Plastome structure, phylogeny and evolution of plastid genes in Reevesia (Helicteroideae, Malvaceae).

Reevesia is an eastern Asian-eastern North American disjunction genus in the family Malvaceae s.l. and comprises approximately 25 species. The relationships within the genus are not well understood. Here, 15 plastomes representing 12 Reevesia species were compared, with the aim of better understanding the species circumscription and phylogenetic relationships within the genus and among genera in the family Malvaceae s.l. The 11 newly sequenced plastomes range between 161,532 and 161, 945 bp in length. The genomes contain 114 unique genes, 18 of which are duplicated in the inverted repeats (IRs). Gene content of these plastomes is nearly identical. All the protein-coding genes are under purifying selection in the Reevesia plastomes compared. The top ten hypervariable regions, SSRs, and the long repeats identified are potential molecular markers for future population genetic and phylogenetic studies. Phylogenetic analysis based on the whole plastomes confirmed the monophyly of Reevesia and a close relationship with Durio (traditional Bombacaceae) in subfamily Helicteroideae, but not with the morphologically similar genera Pterospermum and Sterculia (both of traditional Sterculiaceae). Phylogenetic relationships within Reevesia suggested that two species, R. pubescens and R. thyrsoidea, as newly defined, are not monophyletic. Six taxa, R. membranacea, R. xuefengensis, R. botingensis, R. lofouensis, R. longipetiolata and R. pycnantha, are suggested to be recognized.

Error detection for radiotherapy planning validation based on deep learning networks.

BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. PURPOSE: The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. METHOD: We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. RESULTS: The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. CONCLUSION: When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Double counting heart rate - Littmann sign in electrocardiogram.

This report highlights necessity of correctly and quickly identifying Littmann sign. Littmann sign is not common in clinical practice, which is easily overlooked by most physicians, leading to delays in the treatment of hyperkalemia. A 68 year old patient with hyperkalemia was found to have inconsistent heart rate displayed on electrocardiogram monitoring with cardiac auscultation and synchronous electrocardiogram in the early stages of onset. Hyperkalemia was highly suspected by the Littmann sign. After completing arterial blood gas analysis, hyperkalemia was identified and active potassium lowering treatment was immediately initiated. The Littmann syndrome disappeared, and the patient eventually recovered.

PND-Net: Physics-inspired Non-local Dual-domain Network for Metal Artifact Reduction.

Metal artifacts caused by the presence of metallic implants tremendously degrade the quality of reconstructed computed tomography (CT) images and therefore affect the clinical diagnosis or reduce the accuracy of organ delineation and dose calculation in radiotherapy. Although various deep learning methods have been proposed for metal artifact reduction (MAR), most of them aim to restore the corrupted sinogram within the metal trace, which removes beam hardening artifacts but ignores other components of metal artifacts. In this paper, based on the physical property of metal artifacts which is verified via Monte Carlo (MC) simulation, we propose a novel physics-inspired non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component and develop an image restoration network (IR-Net) to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the data fidelity of anatomical structures in the image domain and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. NSD-Net, IR-Net, and F-Net are jointly trained so that they can benefit from one another. Extensive experiments on simulation and clinical data demonstrate that our method outperforms state-of-the-art MAR methods.

Analysis of false reasons based on the artificial intelligence RRCART model to identify frozen sections of lymph nodes in breast cancer.

BACKGROUND: Breast cancer is the most common malignant tumor in the world. Intraoperative frozen section of sentinel lymph nodes is an important basis for determining whether axillary lymph node dissection is required for breast cancer surgery. We propose an RRCART model based on a deep-learning network to identify metastases in 2362 frozen sections and count the wrongly identified sections and the associated reasons. The purpose is to summarize the factors that affect the accuracy of the artificial intelligence model and propose corresponding solutions. METHODS: We took the pathological diagnosis of senior pathologists as the gold standard and identified errors. The pathologists and artificial intelligence engineers jointly read the images and heatmaps to determine the locations of the identified errors on sections, and the pathologists found the reasons (false reasons) for the errors. Through NVivo 12 Plus, qualitative analysis of word frequency analysis and nodal analysis was performed on the error reasons, and the top-down error reason framework of "artificial intelligence RRCART model to identify frozen sections of breast cancer lymph nodes" was constructed based on the importance of false reasons. RESULTS: There were 101 incorrectly identified sections in 2362 slides, including 42 false negatives and 59 false positives. Through NVivo 12 Plus software, the error causes were node-coded, and finally, 2 parent nodes (high-frequency error, low-frequency error) and 5 child nodes (section quality, normal lymph node structure, secondary reaction of lymph nodes, micrometastasis, and special growth pattern of tumor) were obtained; among them, the error of highest frequency was that caused by normal lymph node structure, with a total of 45 cases (44.55%), followed by micrometastasis, which occurred in 30 cases (29.70%). CONCLUSIONS: The causes of identification errors in examination of sentinel lymph node frozen sections by artificial intelligence are, in descending order of influence, normal lymph node structure, micrometastases, section quality, special tumor growth patterns and secondary lymph node reactions. In this study, by constructing an artificial intelligence model to identify the error causes of frozen sections of lymph nodes in breast cancer and by analyzing the model in detail, we found that poor quality of slices was the preproblem of many identification errors, which can lead to other errors, such as unclear recognition of lymph node structure by computer. Therefore, we believe that the process of artificial intelligence pathological diagnosis should be optimized, and the quality control of the pathological sections included in the artificial intelligence reading should be carried out first to exclude the influence of poor section quality on the computer model. For cases of micrometastasis, we suggest that by differentiating slices into high- and low-confidence groups, low-confidence micrometastatic slices can be separated for manual identification. The normal lymph node structure can be improved by adding samples and training the model in a targeted manner.

Adaptive scatter kernel deconvolution modeling for cone-beam CT scatter correction via deep reinforcement learning.

BACKGROUND: Scattering photons can seriously contaminate cone-beam CT (CBCT) image quality with severe artifacts and substantial degradation of CT value accuracy, which is a major concern limiting the widespread application of CBCT in the medical field. The scatter kernel deconvolution (SKD) method commonly used in clinic requires a Monte Carlo (MC) simulation to determine numerous quality-related kernel parameters, and it cannot realize intelligent scatter kernel parameter optimization, causing limited accuracy of scatter estimation. PURPOSE: Aiming at improving the scatter estimation accuracy of the SKD algorithm, an intelligent scatter correction framework integrating the SKD with deep reinforcement learning (DRL) scheme is proposed. METHODS: Our method firstly builds a scatter kernel model to iteratively convolve with raw projections, and then the deep Q-network of the DRL scheme is introduced to intelligently interact with the scatter kernel to achieve a projection adaptive parameter optimization. The potential of the proposed framework is demonstrated on CBCT head and pelvis simulation data and experimental CBCT measurement data. Furthermore, we have implemented the U-net based scatter estimation approach for comparison. RESULTS: The simulation study demonstrates that the mean absolute percentage error (MAPE) of the proposed method is less than 9.72% and the peak signal-to-noise ratio (PSNR) is higher than 23.90 dB, while for the conventional SKD algorithm, the minimum MAPE is 17.92% and the maximum PSNR is 19.32 dB. In the measurement study, we adopt a hardware-based beam stop array algorithm to obtain the scatter-free projections as a comparison baseline, and our method can achieve superior performance with MAPE < 17.79% and PSNR > 16.34 dB. CONCLUSIONS: In this paper, we propose an intelligent scatter correction framework that integrates the physical scatter kernel model with DRL algorithm, which has the potential to improve the accuracy of the clinical scatter correction method to obtain better CBCT imaging quality.

Bcl-xL mediates interferon-beta secretion by protease-activated receptor 2 deficiency through the mitochondrial permeability transition pore in colorectal cancer metastasis.

Cellular plasticity and immune escape are synergistic drivers of tumor colonization in metastatic organs. Activation of protease-activated receptor 2 (PAR2) signaling promotes metastasis of colorectal carcinoma (CRC). The role of PAR2 in regulating the immune microenvironment and cancer progression remains unclear. We demonstrated that the regulation of liver metastasis by PAR2 requires a competent immune system. PAR2 knockdown enhanced liver infiltration of activated CD8+ T cells prior to metastatic foci formation in an interferon receptor-dependent manner. PAR2 depletion increased interferon (IFN)-ß production via the cGAS-STING and RIG-1 pathways. PAR2 inhibition increased mitochondrial permeability and cytosolic accumulation of mitochondrial DNA, which was reversed by Bcl-xL expression. Strikingly, shRNA against PAR2 with an immune checkpoint blocker (ICB) acted synergistically to suppress liver metastasis. Analysis of single-cell sequence data and 24 paired samples confirmed the regulatory effect of PAR2 on the metastatic immune environment in human CRC. Therefore, PAR2 signaling is involved in stabilizing the mitochondrial membrane and regulating the immune microenvironment through IFN-ß during liver metastasis in CRC. The synergistic effect of the PAR2 inhibitor and ICB provides a potential therapeutic strategy for metastatic CRC treatment.

Gα12 signaling regulates transcriptional and phenotypic responses that promote glioblastoma tumor invasion.

In silico interrogation of glioblastoma (GBM) in The Cancer Genome Atlas (TCGA) revealed upregulation of GNA12 (Gα12), encoding the alpha subunit of the heterotrimeric G-protein G12, concomitant with overexpression of multiple G-protein coupled receptors (GPCRs) that signal through Gα12. Glioma stem cell lines from patient-derived xenografts also showed elevated levels of Gα12. Knockdown (KD) of Gα12 was carried out in two different human GBM stem cell (GSC) lines. Tumors generated in vivo by orthotopic injection of Gα12KD GSC cells showed reduced invasiveness, without apparent changes in tumor size or survival relative to control GSC tumor-bearing mice. Transcriptional profiling of GSC-23 cell tumors revealed significant differences between WT and Gα12KD tumors including reduced expression of genes associated with the extracellular matrix, as well as decreased expression of stem cell genes and increased expression of several proneural genes. Thrombospondin-1 (THBS1), one of the genes most repressed by Gα12 knockdown, was shown to be required for Gα12-mediated cell migration in vitro and for in vivo tumor invasion. Chemogenetic activation of GSC-23 cells harboring a Gα12-coupled DREADD also increased THBS1 expression and in vitro invasion. Collectively, our findings implicate Gα12 signaling in regulation of transcriptional reprogramming that promotes invasiveness, highlighting this as a potential signaling node for therapeutic intervention.

Neutrophil extracellular traps in relationship to efficacy of systemic therapy for metastatic renal cell carcinoma.

BACKGROUND: The efficacy of systemic therapy regimens, such as immune checkpoint inhibitors and tyrosine kinase inhibitors (IO-TKI) and targeted therapy, for metastatic clear cell renal cell carcinoma (ccRCC) remains unpredictable due to the lack of effective biomarkers. Neutrophil extracellular trap (NET) plays an important role in promoting ccRCC. This study explores the NET predictive value of the efficacy in metastatic ccRCC. METHODS: In this retrospective study, patients with metastatic ccRCC who received targeted drugs and IO-TKI were included. Immunofluorescence staining was utilized to quantify the levels of tissue NETs through cell counts of H3Cit(+) and MPO(+) cells. RESULTS: A total of 183 patients with metastatic ccRCC were enrolled, including 150 patients who received TKIs and 33 patients who received IO-TKI. The levels of NETs in tumor tissue were significantly higher than in para-tumor tissue (p < 0.001). In terms of predicting drug efficacy, a correlation between NET levels and progression-free survival (PFS) was observed in the TKI with metachronous metastasis group (HR 1.73 [95% CI 1.02-2.91], log-rank p = 0.037), while no correlation was observed in the TKI with synchronous metastasis group and IO-TKI group. Regarding overall survival (OS), activated NET levels were associated with poor OS in both TKI (HR 1.60 [95% CI 1.05-2.43], log-rank p = 0.017) and IO-TKI group (HR 4.35 [95% CI 1.06-17.82], log-rank p =0.047). IMDC score (HR 1.462 [95% CI 1.030-2.075], p = 0.033) and tumor tissue NET levels (HR 1.733 [95% CI 1.165-2.579], p = 0.007) were independent prognostic risk factors for OS in patients with metastatic ccRCC.NET level was associated with poor OS in both TKI (HR 1.60 [95% CI 1.05-2.43], log-rank p = 0.017). CONCLUSIONS: The active NET levels in tumor tissue can predict drug efficacy in patients with metastatic ccRCC who received systemic therapy. Elevated levels of NETs in tumor tissue were also associated with poor efficacy in OS.

TaSINA2B, interacting with TaSINA1D, positively regulates drought tolerance and root growth in wheat (Triticum aestivum L.).

Wheat (Triticum aestivum L.) is an important food crop mainly grown in arid and semiarid regions worldwide, whose productivity is severely limited by drought stress. Although various E3 ubiquitin (Ub) ligases regulate drought stress, only a few SINA-type E3 Ub ligases are known to participate in such responses. Herein, we identified and cloned 15 TaSINAs from wheat. The transcription level of TaSINA2B was highly induced by drought, osmotic and abscisic acid treatments. Two-type promoters of TaSINA2B were found in 192 wheat accessions; furthermore wheat accessions with promoter TaSINA2BII showed a considerably higher level of drought tolerance and gene expression levels than those characterizing accessions with promoter TaSINA2BI that was mainly caused by a 64 bp insertion in its promoter. Enhanced drought tolerance of TaSINA2B-overexpressing (OE) transgenic wheat lines was found to be associated with root growth promotion. Further, an interaction between TaSINA2B and TaSINA1D was detected through yeast two-hybrid and bimolecular fluorescence complementation assays. And TaSINA1D-OE transgenic wheat lines showed similar drought tolerance and root growth phenotype to those observed when TaSINA2B was overexpressed. Therefore, the variation of TaSINA2B promoter contributed to the drought stress regulation, while TaSINA2B, interacting with TaSINA1D, positively regulated drought tolerance by promoting root growth.

TaTIP41 and TaTAP46 positively regulate drought tolerance in wheat by inhibiting PP2A activity.

Drought is a major environmental stress limiting global wheat (Triticum aestivum) production. Exploring drought tolerance genes is important for improving drought adaptation in this crop. Here, we cloned and characterized TaTIP41, a novel drought tolerance gene in wheat. TaTIP41 is a putative conserved component of target of rapamycin (TOR) signaling, and the TaTIP41 homoeologs were expressed in response to drought stress and abscisic acid (ABA). The overexpression of TaTIP41 enhanced drought tolerance and the ABA response, including ABA-induced stomatal closure, while its downregulation using RNA interference (RNAi) had the opposite effect. Furthermore, TaTIP41 physically interacted with TaTAP46, another conserved component of TOR signaling. Like TaTIP41, TaTAP46 positively regulated drought tolerance. Furthermore, TaTIP41 and TaTAP46 interacted with type-2A protein phosphatase (PP2A) catalytic subunits, such as TaPP2A-2, and inhibited their enzymatic activities. Silencing TaPP2A-2 improved drought tolerance in wheat. Together, our findings provide new insights into the roles of TaTIP41 and TaTAP46 in the drought tolerance and ABA response in wheat, and their potential application in improving wheat environmental adaptability.

Genome-wide association study reveals the genetic variation and candidate gene for grain calcium content in bread wheat.

KEY MESSAGE: This study provides important information on the genetic basis of GCaC in wheat, thus contributing to breeding efforts to improve the nutrient quality of wheat. Calcium (Ca) plays important roles in the human body. Wheat grain provides the main diet for billions of people worldwide but is low in Ca content. Here, grain Ca content (GCaC) of 471 wheat accessions was determined in four field environments. A genome-wide association study (GWAS) was performed to reveal the genetic basis of GCaC using the phenotypic data form four environments and a wheat 660 K single nucleotide polymorphism (SNP) array. Twelve quantitative trait locus (QTLs) for GCaC were identified on chromosomes 1A, 1D, 2A, 3B, 6A, 6D, 7A, and 7D, which was significant in at least two environments. Haplotype analysis revealed that the phenotypic difference between the haplotypes of TraesCS6D01G399100 was significant (P ≤ 0.05) across four environments, suggesting it as an important candidate gene for GCaC. This research enhances our understanding of the genetic architecture of GCaC for further improving the nutrient quality of wheat.

Deep learning based de-overlapping correction of projections from a flat-panel micro array X-ray source: Simulation study.

PURPOSE: Flat-panel X-ray source is an experimental X-ray emitter with target application of static computer tomography (CT), which can save imaging space and time. However, the X-ray cone beams emitted by the densely arranged micro-ray sources are overlapped, causing serious structural overlapping and visual blur in the projection results. Traditional deoverlapping methods can hardly solve this problem well. METHOD: We converted the overlapping cone beam projections to parallel beam projections through a U-like neural network and selected structural similarity (SSIM) loss as the loss function. In this study, we converted three kinds of overlapping cone beam projections of the Shepp-Logan, line-pairs, and abdominal data with two overlapping levels to corresponding parallel beam projections. Training completed, we tested the model using the test set data that was not used at the training phase, and evaluated the difference between the test set conversion results and their corresponding parallel beams through three indicators: mean squared error (MSE), peak signal-to-noise ratio (PSNR) and SSIM. In addition, projections from head phantoms were applied for generalization test. RESULT: In the Shepp-Logan low-overlapping task, we obtained a MSE of 1.624×10-5, a PSNR of 47.892 dB, and a SSIM of 0.998 which are the best results of the six experiments. For the most challenging abdominal task, the MSE, PSNR, and SSIM are 1.563×10-3, 28.0586 dB, and 0.983, respectively. In more generalized data, the model also achieved good results. CONCLUSION: This study proves the feasibility of utilizing the end-to-end U-net for deblurring and deoverlapping in the flat-panel X-ray source domain.

Genome-wide association study for grain zinc concentration in bread wheat (Triticum aestivum L.).

Introduction: Zinc (Zn) deficiency causes serious diseases in people who rely on cereals as their main food source. However, the grain zinc concentration (GZnC) in wheat is low. Biofortification is a sustainable strategy for reducing human Zn deficiency. Methods: In this study, we constructed a population of 382 wheat accessions and determined their GZnC in three field environments. Phenotype data was used for a genome-wide association study (GWAS) using a 660K single nucleotide polymorphism (SNP) array, and haplotype analysis identified an important candidate gene for GZnC. Results: We found that GZnC of the wheat accessions showed an increasing trend with their released years, indicating that the dominant allele of GZnC was not lost during the breeding process. Nine stable quantitative trait loci (QTLs) for GZnC were identified on chromosomes 3A, 4A, 5B, 6D, and 7A. And an important candidate gene for GZnC, namely, TraesCS6D01G234600, and GZnC between the haplotypes of this gene showed, significant difference (P ≤ 0.05) in three environments. Discussion: A novel QTL was first identified on chromosome 6D, this finding enriches our understanding of the genetic basis of GZnC in wheat. This study provides new insights into valuable markers and candidate genes for wheat biofortification to improve GZnC.

A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction.

BACKGROUND: In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. PURPOSE: In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. METHODS: Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. RESULTS: Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2 HU in full-fan case and from 53 to 13 HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half-fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4 HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25 s for the whole iterative scatter correction process. CONCLUSIONS: The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

Complete chloroplast genomes of Sorbus sensu stricto (Rosaceae): comparative analyses and phylogenetic relationships.

BACKGROUND: Sorbus sensu stricto (Sorbus s.s.) is a genus with important economical values because of its beautiful leaves, and flowers and especially the colorful fruits. It belongs to the tribe Maleae of the family Rosaceae, and comprises about 90 species mainly distributed in China. There is on-going dispute about its infrageneric classification and species delimitation as the species are morphologically similar. With the aim of shedding light on the circumscription of taxa within the genus, phylogenetic analyses were performed using 29 Sorbus s.s. chloroplast (cp) genomes (16 newly sequenced) representing two subgenera and eight sections. RESULTS: The 16 cp genomes newly sequenced range between 159,646 bp and 160,178 bp in length. All the samples examined and 22 taxa re-annotated in Sorbus sensu lato (Sorbus s.l.) contain 113 unique genes with 19 of these duplicated in the inverted repeat (IR). Six hypervariable regions including trnR-atpA, petN-psbM, rpl32-trnL, trnH-psbA, trnT-trnL and ndhC-trnV were screened and 44-53 SSRs and 14-31 dispersed repeats were identified as potential molecular markers. Phylogenetic analyses under ML/BI indicated that Sorbus s.l. is polyphyletic, but Sorbus s.s. and the other five segregate genera, Aria, Chamaemespilus, Cormus, Micromeles and Torminalis are monophyletic. Two major clades and four sub-clades resolved with full-support within Sorbus s.s. are not consistent with the existing infrageneric classification. Two subgenera, subg. Sorbus and subg. Albocarmesinae are supported as monophyletic when S. tianschanica is transferred to subg. Albocarmesinae from subg. Sorbus and S. hupehensis var. paucijuga transferred to subg. Sorbus from subg. Albocarmesinae, respectively. The current classification at sectional level is not supported by analysis of cp genome phylogeny. CONCLUSION: Phylogenomic analyses of the cp genomes are useful for inferring phylogenetic relationships in Sorbus s.s. Though genome structure is highly conserved in the genus, hypervariable regions and repeat sequences used are the most promising molecule makers for population genetics, species delimitation and phylogenetic studies.

A novel nomogram can predict pathological T3a upstaged from clinical T1a in localized renal cell carcinoma

ABSTRACT Hypothesis: Nomogram can be built to predict the pathological T3a upstaging from clinical T1a in patients with localized renal cell carcinoma before surgery. Purpose: Renal cell carcinoma (RCC) patients with clinical T1a (cT1a) disease who are upstaged to pathological T3a (pT3a) have reduced survivals after partial nephrectomy. We aimed to develop a nomogram-based model predicting pT3a upstaging in RCC patients with preoperative cT1a based on multiple preoperative blood indexes and oncological characteristics. Materials and Methods: Between 2010 and 2019, 510 patients with cT1a RCC were individually matched according to pT3a upstaging and pathological T1a (pT1a) at a 1:4 ratio using clinicopathologic features. Least absolute shrinkage and selection operator regression analysis was used to identify the most important risk factor from 40 peripheral blood indicators, and a predictive model was established. Multivariate logistic regression analysis was performed with the screened blood parameters and clinical data to identify significant variables. Harrell's concordance index (C-index) was applied to evaluate the accuracy of the model for predicting pT3a upstaging in patients with cT1a RCC. Results: Out of 40 blood indexes, the top ranked predictor was fibrinogen (FIB). Age, the ratio of the tumor maximum and minimum diameter (ROD), FIB, and tumor size were all independent risk factors for pT3a upstaging in multivariate analysis. A predictive ARFS model (Age, ROD, FIB, tumor Size) was established, and the C-index was 0.756 (95% CI, 0.681-0.831) and 0.712 (95% CI, 0.638-0.785) in the training and validation cohorts, respectively. Conclusions: Older age, higher ROD, increased FIB level, and larger tumor size were independent risk factors for upstaging. The ARFS model has a high prediction efficiency for pT3a upstaging in patients with cT1a RCC.

Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases.

The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists' misdiagnosis rate (p < 0.01). The RRCART distinguished more than 80% of the WSIs as a high-accuracy group with an average accuracy reached to 0.995, but the difference with the pathologists' performance was not significant (p > 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.

Overexpression of TaSNAC4-3D in Common Wheat (Triticum aestivum L.) Negatively Regulates Drought Tolerance.

The development and production of bread wheat (Triticum aestivum L.) are widely affected by drought stress worldwide. Many NAC transcription factors (TFs) of stress-associated group (SNAC) are functionally proven to regulate drought tolerance. In this study, we identified 41 TaSNACs that were classified into 14 groups, and the expression of TaSNAC4-3D was induced in the leaf tissue via osmotic or abscisic acid (ABA) treatment. TaSNAC4-3D was localized to the nucleus through the transient expression assay, and the C-terminal region exhibited transcriptional activity via transactivation assays. TaSNAC4-3D was overexpressed in common wheat. The wheat plants with TaSNAC4-3D overexpression was more sensitive to drought stress compared with wild-type (WT) plants. The water loss rate showed no difference between transgenic lines and WT plants. However, drought stress increased H2O2 and O2- accumulation and promoted programmed cell death (PCD) in the leaf tissue of TaSNAC4-3D overexpression lines compared with WT plants. RNA-seq analysis was performed under well-watered and drought conditions, and four strong potential target genes, encoding senescence regulators, were identified by analyzing their promoters containing the NAC recognition sequence (NACRS). Based on these results, our findings revealed that TaSNAC4-3D negatively regulates drought tolerance by inducing oxidative damage in bread wheat.

A novel nomogram can predict pathological T3a upstaged from clinical T1a in localized renal cell carcinoma.

HYPOTHESIS: Nomogram can be built to predict the pathological T3a upstaging from clinical T1a in patients with localized renal cell carcinoma before surgery. PURPOSE: Renal cell carcinoma (RCC) patients with clinical T1a (cT1a) disease who are upstaged to pathological T3a (pT3a) have reduced survivals after partial nephrectomy. We aimed to develop a nomogram-based model predicting pT3a upstaging in RCC patients with preoperative cT1a based on multiple preoperative blood indexes and oncological characteristics. MATERIALS AND METHODS: Between 2010 and 2019, 510 patients with cT1a RCC were individually matched according to pT3a upstaging and pathological T1a (pT1a) at a 1:4 ratio using clinicopathologic features. Least absolute shrinkage and selection operator regression analysis was used to identify the most important risk factor from 40 peripheral blood indicators, and a predictive model was established. Multivariate logistic regression analysis was performed with the screened blood parameters and clinical data to identify significant variables. Harrell's concordance index (C-index) was applied to evaluate the accuracy of the model for predicting pT3a upstaging in patients with cT1a RCC. RESULTS: Out of 40 blood indexes, the top ranked predictor was fibrinogen (FIB). Age, the ratio of the tumor maximum and minimum diameter (ROD), FIB, and tumor size were all independent risk factors for pT3a upstaging in multivariate analysis. A predictive ARFS model (Age, ROD, FIB, tumor Size) was established, and the C-index was 0.756 (95% CI, 0.681-0.831) and 0.712 (95% CI, 0.638-0.785) in the training and validation cohorts, respectively. CONCLUSIONS: Older age, higher ROD, increased FIB level, and larger tumor size were independent risk factors for upstaging. The ARFS model has a high prediction efficiency for pT3a upstaging in patients with cT1a RCC.