Dual-stage semantic segmentation of endoscopic surgical instruments.

BACKGROUND: Endoscopic instrument segmentation is essential for ensuring the safety of robotic-assisted spinal endoscopic surgeries. However, due to the narrow operative region, intricate surrounding tissues, and limited visibility, achieving instrument segmentation within the endoscopic view remains challenging. PURPOSE: This work aims to devise a method to segment surgical instruments in endoscopic video. By designing an endoscopic image classification model, features of frames before and after the video are extracted to achieve continuous and precise segmentation of instruments in endoscopic videos. METHODS: Deep learning techniques serve as the algorithmic core for constructing the convolutional neural network proposed in this study. The method comprises dual stages: image classification and instrument segmentation. MobileViT is employed for image classification, enabling the extraction of key features of different instruments and generating classification results. DeepLabv3+ is utilized for instrument segmentation. By training on distinct instruments separately, corresponding model parameters are obtained. Lastly, a flag caching mechanism along with a blur detection module is designed to effectively utilize the image features in consecutive frames. By incorporating specific parameters into the segmentation model, better segmentation of surgical instruments can be achieved in endoscopic videos. RESULTS: The classification and segmentation models are evaluated on an endoscopic image dataset. In the dataset used for instrument segmentation, the training set consists of 7456 images, the validation set consists of 829 images, and the test set consists of 921 images. In the dataset used for image classification, the training set consists of 2400 images and the validation set consists of 600 images. The image classification model achieves an accuracy of 70% on the validation set. For the segmentation model, experiments are conducted on two common surgical instruments, and the mean Intersection over Union (mIoU) exceeds 98%. Furthermore, the proposed video segmentation method is tested using videos collected during surgeries, validating the effectiveness of the flag caching mechanism and blur detection module. CONCLUSIONS: Experimental results on the dataset demonstrate that the dual-stage video processing method excels in performing instrument segmentation tasks under endoscopic conditions. This advancement is significant for enhancing the intelligence level of robotic-assisted spinal endoscopic surgeries.

Learning to Generate Parameters of ConvNets for Unseen Image Data.

Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e.g., SGD or Adam) to learn network parameters, making training very time- and resource-intensive. In this paper, we propose a new training paradigm and formulate the parameter learning of ConvNets into a prediction task: considering that there exist correlations between image datasets and their corresponding optimal network parameters of a given ConvNet, we explore if we can learn a hyper-mapping between them to capture the relations, such that we can directly predict the parameters of the network for an image dataset never seen during the training phase. To do this, we put forward a new hypernetwork-based model, called PudNet, which intends to learn a mapping between datasets and their corresponding network parameters, then predicts parameters for unseen data with only a single forward propagation. Moreover, our model benefits from a series of adaptive hyper-recurrent units sharing weights to capture the dependencies of parameters among different network layers. Extensive experiments demonstrate that our proposed method achieves good efficacy for unseen image datasets in two kinds of settings: Intra-dataset prediction and Inter-dataset prediction. Our PudNet can also well scale up to large-scale datasets, e.g., ImageNet-1K. It takes 8,967 GPU seconds to train ResNet-18 on the ImageNet-1K using GC from scratch and obtain a top-5 accuracy of 44.65%. However, our PudNet costs only 3.89 GPU seconds to predict the network parameters of ResNet-18 achieving comparable performance (44.92%), more than 2,300 times faster than the traditional training paradigm.

OsCYP706C2 diverts rice strigolactone biosynthesis to a noncanonical pathway branch.

Strigolactones exhibit dual functionality as regulators of plant architecture and signaling molecules in the rhizosphere. The important model crop rice exudes a blend of different strigolactones from its roots. Here, we identify the inaugural noncanonical strigolactone, 4-oxo-methyl carlactonoate (4-oxo-MeCLA), in rice root exudate. Comprehensive, cross-species coexpression analysis allowed us to identify a cytochrome P450, OsCYP706C2, and two methyl transferases as candidate enzymes for this noncanonical rice strigolactone biosynthetic pathway. Heterologous expression in yeast and Nicotiana benthamiana indeed demonstrated the role of these enzymes in the biosynthesis of 4-oxo-MeCLA, which, expectedly, is derived from carlactone as substrate. The oscyp706c2 mutants do not exhibit a tillering phenotype but do have delayed mycorrhizal colonization and altered root phenotype. This work sheds light onto the intricate complexity of strigolactone biosynthesis in rice and delineates its role in symbiosis and development.

Advances of surgical robotics: image-guided classification and application.

Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision. In this review article, we divide clinical image into direct and indirect based on the object of information acquisition, and into continuous, intermittent continuous, and discontinuous according to the target-tracking frequency. The characteristics and applications of the existing surgical robots in each category are introduced based on these two dimensions. Our purpose in conducting this review was to analyze, summarize, and discuss the current evidence on the general rules on the application of image technologies for medical purposes. Our analysis gives insight and provides guidance conducive to the development of more advanced surgical robotics systems in the future.

Specific Activation of Dopamine Receptor D1 Expressing Neurons in the PrL Alleviates CSDS-Induced Anxiety-Like Behavior Comorbidity with Postoperative Hyperalgesia in Male Mice.

Postoperative pain is a type of pain that occurs in clinical patients after surgery. Among the factors influencing the transition from acute postoperative pain to chronic postoperative pain, chronic stress has received much attention in recent years. Here, we investigated the role of dopamine receptor D1/D2 expressing pyramidal neurons in the prelimbic cortex (PrL) in modulating chronic social defeat stress (CSDS)-induced anxiety-like behavior comorbidity with postoperative hyperalgesia in male mice. Our results showed that preoperative CSDS induced anxiety-like behavior and significantly prolonged postoperative pain caused by plantar incision, but did not affect plantar wound recovery and inflammation. Reduced activation of dopamine receptor D1 or D2 expressing neurons in the PrL is a remarkable feature of male mice after CSDS, and chronic inhibition of dopamine receptor D1 or D2 expressing neurons in the PrL induced anxiety-like behavior and persistent postoperative pain. Further studies found that activation of D1 expressing but not D2 expressing neurons in the PrL ameliorated CSDS-induced anxiety-like behavior and postoperative hyperalgesia. Our results suggest that dopamine receptor D1 expressing neurons in the PrL play a crucial role in CSDS-induced anxiety-like behavior comorbidity with postoperative hyperalgesia in male mice.

Targeting HTR2B suppresses non-functioning pituitary adenoma growth and sensitizes cabergoline treatment via inhibiting Gαq/PLC/PKCγ/STAT3 axis.

BACKGROUND: Managing non-functioning pituitary adenomas (NFPAs) is difficult due to limited drug treatments. Cabergoline's (CAB) effectiveness for NFPAs is debated. This study explores the role of HTR2B in NFPAs and its therapeutic potential. METHODS: We conducted screening of bulk RNA-sequencing data to analyze HTR2B expression levels in NFPA samples. In vitro and in vivo experiments were performed to evaluate the effects of HTR2B modulation on tumor growth and cell cycle regulation. Mechanistic insights into the HTR2B-mediated signaling pathway were elucidated using pharmacological inhibitors and molecular interaction assays. RESULTS: Elevated HTR2B expression was detected in NFPA samples, which was associated with increased tumor survival. Inhibition of HTR2B activity resulted in the suppression of tumor growth through modulation of the G2M cell cycle. The inhibition of HTR2B with PRX-08066 was found to block STAT3 phosphorylation and nuclear translocation by interfering with the Gαq/PLC/PKC pathway. A direct interaction between PKC-γ and STAT3 was critical for STAT3 activation. CAB was shown to activate pSTAT3 via HTR2B, reducing its therapeutic potential. However, the combination of an HTR2B antagonist with CAB significantly inhibited tumor cell proliferation in HTR2B-expressing pituitary tumor cell lines, a xenografted pituitary tumor model, and patient-derived samples. Analysis of patient-derived data indicated that a distinct molecular pattern characterized by upregulated HTR2B/PKC-γ and downregulated BTG2/GADD45A may benefit from combination treatment with CAB and PRX-08066. CONCLUSIONS: HTR2B is a potential therapeutic target for NFPAs, and its inhibition could improve CAB efficacy. A dual therapy approach may be beneficial for NFPA patients with high HTR2B expression.

Genome-wide identification and analysis of monocot-specific chimeric jacalins (MCJ) genes in Maize (Zea mays L.).

BACKGROUND: The monocot chimeric jacalins (MCJ) proteins, which contain a jacalin-related lectin (JRL) domain and a dirigent domain (DIR), are specific to Poaceae. MCJ gene family is reported to play an important role in growth, development and stress response. However, their roles in maize have not been thoroughly investigated. RESULTS: In this study, eight MCJ genes in the maize genome (designated as ZmMCJs) were identified, which displayed unequal distribution across four chromosomes. Phylogenetic relationships between the ZmMCJs were evident through the identification of highly conserved motifs and gene structures. Analysis of transcriptome data revealed distinct expression patterns among the ZmMCJ genes, leading to their classification into four different modules, which were subsequently validated using RT-qPCR. Protein structures of the same module are found to be relatively similar. Subcellular localization experiments indicated that the ZmMCJs are mainly located on the cell membrane. Additionally, hemagglutination and inhibition experiments show that only part of the ZmMCJs protein has lectin activity, which is mediated by the JRL structure, and belongs to the mannose-binding type. The cis-acting elements in the promoter region of ZmMCJ genes predicted their involvement response to phytohormones, such as abscisic acid and jasmonic acid. This suggests that ZmMCJ genes may play a significant role in both biotic and abiotic stress responses. CONCLUSIONS: Overall, this study adds new insights into our understanding of the gene-protein architecture, evolutionary characteristics, expression profiles, and potential functions of MCJ genes in maize.

Self-Supervised Monocular Depth Estimation for Endoscopic Imaging.

Endoscopy holds a pivotal role in the early detection and treatment of diverse diseases, with artificial intelligence (AI)-assisted methods increasingly gaining prominence in disease screening. Among them, the depth estimation from endoscopic sequences is crucial for a spectrum of AI-assisted surgical techniques. However, the development of endoscopic depth estimation algorithms presents a formidable challenge due to the unique environmental intricacies and constraints within the dataset. This paper proposes a self-supervised depth estimation network to comprehensively explore the brightness changes in endoscopic images, and fuse different features at multiple levels to achieve an accurate prediction of endoscopic depth. First, a FlowNet is designed to evaluate the brightness changes of adjacent frames by calculating the multi-scale structural similarity. Second, a feature fusion module is presented to capture multi-scale contextual information. Experiments show that the average accuracy of the algorithm is 97.03% in the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED dataset). Based on the training parameters of the SCARED dataset, the algorithm achieves superior performance on the other two datasets (EndoSLAM and KVASIR dataset), indicating that the algorithm has good generalization performance.

PISC-Net: A Comprehensive Neural Network Framework for Predicting Metasurface Infrared Emission Spectra.

Multifunctional metasurfaces have exhibited extensive potential in various fields, owing to their unparalleled capacity for controlling electromagnetic wave characteristics. The precise resolution is achieved through numerical simulation in conventional metasurface design methodologies. Nevertheless, the simulations using these approaches are inherently computationally costly. This paper proposes the Physical Insight Self-Correcting Convolutional Network (PISC-Net), which enables rapid prediction of infrared radiation spectra of metasurfaces with remarkable generalization capacity. In contrast to preceding prediction networks, we have enhanced the cognitive ability of the network to recognize physical mechanisms by designing parameter-communication modules and integrating a priori knowledge grounded in the parameter association mechanism. Additionally, we proposed an effective strategy for constructing data sets that facilitate precise tuning of absorption bands in the entire spectral range (3-14 µm) and serves to reduce the costs associated with data set development. Transfer learning is employed to obtain precise predictions for large-period metasurfaces from limited data sets. This approach demonstrates that a network trained exclusively on simulation data could predict experimental outcomes accurately, as proved by the comparative analysis between simulation, experimental testing, and prediction results. The average mean square error is less than 4%.

Advantages of robot-assisted resection of large mediastinal tumors: a single-center preliminary study.

Current study aims to assess the safety and efficacy of robot-assisted thoracoscopic surgery (RATS) for sizable mediastinal masses with a minimum diameter ≥6 cm, compared with video-assisted thoracoscopic surgery (VATS) and open surgery. This study enrolled 130 patients with mediastinal tumors with no less than 6 cm diameter in Zhongnan Hospital, Wuhan University, including 33 patients who underwent RATS, 52 patients who underwent VATS and 45 patients who underwent open surgery. After classifying based on mass size and whether it has invaded or not, we compared their clinical characteristics and perioperative outcomes. There was no significant difference in age, gender, mass size, myasthenia gravis, mass location, pathological types (p > 0.05) in three groups. Patients undergoing open surgery typically presenting at a more advanced stage (p < 0.05). No obvious difference was discovered in the average postoperative length of stay, operation duration, chest tube duration and average postoperative day 1 drainage output between RATS group and VATS group (p > 0.05), while intraoperative blood loss in RATS group was significantly lower than VATS group (p = 0.046). Moreover, the postoperative length of stay, operation duration, chest tube duration and intraoperative blood loss in RATS group were significantly lower than open surgery group (p < 0.001). RATS is a secure and efficient approach for removing large mediastinal masses at early postoperative period. In comparison with VATS, RATS is associated with lower intraoperative blood loss. Compared with open surgery, RATS is also associated with shorter postoperative length of stay, operation duration, chest tube duration and intraoperative blood loss.

Nano-cutting mechanism of ion implantation-modified SiC: reducing subsurface damage expansion and abrasive wear.

This study utilized ion implantation to modify the material properties of silicon carbide (SiC) to mitigate subsurface damage during SiC machining. The paper analyzed the mechanism of hydrogen ion implantation on the machining performance of SiC at the atomic scale. A molecular dynamics model of nanoscale cutting of an ion-implanted SiC workpiece using a non-rigid regular tetrakaidecahedral diamond abrasive grain was established. The study investigated the effects of ion implantation on crystal structure phase transformation, dislocation nucleation, and defect structure evolution. Results showed ion implantation modification decreased the extension depth of amorphous structures in the subsurface layer, thereby enhancing the surface and subsurface integrity of the SiC workpiece. Additionally, dislocation extension length and volume within the lattice structure were lower in the ion-implanted workpiece compared to non-implanted ones. Phase transformation, compressive pressure, and cutting stress of the lattice in the shear region per unit volume were lower in the ion-implanted workpiece than the non-implanted one. Taking the diamond abrasive grain as the research subject, the mechanism of grain wear under ion implantation was explored. Grain expansion, compression, and atomic volumetric strain wear rate were higher in the non-implanted workpiece versus implanted ones. Under shear extrusion of the SiC workpiece, dangling bonds of atoms in the diamond grain were unstable, resulting in graphitization of the diamond structure at elevated temperatures. This study established a solid theoretical and practical foundation for realizing non-destructive machining at the atomic scale, encompassing both theoretical principles and practical applications.

Discovery of Near-Infrared Heptamethine Cyanine Probes for Imaging-Guided Surgery in Solid Tumors.

Near-infrared (NIR) fluorescence imaging has attracted much attention in image-guided interventions with unique advantages. However, the clinical translation rate of fluorescence probes is extremely low, primarily due to weak lesion signal contrast and poor specificity. To address this dilemma, a series of small-molecule near-infrared fluorescence probes have been designed for tumor imaging. Among them, YQ-04-03 showed notable optical stability and remarkable sensitivity toward tumor targeting. Moreover, within a specific concentration and time range against oxidizing reducing agents and laser, it demonstrated better stability than ICG. The retention time of YQ-04-03 in tumors was significantly longer compared to other nonspecific uptake sites in the subjects, and its tumor-to-normal tissue ratio (TNR) outperformed ICG. Successful resection of in situ hepatocarcinoma and peritoneal carcinoma was achieved using probe imaging guidance, with the smallest visual lesion resected measuring approximately 1 mm3. Ultimately, this probe holds great potential for advancing tumor tracer.

High-density bin-based genetic map reveals a 530-kb chromosome segment derived from wild peanut contributing to late leaf spot resistance.

KEY MESSAGE: Twenty-eight QTLs for LLS disease resistance were identified using an amphidiploid constructed mapping population, a favorable 530-kb chromosome segment derived from wild species contributes to the LLS resistance. Late leaf spot (LLS) is one of the major foliar diseases of peanut, causing serious yield loss and affecting the quality of kernel and forage. Some wild Arachis species possess higher resistance to LLS as compared with cultivated peanut; however, ploidy level differences restrict utilization of wild species. In this study, a synthetic amphidiploid (Ipadur) of wild peanuts with high LLS resistance was used to cross with Tifrunner to construct TI population. In total, 200 recombinant inbred lines were collected for whole-genome resequencing. A high-density bin-based genetic linkage map was constructed, which includes 4,809 bin markers with an average inter-bin distance of 0.43 cM. The recombination across cultivated and wild species was unevenly distributed, providing a novel recombination landscape for cultivated-wild Arachis species. Using phenotyping data collected across three environments, 28 QTLs for LLS disease resistance were identified, explaining 4.35-20.42% of phenotypic variation. The major QTL located on chromosome 14, qLLS14.1, could be consistently detected in 2021 Jiyang and 2022 Henan with 20.42% and 12.12% PVE, respectively. A favorable 530-kb chromosome segment derived from Ipadur was identified in the region of qLLS14.1, in which 23 disease resistance proteins were located and six of them showed significant sequence variations between Tifrunner and Ipadur. Allelic variation analysis indicating the 530-kb segment of wild species might contribute to the disease resistance of LLS. These associate genomic regions and candidate resistance genes are of great significance for peanut breeding programs for bringing durable resistance through pyramiding such multiple LLS resistance loci into peanut cultivars.

A Respiratory Motion Prediction Method Based on LSTM-AE with Attention Mechanism for Spine Surgery.

Respiratory motion-induced vertebral movements can adversely impact intraoperative spine surgery, resulting in inaccurate positional information of the target region and unexpected damage during the operation. In this paper, we propose a novel deep learning architecture for respiratory motion prediction, which can adapt to different patients. The proposed method utilizes an LSTM-AE with attention mechanism network that can be trained using few-shot datasets during operation. To ensure real-time performance, a dimension reduction method based on the respiration-induced physical movement of spine vertebral bodies is introduced. The experiment collected data from prone-positioned patients under general anaesthesia to validate the prediction accuracy and time efficiency of the LSTM-AE-based motion prediction method. The experimental results demonstrate that the presented method (RMSE: 4.39%) outperforms other methods in terms of accuracy within a learning time of 2 min. The maximum predictive errors under the latency of 333 ms with respect to the x, y, and z axes of the optical camera system were 0.13, 0.07, and 0.10 mm, respectively, within a motion range of 2 mm.

Environmental levels of azoxystrobin disturb male zebrafish behavior: Possible roles of oxidative stress, cholinergic system, and dopaminergic system.

A widely applied pesticide of azoxystrobin, is increasingly detected in the water environment. Concern has been raised against its potential detriment to aquatic ecosystems. It has been shown that exposure to azoxystrobin interfere with the locomotor behavior of zebrafish larvae. This study aims to investigate whether exposure to environmental levels of azoxystrobin (2 µg/L, 20 µg/L, and 200 µg/L) changes the behavior of male adult zebrafish. Herein, we evaluated behavioral response (locomotor, anxiety-like, and exploratory behaviors), histopathology, biochemical indicators, and gene expression in male adult zebrafish upon azoxystrobin exposure. The study showed that exposure to azoxystrobin for 42 days remarkably increased the locomotor ability of male zebrafish, resulted in anxiety-like behavior, and inhibited exploratory behavior. After treatment with 200 µg/L azoxystrobin, vasodilatation, and congestion were observed in male zebrafish brains. Exposure to 200 µg/L azoxystrobin notably elevated ROS level, MDA concentration, CAT activity, and AChE activity, while inhibiting SOD activity, GPx activity, ACh concentration, and DA concentration in male zebrafish brains. Moreover, the expression levels of genes related to the antioxidant, cholinergic, and dopaminergic systems were significantly changed. This suggests that azoxystrobin may interfere with the homeostasis of neurotransmitters by causing oxidative stress in male zebrafish brains, thus affecting the behavioral response of male zebrafish.

Integrating transcriptomics and network pharmacology to reveal the mechanisms of total Rhizoma Coptidis alkaloids against nonalcoholic steatohepatitis.

ETHNOPHARMACOLOGICAL RELEVANCE: Non-alcoholic steatohepatitis (NASH) has emerged as a major cause of cirrhosis and hepatocellular carcinoma, posing a significant threat to public health. Rhizoma Coptidis, a traditional Chinese medicinal herb has been shown to have significant curative effects on liver diseases. Total Rhizoma Coptidis Alkaloids (TRCA) is a primarily alkaloid mixture extracted from Rhizoma Coptidis, and its constituents are widely accepted to have hepatoprotective effects. AIM OF THE STUDY: This work aimed to investigate the efficacy and potential mechanisms of TRCA in ameliorating NASH through both in vitro experiments and in vivo mouse models. MATERIALS AND METHODS: The study employed a mice model induced by a high-fat diet (HFD) to evaluate the effectiveness and pharmacological mechanisms of TRCA in alleviating NASH. Transcriptomic sequencing and network pharmacology were used to explore the possible targets and mechanisms of TRCA to ameliorate NASH. Further validation was performed in free fatty acid (FFA)-induced human hepatocytes (LO2) and human hepatocellular carcinoma cells (HepG2). RESULTS: TRCA effectively ameliorated the main features of NASH such as lipid accumulation, hepatitis and hepatic fibrosis in the liver tissue of mice induced by HFD, as well as improved glucose tolerance and insulin resistance in mice. Combined with transcriptomic and network pharmacological analyses, 68 core targets associated with the improvement of NASH by TRCA were obtained. According to the KEGG results, the core targets were significantly enriched in the PI3K-AKT signaling pathway whereas TRCA ameliorated the aberrant down-regulation of the PI3K-AKT signaling pathway induced by HFD. Furthermore, the five highest-ranked genes were obtained by PPI network analysis. Moreover, our findings suggest that TRCA may impede the progression of HFD-induced NASH by regulating the expression of PPARG, MMP9, ALB, CCL2, and EGFR. CONCLUSIONS: TRCA can ameliorate HFD-induced liver injury by modulating aberrant downregulation of the PI3K-AKT signaling pathway. Key proteins such as PPARG, MMP9, ALB, CCL2, and EGFR may be critical targets for TRCA to ameliorate NASH. This finding supports using Rhizoma Coptidis, a well-known herbal medicine, as a potential therapeutic agent for NASH.

Multiprior Learning via Neural Architecture Search for Blind Face Restoration.

Blind face restoration (BFR) aims to recover high-quality (HQ) face images from low-quality (LQ) ones and usually resorts to facial priors for improving restoration performance. However, current methods still suffer from two major difficulties: 1) how to derive a powerful network architecture without extensive hand tuning and 2) how to capture complementary information from multiple facial priors in one network to improve restoration performance. To this end, we propose a face restoration searching network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality. On the basis of FRSNet, we further design our multiple facial prior searching network (MFPSNet) with a multiprior learning scheme. MFPSNet optimally extracts information from diverse facial priors and fuses the information into image features, ensuring that both external guidance and internal features are reserved. In this way, MFPSNet takes full advantage of semantic-level (parsing maps), geometric-level (facial heat maps), reference-level (facial dictionaries), and pixel-level (degraded images) information and, thus, generates faithful and realistic images. Quantitative and qualitative experiments show that the MFPSNet performs favorably on both synthetic and real-world datasets against the state-of-the-art (SOTA) BFR methods. The codes are publicly available at: https://github.com/YYJ1anG/MFPSNet.

Effect of orthopedic insoles on spinal deformity and walking in adolescents with idiopathic scoliosis summary.

Objective: To observe the effects of scoliosis-specific exercise therapy combined with braces and orthopedic insoles on improved spinal deformity and walking ability in adolescents with idiopathic scoliosis (AIS). Method: From September 2019 to September 2020, 60 outpatient AIS patients were distributed into brace group (n = 30) at random and brace combined orthopedic insole group (n = 30). Both groups underwent brace dryness, and the observation group used scoliosis-specific exercise therapy combined with brace therapy, and on this basis, orthopedic insole intervention was added for 8 h per day for 2 months. At the same time, 20 adolescents of the same age with normal spinal development were recruited as a healthy group. GaitScan instruments were used to collect gait and plantar pressure measurements from study subjects. First, the gait and plantar pressure data of AIS patients and healthy groups were compared horizontally to ascertain the abnormal indicators, and then the spinal deformity and the above abnormal indicators were compared between the brace group and the brace combined orthopedic insole group. Outcome: The plantar pressure center drift index (CPEI) in the AIS group was higher than that in the healthy group (F = 3.120, P < 0.05), and there were significant differences in the ratio of medial and lateral heel pressure (M/l) and total foot pressure (P < 0.05) between the AIS group and the healthy group, and no noticeable variations were found in the support phase period, walking speed, and proportion of each phase (P > 0.05). After treatment, the Cobb angle was significantly reduced in both the brace group and the brace combined with orthopedic insole group (P < 0.05), and there was no significant difference between the groups (P > 0.05). There were no significant changes in the pressure ratio of CPEI, M/l and bilateral full foot in the brace group (P > 0.05). The CPEI decreased in the brace combined with orthopedic insole group (P < 0.05), and the pressure ratio of M/l and bilateral full foot tended to 1 (P < 0.05), and was better than that in the brace group (P < 0.05). Conclusion: Patients with AIS may have local and worldwide asymmetric changes in plantar pressure distribution. The addition of orthopedic insoles has limited effect on improving scoliosis deformity in patients with AIS, but it can effectively improve the abnormal biomechanics of patients with AIS, so that the patient's force tends to be balanced.

Phylogenomic Analysis of Cytochrome P450 Gene Superfamily and Their Association with Flavonoids Biosynthesis in Peanut (Arachis hypogaea L.).

Cytochrome P450s (CYPs) constitute extensive enzyme superfamilies in the plants, playing pivotal roles in a multitude of biosynthetic and detoxification pathways essential for growth and development, such as the flavonoid biosynthesis pathway. However, CYPs have not yet been systematically studied in the cultivated peanuts (Arachis hypogaea L.), a globally significant cash crop. This study addresses this knowledge deficit through a comprehensive genome-wide analysis, leading to the identification of 589 AhCYP genes in peanuts. Through phylogenetic analysis, all AhCYPs were systematically classified into 9 clans, 43 gene families. The variability in the number of gene family members suggests specialization in biological functions. Intriguingly, both tandem duplication and fragment duplication events have emerged as pivotal drivers in the evolutionary expansion of the AhCYP superfamily. Ka/Ks analysis underscored the substantial influence of strong purifying selection on the evolution of AhCYPs. Furthermore, we selected 21 genes encoding 8 enzymes associated with the flavonoid pathway. The results of quantitative real-time PCR (qRT-PCR) experiments unveiled stage-specific expression patterns during the development of peanut testa, with discernible variations between pink and red testa. Importantly, we identified a direct correlation between gene expression levels and the accumulation of metabolites. These findings offer valuable insights into elucidating the comprehensive functions of AhCYPs and the underlying mechanisms governing the divergent accumulation of flavonoids in testa of different colors.

Afforestation changed the fungal functional community of paddy fields and dry farmlands differently.

Afforestation currently makes a great contribution to carbon uptake in terrestrial ecosystems, while dramatically affects soil ecosystem functions too. Little is known, however, about the changes in soil fungal functional groups and their interactions following afforestation. Here, based on high-throughput sequencing and FUNGuild annotation, we investigated the functional characteristics of soil fungi as well as environmental factors in a watershed where paddy field and dry farmland were changed to eucalyptus plantation. The results showed that afforestation on paddy field resulted in greater changes in diversity, community structure and taxon interactions of fungal functional groups than afforestation on dry farmland. The most complex and distinctive community structure was found in eucalyptus plantation, as well as the greatest taxon interactions, and the lowest alpha-diversity of functional guilds of symbiotrophic fungi because of the dominant ectomycorrhizal fungi. Paddy field exhibited the highest proportion of saprotrophic fungi, but the lowest taxonomic diversity of saprotrophic and pathotrophic fungi. The taxonomic diversity of undefined saprotrophic fungi shaped the differences in community structure and network complexity between eucalyptus plantation and cropland. Limited cooperation within dominant fungi was the main reason for the establishment of a loose co-occurrence network in paddy field. From croplands to artificial forests, reduced soil pH boosted the taxonomic diversity of fungal functional groups. All of these findings suggested that afforestation may lead to an increase in the taxonomic diversity of soil fungal functional groups, which would further intensify the taxon interactions.