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Eggplant is a highly significant vegetable crop and extensively cultivated worldwide. Sepal color is considered one of the major commercial traits of eggplant. Eggplant sepals develop from petals, and sepals have the ability to change color by accumulating anthocyanins, but whether the eggplants in sepal and their biosynthetic pathways are the same as those in petals is not known. To date, little is known about the underlying mechanisms of sepal color formation. In this study, we performed bulked segregant analysis and transcriptome sequencing using eggplant sepals and obtained 1,452,898 SNPs and 182,543 InDel markers, respectively, as well as 123.65 Gb of clean data using transcriptome sequencing. Through marker screening, the genes regulating eggplant sepals were localized to an interval of 2.6 cM on chromosome 10 by bulked segregant analysis sequencing and transcriptome sequencing and co-analysis, combined with screening of molecular markers by capillary electrophoresis. Eight possible candidate genes were then screened to further interpret the regulatory incentives for the eggplant sepal color. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of the effects of diverse greenhouse vegetable production systems (GVPS) on vegetable yield, soil water consumption, and nitrogen (N) fates could provide a scientific basis for identifying optimum water and fertilizer management practices for GVPS. This research was conducted from 2013 to 2015 in a greenhouse vegetable field in Quzhou County, North China. Three production systems were designed: conventional (CON), integrated (INT), and organic (ORG) systems. The WHCNS-Veg model was employed for simulating vegetable growth, water dynamics, and fates of N, as well as water and N use efficiencies (WUE and NUE) for four continuous growing seasons. The simulation results revealed that nitrate leaching and gaseous N emissions constituted the predominant N loss within GVPS, which separately accounted for 11.5–59.4% and 6.0–21.1% of the N outputs. The order of vegetable yield, N uptake, WUE, and NUE under different production systems was ORG > INT > CON, while the order of nitrate leaching and gaseous N loss was CON > INT > ORG. Compared to CON, ORG exhibited a significant increase in yield, N uptake, WUE, and NUE by 24.6%, 24.2%, 26.1%, and 89.7%, respectively, alongside notable reductions in nitrate leaching and gaseous N loss by 67.7% and 63.2%, respectively. The ORG system should be recommended to local farmers. [ABSTRACT FROM AUTHOR]

Background: Deep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos. Methods: A total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test. Results: At the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880–0.958) for caries detection and 0.902 (95% CI, 0.853–0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection. Conclusions: The proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection. [ABSTRACT FROM AUTHOR]

As the core component of the wind turbine generation gearbox, the gear-rotor-bearing transmission system typically operates in harsh environments, inevitably leading to the occurrence of composite faults in the system, which exacerbates system vibration. Therefore, it is necessary to study the vibration characteristics of wind turbine helical gear-rotor-bearing transmission systems with composite faults. This paper uses an improved energy method to calculate the theoretical time-varying mesh stiffness of a helical gear with a root crack failure. On the premise of considering the time-varying meshing stiffness of the faulty helical gear, the gear eccentric fault, and the nonlinear support force of the faulty bearing, a multi-degree-of-freedom helical gear-rotor-bearing transmission system with compound faults was established by using the lumped parameter method. The dynamic model of the system was solved based on the Runge–Kutta method, and the vibration response of the system under healthy conditions, single faults with gear eccentricity, single faults with tooth root cracks, and coupled bearing composite faults were simulated and analyzed. The results show that the simulation results based on KISSsoft software 2018 version verify the effectiveness of the improved energy method; the existence of single faults and composite faults will cause the fault characteristics in the time domain and frequency domain responses. In this paper, the influence of a single fault and a complex fault on the time domain and frequency domain of the system is mainly discovered through the fault study of the helical rotor-bearing system, and the influence of the fault degree on the vibration of the gear motion system is discussed. The greater the degree of the fault, the more vibration of the system occurs; accordingly, when the system is under the coupling of tooth root crack and bearing fault, there is a significant difference compared with the healthy system and the single fault system. The system vibration has obvious time domain and frequency domain signal characteristics, including periodic pulse impacts caused by gear faults and time domain impact caused by bearing. The fault characteristic frequencies can also be found in the frequency domain. In this paper, the fault study of a helical gear of wind turbine generation provides a reference for the theoretical analysis of the vibration characteristics of the helical gear-rotor-bearing system under various fault conditions, lays a solid foundation for the simulation and subsequent diagnosis of the composite fault signal of the system, and provides help for the fault diagnosis of wind turbine gearboxes in the future. [ABSTRACT FROM AUTHOR]

The low electrical conductivity limits the application of T−Nb2O5 as anode materials for practical applications. The large‐grain T−Nb2O5 nanowires with nitrogen doping are obtained by simple hydrothermal and annealing treatments. After nitriding with ammonia, an amorphous layer of NbON as an intermediate is formed and then the new crystalline NbN is generated with the deepening of the nitriding degree, causing homogeneous pores on the surface of the T−Nb2O5 and thus increasing the specific surface area. Meanwhile, the formation of the nitriding layer enhances the electrical conductivity, inducing a pseudo‐capacitance mechanism. Therefore, the N−T−Nb2O5 nanowire demonstrates superior electrochemical performance. Specifically, the 45N−T−Nb2O5 nanowires delivered a first reversible specific capacity of 238.33 mAh g−1 at 0.1 C, higher than of theoretical capacity (201.6 mAh g−1). However, the excessive nitride will be completely converted into NbN, reducing the initial lithium storage of the material due to the lack of a lithium storage site, which has not been previously discussed. Besides, at the high rate of 100 C, with the deepening of nitride degree of the T−Nb2O5, the higher the rate performance. Therefore, to find the balance between the high rate performance and the initial lithium storage amount, the nitride degree should be strictly controlled. [ABSTRACT FROM AUTHOR]

Prime editing allows precise installation of any single base substitution and small insertions and deletions without requiring homologous recombination or double-strand DNA breaks in eukaryotic cells. However, the applications in bacteria are hindered and the underlying mechanisms that impede efficient prime editing remain enigmatic. Here, we report the determination of vital cellular factors that affect prime editing in bacteria. Genetic screening of 129 Escherichia coli transposon mutants identified sbcB, a 3ʹ→5ʹ DNA exonuclease, as a key genetic determinant in impeding prime editing in E. coli, combinational deletions of which with two additional 3ʹ→5ʹ DNA exonucleases, xseA and exoX, drastically enhanced the prime editing efficiency by up to 100-fold. Efficient prime editing in wild-type E. coli can be achieved by simultaneously inhibiting the DNA exonucleases via CRISPRi. Our results pave the way for versatile applications of prime editing for bacterial genome engineering. Prime editing in bacteria is currently inefficient. Here the authors report BacPE, a versatile prime editing platform in Escherichia coli that works by inhibiting 3′→5′ DNA exonucleases, highlighting the intrinsic genetic factors that are adverse to efficient prime editing. [ABSTRACT FROM AUTHOR]

The clinical translation of tumor hypoxia intervention modalities still falls short of expectation, restricted by poor biocompatibility of oxygen-carrying materials, unsatisfactory oxygen loading performance, and abnormally high cellular oxygen consumption-caused insufficient hypoxia relief. Herein, a carrier-free oxygen nano-tank based on modular fluorination prodrug design and co-assembly nanotechnology is elaborately exploited, which is facilely fabricated through the molecular nanoassembly of a fluorinated prodrug (FSSP) of pyropheophorbide a (PPa) and an oxygen consumption inhibitor (atovaquone, ATO). The nano-tank adeptly achieves sufficient oxygen enrichment while simultaneously suppressing oxygen consumption within tumors for complete tumor hypoxia alleviation. Significant, the fluorination module in FSSP not only confers favorable co-assemblage of FSSP and ATO, but also empowers the nanoassembly to readily carry oxygen. As expected, it displays excellent oxygen carrying capacity, favorable pharmacokinetics, on-demand laser-triggerable ATO release, closed-loop tumor hypoxia relief, and significant enhancement to PPa-mediated PDT in vitro and in vivo. This study provides a novel nanotherapeutic paradigm for tumor hypoxia intervention-enhanced cancer therapy., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Exploring the bacterial community in the S. glauca rhizosphere was of great value for understanding how this species adapted to the saline-alkali environment and for the rational development and use of saline-alkali soils. In this study, high-throughput sequencing technology was used to investigate the diversity characteristics and distribution patterns of soil bacterial communities in the rhizosphere of S.glauca -dominated communities in the Hetao Irrigation Distract, Inner Mongolia, China. The relationships among bacterial characteristics, soil physicochemical properties and vegetation in four sampling sites were analyzed. The soil bacterial communities in the rhizosphere of S. glauca -dominated communities were mainly composed of 16 phyla (i.e., Proteobacteria, Actinobacteria, Bacteroidetes, Gemmatimonadetes, Chloroflexi, Acidobacteria, Firmicutes, Planctomycetes, Deinococcus-Thermus, Verrucomicrobia, Saccharibacteria, Cyanobacteria, Nitrospirae, JL-ETNP-Z39, Parcubacteria and Chlorobi ), and these populations accounted for more than 99% of the total bacterial community. At the genus level, the main bacterial communities comprised Halomonas, Nitriliruptor, Euzebya and Pelagibius , which accounted for 15.70% of the total bacterial community. An alpha diversity analysis indicated that the richness and diversity of rhizosphere soil bacteria differed significantly among the sampling sites, and the bacterial richness and diversity indices of severe saline-alkali land were higher than those of light and moderate saline-alkali land. The principal component analysis (PCA) and linear discriminant analysis effect size (LEfSe) showed significant differences in the species composition of the rhizosphere soil bacterial community among different sampling sites. A correlation analysis showed that the number of bacterial species exhibited the highest correlation with the soil water content (SWC). The richness and evenness indices were significantly correlated with the SWC and SO 4 2- , K + and Mg 2+ concentrations. The electrical conductivity (EC), soluble ions (Na + , CO 3 2- + HCO 3 - , K + , Ca 2+ , Mg 2+ , and SO 4 2+ ), SWC and vegetation coverage (VC) were the main drivers affecting the changes in its community structure. The bacterial community in the rhizosphere of S. glauca enhanced the adaptability of S. glauca to saline-alkali environment by participating in the cycling process of nutrient elements, the decomposition of organic matter and the production of plant growth regulating substances. These results provided a theoretical reference for further study on the relationship among rhizosphere soil microorganisms and salt tolerance in halophytes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Dong, Wang, Li, Zhang, Li, Song, Chang, Feng, Pang and Wang.)

Enchondromas are a common tumor in bone that can occur as multiple lesions in enchondromatosis, which is associated with deformity of the effected bone. These lesions harbor mutations in IDH and driving expression of a mutant Idh1 in Col2 expressing cells in mice causes an enchondromatosis phenotype. In this study we compared growth plates from E18.5 mice expressing a mutant Idh1 with control littermates using single cell RNA sequencing. Data from Col2 expressing cells were analyzed using UMAP and RNA pseudo-time analyses. A unique cluster of cells was identified in the mutant growth plates that expressed genes known to be upregulated in enchondromas. There was also a cluster of cells that was underrepresented in the mutant growth plates that expressed genes known to be important in longitudinal bone growth. Immunofluorescence showed that the genes from the unique cluster identified in the mutant growth plates were expressed in multiple growth plate anatomic zones, and pseudo-time analysis also suggested these cells could arise from multiple growth plate chondrocyte subpopulations. This data identifies subpopulations of cells in control and mutant growth plates, and supports the notion that a mutant Idh1 alters the subpopulations of growth plate chondrocytes, resulting a subpopulation of cells that become enchondromas at the expense of other populations that contribute to longitudinal growth., Competing Interests: Additional Declarations: No competing interests reported.

Graph neural networks (GNN) suffer from severe inefficiency due to the exponential growth of node dependency with the increase of layers. It extremely limits the application of stochastic optimization algorithms so that the training of GNN is usually time-consuming. To address this problem, we propose to decouple a multi-layer GNN as multiple simple modules for more efficient training, which is comprised of classical forward training (FT) and designed backward training (BT). Under the proposed framework, each module can be trained efficiently in FT by stochastic algorithms without distortion of graph information owing to its simplicity. To avoid the only unidirectional information delivery of FT and sufficiently train shallow modules with the deeper ones, we develop a backward training mechanism that makes the former modules perceive the latter modules, inspired by the classical backward propagation algorithm. The backward training introduces the reversed information delivery into the decoupled modules as well as the forward information delivery. To investigate how the decoupling and greedy training affect the representational capacity, we theoretically prove that the error produced by linear modules will not accumulate on unsupervised tasks in most cases. The theoretical and experimental results show that the proposed framework is highly efficient with reasonable performance, which may deserve more investigation.

Objective. Quantitative susceptibility mapping (QSM) is a new imaging technique for non-invasive characterization of the composition and microstructure of in vivo tissues, and it can be reconstructed from local field measurements by solving an ill-posed inverse problem. Even for deep learning networks, it is not an easy task to establish an accurate quantitative mapping between two physical quantities of different units, i.e. field shift in Hz and susceptibility value in ppm for QSM. Approach . In this paper, we propose a spatially adaptive regularization based three-dimensional reconstruction network SAQSM. A spatially adaptive module is specially designed and a set of them at different resolutions are inserted into the network decoder, playing a role of cross-modality based regularization constraint. Therefore, the exact information of both field and magnitude data is exploited to adjust the scale and shift of feature maps, and thus any information loss or deviation occurred in previous layers could be effectively corrected. The network encoding has a dynamic perceptual initialization, which enables the network to overcome receptive field intervals and also strengthens its ability to detect features of various sizes. Main results . Experimental results on the brain data of healthy volunteers, clinical hemorrhage and simulated phantom with calcification demonstrate that SAQSM can achieve more accurate reconstruction with less susceptibility artifacts, while perform well on the stability and generalization even for severe lesion areas. Significance . This proposed framework may provide a valuable paradigm to quantitative mapping or multimodal reconstruction., (© 2024 Institute of Physics and Engineering in Medicine.)