Clinical risk factors for preterm birth and evaluating maternal psychology in the postpartum period.

BACKGROUND: Although the specific pathogenesis of preterm birth (PTB) has not been thoroughly clarified, it is known to be related to various factors, such as pregnancy complications, maternal socioeconomic factors, lifestyle habits, reproductive history, environmental and psychological factors, prenatal care, and nutritional status. PTB has serious implications for newborns and families and is associated with high mortality and complications. Therefore, the prediction of PTB risk can facilitate early intervention and reduce its resultant adverse consequences. AIM: To analyze the risk factors for PTB to establish a PTB risk prediction model and to assess postpartum anxiety and depression in mothers. METHODS: A retrospective analysis of 648 consecutive parturients who delivered at Shenzhen Bao'an District Songgang People's Hospital between January 2019 and January 2022 was performed. According to the diagnostic criteria for premature infants, the parturients were divided into a PTB group (n = 60) and a full-term (FT) group (n = 588). Puerperae were assessed by the Self-rating Anxiety Scale (SAS) and Self-rating Depression Scale (SDS), based on which the mothers with anxiety and depression symptoms were screened for further analysis. The factors affecting PTB were analyzed by univariate analysis, and the related risk factors were identified by logistic regression. RESULTS: According to univariate analysis, the PTB group was older than the FT group, with a smaller weight change and greater proportions of women who underwent artificial insemination and had gestational diabetes mellitus (P < 0.05). In addition, greater proportions of women with reproductive tract infections and greater white blood cell (WBC) counts (P < 0.05), shorter cervical lengths in the second trimester and lower neutrophil percentages (P < 0.001) were detected in the PTB group than in the FT group. The PTB group exhibited higher postpartum SAS and SDS scores than did the FT group (P < 0.0001), with a higher number of mothers experiencing anxiety and depression (P < 0.001). Multivariate logistic regression analysis revealed that a greater maternal weight change, the presence of gestational diabetes mellitus, a shorter cervical length in the second trimester, a greater WBC count, and the presence of maternal anxiety and depression were risk factors for PTB (P < 0.01). Moreover, the risk score of the FT group was lower than that of the PTB group, and the area under the curve of the risk score for predicting PTB was greater than 0.9. CONCLUSION: This study highlights the complex interplay between postpartum anxiety and PTB, where maternal anxiety may be a potential risk factor for PTB, with PTB potentially increasing the incidence of postpartum anxiety in mothers. In addition, a greater maternal weight change, the presence of gestational diabetes mellitus, a shorter cervical length, a greater WBC count, and postpartum anxiety and depression were identified as risk factors for PTB.

H2B oncohistones cause homologous recombination defect and genomic instability through reducing H2B monoubiquitination in Schizosaccharomyces pombe.

Canonical oncohistones are histone H3 mutations in the N-terminal tail associated with tumors and affect gene expression by altering H3 post-translational modifications (PTMs) and the epigenetic landscape. Noncanonical oncohistone mutations occur in both tails and globular domains of all four core histones and alter gene expression by perturbing chromatin remodeling. However, the effects and mechanisms of noncanonical oncohistones remain largely unknown. Here we characterized 16 noncanonical H2B oncohistones in the fission yeast Schizosaccharomyces pombe. We found that seven of them exhibited temperature sensitivities and 11 exhibited genotoxic sensitivities. A detailed study of two of these onco-mutants H2BG52D and H2BP102L revealed that they were defective in homologous recombination (HR) repair with compromised histone eviction and Rad51 recruitment. Interestingly, their genotoxic sensitivities and HR defects were rescued by the inactivation of the H2BK119 deubiquitination function of Ubp8 in the Spt-Ada-Gcn5-Acetyltransferase (SAGA) complex. The levels of H2BK119 monoubiquitination (H2Bub) in the H2BG52D and H2BP102L mutants are reduced in global genome and at local DNA break sites presumably due to enhanced recruitment of Ubp8 onto nucleosomes and are recovered upon loss of H2B deubiquitination function of the SAGA complex. Moreover, H2BG52D and H2BP102L heterozygotes exhibit genotoxic sensitivities and reduced H2Bub in cis. We therefore conclude that H2BG52D and H2BP102L oncohistones affect HR repair and genome stability via the reduction of H2Bub and propose that other noncanonical oncohistones may also affect histone PTMs to cause diseases.

Tea quality estimation based on multi-source information from leaf and soil using machine learning algorithm.

Mineral nutrients play a significant role in influencing the quality of tea. In order to detect the quantitative relationships between tea quality and mineral elements from the soil and tea plant, samples of soil and tea leaves from 'Baiyeyihao' and 'Huangjinya' cultivars were collected from 160 tea plantations, and these were used to determine 16 types of soil mineral elements, 16 leaf nutrient elements, and 10 key tea quality compositions. Three predictive models including linear regression, multiple linear regression (MLR) and random forest (RF) were applied to predict the main constituents of tea quality. The usage of mineral elements in the soil and tea leaves improved the estimation accuracy of tea quality compositions, the RF performed best for EGCG (R2 = 0.67-0.77), amino acid (R2 = 0.61-0.88), tea polyphenols (R2 = 0.68-0.77) and caffeine (R2 = 0.59-0.68), while the MLR performed well for predicting the soluble sugars (R2 = 0.54-0.84). The multi-source information demonstrated a superior accuracy in predicting the biochemical components of tea when compared to individual mineral elements.

Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 1,986 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that: 1) challenge metric was permissive with the false positive predictions; and 2) size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download.

ICI 182,780 Attenuates Selective Upregulation of Uterine Artery Cystathionine ß-Synthase Expression in Rat Pregnancy.

Endogenous hydrogen sulfide (H2S) produced by cystathionine ß-synthase (CBS) and cystathionine-γ lyase (CSE) has emerged as a novel uterine vasodilator contributing to pregnancy-associated increases in uterine blood flow, which safeguard pregnancy health. Uterine artery (UA) H2S production is stimulated via exogenous estrogen replacement and is associated with elevated endogenous estrogens during pregnancy through the selective upregulation of CBS without altering CSE. However, how endogenous estrogens regulate uterine artery CBS expression in pregnancy is unknown. This study was conducted to test a hypothesis that endogenous estrogens selectively stimulate UA CBS expression via specific estrogen receptors (ER). Treatment with E2ß (0.01 to 100 nM) stimulated CBS but not CSE mRNA in organ cultures of fresh UA rings from both NP and P (gestational day 20, GD20) rats, with greater responses to all doses of E2ß tested in P vs. NP UA. ER antagonist ICI 182,780 (ICI, 1 µM) completely attenuated E2ß-stimulated CBS mRNA in both NP and P rat UA. Subcutaneous injection with ICI 182,780 (0.3 mg/rat) of GD19 P rats for 24 h significantly inhibited UA CBS but not mRNA expression, consistent with reduced endothelial and smooth muscle cell CBS (but not CSE) protein. ICI did not alter mesenteric and renal artery CBS and CSE mRNA. In addition, ICI decreased endothelial nitric oxide synthase mRNA in UA but not in mesenteric or renal arteries. Thus, pregnancy-augmented UA CBS/H2S production is mediated by the actions of endogenous estrogens via specific ER in pregnant rats.

Myocardial Perfusion in ST-Segment Elevation Myocardial Infarction Patients After Percutaneous Coronary Intervention: Influencing Factors and Intervention Strategies.

Aim We aim to explore the factors influencing myocardial perfusion in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PPCI) and evaluate the effects of different intervention strategies on myocardial perfusion improvement. Methods A retrospective analysis was conducted on 300 patients with STEMI who underwent primary percutaneous coronary intervention (PPCI) at our hospital between January 2020 and December 2022. Based on post-procedural coronary angiography results using the thrombolysis in myocardial infarction (TIMI) blood flow grade and myocardial blush grade (MBG), patients were categorized into two groups: the normal perfusion group (TIMI grade 3 or MBG 2-3, n=180) and the impaired perfusion group (TIMI grades 0-2 or MBG 0-1, n=120). The impaired perfusion group was further divided using a random number table into the thrombus aspiration-only group (control group, n=60) and the thrombus aspiration combined with nicorandil group (nicorandil group, n=60). A 1:1 propensity score matching method was employed to adjust for baseline characteristics between the groups. Clinical characteristics, hematological parameters, coronary lesion features, and percutaneous coronary intervention (PCI) technical parameters were compared between the matched groups. Additionally, a multivariate logistic regression analysis was performed to identify independent risk factors influencing myocardial perfusion. Furthermore, the post-procedural myocardial perfusion, cardiac function, and clinical prognosis were compared between the control and nicorandil groups. Results After matching, the baseline characteristics of the two groups were compared. The impaired perfusion group had older age, higher proportion of male patients, higher rates of diabetes and hypertension, longer time from symptom onset to balloon dilation, higher peak cardiac troponin I (cTnI) levels, higher proportion of left main or multivessel involvement, heavier coronary lesion burden, and lower balloon inflation pressure (P<0.05). Multivariate logistic regression analysis revealed that age of ≥65 years (odds ratio {OR}=2.34, 95% confidence interval {CI}=1.23-4.46, P<0.01), time from symptom onset to balloon dilation of ≥6 hours (OR=3.12, 95% CI=1.67-5.83, P<0.01), peak cTnI level of ≥100 ng/mL (OR=4.27, 95% CI=2.18-8.36, P<0.01), left main or multivessel involvement (OR=2.86, 95% CI=1.51-5.41, P<0.01), and balloon inflation pressure of <8 atm (OR=3.45, 95% CI=1.79-6.65, P<0.01) were independent risk factors affecting myocardial perfusion. In the intervention analysis, the nicorandil group showed superior post-procedural TIMI blood flow grade, MBG, left ventricular ejection fraction (LVEF), and New York Heart Association (NYHA) functional classification compared to the control group (P<0.05). During a six-month follow-up, the nicorandil group had a lower incidence of major adverse cardiovascular events (MACE) compared to the control group (P<0.05). Conclusion Age, time from symptom onset to balloon dilation, peak cTnI level, extent of coronary artery lesions, and balloon inflation pressure were identified as independent risk factors affecting myocardial perfusion in STEMI patients after PCI. Compared to simple thrombus aspiration, thrombus aspiration combined with nicorandil demonstrated better improvement in myocardial perfusion, cardiac function, and clinical outcomes for patients with impaired perfusion.

A pathogenesis-related protein 1 of Cucurbita moschata responds to powdery mildew infection.

Pumpkin (Cucurbita moschata Duch.) productivity is severely hindered by powdery mildew (PM) worldwide. The causative agent of pumpkin PM is Podosphaera xanthii, a biotrophic fungus. Pathogenesis-related protein 1 (PR1) homolog was previously identified from transcriptomic analysis of a PM-resistant pumpkin. Here, we investigated the effects of CmPR1 gene from pumpkin for resistance to PM. Subcellular localization assay revealed that CmPR1 is a cytoplasmic protein in plants. The expression of CmPR1 gene was strongly induced by P. xanthii inoculation at 48 h and exogenous ethylene (ET), jasmonic acid (JA) and NaCl treatments, but repressed by H2O2 and salicylic acid (SA) treatments. Visual disease symptoms, histological observations of fungal growth and host cell death, and accumulation of H2O2 in transgenic tobacco plants indicated that CmPR1 overexpression significantly enhanced the resistance to Golovinomyces cichoracearum compared to wild type plants during PM pathogens infection, possibly due to inducing cell death and H2O2 accumulation near infected sites. The expression of PR1a was significantly induced in transgenic tobacco plants in response to G. cichoracearum, suggesting that CmPR1 overexpression positively modulates the resistance to PM via the SA signaling pathway. These findings indicate that CmPR1 is a defense response gene in C. moschata and can be exploited to develop disease-resistant crop varieties.

Context-Aware Proposal-Boundary Network With Structural Consistency for Audiovisual Event Localization.

Audiovisual event localization aims to localize the event that is both visible and audible in a video. Previous works focus on segment-level audio and visual feature sequence encoding and neglect the event proposals and boundaries, which are crucial for this task. The event proposal features provide event internal consistency between several consecutive segments constructing one proposal, while the event boundary features offer event boundary consistency to make segments located at boundaries be aware of the event occurrence. In this article, we explore the proposal-level feature encoding and propose a novel context-aware proposal-boundary (CAPB) network to address audiovisual event localization. In particular, we design a local-global context encoder (LGCE) to aggregate local-global temporal context information for visual sequence, audio sequence, event proposals, and event boundaries, respectively. The local context from temporally adjacent segments or proposals contributes to event discrimination, while the global context from the entire video provides semantic guidance of temporal relationship. Furthermore, we enhance the structural consistency between segments by exploiting the above-encoded proposal and boundary representations. CAPB leverages the context information and structural consistency to obtain context-aware event-consistent cross-modal representation for accurate event localization. Extensive experiments conducted on the audiovisual event (AVE) dataset show that our approach outperforms the state-of-the-art methods by clear margins in both supervised event localization and cross-modality localization.

Semantic and Relation Modulation for Audio-Visual Event Localization.

We study the problem of localizing audio-visual events that are both audible and visible in a video. Existing works focus on encoding and aligning audio and visual features at the segment level while neglecting informative correlation between segments of the two modalities and between multi-scale event proposals. We propose a novel Semantic and Relation Modulation Network (SRMN) to learn the above correlation and leverage it to modulate the related auditory, visual, and fused features. In particular, for semantic modulation, we propose intra-modal normalization and cross-modal normalization. The former modulates features of a single modality with the event-relevant semantic guidance of the same modality. The latter modulates features of two modalities by establishing and exploiting the cross-modal relationship. For relation modulation, we propose a multi-scale proposal modulating module and a multi-alignment segment modulating module to introduce multi-scale event proposals and enable dense matching between cross-modal segments, which strengthen correlations between successive segments within one proposal and between all segments. With the features modulated by the correlation information regarding audio-visual events, SRMN performs accurate event localization. Extensive experiments conducted on the public AVE dataset demonstrate that our method outperforms the state-of-the-art methods in both supervised event localization and cross-modality localization tasks.

Graph Representation Learning for Large-Scale Neuronal Morphological Analysis.

The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphological analysis, such as identifying neuron types and large-scale neuron retrieval, all of which require accurate measuring and efficient matching algorithms. Recently, many studies have been conducted to describe neuronal morphologies quantitatively using predefined measurements. However, hand-crafted features are usually inadequate for distinguishing fine-grained differences among massive neurons. In this article, we propose a novel morphology-aware contrastive graph neural network (MACGNN) for unsupervised neuronal morphological representation learning. To improve the retrieval efficiency in large-scale neuronal morphological datasets, we further propose Hash-MACGNN by introducing an improved deep hash algorithm to train the network end-to-end to learn binary hash representations of neurons. We conduct extensive experiments on the largest dataset, NeuroMorpho, which contains more than 100 000 neurons. The experimental results demonstrate the effectiveness and superiority of our MACGNN and Hash-MACGNN for large-scale neuronal morphological analysis.

Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning.

Emerging deep learning-based methods have enabled great progress in automatic neuron segmentation from Electron Microscopy (EM) volumes. However, the success of existing methods is heavily reliant upon a large number of annotations that are often expensive and time-consuming to collect due to dense distributions and complex structures of neurons. If the required quantity of manual annotations for learning cannot be reached, these methods turn out to be fragile. To address this issue, in this article, we propose a two-stage, semi-supervised learning method for neuron segmentation to fully extract useful information from unlabeled data. First, we devise a proxy task to enable network pre-training by reconstructing original volumes from their perturbed counterparts. This pre-training strategy implicitly extracts meaningful information on neuron structures from unlabeled data to facilitate the next stage of learning. Second, we regularize the supervised learning process with the pixel-level prediction consistencies between unlabeled samples and their perturbed counterparts. This improves the generalizability of the learned model to adapt diverse data distributions in EM volumes, especially when the number of labels is limited. Extensive experiments on representative EM datasets demonstrate the superior performance of our reinforced consistency learning compared to supervised learning, i.e., up to 400% gain on the VOI metric with only a few available labels. This is on par with a model trained on ten times the amount of labeled data in a supervised manner. Code is available at https://github.com/weih527/SSNS-Net.

Hypoglycaemic and anti-ageing activities of green alga Ulva lactuca polysaccharide via gut microbiota in ageing-associated diabetic mice.

Ageing-related type 2 diabetes is a significant public health problem. Particularly, the number of cases and fatality rates of ageing-associated diabetes increase with population ageing. This study aimed to investigate the structural characterisation of Ulva lactuca polysaccharide (ULP) and the hypoglycaemic effect on ageing-associated diabetic mice using gut microbiota variation. Sugar residuals analysis showed that the purified ULP (ULP-1) comprised ß-D-Xylp-(1→3)-ß-D-Arap-(1→6)-ß-D-Galp-(1→6)-ß-D-Glcp linked to [→α-L-Rhap-(1→4)-ß-D-GlcpA→]n and α-D-Manp-(1→4)-α-L-Rhap(2SO3-)-(1→2)-α-L-Rhap(4SO3-)-(1→2)-α-L-Arap-(1→2)-α-L-Rhap-(1→ as its side chains at ß-D-Glcp. Moreover, ULP modulated the expression levels of p16Ink4a, MMP2, FoxO1, GLP-1/GLP-1R, STAT3, and GLUT4 to improve the status of ageing and diabetes, which was concurrent with the increased abundance of Dubosiella, Enterococcus, Romboutsia, Bifidobacterium, Kurthia, Clostridium_sensu_stricto_1, Corynebacterium, Faecalibaculum, Aerococcus and Vibrio. Notably, Dubosiella, Romboutsia, Bifidobacterium, Turicibacter and Clostridium_sensu_stricto_1 could serve as important intermediates for delaying ageing and diabetes. Additionally, the ULP-1 structure is strongly binding interaction with the target protein through hydrogen bonding and Van der Waals force, especially for GLP-1 (-10.34 kcal/mol), p16Ink4a (-10.51 kcal/mol) and GLP-1R (-8.57 kcal/mol). Moreover, the average length of the hydrogen bond was observed to be 2.36 MPa, which is smaller than that of the traditional hydrogen bond. Therefore, ULP has the potential to function as a nutraceutical to delay or prevent the development of ageing-related type 2 diabetes.

Offspring production of haploid spermatid-like cells derived from mouse female germline stem cells with chromatin condensation.

BACKGROUND: During male meiosis, the Y chromosome can form perfect pairing with the X chromosome. However, it is unclear whether mammalian Female germline stem cells (FGSCs) without a Y chromosome can transdifferentiate into functional haploid spermatid-like cells (SLCs). RESULTS: We found that spermatogenesis was restarted by transplanting FGSCs into Kitw/wv mutant testes. Complete meiosis and formation of SLCs was induced in vitro by testicular cells of Kitw/wv mutant mice, cytokines and retinoic acid. Healthy offspring were produced by sperm and SLCs derived from the in vivo and in vitro transdifferentiation of FGSCs, respectively. Furthermore, high-throughput chromosome conformation capture sequencing(Hi-C-seq) and "bivalent" (H3K4me3-H3K27me3) micro chromatin immunoprecipitation sequencing (µChIP-seq) experiments showed that stimulated by retinoic acid gene 8 (STRA8)/protamine 1 (PRM1)-positive transdifferentiated germ cells (tGCs) and male germ cells (mGCs) display similar chromatin dynamics and chromatin condensation during in vitro spermatogenesis. CONCLUSION: This study demonstrates that sperm can be produced from FGSCs without a Y chromosome. This suggests a strategy for dairy cattle breeding to produce only female offspring with a high-quality genetic background.

Lipidomics analysis unravels changes from flavor precursors in different processing treatments of purple-leaf tea.

BACKGROUND: Lipids are one of the most important bioactive compounds, affecting the character and quality of tea. However, the contribution of lipids to tea productions is still elusive. Here, we systematically identified the lipid profiles of green, oolong, and black teas in purple-leaf tea (Jinmingzao, JMZ) and green-leaf tea (Huangdan, HD), respectively. RESULTS: The lipids analysis showed regular accumulation in tea products with different manufacturing processes, among which the fatty acids, glycerolipids, glycerophospholipids, and sphingolipids contribute to the quality characteristics of tea products, including typical fatty acyl (FA), monogalactosyldiacylglycerol (MGDG), digalactosyldiacylglycerols (DGDG), and phosphatidylcholine (PC). Compared tea materials with products, levels of fatty acids were up-regulated, while glycerolipids and glycerophospholipids were down-regulated in tea products. FA 18:3, FA 16:0, MGDG 36:6, DGDG 36:6, PC 34:3, and PC 36:6 were the negative contributors to green tea flavor formation of purple-leaf tea. The pathway analysis of significant lipids in materials and products of purple-leaf tea were enriched linolenic acid metabolism pathway and glycerolipid metabolism. CONCLUSION: This study provides insights into the lipid metabolism profiles of different tea leaf colors, and found that fatty acids are essential precursors of black tea flavor formation. © 2021 Society of Chemical Industry.

Weakly supervised learning analysis of Aß plaque distribution in the whole rat brain.

Alzheimer's disease (AD) is a great challenge for the world and hardly to be cured, partly because of the lack of animal models that fully mimic pathological progress. Recently, a rat model exhibiting the most pathological symptoms of AD has been reported. However, high-resolution imaging and accurate quantification of beta-amyloid (Aß) plaques in the whole rat brain have not been fulfilled due to substantial technical challenges. In this paper, a high-efficiency data analysis pipeline is proposed to quantify Aß plaques in whole rat brain through several terabytes of image data acquired by a high-speed volumetric imaging approach we have developed previously. A novel segmentation framework applying a high-performance weakly supervised learning method which can dramatically reduce the human labeling consumption is described in this study. The effectiveness of our segmentation framework is validated with different metrics. The segmented Aß plaques were mapped to a standard rat brain atlas for quantitative analysis of the Aß distribution in each brain area. This pipeline may also be applied to the segmentation and accurate quantification of other non-specific morphology objects.

Contrastive Learning for Mitochondria Segmentation.

Mitochondria segmentation in electron microscopy images is essential in neuroscience. However, due to the image degradation during the imaging process, the large variety of mitochondrial structures, as well as the presence of noise, artifacts and other sub-cellular structures, mitochondria segmentation is very challenging. In this paper, we propose a novel and effective contrastive learning framework to learn a better feature representation from hard examples to improve segmentation. Specifically, we adopt a point sampling strategy to pick out representative pixels from hard examples in the training phase. Based on these sampled pixels, we introduce a pixel-wise label-based contrastive loss which consists of a similarity loss term and a consistency loss term. The similarity term can increase the similarity of pixels from the same class and the separability of pixels from different classes in feature space, while the consistency term is able to enhance the sensitivity of the 3D model to changes in image content from frame to frame. We demonstrate the effectiveness of our method on MitoEM dataset as well as FIB-SEM dataset and show better or on par with state-of-the-art results.

Corrigendum: Expression of Pumpkin CmbHLH87 Gene Improves Powdery Mildew Resistance in Tobacco.

[This corrects the article DOI: 10.3389/fpls.2020.00163.].

Weakly Supervised Neuron Reconstruction From Optical Microscopy Images With Morphological Priors.

Manually labeling neurons from high-resolution but noisy and low-contrast optical microscopy (OM) images is tedious. As a result, the lack of annotated data poses a key challenge when applying deep learning techniques for reconstructing neurons from noisy and low-contrast OM images. While traditional tracing methods provide a possible way to efficiently generate labels for supervised network training, the generated pseudo-labels contain many noisy and incorrect labels, which lead to severe performance degradation. On the other hand, the publicly available dataset, BigNeuron, provides a large number of single 3D neurons that are reconstructed using various imaging paradigms and tracing methods. Though the raw OM images are not fully available for these neurons, they convey essential morphological priors for complex 3D neuron structures. In this paper, we propose a new approach to exploit morphological priors from neurons that have been reconstructed for training a deep neural network to extract neuron signals from OM images. We integrate a deep segmentation network in a generative adversarial network (GAN), expecting the segmentation network to be weakly supervised by pseudo-labels at the pixel level while utilizing the supervision of previously reconstructed neurons at the morphology level. In our morphological-prior-guided neuron reconstruction GAN, named MP-NRGAN, the segmentation network extracts neuron signals from raw images, and the discriminator network encourages the extracted neurons to follow the morphology distribution of reconstructed neurons. Comprehensive experiments on the public VISoR-40 dataset and BigNeuron dataset demonstrate that our proposed MP-NRGAN outperforms state-of-the-art approaches with less training effort.

Chromatin accessibility and translational landscapes of tea plants under chilling stress.

Plants have evolved regulatory mechanisms at multiple levels to regulate gene expression in order to improve their cold adaptability. However, limited information is available regarding the stress response at the chromatin and translational levels. Here, we characterize the chromatin accessibility, transcriptional, and translational landscapes of tea plants in vivo under chilling stress for the first time. Chilling stress significantly affected both the transcription and translation levels as well as the translation efficiency of tea plants. A total of 3010 genes that underwent rapid and independent translation under chilling stress were observed, and they were significantly enriched in the photosynthesis-antenna protein and phenylpropanoid biosynthesis pathways. A set of genes that were significantly responsive to cold at the transcription and translation levels, including four (+)-neomenthol dehydrogenases (MNDs) and two (E)-nerolidol synthases (NESs) arranged in tandem on the chromosomes, were also found. We detected potential upstream open reading frames (uORFs) on 3082 genes and found that tea plants may inhibit the overall expression of genes by enhancing the translation of uORFs under chilling stress. In addition, we identified distal transposase hypersensitive sites (THSs) and proximal THSs and constructed a transcriptional regulatory network for tea plants under chilling stress. We also identified 13 high-confidence transcription factors (TFs) that may play a crucial role in cold regulation. These results provide valuable information regarding the potential transcriptional regulatory network in plants and help to clarify how plants exhibit flexible responses to chilling stress.

Genetic basis of high aroma and stress tolerance in the oolong tea cultivar genome.

Tea plants (Camellia sinensis) are commercially cultivated in >60 countries, and their fresh leaves are processed into tea, which is the most widely consumed beverage in the world. Although several chromosome-level tea plant genomes have been published, they collapsed the two haplotypes and ignored a large number of allelic variations that may underlie important biological functions in this species. Here, we present a phased chromosome-scale assembly for an elite oolong tea cultivar, "Huangdan", that is well known for its high levels of aroma. Based on the two sets of haplotype genome data, we identified numerous genetic variations and a substantial proportion of allelic imbalance related to important traits, including aroma- and stress-related alleles. Comparative genomics revealed extensive structural variations as well as expansion of some gene families, such as terpene synthases (TPSs), that likely contribute to the high-aroma characteristics of the backbone parent, underlying the molecular basis for the biosynthesis of aroma-related chemicals in oolong tea. Our results uncovered the genetic basis of special features of this oolong tea cultivar, providing fundamental genomic resources to study evolution and domestication for the economically important tea crop.