances in single-cell chromatin accessibility sequencing (scCAS) technologies have enabled the characterization of epigenomic heterogeneity and increased the demand for automatic annotation of cell types. However, there are few computational methods tailored for cell type annotation in scCAS data and the existing methods perform poorly for differentiating and imbalanced cell types. Here, we propose CASCADE, a novel annotation method based on simulation- and denoising-based strategies. With comprehensive experiments on a number of scCAS datasets, we showed that CASCADE can effectively distinguish the patterns of different cell types and mitigate the effect of high noise levels, and thus achieve significantly better annotation performance for differentiating and imbalanced cell types. Besides, we performed model ablation experiments to show the contribution of modules in CASCADE and conducted extensive experiments to demonstrate the robustness of CASCADE to batch effect, imbalance degree, data sparsity, and number of cell types. Moreover, CASCADE significantly outperformed baseline methods for accurately annotating the cell types in newly sequenced data. We anticipate that CASCADE will greatly assist with characterizing cell heterogeneity in scCAS data analysis.

Stretchable and Durable Bacterial Cellulose-Based Thermocell with Improved Thermopower Density for Low-Grade Heat Harvesting.

Low-grade heat exists ubiquitously in the environment, and gel-state thermogalvanic cells (GTCs) can directly convert thermal energy into electricity by a redox reaction. However, their low ionic conductivity and poor mechanical properties are still insufficient for their potential applications. Here, we designed a bacterial cellulose (BC) nanofiber-macromolecular entanglement network to balance the GTC's thermopower and mechanical properties. Therefore, the BC-GTC shows a Seebeck coefficient of 3.84 mV K-1, an ionic conductivity of 108.5 mS cm-1, and a high specific output power density of 1760 µW m-2 K-2, which are much higher than most current literature. Further connecting 15 units of BC-GTCs, the output voltage of 3.35 V can be obtained at a temperature gradient of 65 K, which can directly power electronic devices such as electronic calculators, thermohydrometers, fans, and light-emitting diodes (LEDs). This work offers a promising method for developing high-performance and durable GTC in sustainable green energy.

A WRKY transcription factor, PyWRKY71, increased the activities of antioxidant enzymes and promoted the accumulation of cadmium in poplar.

Cadmium (Cd) pollution poses significant threats to the ecological environment and human health. Currently, phytoremediation is recognized as an environmentally friendly approach for mitigating Cd pollution, with increasing attention on the utilization of transgenic plants in Cd-contaminated soil remediation. In this study, we isolated and cloned PyWRKY71 from Populus yunnanensis and conducted a pot experiment to validate its enhanced functionality in conferring Cd tolerance to woody plants (poplar). During the experiment, the increase in plant height of the OE-87 line (overexpression poplar) was 1.46 times than that of the wild type (WT). Moreover, PyWRKY71 significantly promoted the accumulation of Cd in poplar, especially in the roots, where the Cd content in the OE-45 and OE-87 lines was 1.42 times than that in the WT. The chlorophyll content of transgenic poplar leaves was higher than that of the WT, reflecting a protective mechanism of PyWRKY71. Additionally, the activities of other antioxidants, including POD, SOD, CAT, and MDA, were elevated in transgenic poplars, bolstering their tolerance to Cd stress. In summary, PyWRKY71 exhibits substantial potential in regulating plant tolerance to Cd stress. This study not only provides a solid scientific foundation but also introduces a novel modified poplar variety for the remediation of Cd pollution.

A longan yield estimation approach based on UAV images and deep learning.

Longan yield estimation is an important practice before longan harvests. Statistical longan yield data can provide an important reference for market pricing and improving harvest efficiency and can directly determine the economic benefits of longan orchards. At present, the statistical work concerning longan yields requires high labor costs. Aiming at the task of longan yield estimation, combined with deep learning and regression analysis technology, this study proposed a method to calculate longan yield in complex natural environment. First, a UAV was used to collect video images of a longan canopy at the mature stage. Second, the CF-YD model and SF-YD model were constructed to identify Cluster_Fruits and Single_Fruits, respectively, realizing the task of automatically identifying the number of targets directly from images. Finally, according to the sample data collected from real orchards, a regression analysis was carried out on the target quantity detected by the model and the real target quantity, and estimation models were constructed for determining the Cluster_Fruits on a single longan tree and the Single_Fruits on a single Cluster_Fruit. Then, an error analysis was conducted on the data obtained from the manual counting process and the estimation model, and the average error rate regarding the number of Cluster_Fruits was 2.66%, while the average error rate regarding the number of Single_Fruits was 2.99%. The results show that the method proposed in this paper is effective at estimating longan yields and can provide guidance for improving the efficiency of longan fruit harvests.

SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains.

Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics.

TSHVNet: Simultaneous Nuclear Instance Segmentation and Classification in Histopathological Images Based on Multiattention Mechanisms.

Accurate nuclear instance segmentation and classification in histopathologic images are the foundation of cancer diagnosis and prognosis. Several challenges are restricting the development of accurate simultaneous nuclear instance segmentation and classification. Firstly, the visual appearances of different category nuclei could be similar, making it difficult to distinguish different types of nuclei. Secondly, it is thorny to separate highly clustering nuclear instances. Thirdly, rare current studies have considered the global dependencies among diverse nuclear instances. In this article, we propose a novel deep learning framework named TSHVNet which integrates multiattention modules (i.e., Transformer and SimAM) into the state-of-the-art HoVer-Net for the sake of a more accurate nuclear instance segmentation and classification. Specifically, the Transformer attention module is employed on the trunk of the HoVer-Net to model the long-distance relationships of diverse nuclear instances. The SimAM attention modules are deployed on both the trunk and branches to apply the 3D channel and spatial attention to assign neurons with appropriate weights. Finally, we validate the proposed method on two public datasets: PanNuke and CoNSeP. The comparison results have shown the outstanding performance of the proposed TSHVNet network among the state-of-art methods. Particularly, as compared to the original HoVer-Net, the performance of nuclear instance segmentation evaluated by the PQ index has shown 1.4% and 2.8% increases on the CoNSeP and PanNuke datasets, respectively, and the performance of nuclear classification measured by F1_score has increased by 2.4% and 2.5% on the CoNSeP and PanNuke datasets, respectively. Therefore, the proposed multiattention-based TSHVNet is of great potential in simultaneous nuclear instance segmentation and classification.

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images.

Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets via equation fitting. Then, the data obtained from the manual counting process and the estimation model for the other five litchi trees in the real orchard are statistically analysed. The average error rate for the number of flower clusters is 4.20%, the average error rate for the number of flushes is 2.85%, and the average error for the flowering rate is 1.135%. The experimental results show that the proposed method is effective for estimating the litchi flowering rate and can provide guidance regarding the management of the flowering periods of litchi orchards.

Bioprocess-Inspired Room-Temperature Synthesis of Enamel-like Fluorapatite/Polymer Nanocomposites Controlled by Magnesium Ions.

Tooth enamel is composed of arrayed fluorapatite (FAP) or hydroxyapatite nanorods modified with Mg-rich amorphous layers. Although it is known that Mg2+ plays an important role in the formation of enamel, there is limited research on the regulatory role of Mg2+ in the synthesis of enamel-like materials. Therefore, we focus on the regulatory behavior of Mg2+ in the fabrication of biomimetic mineralized enamel-like structural materials. In the present study, we adopt a bioprocess-inspired room-temperature mineralization technique to synthesize a multilayered array of enamel-like columnar FAP/polymer nanocomposites controlled by Mg2+ (FPN-M). The results reveal that the presence of Mg2+ induced the compaction of the array and the formation of a unique Mg-rich amorphous-reinforced architecture. Therefore, the FPN-M array exhibits excellent mechanical properties. The hardness (2.42 ± 0.01 GPa) and Young's modulus (81.5 ± 0.6 GPa) of the as-prepared FPN-M array are comparable to those of its biological counterparts; furthermore, the enamel-like FPN-M array is translucent. The hardness and Young's modulus of the synthetic array of FAP/polymer nanocomposites without Mg2+ control (FPN) are 0.51 ± 0.04 and 43.5 ± 1.6 GPa, respectively. The present study demonstrates a reliable bioprocess-inspired room-temperature fabrication technique for the development of advanced high-performance composite materials.

Using machine learning to examine street green space types at a high spatial resolution: Application in Los Angeles County on socioeconomic disparities in exposure.

Background: Compared to commonly-used green space indicators from downward-facing satellite imagery, street view-based green space may capture different types of green space and represent how environments are perceived and experienced by people on the ground, which is important to elucidate the underlying mechanisms linking green space and health. Objectives: This study aimed to evaluate machine learning models that can classify the type of vegetation (i.e., tree, low-lying vegetation, grass) from street view images; and to investigate the associations between street green space and socioeconomic (SES) factors, in Los Angeles County, California. Methods: SES variables were obtained from the CalEnviroScreen3.0 dataset. Microsoft Bing Maps images in conjunction with deep learning were used to measure total and types of street view green space, which were compared to normalized difference vegetation index (NDVI) as commonly-used satellite-based green space measure. Generalized linear mixed model was used to examine associations between green space and census tract SES, adjusting for population density and rural/urban status. Results: The accuracy of the deep learning model was high with 92.5% mean intersection over union. NDVI were moderately correlated with total street view-based green space and tree, and weakly correlated with low-lying vegetation and grass. Total and three types of green space showed significant negative associations with neighborhood SES. The percentage of total green space decreased by 2.62 [95% confidence interval (CI): -3.02, -2.21, p < 0.001] with each interquartile range increase in CalEnviroScreen3.0 score. Disadvantaged communities contained approximately 5% less average street green space than other communities. Conclusion: Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. In Los Angeles County, disadvantaged communities had substantively less street green space. Governments and urban planners need to consider the type and visibility of street green space from pedestrian's perspective.

Electrospun PLGA membrane incorporated with andrographolide-loaded mesoporous silica nanoparticles for sustained antibacterial wound dressing.

AIM: To assess the wound-healing ability of poly(lactic-co-glycolic acid) (PLGA) nanofibrous wound dressing incorporated with andrographolide (Andro)-loaded mesoporous silica nanoparticles (MSNs). MATERIALS & METHODS: PLGA/Andro-MSNs nanofibrous membrane wound dressings were produced by electrospinning. The effects of MSNs on the hydrophilicity, degradation and mechanical strength of membranes were evaluated. The cumulative release of Andro in vitro was obtained. Cell culture and in vivo tests on infectious models were carried out. RESULTS: The PLGA/Andro-MSNs membrane showed a sustained release of Andro. The incorporation of MSNs into PLGA improved the hydrophilicity of the nanofibrous membranes. Cell culture and in vivo tests showed that PLGA/Andro-MSNs membrane can promote epidermal cell adhesion and reduce inflammation process. CONCLUSION: PLGA/Andro-MSNs nanofibrous membrane exhibited an efficient wound-healing ability.

Hydroxypropyltrimethyl Ammonium Chloride Chitosan Functionalized-PLGA Electrospun Fibrous Membranes as Antibacterial Wound Dressing: In Vitro and In Vivo Evaluation.

A novel poly(lactic-co-glycolic acid) (PLGA)-hydroxypropyltrimethyl ammonium chloride chitosan (HACC) composite nanofiber wound dressing was prepared through electrospinning and the entrapment-graft technique as an antibacterial dressing for cutaneous wound healing. HACC with 30% degrees of substitution (DS) was immobilized onto the surface of PLGA membranes via the reaction between carboxyl groups in PLGA after alkali treatment and the reactive groups (⁻NH2) in HACC molecules. The naked PLGA and chitosan graft PLGA (PLGA-CS) membranes served as controls. The surface immobilization was characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier transform infrared (FTIR), thermogravimetric analysis (TGA) and energy dispersive X-ray spectrometry (EDX). The morphology studies showed that the membranes remain uniform after the immobilization process. The effects of the surface modification by HACC and CS on the biological properties of the membranes were also investigated. Compared with PLGA and PLGA-CS, PLGA-HACC exhibited more effective antibacterial activity towards both Gram-positive (S. aureus) and Gram-negative (P. aeruginosa) bacteria. The newly developed fibrous membranes were evaluated in vitro for their cytotoxicity using human dermal fibroblasts (HDFs) and human keratinocytes (HaCaTs) and in vivo using a wound healing mice model. It was revealed that PLGA-HACC fibrous membranes exhibited favorable cytocompatibility and significantly stimulated adhesion, spreading and proliferation of HDFs and HaCaTs. PLGA-HACC exhibited excellent wound healing efficacy, which was confirmed using a full thickness excision wound model in S. aureus-infected mice. The experimental results in this work suggest that PLGA-HACC is a strong candidate for use as a therapeutic biomaterial in the treatment of infected wounds.

Sideline Management of Joint Dislocations.

Athletes can sustain a large variety of injuries from simple soft tissue sprains to complex fractures and joint dislocations. This article reviews and provides the most recent information for sports medicine professionals on the management of simple and complex joint dislocations, i.e., irreducible and/or associated with a fracture, from the sidelines without the benefit of imaging. For each joint, the relevant anatomy, common mechanisms, sideline assessment, reduction techniques, initial treatment, and potential complications will be discussed, which allow for the safe and prompt return of athletes to the field of play.