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Purposes: Developing the cultivation and utilization of tiger nuts on marginal land is an effective approach to tackle the shortage of arable land in northern China. However, key information on their growth is unclear. This study aims to reveal the adaption potential of tiger nuts to on marginal land to help improve and adjust management practices. Methods: Three treatments were arranged to plant tiger nut in marginal land: no fertilization (N:P = 0:0), traditional fertilization (N:P = 15:15), and additional nitrogen fertilizer (N:P = 60:15). Functional traits of tiger nut and soil nutrients over three growth periods were investigated. Results: Under no fertilization, tiger nuts extended roots and toughened leaves to maximize nutrient absorption, and then quickly transported those nutrients to tubers. However, the lack of available phosphorus in the soil prevented the expansion of tubers. With the addition of fertilizers, tiger nuts produced more individuals, and their abundant belowground tissues resulted in soil organic matter increase. Under traditional fertilization, the tuber remains the main reproductive organ for tiger nuts, and phosphorus has a significant effect on the expansion of tubers. But under additional nitrogen fertilizer, the main reproductive organ changed to the stolon, nitrogen and phosphorus acted on stolons to promote their extension. Conclusion: Tiger nuts outstandingly adapt to marginal land environments and adjust their adaptation strategies according to varying soil nutrient conditions, Best tuber production will be achieved in soils with higher phosphorus availability, and a significant nitrogen proportion increase in fertilizers is necessary for enhancing aboveground production. [ABSTRACT FROM AUTHOR]

Objective: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.Materials and methods: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.Results: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.Conclusion: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs. [ABSTRACT FROM AUTHOR]

Inspired by an open question by Chemin, Zhang and Zhang about the regularity of the 3D Navier–Stokes equations with one initially small component, we investigate symmetry breaking and symmetry preservation. Our results fall in three classes. First we prove strong symmetry breaking. Specifically, we demonstrate third component norm inflation (3rdNI) and Isotropic Norm Inflation (INI) starting from zero third component. Second we prove symmetry breaking for initially zero third component, even in the presence of a favorable initial pressure gradient. Third we study certain symmetry preserving solutions with a shear flow structure. Specifically, we give applications to the inviscid limit and exhibit explicit solutions that inviscidly damp to the Kolmogorov flow. [ABSTRACT FROM AUTHOR]

Background: Laparoscopic liver resection (LLR) has now been established as a safe and minimally invasive technique that is deemed feasible for treating hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). However, the role of LLR in treating combined hepatocellular-cholangiocarcinoma (cHCC-CC) patients has been rarely reported. This study aimed to assess the efficacy of LLR when compared with open liver resection (OLR) procedure for patients with cHCC-CC. Methods: A total of 229 cHCC-CC patients who underwent hepatic resection (34 LLR and 195 OLR patients) from January 2014 to December 2018 in Zhongshan Hospital, Fudan University were enrolled and underwent a 1:2 propensity score matching (PSM) analysis between the LLR and OLR groups to compare perioperative and oncologic outcomes. Overall survival (OS) and recurrence-free survival (RFS) parameters were assessed by the log-rank test and the sensitivity analysis. Results: A total of 34 LLR and 68 OLR patients were included after PSM analysis. The LLR group displayed a shorter postoperative hospital stay (6.61 vs. 8.26 days; p value < 0.001) when compared with the OLR group. No significant differences were observed in the postoperative complications' incidence or a negative surgical margin rate between the two groups (p value = 0.409 and p value = 1.000, respectively). The aspartate aminotransferase (AST), alanine aminotransferase (ALT), and inflammatory indicators in the LLR group were significantly lower than those in the OLR group on the first and third postoperative days. Additionally, OS and RFS were comparable in both the LLR and OLR groups (p value = 0.700 and p value = 0.780, respectively), and similar results were obtained by conducting a sensitivity analysis. Conclusion: LLR can impart less liver function damage, better inflammatory response attenuation contributing to a faster recovery, and parallel oncologic outcomes when compared with OLR. Therefore, LLR can be recommended as a safe and effective therapeutic modality for treating selected cHCC-CC patients, especially for those with small tumors in favorable location. [ABSTRACT FROM AUTHOR]

Introduction: Real-world data on the comparative effectiveness of endothelin receptor antagonists (ERAs; macitentan, bosentan, ambrisentan) for pulmonary arterial hypertension (PAH), particularly in Asian countries, are scarce. We evaluated the persistence of these ERAs before and after macitentan approval in Japan (2015). Methods: We used real-world data from the Japanese Medical Data Vision administrative claims database between April 2008 and November 2020. Patients with PAH were identified from the dataset. Persistence to ERA treatment before and after approval of macitentan in Japan was defined as the time between start of the index ERA and treatment discontinuation or death. Propensity score adjustment was applied to minimize confounding effects among treatment groups. Results: In the pre-macitentan approval cohort, 153 and 51 patients received bosentan and ambrisentan, respectively. In the post-macitentan approval cohort, 331, 284, and 91 patients received macitentan, bosentan, and ambrisentan, respectively. Unadjusted median persistence for ambrisentan- and bosentan-treated patients was 19 and 10 months, respectively (adjusted HR 0.87 [95% CI 0.61–1.24]; P = 0.434 [bosentan as reference]). In the post-macitentan approval cohort, unadjusted median persistence was 18 months for macitentan-treated patients versus 6 and 8 months for ambrisentan- and bosentan-treated patients, respectively. Adjusted HRs for ambrisentan and bosentan were 1.48 (95% CI 1.12–1.95; P = 0.006) and 1.63 (95% CI 1.30–2.04; P < 0.001 [macitentan as reference]), respectively. Conclusions: Real-world data for Japanese patients with PAH showed that persistence was significantly higher for macitentan, versus ambrisentan and bosentan, since its approval. [ABSTRACT FROM AUTHOR]

An urban subway network system is a complex public transportation system. To compare rail transit braking digital command control strategies based on neural network, this article analyzes and studies the characteristics of subway vehicle driver controllers and their design methods from three aspects: mechanical, electrical and software-assisted design. The learning rule of the BP neural network is called the mentor system learning rule, which is a kind of error-correcting algorithm. In the learning and training process, the expected output value needs to be given. The weights and thresholds of the BP neural network are optimized by selecting the parameters of the SA algorithm. The search method of SA is heuristic, and it has the following advantages: The selection of the initial solution does not affect the optimal solution. The simplified model extracts the core data processing individual analysis. In this paper, the physical data are extracted from the physical entity operation process for analysis, and the twin model is established to extract the twin data for analysis. This paper uses the characteristics of physical data to test the modeling effect and utilizes the twin data to carry out algorithm experiments on physical data. The ultimate goal is to use twin data to predict the state information of physical entities. The network error in the scheme designed by the article is 6%. The smooth implementation of this research constitutes an important reference for the design of subway train network control systems in other cities in China. Therefore, this research has great application value. [ABSTRACT FROM AUTHOR]

Understanding of the complex mechanical behavior of metal-organic frameworks (MOF) beyond their elastic limit will allow the design of real-world applications in chemical engineering, optoelectronics, energy conversion apparatus, and sensing devices. Through in situ compression of micropillars, the uniaxial stress-strain curves of a copper paddlewheel MOF (HKUST-1) were determined along two unique crystallographic directions, namely the (100) and (111) facets. We show strongly anisotropic elastic response where the ratio of the Young's moduli are E(111) ≈ 3.6 × E(100), followed by extensive plastic flows. Likewise, the yield strengths are considerably different, in which Y(111) ≈ 2 × Y(100) because of the underlying framework anisotropy. We measure the fracture toughness using micropillar splitting. While in situ tests revealed differential cracking behavior, the resultant toughness values of the two facets are comparable, yielding Kc ~ 0.5 MPa m . This work provides insights of porous framework ductility at the micron scale under compression and failure by bonds breakage. Understanding the complex mechanical behaviour of metal–organic frameworks is of importance for their real-world application. Here, an in situ micropillar compression method is demonstrated to provide valuable insights into the anisotropic mechanical properties of the HKUST-1 single crystal. [ABSTRACT FROM AUTHOR]

The development of biodegradable materials from biomass is a crucial strategy for leveraging biomass resources. A key component in this process is 2,5-bis(hydroxymethyl)furan (BHMF), a significant biomass-derived platform chemical. However, producing BHMF with high selectivity through the selective hydrogenation of 5-hydroxymethylfurfural, while avoiding the creation of unwanted byproducts, is a complex challenge. In our research, we have developed a novel catalyst featuring a magnetic core. This catalyst, which consists of platinum loaded onto a graphene-like shell, has been applied to the catalytic hydrogenation of HMF in an aqueous environment. Under the optimized conditions of 373 K, 3 MPa H2, and 1 h reaction time, a BHMF yield exceeding 99% was achieved. This method presents a promising and effective approach for the high-value conversion of biomass waste into polymer materials and pharmaceutical chemicals.Graphical Abstract: The development of biodegradable materials from biomass is a crucial strategy for leveraging biomass resources. A key component in this process is 2,5-bis(hydroxymethyl)furan (BHMF), a significant biomass-derived platform chemical. However, producing BHMF with high selectivity through the selective hydrogenation of 5-hydroxymethylfurfural, while avoiding the creation of unwanted byproducts, is a complex challenge. In our research, we have developed a novel catalyst featuring a magnetic core. This catalyst, which consists of platinum loaded onto a graphene-like shell, has been applied to the catalytic hydrogenation of HMF in an aqueous environment. Under the optimized conditions of 373 K, 3 MPa H2, and 1 h reaction time, a BHMF yield exceeding 99% was achieved. This method presents a promising and effective approach for the high-value conversion of biomass waste into polymer materials and pharmaceutical chemicals. [ABSTRACT FROM AUTHOR]