In Arabidopsis and rice, miR159-regulated GAMYB-like family transcription factors function in flower development and gibberellin (GA) signaling in cereal aleurone cells. In this study, the ...involvement of miR159 in the regulation of its putative target TaGAMYB and its relationship to wheat development were investigated. First, we demonstrated that cleavage of TaGAMYB1 and TaGAMYB2 was directed by miR159 using 5'-RACE and a transient expression system. Second, we overexpressed TamiR159, TaGAMYB1 and mTaGAMYB1 (impaired in the miR159 binding site) in transgenic rice, revealing that the accumulation in rice of mature miR159 derived from the precursor of wheat resulted in delayed heading time and male sterility. In addition, the number of tillers and primary branches in rice overexpressing mTaGAMYB1 increased relative to the wild type. Our previous study reported that TamiR159 was downregulated after two hours of heat stress treatment in wheat (Triticum aestivum L.). Most notably, the TamiR159 overexpression rice lines were more sensitive to heat stress relative to the wild type, indicating that the downregulation of TamiR159 in wheat after heat stress might participate in a heat stress-related signaling pathway, in turn contributing to heat stress tolerance.
Seed germination is important for grain yield and quality and rapid, near-simultaneous germination helps in cultivation; however, cultivars that germinate too readily can undergo preharvest sprouting ...(PHS), which causes substantial losses in areas that tend to get rain around harvest time. Moreover, our knowledge of mechanisms regulating seed germination in wheat (Triticum aestivum) remains limited. In this study, we analyzed function of a wheat-specific microRNA 9678 (miR9678), which is specifically expressed in the scutellum of developing and germinating seeds. Overexpression of miR9678 delayed germination and improved resistance to PHS in wheat through reducing bioactive gibberellin (GA) levels; miR9678 silencing enhanced germination rates. We provide evidence that miR9678 targets a long noncoding RNA (WSGAR) and triggers the generation of phased small interfering RNAs that play a role in the delay of seed germination. Finally, we found that abscisic acid (ABA) signaling proteins bind the promoter of miR9678 precursor and activate its expression, indicating that miR9678 affects germination by modulating the GA/ABA signaling.
In 2003, Railsback proposed the Earth Scientist’s Periodic Table, which displays a great deal of elemental geology information in accordance with the natural environment of the earth. As an applied ...science, metallurgy is based on mineral composition and element behavior, that is similar to geochemistry. In this paper, connections and similarities between geology and metallurgy are identified, based on geochemical laws and numerous metallurgical cases. An obvious connection is that simple cations with high and low ionic potential are commonly extracted by hydrometallurgy, while those with intermediate ionic potential are extracted by pyrometallurgy. In addition, element affinity in geology is associated with element migration in metallurgic phases. To be specific, in pyrometallurgy, lithophile elements tend to gather in slags, chalcophile elements prefer the matte phase, siderophile elements are easily absorbed into metal melt, and atmophile elements readily enter the gas phase. Furthermore, in hydrometallurgy, the principles of hard/soft acids and bases (HSABs) offer an explanation of how precipitation and dissolution occur in different solutions, especially for fluoride and chloride. This article provides many metallurgical examples based on the principles of geochemistry to verify these similarities and connections.
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Abstract
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.
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► Pristine pyrite was produced using rigorously anoxic conditions. ► As(III) adsorbed over a broader pH range with this material. ► As(V) was adsorbed, reduced to As(III), and was not ...released to solution. ► Results with surface-oxidized pyrite were similar to literature reports for pyrite. ► The need for unusual experimental techniques is described.
Reactions of As(III) and As(V) with pyrite were investigated using pristine pyrite (produced and reacted in a rigorously anoxic environment with PO2<10−8atm) and using surface-oxidized pyrite (produced under anoxic conditions, exposed to air, then stored and reacted under rigorously anoxic conditions). Results with surface-oxidized pyrite were similar to previously reported arsenic-pyrite results. However As(III) adsorbed over a broader pH range on pristine pyrite than on surface-oxidized pyrite, As(V) adsorbed over a narrower pH range on pristine pyrite than on surface-oxidized pyrite, and adsorbed As(V) on pristine pyrite was reduced to As(III) but adsorbed As(V) was not reduced with surface-oxidized pyrite. Reduction of As(V) with pristine pyrite was first-order in total As(V), Fe(II) was released, and sulfur was oxidized. The proposed mechanism for pyrite oxidation by As(V) was similar to the published mechanism for oxidation by O2 and rates were compared. The results can be used to predict the removals of As(V) and As(III) on pyrite in continuously anoxic environments or on pyrite in intermittently oxic/anoxic environments. Rigorous cleanup and continuous maintenance of strictly anoxic conditions are required if commercial or produced pyrites are to be used as surrogates for pristine pyrite.
Abstract Background The functional integrity of the anterior cruciate ligament (ACL) influences surgical decision-making in patients with knee osteoarthritis (KOA). This study aimed to compare the ...diagnostic value of radiography and magnetic resonance imaging (MRI) in determining the functional status of ACL. Methods We analyzed 306 knees retrospectively using preoperative hip-to-ankle anteroposterior standing (APS) radiographs, anteroposterior (AP) and lateral knee radiographs, AP valgus stress (VS) force radiographs, and standard orthogonal MRI. Based on the intraoperative visualization, the knees were grouped into ACL functionally-intact and ACL functionally-deficient (ACLD) groups. The diagnostic validity and reliability were calculated based on the radiograph parameters such as hip-knee-ankle angle (HKA), medial proximal tibial angle (MPTA), lateral distal femoral angle (LDFA), posterior tibial slope (PTS), sagittal tibiofemoral subluxation (STFS), coronal tibiofemoral subluxation (CTFS), joint line convergence angle (JLCA), the maximum wear point of the proximal tibia plateau (MWPPT%), and MRI parameters including ACL grades and MWPPT%. Results HKA, MPTA, PTS, STFS, JLCA, and CTFS on APS and AP radiographs, and MWPPT% on radiographs and MRI showed a significant diagnostic value ( P < 0.05). There were no statistically significant differences in the single parameters from radiographs and MRI. After constructing the logistic regression models, MRI showed higher sensitivity, specificity, and accuracy, reaching 96.8%, 79.9%, and 83.3%, respectively ( P < 0.001). Conclusions In patients with KOA, the diagnostic value of single radiographic or MRI parameter in assessing the functional integrity of the ACL are equivalent. However, by constructing predictive models, MRI could significantly improve diagnostic validity compared with radiography.
Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early ...identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19.
Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively.
The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval CI: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the model achieved AUCs of 0.955 (0.884-1.00) and 0.923 (0.864-0.983) and accuracies of 97.0 and 81.6% in the Honghu and Nanchang subgroups, respectively.
Our deep learning-based model can accurately predict disease severity as well as disease progression in COVID-19 patients using CT imaging, offering promise for guiding clinical treatment.
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.
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.
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%).
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.
Introduction:
The pathological rare category of thyroid is a type of lesion with a low incidence rate and is easily misdiagnosed in clinical practice, which directly affects a patient’s treatment ...decision. However, it has not been adequately investigated to recognize the rare, benign, and malignant categories of thyroid using the deep learning method and recommend the rare to pathologists.
Methods:
We present an empirical decision tree based on the binary classification results of the patch-based UNet model to predict rare categories and recommend annotated lesion areas to be rereviewed by pathologists.
Results:
Applying this framework to 1,374 whole-slide images (WSIs) of frozen sections from thyroid lesions, we obtained an area under a curve of 0.946 and 0.986 for the test datasets with and without WSIs, respectively, of rare types. However, the recognition error rate for the rare categories was significantly higher than that for the benign and malignant categories (
p
< 0.00001). For rare WSIs, the addition of the empirical decision tree obtained a recall rate and precision of 0.882 and 0.498, respectively; the rare types (only 33.4% of all WSIs) were further recommended to be rereviewed by pathologists. Additionally, we demonstrated that the performance of our framework was comparable to that of pathologists in clinical practice for the predicted benign and malignant sections.
Conclusion:
Our study provides a baseline for the recommendation of the uncertain predicted rare category to pathologists, offering potential feasibility for the improvement of pathologists’ work efficiency.
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