255 research outputs found
Functional Mapping of Plant Growth in Arabidopsis thaliana
Most traits important to agriculture, biology, and biomedicine are complex traits, determined by both genetic and environmental factors. The complex traits that change their phenotypes over different stages of development are called dynamic traits. Traditional quantitative trait loci (QTLs) mapping approaches ignore the dynamic changes of complex traits. Functional mapping, as a powerful statistical tool, can not only map QTLs that control the developmental pattern and process of complex traits, but also describe the dynamic changes of complex traits. In this study, we used functional mapping to identify those QTLs that affect height growth in 10th generation recombinant inbred lines derived from two different Arabidopsis thaliana accessions. Functional mapping identified 48 QTLs that are related to height traits. The growth curves of different genotypes can be drawn for each significant locus. By GO gene function annotations, we found that these QTLs detected are associated with the synthesis of biological macromolecules and the regulation of biological functions. Our findings provide unique insights into the genetic control of height growth of A. thaliana and will provide a theoretical basis for the study of complex traits
Instability and Momentum Bifurcation of molecular BEC in Exotic Dispersion with Shaken Lattice
We place a molecular Bose-Einstein condensate in a 1D shaken lattice with a
Floquet-engineered dispersion, and observe the dynamics in both position and
momentum space. At the initial condition of zero momentum, our engineered
dispersion is inverted, and therefore unstable. We observe that the condensate
is destabilized by the lattice shaking as expected, but rather than decaying
incoherently or producing jets, as in other unstable condensates, under our
conditions the condensate bifurcates into two portions in momentum space, with
each portion subsequently following semi-classical trajectories that suffer
minimal spreading in momentum space as they evolve. We can model the evolution
with a Gross-Pitaevskii equation, which suggests the initial bifurcation is
facilitate by a nearly linear "inverted V"-shaped dispersion at the zone
center, while the lack of spreading in momentum space is facilitated by
interactions, as in a soliton. We propose that this relatively clean
bifurcation in momentum space has applications for counter-diabatic preparation
of exotic ground states in many-body quantum simulation schemes
Underwater target detection based on improved YOLOv7
Underwater target detection is a crucial aspect of ocean exploration.
However, conventional underwater target detection methods face several
challenges such as inaccurate feature extraction, slow detection speed and lack
of robustness in complex underwater environments. To address these limitations,
this study proposes an improved YOLOv7 network (YOLOv7-AC) for underwater
target detection. The proposed network utilizes an ACmixBlock module to replace
the 3x3 convolution block in the E-ELAN structure, and incorporates jump
connections and 1x1 convolution architecture between ACmixBlock modules to
improve feature extraction and network reasoning speed. Additionally, a
ResNet-ACmix module is designed to avoid feature information loss and reduce
computation, while a Global Attention Mechanism (GAM) is inserted in the
backbone and head parts of the model to improve feature extraction.
Furthermore, the K-means++ algorithm is used instead of K-means to obtain
anchor boxes and enhance model accuracy. Experimental results show that the
improved YOLOv7 network outperforms the original YOLOv7 model and other popular
underwater target detection methods. The proposed network achieved a mean
average precision (mAP) value of 89.6% and 97.4% on the URPC dataset and
Brackish dataset, respectively, and demonstrated a higher frame per second
(FPS) compared to the original YOLOv7 model. The source code for this study is
publicly available at https://github.com/NZWANG/YOLOV7-AC. In conclusion, the
improved YOLOv7 network proposed in this study represents a promising solution
for underwater target detection and holds great potential for practical
applications in various underwater tasks
Coating titania nanoparticles with epoxy-containing catechol polymers via Cu(0)-living radical polymerization as intelligent enzyme carriers
Immobilization of enzyme could offer the biocatalyst with increased stability and important recoverability, which plays a vital role in the enzyme’s industrial applications. In this study, we present a new strategy to build an intelligent enzyme carrier by coating titania nanoparticles with thermoresponsive epoxy-functionalized polymers. Zero-valent copper-mediated living radical polymerization (Cu(0)-LRP) was utilized herein to copolymerize N-isopropylacrylamide (NIPAM) and glycidyl acrylate (GA) directly from an unprotected dopamine-functionalized initiator to obtain an epoxy-containing polymer with terminal anchor for the “grafting to” or “one-pot” modification of titania nanoparticles. A rhodamine B-labeled laccase has been subsequently used as a model enzyme for successful immobilization to yield an intelligent titania/laccase hybrid bifunctional catalyst. The immobilized laccase has shown excellent thermal stability under ambient or even relatively high temperature above the lower critical solution temperature (LCST) at which temperature the hybrid particles could be facilely recovered for reuse. The enzyme activity could be maintained during the repeated use after recovery and enzymatic degradation of bisphenol A was proven to be efficient. The photocatalytic ability of titania was also investigated by fast degradation of rhodamine B under the excitation of simulated sunlight. Therefore, this study has provided a facile strategy for the immobilization of metal oxide catalysts with enzymes, which constructs a novel bifunctional catalyst that will be promising for the “one-pot” degradation of different organic pollutants
Characterization of marine shale in Western Hubei Province based on unmanned aerial vehicle oblique photographic data
The marine shale in the Sinian Doushantuo Formation of Qinglinkou outcrop section is well developed, but the current characterization methods for outcrops are unsatisfactory. In this paper, the data of outcrop in the field study area were collected by Unmanned Aerial Vehicle, then processed and interpreted by oblique photography technology combined with manual investigation. Subsequently, we established a quantitative geological knowledge database of the shale formations and carried out the typical section of anatomy analysis. The results showed that the high-precision image information captured by unmanned aerial vehicle oblique photography technology can be well coupled with a three-dimensional coordinate system. The three-dimensional digital model was used to characterize the lithologic assemblage, thickness and distribution characteristics of the target reservoir. Based on this digital model, we established the three-dimensional lithology and the total organic carbon models of the outcrop area. The spatial distribution characteristics of interbedding between marine dolomite and shale in the outcrop area were displayed, and the distribution of total organic carbon was revealed under lithological constraints. The models are beneficial for the analysis and prediction of the lithology and total organic carbon, which is of great significance to the understanding of shale gas sweet spots.Cited as: Yin, S., Feng, K., Nie, X., Chen, Q., Liu, Y., Wang, P. Characterization of marine shale in Western Hubei Province based on unmanned aerial vehicle oblique photographic data. Advances in Geo-Energy Research, 2022, 6(3): 252-263. https://doi.org/10.46690/ager.2022.03.0
Organic & Hybrid Photonic Crystals for Controlling Light-Matter Interaction Processes
Organic & Hybrid Photonic Crystals for Controlling Light-Matter Interaction Processe
Ruminal microbiota and muscle metabolome characteristics of Tibetan plateau yaks fed different dietary protein levels
IntroductionThe dietary protein level plays a crucial role in maintaining the equilibrium of rumen microbiota in yaks. To explore the association between dietary protein levels, rumen microbiota, and muscle metabolites, we examined the rumen microbiome and muscle metabolome characteristics in yaks subjected to varying dietary protein levels.MethodsIn this study, 36 yaks were randomly assigned to three groups (n = 12 per group): low dietary protein group (LP, 12% protein concentration), medium dietary protein group (MP, 14% protein concentration), and high dietary protein group (HP, 16% protein concentration).Results16S rDNA sequencing revealed that the HP group exhibited the highest Chao1 and Observed_species indices, while the LP group demonstrated the lowest. Shannon and Simpson indices were significantly elevated in the MP group relative to the LP group (P < 0.05). At the genus level, the relative abundance of Christensenellaceae_R-7_group in the HP group was notably greater than that in the LP and MP groups (P < 0.05). Conversely, the relative abundance of Rikenellaceae_RC9_gut_group displayed an increasing tendency with escalating feed protein levels. Muscle metabolism analysis revealed that the content of the metabolite Uric acid was significantly higher in the LP group compared to the MP group (P < 0.05). The content of the metabolite L-(+)-Arabinose was significantly increased in the MP group compared to the HP group (P < 0.05), while the content of D-(-)-Glutamine and L-arginine was significantly reduced in the LP group (P < 0.05). The levels of metabolites 13-HPODE, Decanoylcarnitine, Lauric acid, L-(+)-Arabinose, and Uric acid were significantly elevated in the LP group relative to the HP group (P < 0.05). Furthermore, our observations disclosed correlations between rumen microbes and muscle metabolites. The relative abundance of NK4A214_group was negatively correlated with Orlistat concentration; the relative abundance of Christensenellaceae_R-7_group was positively correlated with D-(-)-Glutamine and L-arginine concentrations.DiscussionOur findings offer a foundation for comprehending the rumen microbiome of yaks subjected to different dietary protein levels and the intimately associated metabolic pathways of the yak muscle metabolome. Elucidating the rumen microbiome and muscle metabolome of yaks may facilitate the determination of dietary protein levels
CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation
It is a challenging task to accurately perform semantic segmentation due to
the complexity of real picture scenes. Many semantic segmentation methods based
on traditional deep learning insufficiently captured the semantic and
appearance information of images, which put limit on their generality and
robustness for various application scenes. In this paper, we proposed a novel
strategy that reformulated the popularly-used convolution operation to
multi-layer convolutional sparse coding block to ease the aforementioned
deficiency. This strategy can be possibly used to significantly improve the
segmentation performance of any semantic segmentation model that involves
convolutional operations. To prove the effectiveness of our idea, we chose the
widely-used U-Net model for the demonstration purpose, and we designed CSC-Unet
model series based on U-Net. Through extensive analysis and experiments, we
provided credible evidence showing that the multi-layer convolutional sparse
coding block enables semantic segmentation model to converge faster, can
extract finer semantic and appearance information of images, and improve the
ability to recover spatial detail information. The best CSC-Unet model
significantly outperforms the results of the original U-Net on three public
datasets with different scenarios, i.e., 87.14% vs. 84.71% on DeepCrack
dataset, 68.91% vs. 67.09% on Nuclei dataset, and 53.68% vs. 48.82% on CamVid
dataset, respectively
Computer-aided autotransplantation of teeth with 3D printed surgical guides and arch bar: a preliminary experience
Background/Aim Autotransplantation of teeth is a method to restore the missing teeth and computer-aided techniques have been applied in this field. The aim of this study was to describe a novel approach for computer-aided autotransplantation of teeth and to preliminarily assess its feasibility, accuracy, and stability. Methods Eight wisdom teeth with complete root formation of eight adult patients were autotransplanted. Individual replicas of donor teeth with local splints, surgical templates, and arch bars were virtually designed and fabricated using three-dimensional printing, these were then applied in the autotransplantation surgeries. Clinical and radiological outcomes were observed, the extra-alveolar time, success rate, and 1-year survival rate were analyzed, and accuracy and stability of this approach were evaluated. Results The extra-alveolar time of donor teeth were less than 3 min. The average follow-up duration was 2.00 ± 1.06 years. All autotransplanted teeth showed normal masticatory function. Ankylosis was found in one patient, and the overall success rate was 87.5%, whereas the 1-year survival rate was 100%. Linear differences between the designed and the immediate autotransplanted positions at crowns and apexes of the donor teeth were 1.43 ± 0.57 and 1.77 ± 0.67 mm, respectively. Linear differences between immediate and the stable positions at crowns and apexes of the donor teeth were 0.66 ± 0.36 and 0.67 ± 0.48 mm, respectively. Conclusion The present study illustrated the feasibility, clinical satisfied accuracy, and stability of a novel approach for computer-aided autotransplantation of teeth. This new approach facilitated the surgical procedure and might be a viable and predictable method for autotransplantation of teeth
Eight RGS and RGS-like Proteins Orchestrate Growth, Differentiation, and Pathogenicity of Magnaporthe oryzae
A previous study identified MoRgs1 as an RGS protein that negative regulates G-protein signaling to control developmental processes such as conidiation and appressorium formation in Magnaporthe oryzae. Here, we characterized additional seven RGS and RGS-like proteins (MoRgs2 through MoRgs8). We found that MoRgs1 and MoRgs4 positively regulate surface hydrophobicity, conidiation, and mating. Indifference to MoRgs1, MoRgs4 has a role in regulating laccase and peroxidase activities. MoRgs1, MoRgs2, MoRgs3, MoRgs4, MoRgs6, and MoRgs7 are important for germ tube growth and appressorium formation. Interestingly, MoRgs7 and MoRgs8 exhibit a unique domain structure in which the RGS domain is linked to a seven-transmembrane motif, a hallmark of G-protein coupled receptors (GPCRs). We have also shown that MoRgs1 regulates mating through negative regulation of Gα MoMagB and is involved in the maintenance of cell wall integrity. While all proteins appear to be involved in the control of intracellular cAMP levels, only MoRgs1, MoRgs3, MoRgs4, and MoRgs7 are required for full virulence. Taking together, in addition to MoRgs1 functions as a prominent RGS protein in M. oryzae, MoRgs4 and other RGS and RGS-like proteins are also involved in a complex process governing asexual/sexual development, appressorium formation, and pathogenicity
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