Entropy and distance-guided super self-ensembling for optic disc and cup segmentation.

Segmenting the optic disc (OD) and optic cup (OC) is crucial to accurately detect changes in glaucoma progression in the elderly. Recently, various convolutional neural networks have emerged to deal with OD and OC segmentation. Due to the domain shift problem, achieving high-accuracy segmentation of OD and OC from different domain datasets remains highly challenging. Unsupervised domain adaptation has taken extensive focus as a way to address this problem. In this work, we propose a novel unsupervised domain adaptation method, called entropy and distance-guided super self-ensembling (EDSS), to enhance the segmentation performance of OD and OC. EDSS is comprised of two self-ensembling models, and the Gaussian noise is added to the weights of the whole network. Firstly, we design a super self-ensembling (SSE) framework, which can combine two self-ensembling to learn more discriminative information about images. Secondly, we propose a novel exponential moving average with Gaussian noise (G-EMA) to enhance the robustness of the self-ensembling framework. Thirdly, we propose an effective multi-information fusion strategy (MFS) to guide and improve the domain adaptation process. We evaluate the proposed EDSS on two public fundus image datasets RIGA+ and REFUGE. Large amounts of experimental results demonstrate that the proposed EDSS outperforms state-of-the-art segmentation methods with unsupervised domain adaptation, e.g., the Dicemean score on three test sub-datasets of RIGA+ are 0.8442, 0.8772 and 0.9006, respectively, and the Dicemean score on the REFUGE dataset is 0.9154.

Retinal vessel segmentation based on multi-scale feature and style transfer.

Retinal vessel segmentation is very important for diagnosing and treating certain eye diseases. Recently, many deep learning-based retinal vessel segmentation methods have been proposed; however, there are still many shortcomings (e.g., they cannot obtain satisfactory results when dealing with cross-domain data or segmenting small blood vessels). To alleviate these problems and avoid overly complex models, we propose a novel network based on a multi-scale feature and style transfer (MSFST-NET) for retinal vessel segmentation. Specifically, we first construct a lightweight segmentation module named MSF-Net, which introduces the selective kernel (SK) module to increase the multi-scale feature extraction ability of the model to achieve improved small blood vessel segmentation. Then, to alleviate the problem of model performance degradation when segmenting cross-domain datasets, we propose a style transfer module and a pseudo-label learning strategy. The style transfer module is used to reduce the style difference between the source domain image and the target domain image to improve the segmentation performance for the target domain image. The pseudo-label learning strategy is designed to be combined with the style transfer module to further boost the generalization ability of the model. Moreover, we trained and tested our proposed MSFST-NET in experiments on the DRIVE and CHASE_DB1 datasets. The experimental results demonstrate that MSFST-NET can effectively improve the generalization ability of the model on cross-domain datasets and achieve improved retinal vessel segmentation results than other state-of-the-art methods.

BGF-Net: Boundary guided filter network for medical image segmentation.

How to fuse low-level and high-level features effectively is crucial to improving the accuracy of medical image segmentation. Most CNN-based segmentation models on this topic usually adopt attention mechanisms to achieve the fusion of different level features, but they have not effectively utilized the guided information of high-level features, which is often highly beneficial to improve the performance of the segmentation model, to guide the extraction of low-level features. To address this problem, we design multiple guided modules and develop a boundary-guided filter network (BGF-Net) to obtain more accurate medical image segmentation. To the best of our knowledge, this is the first time that boundary guided information is introduced into the medical image segmentation task. Specifically, we first propose a simple yet effective channel boundary guided module to make the segmentation model pay more attention to the relevant channel weights. We further design a novel spatial boundary guided module to complement the channel boundary guided module and aware of the most important spatial positions. Finally, we propose a boundary guided filter to preserve the structural information from the previous feature map and guide the model to learn more important feature information. Moreover, we conduct extensive experiments on skin lesion, polyp, and gland segmentation datasets including ISIC 2016, CVC-EndoSceneStil and GlaS to test the proposed BGF-Net. The experimental results demonstrate that BGF-Net performs better than other state-of-the-art methods.

Nitrogen-doped hollow mesoporous carbon spheres for efficient oxygen reduction.

Extensive investigations have been devoted to nitrogen-doped carbon materials as catalysts for the oxygen reduction reaction (ORR) in various conversion technologies. In this study, we introduce nitrogen-doped carbon materials with hollow spherical structures. These materials demonstrate significant potential in ORR activity within alkaline media, showing a half-wave potential of 0.87 V versus the reversible hydrogen electrode (RHE). Nitrogen-doped hollow carbon spheres (N-CHS) exhibit unique characteristics such as a thin carbon shell layer, hollow structure, large surface area, and distinct pore features. These features collectively create an optimal environment for facilitating the diffusion of reactants, thereby enhancing the exposure of active sites and improving catalytic performance. Building upon the promising qualities of N-CHS as a catalyst support, we employ heme chloride (1 wt%) as the source of iron for Fe doping. Through the carbonization process, Fe-N active sites are effectively formed, displaying a half-wave potential of 0.9 V versus RHE. Notably, when implemented as a cathode catalyst in zinc-air batteries, this catalyst exhibits an impressive power density of 162.6 mW cm-2.

Dense-Packed RuO2 Nanorods with In Situ Generated Metal Vacancies Loaded on SnO2 Nanocubes for Proton Exchange Membrane Water Electrolyzer with Ultra-Low Noble Metal Loading.

Proton exchange membrane water electrolyzer (PEMWE) is a green hydrogen production technology that can be coupled with intermittent power sources such as wind and photoelectric power. To achieve cost-effective operations, low noble metal loading on the anode catalyst layer is desired. In this study, a catalyst with RuO2 nanorods coated outside SnO2 nanocubes is designed, which forms continuous networks and provides high conductivity. This allows for the reduction of Ru contents in catalysts. Furthermore, the structure evolutions on the RuO2 surface are carefully investigated. The etched RuO2 surfaces are seen as the consequence of Co leaching, and theoretical calculations demonstrate that it is more effective in driving oxygen evolution. For electrochemical tests, the catalysts with 23 wt% Ru exhibit an overpotential of 178 mV at 10 mA cm-2 , which is much higher than most state-of-art oxygen evolution catalysts. In a practical PEMWE, the noble metal Ru loading on the anode side is only 0.3 mg cm-2 . The cell achieves 1.61 V at 1 A cm-2 and proper stability at 500 mA cm-2 , demonstrating the effectiveness of the designed catalyst.

Analysis of the Small Auxin-Up RNA (SAUR) Genes Regulating Root Growth Angle (RGA) in Apple.

Small auxin upregulated RNAs (SAURs) are primary auxin response genes; the function of regulating root growth angle (RGA) is unclear in the apple rootstock. We firstly identified 96 MdSAUR genes families from new apple genome GDDH13 using the resequence database of 'Baleng Crab (BC)' and 'M9'. A total of 25 MdSAUR genes, regulating the formation of RGA, were screened for the expression profiles in stems and roots and the allelic variants of quantitative trait loci (QTL). Finally, through the joint analysis of network and protein-protein interaction, MdSAUR2, MdSAUR29, MdSAUR60, MdSAUR62, MdSAUR69, MdSAUR71, and MdSAUR84 were screened as the main candidate genes for regulating RGA. This study provides a new insight for further revealing the regulatory mechanism of RGA in apple dwarf rootstocks.

Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning.

Purpose: To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC). Methods: Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis. Results: No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features (p < 0.05). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists. Conclusion: The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.

Clinical utility of cryptococcal antigen detection in transthoracic needle aspirate by lateral flow assay for diagnosing non-HIV pulmonary cryptococcosis: A multicenter retrospective study.

Lateral flow immunoassay (LFA) detection of cryptococcal capsular polysaccharide antigen (CrAg) is reported to be the most rapid and convenient laboratory method for diagnosing cryptococcosis. Its clinical diagnostic use, however, is not well studied. We retrospectively analyzed the data from 97 patients with suspected pulmonary cryptococcosis (PC) at 2 tertiary care centers. CrAg in both serum and lung aspirate specimens were examined by LFA. We divided the patients who were diagnosed with PC into group I, patients positive for CrAg in both the serum and lung aspirate, and group II, patients positive for CrAg in the lung aspirate but not in the serum. We analyzed the differences in imaging distribution, morphological characteristics, and concomitant signs between the 2 groups. Of all 97 patients, 47 were diagnosed with PC. Lung aspirates were positive for CrAg in 46/47 patients with PC (sensitivity 97.9%, specificity 100%, positive predictive value = 100%, negative predictive value = 98%). There were no false positive results in the noncryptococcosis patients, revealing a diagnostic accuracy of 99%. Serum CrAg tests were positive in 36/47 patients with PC (sensitivity 76.6%, specificity 100%, accuracy 88.7%, positive predictive value = 100%, negative predictive value = 82%). Chest imaging data showed a statistically significant greater number of single lesions in group II than in group I (P < .05). More lesions accompanied by halo signs were showed in group I (P < .01), whereas more accompanied by pleural stretch signs were found in group II (P < .01). The LFA-positive rate of CrAg in lung aspirate samples was higher than that of the serum samples, especially in patients with single pulmonary lesion or in those accompanied by pleural stretch. The direct measurement of CrAg in lung aspirate is a rapid, useful alternative diagnostic method for PC confirmation.

Role of Mitophagy-based TLR9 Signal Pathway in Neonatal Ventilator-induced Lung Injury.

The study focused on the role of mitophagy in neonatal ventilator-induced lung injury (VILI). Immunoassays were used to study the TLR9 signaling pathway of neonatal VILI, expected to provide a feasible solution for neonatal VILI. The mice were randomly divided into four groups, group A: spontaneous breathing group; group B: normal tidal volume (VT) group (VT=9mL/kg); group C: high VT group (VT=39mL/kg); and group D: ODN2088 (400µg/ Only) intervention + high VT group. The four groups were compared for the expression of inflammatory factors. It was found that as the culture time increased, the expression of TLR9, MyD88, and NF-κBp65 in the lung tissue of the large VT group was significantly higher than those in the spontaneous breathing group and normal VT group, and the differences were statistically significant; and TLR9 inhibitors could activate the TLR9-MyD88 signaling pathway to up-regulate the expression of NF-κB, mediating the release of inflammatory factors to cause VILI.

iTRAQ-based proteomic analysis of heteromorphic leaves reveals eco-adaptability of Populus euphratica Oliv.

BACKGROUND: Heterophylly is regard as adaptation to different environments in plant, and Populus euphratica is an important heterophyllous woody plant. However, information on its molecular mechanism in eco-adaptability remains obscure. RESULTS: In this research, proteins were identified by isobaric tags for relative and absolute quantitation (iTRAQ) technology in lanceolate, ovate, and dentate broad-ovate leaves from adult P. euphratica trees, respectively. Besides, chlorophyll content, net photosynthetic rate, stomatal conductance, transpiration rate and peroxidase activity in these heteromorphic leaves were investigated. A total number of 2,689 proteins were detected in the heteromorphic leaves, of which 56, 73, and 222 differential abundance proteins (DAPs) were determined in ovate/lanceolate, dentate broad-ovate/lanceolate, and dentate broad-ovate/ovate comparison groups. Bioinformatics analysis suggested these altered proteins related to photosynthesis, stress tolerance, respiration and primary metabolism accumulated in dentate broad-ovate and ovate leaves, which were consistent with the results of physiological parameters and Real-time Quantitative PCR experiments. CONCLUSION: This research demonstrated the mechanism of the differential abundance proteins in providing an optimal strategy of resource utilization and survival for P. euphratica, that could offer clues for further investigations into eco-adaptability of heterophyllous woody plants.

Curv-Net: Curvilinear structure segmentation network based on selective kernel and multi-Bi-ConvLSTM.

PURPOSE: Accurately segmenting curvilinear structures, for example, retinal blood vessels or nerve fibers, in the medical image is essential to the clinical diagnosis of many diseases. Recently, deep learning has become a popular technology to deal with the image segmentation task, and it has obtained remarkable achievement. However, the existing methods still have many problems when segmenting the curvilinear structures in medical images, such as losing the details of curvilinear structures, producing many false-positive segmentation results. To mitigate these problems, we propose a novel end-to-end curvilinear structure segmentation network called Curv-Net. METHODS: Curv-Net is an effective encoder-decoder architecture constructed based on selective kernel (SK) and multibidirectional convolutional LSTM (multi-Bi-ConvLSTM). To be specific, we first employ the SK module in the convolutional layer to adaptively extract the multi-scale features of the input image, and then we design a multi-Bi-ConvLSTM as the skip concatenation to fuse the information learned in the same stage and propagate the feature information from the deep stages to the shallow stages, which can enable the feature captured by Curv-Net to contain more detail information and high-level semantic information simultaneously to improve the segmentation performance. RESULTS: The effectiveness and reliability of our proposed Curv-Net are verified on three public datasets: two color fundus datasets (DRIVE and CHASE_DB1) and one corneal nerve fiber dataset (CCM-2). We calculate the accuracy (ACC), sensitivity (SE), specificity (SP), Dice similarity coefficient (Dice), and area under the receiver (AUC) for the DRIVE and CHASE_DB1 datasets. The ACC, SE, SP, Dice, and AUC of the DRIVE dataset are 0.9629, 0.8175, 0.9858, 0.8352, and 0.9810, respectively. For the CHASE_DB1 dataset, the values are 0.9810, 0.8564, 0.9899, 0.8143, and 0.9832, respectively. To validate the corneal nerve fiber segmentation performance of the proposed Curv-Net, we test it on the CCM-2 dataset and calculate Dice, SE, and false discovery rate (FDR) metrics. The Dice, SE, and FDR achieved by Curv-Net are 0.8114 ± 0.0062, 0.8903 ± 0.0113, and 0.2547 ± 0.0104, respectively. CONCLUSIONS: Curv-Net is evaluated on three public datasets. Extensive experimental results demonstrate that Curv-Net outperforms the other superior curvilinear structure segmentation methods.

Screening of Differentially Expressed Genes and Localization Analysis of Female Gametophyte at the Free Nuclear Mitosis Stage in Pinus tabuliformis Carr.

Female sterility is a common phenomenon in the plant world, and systematic research has not been carried out in gymnosperms. In this study, the ovules of No. 28 sterile line and No. 15 fertile line Pinus tabuliformis were used as materials, and a total of 18 cDNA libraries were sequenced by the HiSeqTM 4000 platform to analyze the differentially expressed genes (DEGs) and simple sequence repeats (SSRs) between the two lines. In addition, this study further analyzed the DEGs involved in the signal transduction of plant hormones, revealing that the signal pathways related to auxin, cytokinin, and gibberellin were blocked in the sterile ovule. Additionally, real-time fluorescent quantitative PCR verified that the expression trend of DEGs related to plant hormones was consistent with the results of high-throughput sequencing. Frozen sections and fluorescence in situ hybridization (FISH) were used to study the temporal and spatial expression patterns of PtRab in the ovules of P. tabuliformis. It was found that PtRab was significantly expressed in female gametophytes and rarely expressed in the surrounding diploid tissues. This study further explained the molecular regulation mechanism of female sterility in P. tabuliformis, preliminarily mining the key factors of ovule abortion in gymnosperms at the transcriptional level.

Combined Transcriptome Analysis Reveals the Ovule Abortion Regulatory Mechanisms in the Female Sterile Line of Pinus tabuliformis Carr.

Ovule abortion is a common phenomenon in plants that has an impact on seed production. Previous studies of ovule and female gametophyte (FG) development have mainly focused on angiosperms, especially in Arabidopsis thaliana. However, because it is difficult to acquire information about ovule development in gymnosperms, this remains unclear. Here, we investigated the transcriptomic data of natural ovule abortion mutants (female sterile line, STE) and the wild type (female fertile line, FER) of Pinus tabuliformis Carr. to evaluate the mechanism of ovule abortion during the process of free nuclear mitosis (FNM). Using single-molecule real-time (SMRT) sequencing and next-generation sequencing (NGS), 18 cDNA libraries via Illumina and two normalized libraries via PacBio, with a total of almost 400,000 reads, were obtained. Our analysis showed that the numbers of isoforms and alternative splicing (AS) patterns were significantly variable between FER and STE. The functional annotation results demonstrate that genes involved in the auxin response, energy metabolism, signal transduction, cell division, and stress response were differentially expressed in different lines. In particular, AUX/IAA, ARF2, SUS, and CYCB had significantly lower expression in STE, showing that auxin might be insufficient in STE, thus hindering nuclear division and influencing metabolism. Apoptosis in STE might also have affected the expression levels of these genes. To confirm the transcriptomic analysis results, nine pairs were confirmed by quantitative real-time PCR. Taken together, these results provide new insights into ovule abortion in gymnosperms and further reveal the regulatory mechanisms of ovule development.

An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network.

Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.

Intricate genetic variation networks control the adventitious root growth angle in apple.

BACKGROUND: The root growth angle (RGA) typically determines plant rooting depth, which is significant for plant anchorage and abiotic stress tolerance. Several quantitative trait loci (QTLs) for RGA have been identified in crops. However, the underlying mechanisms of the RGA remain poorly understood, especially in apple rootstocks. The objective of this study was to identify QTLs, validate genetic variation networks, and develop molecular markers for the RGA in apple rootstock. RESULTS: Bulked segregant analysis by sequencing (BSA-seq) identified 25 QTLs for RGA using 1955 hybrids of the apple rootstock cultivars 'Baleng Crab' (Malus robusta Rehd., large RGA) and 'M9' (M. pumila Mill., small RGA). With RNA sequencing (RNA-seq) and parental resequencing, six major functional genes were identified and constituted two genetic variation networks for the RGA. Two single nucleotide polymorphisms (SNPs) of the MdLAZY1 promoter damaged the binding sites of MdDREB2A and MdHSFB3, while one SNP of MdDREB2A and MdIAA1 affected the interactions of MdDREB2A/MdHSFB3 and MdIAA1/MdLAZY1, respectively. A SNP within the MdNPR5 promoter damaged the interaction between MdNPR5 and MdLBD41, while one SNP of MdLBD41 interrupted the MdLBD41/MdbHLH48 interaction that affected the binding ability of MdLBD41 on the MdNPR5 promoter. Twenty six SNP markers were designed on candidate genes in each QTL interval, and the marker effects varied from 0.22°-26.11°. CONCLUSIONS: Six diagnostic markers, SNP592, G122, b13, Z312, S1272, and S1288, were used to identify two intricate genetic variation networks that control the RGA and may provide new insights into the accuracy of the molecular markers. The QTLs and SNP markers can potentially be used to select deep-rooted apple rootstocks.

Spatiotemporal Interaction Residual Networks with Pseudo3D for Video Action Recognition.

Action recognition is a significant and challenging topic in the field of sensor and computer vision. Two-stream convolutional neural networks (CNNs) and 3D CNNs are two mainstream deep learning architectures for video action recognition. To combine them into one framework to further improve performance, we proposed a novel deep network, named the spatiotemporal interaction residual network with pseudo3D (STINP). The STINP possesses three advantages. First, the STINP consists of two branches constructed based on residual networks (ResNets) to simultaneously learn the spatial and temporal information of the video. Second, the STINP integrates the pseudo3D block into residual units for building the spatial branch, which ensures that the spatial branch can not only learn the appearance feature of the objects and scene in the video, but also capture the potential interaction information among the consecutive frames. Finally, the STINP adopts a simple but effective multiplication operation to fuse the spatial branch and temporal branch, which guarantees that the learned spatial and temporal representation can interact with each other during the entire process of training the STINP. Experiments were implemented on two classic action recognition datasets, UCF101 and HMDB51. The experimental results show that our proposed STINP can provide better performance for video recognition than other state-of-the-art algorithms.

Salicylic acid-primed defence response in octoploid strawberry 'Benihoppe' leaves induces resistance against Podosphaera aphanis through enhanced accumulation of proanthocyanidins and upregulation of pathogenesis-related genes.

BACKGROUND: Podosphaera aphanis, a predominately biotrophic fungal pathogen, causes significant yield losses of strawberry. China is the largest strawberry producer in the world, and selecting for powdery mildew-resistant cultivars is desirable. However, the resistance mechanism against P. aphanis in the octoploid strawberry remains unclear. RESULTS: To understand possible mechanisms of disease resistance, we inoculated strawberry leaves with P. aphanis, and examined the expression profiles of candidate genes and the biochemical phenotypes in strawberry leaves of two groups. The unigenes obtained from ddH2O- and SA-pretreated leaves resulted in a total of 48,020 and 45,896 genes, respectively. KEGG enrichment showed that phenylpropanoid biosynthesis and plant hormone signal transduction pathways were enriched to a noticeable extent. DEG analysis showed that key TFs genes associated with the SA signaling pathway could play important role in the strawberry-P. aphanis interaction. In particular, FaWRKY70, FaJAZ1 and FaMYC2-like, involved in regulating the antagonistic effect of SA and JA signaling pathway, leading to increased expression of SA-responsive genes (in particular PR1, PR2, PR3, and PR5) compared to a decline in expression of JA-responsive genes (FaJAR1, FaAOS, and FaLOX2). Furthermore, SA pretreatment induced accumulation of PAs by activating the MBW complex and inhibit powdery mildew growth. CONCLUSIONS: This study describes the role of the proanthocyanidins (PAs), pathogenesis-related (PR) genes, SA, and transcription factors in regulatory model against P. aphanis, which coincided with an early activation of defense, leading to the accumulation of PAs and the PR proteins.

Integrative transcriptomics and proteomics analysis constructs a new molecular model for ovule abortion in the female-sterile line of Pinus tabuliformis Carr.

Ovule development is critical to plant reproduction and free nuclear mitosis of megagametophyte (FNMM) is vital for ovule development. However, most results of ovule development were based on the studies in angiosperms, and its molecular regulation remained largely unknown in gymnosperms, particularly, during FNMM. In this context, we studied the genome-wide difference between sterile line (SL) and fertile line (FL) ovules using transcriptomics and proteomics approaches in Pinus tabuliformis Carr. Comparative analyses revealed that genes involved in DNA replication, DNA damage repair, Cell cycle, Apoptosis and Energy metabolism were highlighted. Further results showed the low expressions of MCM 2-7, RRM1, etc. perhaps led to abnormal DNA replication and damage repair, and the significantly different expressions of PARP2, CCs1, CCs3, etc. implied that the accumulated DNA double-stranded breaks were failed to be repaired and the cell cycle was arrested at G2/M in SL ovules, potentially resulting in the occurrence of apoptosis. Moreover, the deficiency of ETF-QO might hinder FNMM. Consequently, FNMM stopped and ovule aborted in SL ovules. Our results suggested a selective regulatory mechanism led to FNMM half-stop and ovule abortion in P. tabuliformis and these insights could be exploited to investigate the molecular regulations of ovule development in woody gymnosperms.

The Effects of Electroacupuncture in a Rat Model of Cerebral Ischemia-Reperfusion Injury Following Middle Cerebral Artery Occlusion Involves MicroRNA-223 and the PTEN Signaling Pathway.

BACKGROUND In China, electroacupuncture (EA) is used to treat the symptoms of ischemic stroke. However, the mechanisms involved in the effects of EA in cerebral ischemia remain to be investigated. This study aimed to investigate the molecular mechanism underlying the effects of EA in a rat model of cerebral ischemia-reperfusion injury (CIRI) induced by middle cerebral artery occlusion (MCAO). MATERIAL AND METHODS Seventy-five male Sprague-Dawley rats were divided into five groups: the sham group (with sham surgery), the model group (the MCAO model), the EA group (treated with EA), the EA control group, and the EA+antagomir-223-3p group. Rats in the model of CIRI underwent MCAO for 90 minutes. EA was performed on the second postoperative day and was performed at the Waiguan (TE5) and Zusanli (ST36) acupoints. The rat brains were evaluated for structural and molecular markers. RESULTS EA treatment significantly upregulated the expression of microRNA-223 (miR-223), NESTIN, and NOTCH1, and downregulated the expression of PTEN in the subventricular zone (SVZ) and hippocampus. The luciferase reporter assay supported that PTEN was a direct target of miR-223, and antagomiR-223-3p reversed the effects of EA and reduced the increase in NESTIN and inhibition of PTEN expression associated with EA treatment. There was a negative correlation between PTEN expression and the number of neural stem cells (NSCs). CONCLUSIONS In a rat model of CIRI following MCAO, EA activated the NOTCH pathway, promoted the expression of miR-223, increased the number of NSCs, and reduced the expression of PTEN.

Association between Smoking and Liver Fibrosis among Patients with Nonalcoholic Fatty Liver Disease.

Objective: We aimed at analyzing the role of smoking in hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD) and at exploring the related risk factors. Methods: This was a cross-sectional study that included a total of 225 patients with NAFLD. Among them, 127 were nonsmokers and 98 were smokers. Liver significant fibrosis was diagnosed when the liver stiffness (LS) value was higher than 7.4 kPa. The diagnostic criterion for NAFLD was a controlled attenuation parameter (CAP) value of >238 dB/m. The CAP and LS values were measured using FibroScan. Results: FibroScan showed that the LS value in the smokers was significantly higher than that in the nonsmokers (10.12 ± 10.38 kPa vs. 7.26 ± 6.42 kPa, P=0.013). The proportions of patients with liver significant fibrosis and advanced liver fibrosis among the smokers were significantly higher than those among the nonsmokers (P=0.046). Univariate analysis showed that age, weight, high AST level, low PLT level, and smoking were the risk factors associated with liver fibrosis in the smokers with NAFLD while multivariate analysis showed that age (OR = 1.029, P=0.021), high AST level (OR = 1.0121, P=0.025), and smoking (OR = 1.294, P=0.015) were the independent risk factors associated with liver fibrosis in the patients with NAFLD. Moreover, high AST level (OR = 1.040, P=0.029), smoking index (OR = 1.220, P=0.019), and diabetes mellitus (OR = 1.054, P=0.032) were the independent risk factors for liver fibrosis among the smokers with NAFLD. Conclusion: This study showed that smoking was closely associated with liver fibrosis among the patients with NAFLD. For patients with NAFLD who smoke, priority screening and timely intervention should be provided if they are at risk of liver fibrosis.