Search

Logs require multiple verifications to ensure accurate volume and quantity measurements. Log end detection is a crucial step in measuring log volume and counting logs. Currently, this task primarily relies on the Mask R-CNN instance segmentation model. However, the Feature Pyramid Network (FPN) in Mask R-CNN may compromise accuracy due to feature redundancy during multi-scale fusion, particularly with small objects. Moreover, counting logs in a single image is challenging due to their large size and stacking. To address the above issues, we propose an improved log segmentation model based on Cascade Mask R-CNN. This method uses ResNet for multi-scale feature extraction and integrates a hierarchical Convolutional Block Attention Module (CBAM) to refine feature weights and enhance object emphasis. Then, a Region Proposal Network (RPN) is employed to generate log segmentation proposals. Finally, combined with Deep SORT, the model tracks log ends in video streams and counts the number of logs in the stack. Experiments demonstrate the effectiveness of our method, achieving an average precision ( A P ) of 82.3, A P s of 75.3 for small, A P m of 70.9 for medium, and A P l of 86.2 for large objects. These results represent improvements of 1.8%, 3.7%, 2.6%, and 1.4% over Mask R-CNN, respectively. The detection rate reached 98.6%, with a counting accuracy of 95%. Compared to manually measured volumes, our method shows a low error rate of 4.07%. [ABSTRACT FROM AUTHOR]

Atypical porcine pestivirus (APPV) can cause congenital tremor type A-II in neonatal piglets, posing a significant threat to swine herd health globally. Our previous study demonstrated that the Mut domains, comprising 112 amino acids at the N-terminus, are the primary functional regions of the E2 protein of APPV. This study identified 14 host cellular proteins that exhibit potential interactions with the Mut domains of the E2 protein using yeast two-hybrid screening. Using bioinformatics analysis, we discovered that the Mut domains of the E2 protein might exert regulatory effects on apoptosis by modulating energy metabolism within the mitochondria. We also conducted co-immunoprecipitation, glutathione S-transferase pull-down, and immunofluorescence assays to confirm the interaction between the Mut domains of the E2 protein and cathepsin H and signal sequence receptor subunit 4 (SSR4). Ultimately, SSR4 enhanced APPV replication in vitro. In summary, our study successfully elucidated the interactions between the Mut domains of the E2 protein and host cell protein, predicted the potential pathways implicated in these interactions, and demonstrated SSR4 involvement in APPV infection. These significant findings contribute valuable knowledge toward a deeper understanding of APPV pathogenesis and the role of the Mut domains of the E2 protein in this intricate process. [ABSTRACT FROM AUTHOR]

The Dendrolimus houi, a phytophagous pest, displays a broad range of adaptations and often inflicts localized damage to its hosts. Cupressus funebris, an indigenous timber species in China, is significantly impacted by D. houi. Investigating the suitable habitat distribution and changes in D. houi and its host plant, C. funebris, within the context of climate warming, is essential for understanding D. houi's development and providing novel insights for managing D. houi and conserving C. funebris resources. In this study, MaxEnt was employed to simulate the distribution of D. houi and its host plant, C. funebris, in their suitable habitats, evaluating the influence of environmental factors on their distribution and determining changes under a warming scenario. MaxEnt model parameters were adjusted using the Kuenm data package based on available distribution and climatic data. The minimum temperature of the coldest month emerged as the primary environmental factor influencing the distribution of a suitable habitat for D. houi and C. funebris, with a percentage contribution of environmental factors over 60%. There was a substantial similarity in the suitable habitat distributions of D. houi and C. funebris, with varying degrees of expansion in the total habitat area under different temporal and climatic scenarios. Intersection analysis results indicated that the 2041–2060 period, especially under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios, is a critical phase for D. houi control. The habitat expansion of D. houi and C. funebris due to climate change was observed, with the distribution center of D. houi shifting northeast and that of C. funebris shifting northwest. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) is an important research topic in the field of forestry, with implications for carbon cycles and carbon sinks. Malania oleifera Chun et S. K. Lee (M. oleifera) is a valuable plant species that is listed on the National Second-Class Protected Plant checklist and has received global attention for its conservation and resource utilization. To obtain accurate AGB of individual M. oleifera trees in a fast, low-finance-cost and low-labor-cost way, this study first attempted to estimate individual M. oleifera tree AGB by combining the centimeter-level resolution RGB imagery derived from unmanned aerial vehicles (UAVs) and the deep learning model of Mask R-CNN. Firstly, canopy area (CA) was obtained from the 3.5 cm high-resolution UAV-RGB imagery using the Mask R-CNN; secondly, to establish an allometric growth model between the diameter at breast height (DBH) and CA, the correlation analysis of both was conducted; thirdly, the AGB estimation method of individual M. oleifera trees was presented based on an empirical equation. The study showed that: (1) The deep learning model of Mask R-CNN achieved an average segmentation accuracy of 90% in the mixed forests to the extraction of the canopy of M. oleifera trees from UAV-RGB imagery. (2) The correlation between the extracted CA and field-measured DBH reached an R2 of 0.755 (n = 96). (3) The t-test method was used to verify the predicted and observed values of the CA-DBH model presented in this study, and the difference in deviation was not significant (p > 0.05). (4) AGB of individual M. oleifera was estimated for the first time. This study provides a reference method for the estimation of individual tree AGB of M. oleifera based on centimeter-level resolution UAV-RGB images and the Mask R-CNN deep learning. [ABSTRACT FROM AUTHOR]

Wood logs need to be measured for size when passing through customs to verify their quantity and volume. Due to the large number of wood logs needs through customs, a fast and accurate measurement method is required. The traditional log measurement methods are inefficient, have significant errors in determining the long and short diameters of the wood, and are difficult to achieve fast measurements in complex wood stacking environments. We use a Mask R-CNN instance segmentation model to detect the contour of the wood log and employ a binocular stereo camera to measure the log diameter. A rotation search algorithm centered on the wood contour is proposed to find long and short diameters and to optimal log size according to the Chinese standard. The experiments show that the Mask R-CNN we trained obtains 0.796 average precision and 0.943 I O U m a s k , and the recognition rate of wood log ends reaches 98.2%. The average error of the short diameter of the measurement results is 5.7 mm, the average error of the long diameter is 7.19 mm, and the average error of the diameter of the wood is 5.3 mm. [ABSTRACT FROM AUTHOR]

Tomicus yunnanensis Kirkendall (Coleoptera: Scolytinae) is a stem-boring pest that endangers Pinus yunnanensis Franch (Pinales:Pinoideae), which seriously affects the ecological environment safety in southwest China. In order to understand the potential distribution pattern and change in the potential distribution of P. yunnanensis and T. yunnanensis, this study used the maximum entropy model to predict the distribution of potentially suitable areas for P. yunnanensis and T. yunnanensis and explored the relationships between their different spatiotemporal distributions based on change analysis. The experimental results show that altitude is the main factor restricting the current distribution of P. yunnanensis. The current suitable areas of P. yunnanensis are mainly distributed in Yunnan, Sichuan and Guizhou. The minimum temperature of the coldest month is the main factor affecting the current distribution of T. yunnanensis. The current suitable areas of T. yunnanensis are mainly distributed in Yunnan, Sichuan and Tibet. Under future climate scenarios, the total suitable areas of P. yunnanensis and T. yunnanensis are expected to increase. The suitable areas tend to move to higher altitudes in the west and higher latitudes in the north. At the same time, this study finds that there is an obvious bottleneck of expansion to northeastern Sichuan near the Daba Mountains. The results of intersection analysis showed that, with future climate change, P. yunnanensis and T. yunnanensis mainly showed lowly suitable (or unsuitable)—lowly suitable (or unsuitable) to moderately (or highly) suitable—and moderate (or high) variation patterns of suitable areas under the SSP1-2.6 climate scenario. These results will provide an important basis for the breeding of P. yunnanensis and controlling T. yunnanensis. [ABSTRACT FROM AUTHOR]

Wood recognition is necessary for work in the wood trade activities. The advantage of the one-class wood classification method is more generalization, and it only needs positive samples and does not need negative samples in the training phase, so it is suitable for rare wood species inspection. This paper proposed an improved method based on the one-class support vector machine (OCSVM) for wood species recognition. It uses cross-section images acquired with a magnifying glass, which uses a pre-trained VGG16 model for feature extraction, a normal distribution test for key features filtering, and OCSVM to determine the wood species. The results showed that the approach achieved a mean recall of 0.842 for both positive and negative samples, which indicates this method has good performance for wood recognition. In a negative public dataset, the negative recall reached as high as 0.989, which showed that this method has good generalization. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a computer-aided method to quantify the obvious degree of growth ring boundaries of softwood species, based on data analysis with some image processing technologies. For this purpose, a 5× magnified cross-section color micro-image of softwood was cropped into 20 sub-images, and then every image was binarized as a gray image according to an automatic threshold value. After that, the number of black pixels in the gray image was counted row by row and the number of black pixels was binarized to 0 or 100. Finally, a transition band from earlywood to latewood on the sub-image was identified. If everything goes as planned, the growth ring boundaries of the sub-image would be distinct. Otherwise would be indistinct or absent. If more than 50% sub-images are distinct, with the majority voting method, the growth ring boundaries of softwood would be distinct, otherwise would be indistinct or absent. The proposed method has been visualized as a growth-ring-boundary detecting system based on the.NET Framework. A sample of 100 micro-images (see S1 Fig via https://github.com/senly2019/Lin-Qizhao/) of softwood cross-sections were selected for evaluation purposes. In short, this detecting system computes the obvious degree of growth ring boundaries of softwood species by image processing involving image importing, image cropping, image reading, image grayscale, image binarization, data analysis. The results showed that the method used avoided mistakes made by the manual comparison method of identifying the presence of growth ring boundaries, and it has a high accuracy of 98%. [ABSTRACT FROM AUTHOR]

Hole transport in the p-type metal-oxide-semiconductor field-effect-transistor (p-MOSFET) inversion layer under arbitrary stress, surface, and channel orientation is investigated by employing a six-band k·p model and finite difference formalism. The piezoresistance coefficients are calculated and measured at stresses up to 300 MPa via wafer-bending experiments for stresses of technological importance: uniaxial and biaxial stresses on (001) and (110) surface oriented p-MOSFETs with <110> and <111> channels. With good agreement in the measured and calculated small stress piezoresistance coefficients, k·p calculations are used to give physical insights into hole mobility enhancement at large stress (∼3 GPa). The results show that the maximum hole mobility is similar for (001)/<110>, (110)/<110>, and (110)/<111> p-MOSFETs under uniaxial stress, although the enhancement factor is different. Strong quantum confinement and a low density of states cause less stress-induced mobility enhancement for (110) p-MOSFETs. For (001) p-MOSFETs, the dominant factor for the improved hole mobility is reduced conductivity effective mass at small stress and lower phonon scattering rate at large stress. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are a kind of noncoding RNA, which plays an essential role in gene regulation by binding to messenger RNAs (mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulation, which is of great significance for the study of cancer and other diseases. Many bioinformatics methods have been proposed to solve this problem, but the previous research did not further study the encoding of the nucleotide sequence. In this paper, we developed a novel method combining word embedding and deep learning for human miRNA targets at the site-level prediction, which is inspired by the similarity between natural language and biological sequences. First, the word2vec model was used to mine the distribution representation of miRNAs and mRNAs. Then, the embedding is extracted automatically via the stacked bidirectional long short-term memory (BiLSTM) network. By testing, our method can effectively improve the accuracy, sensitivity, specificity, and F -measure of other methods. Through our research, it is proved that the distributed representation can improve the accuracy of the deep learning model and better solve the miRNA target site prediction problem. [ABSTRACT FROM AUTHOR]

Power law is an important feature of phenomena in long memory behaviors. Zipf ever found power law in the distribution of the word frequencies. In physics, the terms order and disorder are Thermodynamic or statistical physics concepts originally and a lot of research work has focused on self-organization of the disorder ingredients of simple physical systems. It is interesting what make disorder–order transition. We devise an experiment-based method about random symbolic sequences to research regular pattern between disorder and order. The experiment results reveal power law is indeed an important regularity in transition from disorder to order. About these results the preliminary study and analysis has been done to explain the reasons. [ABSTRACT FROM AUTHOR]

Background: Porcine reproductive and respiratory syndrome virus (PRRSV) is the cause of an economically important swine disease that has devastated the swine industry since the late 1980s. The aim of the present study was to investigate the interaction between reactive oxygen species (ROS) and NF-κB by PRRSV infection., Results: We isolated the local strain of PRRSV from southwest China, designated YN-2011, then sequenced and analyzed the genome. YN-2011 was then used to evaluate the interaction of ROS and NF-κB. In PRRSV infected MARC-145 cells, there was a time-dependent increase in ROS and Maleic Dialdehyde (MDA). Accordingly, NF-κB activation was also increased as PRRSV infection progressed. Degradation of IκB mRNA was detected late in PRRSV infection, and overexpression of the dominant negative form of IκBα significantly suppressed NF-κB induced by PRRSV., Conclusions: The results indicate that the generation of ROS is involved in PRRSV replication and this progression is associated with the alteration in NF-κB activity induced by ROS. These results should extend our better understanding the interaction between PRRSV and host MARC-145 cells.

The mammalian major histocompatibility complex (MHC) plays important roles in pathogen recognition and disease resistance. In the present study, the coding sequence and the 5'- and 3'-untranslated regions of MHC class II DR alpha chain (the DRA gene) from rare gayal and gaytle were cloned and analyzed to dissect structural and functional variations. The nucleotide and amino acid sequences for the DRA genes in gayal ( Bofr-DRA ) and gaytle ( Bofr × BoLA-DRA ) were almost identical to those for cattle and yak (99%). Compared to yak, two amino acids substitutions in the signal peptide (SP) domain for gayal were found within all Bos animals. Except for only one replacement in the amino acid within the α2 domain of the DRA protein in gayal, the additional residues were highly conserved across the species investigated. The 20 peptide-binding sites (PBS) of Bofr-DRA and Bofr × BoLA-DRA were essentially reserved in the α1 domain among all species investigated. The lesser degree of substitution in Bofr-DRA is concordant with the concept that the DRA gene is highly conserved among all mammals. The very high degree of conservativity of the DRA gene among ruminants, including gayal, suggests its recent evolutionary separation.

In the present study, exon 2 of major histocompatibility complex (MHC) class II DQB gene from 39 gayals ( Bos frontalis ) was isolated, characterized and compared with previously reported patterns for other bovidae. It was revealed by sequence analyses that there are 36 DQB exon 2 variants among 39 gayals. These variants exhibited a high degree of nucleotide and amino acid substitutions with most amino acid variations occurring at positions forming the peptide-binding sites (PBS). The DQB loci were analysed for patterns of synonymous ( d S ) and non-synonymous ( d N ) substitution. The gayals were observed to be under strong balancing selection in the DQB exon 2 PBS ( d N = 0.094, P = 0.001). It appears that this variability among gayals could confer the ability to mount immune responses to a wide variety of peptides or pathogens.