The complete chloroplast genome of common walnut (Juglans regia)

Common walnut (Juglans regia L.) is cultivated in temperate regions worldwide for its wood and nuts. The complete chloroplast genome of J. regia was sequenced using the Illumina MiSeq platform. This is the first complete chloroplast sequence for the Juglandaceae, a family that includes numerous species of economic importance. The chloroplast genome of J. regia was 160 367 bp in length, with 36.11% GC content. It contains a pair of inverted repeats (26 035 bp) which were separated by a large single copy (89 872 bp) and a small single copy region (18 425 bp). A total of 137 genes were annotated, which included 86 protein-coding genes, three pseudogenes (two ycf15 and one infA), 40 tRNA genes and eight rRNA genes. The neighbour-joining phylogenetic analysis with the reported chloroplast genomes showed that common walnut chloroplasts are most closely related to those of the Fagaceae family

Climatic and Soil Factors Shape the Demographical History and Genetic Diversity of a Deciduous Oak (Quercus liaotungensis) in Northern China

Past and current climatic changes have affected the demography, patterns of genetic diversity, and genetic structure of extant species. The study of these processes provides valuable information to forecast evolutionary changes and to identify conservation priorities. Here, we sequenced two functional nuclear genes and four chloroplast DNA regions for 105 samples from 21 populations of Quercus liaotungensis across its distribution range. Coalescent-based Bayesian analysis, approximate Bayesian computation (ABC), and ecological niche modeling (ENM) were integrated to investigate the genetic patterns and demographical history of this species. Association estimates including Mantel tests and multiple linear regressions were used to infer the effects of geographical and ecological factors on temporal genetic variation and diversity of this oak species. Based on multiple loci, Q. liaotungensis populations clustered into two phylogenetic groups; this grouping pattern could be the result of adaptation to habitats with different temperature and precipitation seasonality conditions. Demographical reconstructions and ENMs suggest an expansion decline trend of this species during the Quaternary climatic oscillations. Association analyses based on nuclear data indicated that intraspecific genetic differentiation of Q. liaotungensis was clearly correlated with ecological distance; specifically, the genetic diversity of this species was significantly correlated with temperature seasonality and soil pH, but negatively correlated with precipitation. Our study highlights the impact of Pleistocene climate oscillations on the demographic history of a tree species in Northern China, and suggests that climatic and soil conditions are the major factors shaping the genetic diversity and population structure of Q. liaotungensis

Remote Sensing Image Detection Based on YOLOv4 Improvements

Remote sensing image target object detection and recognition are widely used both in military and civil fields. There are many models proposed for this purpose, but their effectiveness on target object detection in remote sensing images is not ideal due to the influence of climate conditions, obstacles and confusing objects presented in images, image clarity, and associated problems with small-target and multi-target detection and recognition. Therefore, how to accurately detect target objects in images is an urgent problem to be solved. To this end, a novel model, called YOLOv4_CE, is proposed in this paper, based on the classical YOLOv4 model with added improvements, resulting from replacing the backbone feature-extraction CSPDarknet53 network with a ConvNeXt-S network, replacing the Complete Intersection over Union (CIoU) loss with the Efficient Intersection over Union (EIoU) loss, and adding a coordinate attention mechanism to YOLOv4, as to improve its remote sensing image detection capabilities. The results, obtained through experiments conducted on two open data sets, demonstrate that the proposed YOLOv4_CE model outperforms, in this regard, both the original YOLOv4 model and four other state-of-the-art models, namely Faster R-CNN, Gliding Vertex, Oriented R-CNN, and EfficientDet, in terms of the mean average precision (mAP) and F1 score, by achieving respective values of 95.03% and 0.933 on the NWPU VHR-10 data set, and 95.89% and 0.937 on the RSOD data set.National Key Research and Development Program of China under Grant 2017YFE0135700; MES under Grant No. D01-168/28.07.2022 for NCDSC part of the Bulgarian National Roadmap on RIs; Telecommunications Research Centre (TRC), University of Limerick, Ireland

Multi-tissue integrative analysis of personal epigenomes

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics

The Effect of Clay on the Shear Strength of Microbially Cured Sand Particles

Microbial solidification of sand has obvious effects: energy-saving and environmental protection. It is a green and sustainable soil consolidation technology with low energy consumption, which meets the needs of high-quality development of modern economy and society. However, when clay is doped in sand, clay has an uncertain influence on the effectiveness of the microbial solidification of sand. Therefore, triaxial consolidation undrained tests before and after microbial solidification of sands with different clay content are carried out in this paper. The effects of clay content on the solidification effect of sands are compared and analyzed. The variation laws of shear strength, unconfined compressive strength, internal friction angle and the cohesion of sands with different clay content before and after microbial solidification are discussed. The failure modes of sand samples were studied and the influence mechanism of clay on the microbial solidification of sand was revealed from a micro perspective. The test results show that the failure strain and unconfined compressive strength of microbial-induced calcium carbonate precipitation (MICP) treated samples increase first and then decrease with the increase in the clay content. The unconfined compressive strength is the highest when the clay content is 9%, and the samples with low clay content (3~9%) can still retain good integrity after being destroyed. As the content of clay in the sand–clay mixture increases, the internal friction angle of the sample decreases and the cohesion increases. After MICP treatment, the internal friction angle and cohesion of the sand increase first and then decrease with the increase in clay content. There are three main contact modes between sand-clay-CaCO3. When clay content is low, clay plays a filling role. The contact mode between sand-clay and CaCO3 is mainly between sand particles and calcium carbonate and between clay particles and calcium carbonate. When clay content is high, the contact mode between particles is mainly between clay particles and calcium carbonate. Higher clay content wraps sand particles, prevents contact between calcium carbonate and sand particles and reduces the strength of sand

Cross-Section Dimension Measurement of Construction Steel Pipe Based on Machine Vision

Currently, the on-site measuring of the size of a steel pipe cross-section for scaffold construction relies on manual measurement tools, which is a time-consuming process with poor accuracy. Therefore, this paper proposes a new method for steel pipe size measurements that is based on edge extraction and image processing. Our primary aim is to solve the problems of poor accuracy and waste of labor in practical applications of construction steel pipe inspection. Therefore, the developed method utilizes a convolutional neural network and image processing technology to find an optimum solution. Our experiment revealed that the edge image that is proposed in the existing convolutional neural network technology is relatively rough and is unable to calculate the steel pipe’s cross-sectional size. Thus, the suggested network model optimizes the current technology and combines it with image processing technology. The results demonstrate that compared with the richer convolutional features (RCF) network, the optimal dataset scale (ODS) is improved by 3%, and the optimal image scale (OIS) is improved by 2.2%. At the same time, the error value of the Hough transform can be effectively reduced after improving the Hough algorithm

Effect of Nutrient Solution Composition on Bio-Cemented Sand

Microbial-induced carbonate precipitation is an environmentally friendly foundation treatment technology that effectively improves soil engineering performance. The various nutrient components of liquid curing compounds significantly influence the curing effect. On the basis of penetration, dry density, water absorption, and unconfined compressive strength tests, this study showed the effect of nutrient solution composition, including urea, calcium chloride, sodium bicarbonate, ammonium chloride, and nutrient broth, on the physicomechanical properties of bio-cemented sand. The morphological differences of calcium carbonate precipitates under nutrient solution composition were compared through scanning electron microscopy (SEM). Results showed that the curing effect of compound nutrient solution was improved compared with the basic nutrient solution (urea and calcium chloride). Among the individual components added, ammonium chloride had the most remarkable effect, followed by sodium bicarbonate and nutrient broth. Among the paired components added, sodium bicarbonate + ammonium chloride had the most significant effect, followed by sodium bicarbonate + nutrient broth and ammonium chloride + nutrient broth. The strength of bio-cemented sand cured with compound nutrient solution containing five components could reach 3.43 MPa, which was 1.92 times higher than the strength of the basic nutrient solution. As shown by the SEM image, the calcium carbonate precipitation in the solidified sand was distributed in the clearance of sand particles, effectively bonding the sand particles. The calcium carbonate obtained by the composition of the compound nutrient solution precipitated the sand particles, and some of the sand particles were wrapped. Moreover, the amount of precipitation was evidently greater than that of the basic nutrient solution. Compared with the basic nutrient solution, the compound nutrient solution effectively reduced the apparent porosity and average pore size of the sand. Thus, the curing effect of the compound nutrient solution was better than that of the basic nutrient solution

Effect of final cooling temperature on the microstructure and mechanical properties of high-strength anti-seismic rebar

Rebar is an important material in the major structural engineering, and its fine structure has a very important effect on the performance of the rebar. In this work, the Gleeble-3800 thermal simulator was used to simulate and control the final cooling temperature process to explore the effect of the precipitation behavior of the microalloying elements on the microstructure and mechanical properties of the rebar. The electron backscatter diffraction (EBSD), high-resolution transmission electron microscope (TEM), and universal tensile testing machine were used to characterize the microstructural transformation and mechanical properties of high-strength anti-seismic rebar. The results shows that under the conditions of different final cooling temperatures, the microstructure of the rebar were mainly composed of ferrite and pearlite. When the final cooling temperature decreased from 750 °C to 650 °C, the ferrite grain size decreased from 0.01237 mm to 0.00678 mm and the pearlite lamellar spacing decreased from 0.226 μ m to 0.114 μ m. The EBSD results found that the most of ferrite grains with larger misorientation angle (20° ∼ 60°) formed by the different austenite grains. The TEM results found that the main precipitates were (Nb, Ti, V) C, which precipitated on the ferrite matrix, and the shapes were oval, and the average particle sizes were about 20 ∼ 30 nm. When the final cooling temperature was 650 °C, the tensile strength and yield strength of the rebar reached 712.94 MPa and 562.97 MPa, respectively, and strength yield ratio was 1.27. With the decreases in the final cooling temperature, the tensile strength and yield strength of the rebar gradually increased

Effect of Nutrient Solution Composition on Bio-Cemented Sand

Microbial-induced carbonate precipitation is an environmentally friendly foundation treatment technology that effectively improves soil engineering performance. The various nutrient components of liquid curing compounds significantly influence the curing effect. On the basis of penetration, dry density, water absorption, and unconfined compressive strength tests, this study showed the effect of nutrient solution composition, including urea, calcium chloride, sodium bicarbonate, ammonium chloride, and nutrient broth, on the physicomechanical properties of bio-cemented sand. The morphological differences of calcium carbonate precipitates under nutrient solution composition were compared through scanning electron microscopy (SEM). Results showed that the curing effect of compound nutrient solution was improved compared with the basic nutrient solution (urea and calcium chloride). Among the individual components added, ammonium chloride had the most remarkable effect, followed by sodium bicarbonate and nutrient broth. Among the paired components added, sodium bicarbonate + ammonium chloride had the most significant effect, followed by sodium bicarbonate + nutrient broth and ammonium chloride + nutrient broth. The strength of bio-cemented sand cured with compound nutrient solution containing five components could reach 3.43 MPa, which was 1.92 times higher than the strength of the basic nutrient solution. As shown by the SEM image, the calcium carbonate precipitation in the solidified sand was distributed in the clearance of sand particles, effectively bonding the sand particles. The calcium carbonate obtained by the composition of the compound nutrient solution precipitated the sand particles, and some of the sand particles were wrapped. Moreover, the amount of precipitation was evidently greater than that of the basic nutrient solution. Compared with the basic nutrient solution, the compound nutrient solution effectively reduced the apparent porosity and average pore size of the sand. Thus, the curing effect of the compound nutrient solution was better than that of the basic nutrient solution

Detection of River Floating Garbage Based on Improved YOLOv5

The random dumping of garbage in rivers has led to the continuous deterioration of water quality and affected people’s living environment. The accuracy of detection of garbage floating in rivers is greatly affected by factors such as floating speed, night/daytime natural light, viewing angle and position, etc. This paper proposes a novel detection model, called YOLOv5_CBS, for the detection of garbage objects floating in rivers, based on improvements of the YOLOv5 model. Firstly, a coordinate attention (CA) mechanism is added to the original C3 module (without compressing the number of channels in the bottleneck), forming a new C3-CA-Uncompress Bottleneck (CCUB) module for improving the size of the receptive field and allowing the model to pay more attention to important parts of the processed images. Then, the Path Aggregation Network (PAN) in YOLOv5 is replaced with a Bidirectional Feature Pyramid Network (BiFPN), as proposed by other researchers, to enhance the depth of information mining and improve the feature extraction capability and detection performance of the model. In addition, the Complete Intersection over Union (CIoU) loss function, which was originally used in YOLOv5 for the calculation of location score of the compound loss, is replaced with the SCYLLA-IoU (SIoU) loss function, so as to speed up the model convergence and improve its regression precision. The results, obtained through experiments conducted on two datasets, demonstrate that the proposed YOLOv5_CBS model outperforms the original YOLOv5 model, along with three other state-of-the-art models (Faster R-CNN, YOLOv3, and YOLOv4), when used for river floating garbage objects detection, in terms of the recall, average precision, and F1 score achieved by reaching respective values of 0.885, 90.85%, and 0.8669 on the private dataset, and 0.865, 92.18%, and 0.9006 on the Flow-Img public dataset