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Background: The recent release of the reference genome sequence assembly of flax, a self-pollinated crop with 15 chromosome pairs, into chromosome-scale pseudomolecules enables the characterization of gene families. The ABC transporter and HMA gene families are important in the control of cadmium (Cd) accumulation in crops. To date, the genome-wide analysis of these two gene families has been successfully conducted in some plant species, but no systematic evolutionary analysis is available for the flax genome. Results: Here we describe the ABC transporter and HMA gene families in flax to provide a comprehensive overview of its evolution and some support towards the functional annotation of its members. The 198 ABC transporter and 12 HMA genes identified in the flax genome were classified into eight ABC transporter and four HMA subfamilies based on their phylogenetic analysis and domains’ composition. Nine of these genes, i.e., LuABCC9, LuABCC10, LuABCG58, LuABCG59, LuABCG71, LuABCG72, LuABCG73, LuHMA3, and LuHMA4, were orthologous with the Cd associated genes in Arabidopsis, rice and maize. Ten motifs were identified from all ABC transporter and HMA genes. Also, several motifs were conserved among genes of similar length, but each subfamily each had their own motif structures. Both the ABC transporter and HMA gene families were highly conserved among subfamilies of flax and with those of Arabidopsis. While four types of gene duplication were observed at different frequencies, whole-genome or segmental duplications were the most frequent with 162 genes, followed by 29 dispersed, 14 tandem and 4 proximal duplications, suggesting that segmental duplications contributed the most to the expansion of both gene families in flax. The rates of non-synonymous to synonymous (Ka/Ks) mutations of paired duplicated genes were for the most part lower than one, indicative of a predominant purifying selection. Only five pairs of genes clearly exhibited positive selection with a Ka/Ks ratio greater than one. Gene ontology analyses suggested that most flax ABC transporter and HMA genes had a role in ATP binding, transport, catalytic activity, ATPase activity, and metal ion binding. The RNA-Seq analysis of eight different organs demonstrated diversified expression profiling patterns of the genes and revealed their functional or sub-functional conservation and neo-functionalization. Conclusion: Characterization of the ABC transporter and HMA gene families will help in the functional analysis of candidate genes in flax and other crop species.

Artificial intelligence (AI) has made significant progress in the medical field in the last decade. The AI-powered analysis methods of medical images and clinical records can now match the abilities of clinical physicians. Due to the challenges posed by the unique group of fetuses and the dynamic organ of the heart, research into the application of AI in the prenatal diagnosis of congenital heart disease (CHD) is particularly active. In this review, we discuss the clinical questions and research methods involved in using AI to address prenatal diagnosis of CHD, including imaging, genetic diagnosis, and risk prediction. Representative examples are provided for each method discussed. Finally, we discuss the current limitations of AI in prenatal diagnosis of CHD, namely Volatility, Insufficiency and Independence (VII), and propose possible solutions.

Background Fetal arrhythmias frequently co-occur with congenital heart disease in fetuses. The peaks observed in M-mode fetal echocardiograms serve as pivotal diagnostic markers for fetal arrhythmias. However, speckles, artifacts, and noise pose notable challenges for accurate image analysis. While current deep learning networks mainly overlook cardiac cyclic information, this study concentrated on the integration of such features, leveraging contextual constraints derived from cardiac cyclical features to improve diagnostic accuracy. Methods This study proposed a novel deep learning architecture for diagnosing fetal arrhythmias. The architecture presented a loss function tailored to the cardiac cyclical information and formulated a diagnostic algorithm for classifying fetal arrhythmias. The training and validation processes utilized a dataset comprising 4440 patches gathered from 890 participants. Results Incorporating cyclic loss significantly enhanced the performance of deep learning networks in predicting peak points for diagnosing fetal arrhythmia, resulting in improvements ranging from 7.11% to 14.81% in F1-score across different network combinations. Particularly noteworthy was the 18.2% improvement in the F1-score for the low-quality group. Additionally, the precision of diagnosing fetal arrhythmia across four categories exhibited improvement, with an average improvement rate of 20.6%. Conclusion This study introduced a cyclic loss mechanism based on the cardiac cycle information. Comparative evaluations were conducted using baseline methods and state-of-the-art deep learning architectures with the fetal echocardiogram dataset. These evaluations demonstrated the proposed framework’s superior accuracy in diagnosing fetal arrhythmias. It is also crucial to note that further external testing is essential to assess the model’s generalizability and clinical value.

The recent release of the reference genome sequence assembly of flax, a self-pollinated crop with 15 chromosome pairs, into chromosome-scale pseudomolecules enables the characterization of gene families. The ABC transporter and HMA gene families are important in the control of cadmium (Cd) accumulation in crops. To date, the genome-wide analysis of these two gene families has been successfully conducted in some plant species, but no systematic evolutionary analysis is available for the flax genome. Here we describe the ABC transporter and HMA gene families in flax to provide a comprehensive overview of its evolution and some support towards the functional annotation of its members. The 198 ABC transporter and 12 HMA genes identified in the flax genome were classified into eight ABC transporter and four HMA subfamilies based on their phylogenetic analysis and domains' composition. Nine of these genes, i.e., LuABCC9, LuABCC10, LuABCG58, LuABCG59, LuABCG71, LuABCG72, LuABCG73, LuHMA3, and LuHMA4, were orthologous with the Cd associated genes in Arabidopsis, rice and maize. Ten motifs were identified from all ABC transporter and HMA genes. Also, several motifs were conserved among genes of similar length, but each subfamily each had their own motif structures. Both the ABC transporter and HMA gene families were highly conserved among subfamilies of flax and with those of Arabidopsis. While four types of gene duplication were observed at different frequencies, whole-genome or segmental duplications were the most frequent with 162 genes, followed by 29 dispersed, 14 tandem and 4 proximal duplications, suggesting that segmental duplications contributed the most to the expansion of both gene families in flax. The rates of non-synonymous to synonymous (Ka/Ks) mutations of paired duplicated genes were for the most part lower than one, indicative of a predominant purifying selection. Only five pairs of genes clearly exhibited positive selection with a Ka/Ks ratio greater than one. Gene ontology analyses suggested that most flax ABC transporter and HMA genes had a role in ATP binding, transport, catalytic activity, ATPase activity, and metal ion binding. The RNA-Seq analysis of eight different organs demonstrated diversified expression profiling patterns of the genes and revealed their functional or sub-functional conservation and neo-functionalization. Characterization of the ABC transporter and HMA gene families will help in the functional analysis of candidate genes in flax and other crop species.

This paper introduces a novel cache replacement strategy, named Cluster-Based Content Caching (CBCC). CBCC extracts the content feature and uses it to predict popularity for cache replacement. The prediction of expected popularity of new content is based on the clustering algorithm dynamically. If the content is not in the cache list and the list is full, CBCC can help the Cache Node (CN) to make smarter decisions about whether cache new content or not and which content will be replaced based on a popularity prior queue, intending to increase the number of cache hitting in the near future. This strategy can reduce the pressure on the backbone and further improve user satisfaction. We evaluate the performance of our algorithm based on an open Douban Movie Comment Dataset. This dataset can be used to simulate users’ requests. Simulation results show the feasibility and effectiveness of the proposed algorithm.

Common bean (Phaseolus vulgaris L.) is a food crop that is an important source of dietary proteins and carbohydrates. Marsh spot is a physiological disorder that diminishes seed quality in beans. Prior research suggested that this disease is likely caused by manganese (Mn) deficiency during seed development and that marsh spot resistance is controlled by at least four genes. In this study, genetic mapping was performed to identify quantitative trait loci (QTL) and the potential candidate genes associated with marsh spot resistance. All 138 recombinant inbred lines (RILs) from a bi-parental population were evaluated for marsh spot resistance during five years from 2015 to 2019 in sandy and heavy clay soils in Morden, Manitoba, Canada. The RILs were sequenced using a genotyping by sequencing approach. A total of 52,676 single nucleotide polymorphisms (SNPs) were identified and filtered to generate a high-quality set of 2066 SNPs for QTL mapping. A genetic map based on 1273 SNP markers distributed on 11 chromosomes and covering 1599 cm was constructed. A total of 12 stable and 4 environment-specific QTL were identified using additive effect models, and an additional two epistatic QTL interacting with two of the 16 QTL were identified using an epistasis model. Genome-wide scans of the candidate genes identified 13 metal transport-related candidate genes co-locating within six QTL regions. In particular, two QTL (QTL.3.1 and QTL.3.2) with the highest R2 values (21.8% and 24.5%, respectively) harbored several metal transport genes Phvul.003G086300, Phvul.003G092500, Phvul.003G104900, Phvul.003G099700, and Phvul.003G108900 in a large genomic region of 16.8–27.5 Mb on chromosome 3. These results advance the current understanding of the genetic mechanisms of marsh spot resistance in cranberry common bean and provide new genomic resources for use in genomics-assisted breeding and for candidate gene isolation and functional characterization.

Automatic view positioning is crucial for cardiac computed tomography (CT) examinations, including disease diagnosis and surgical planning. However, it is highly challenging due to individual variability and large 3D search space. Existing work needs labor-intensive and time-consuming manual annotations to train view-specific models, which are limited to predicting only a fixed set of planes. However, in real clinical scenarios, the challenge of positioning semantic 2D slices with any orientation into varying coordinate space in arbitrary 3D volume remains unsolved. We thus introduce a novel framework, AVP-AP, the first to use Atlas Prompting for self-supervised Automatic View Positioning in the 3D CT volume. Specifically, this paper first proposes an atlas prompting method, which generates a 3D canonical atlas and trains a network to map slices into their corresponding positions in the atlas space via a self-supervised manner. Then, guided by atlas prompts corresponding to the given query images in a reference CT, we identify the coarse positions of slices in the target CT volume using rigid transformation between the 3D atlas and target CT volume, effectively reducing the search space. Finally, we refine the coarse positions by maximizing the similarity between the predicted slices and the query images in the feature space of a given foundation model. Our framework is flexible and efficient compared to other methods, outperforming other methods by 19.8% average structural similarity (SSIM) in arbitrary view positioning and achieving 9% SSIM in two-chamber view compared to four radiologists. Meanwhile, experiments on a public dataset validate our framework's generalizability.

Background The recent release of the reference genome sequence assembly of flax, a self-pollinated crop with 15 chromosome pairs, into chromosome-scale pseudomolecules enables the characterization of gene families. The ABC transporter and HMA gene families are important in the control of cadmium (Cd) accumulation in crops. To date, the genome-wide analysis of these two gene families has been successfully conducted in some plant species, but no systematic evolutionary analysis is available for the flax genome. Results Here we describe the ABC transporter and HMA gene families in flax to provide a comprehensive overview of its evolution and some support towards the functional annotation of its members. The 198 ABC transporter and 12 HMA genes identified in the flax genome were classified into eight ABC transporter and four HMA subfamilies based on their phylogenetic analysis and domains’ composition. Nine of these genes, i.e., LuABCC9, LuABCC10, LuABCG58, LuABCG59, LuABCG71, LuABCG72, LuABCG73, LuHMA3, and LuHMA4, were orthologous with the Cd associated genes in Arabidopsis, rice and maize. Ten motifs were identified from all ABC transporter and HMA genes. Also, several motifs were conserved among genes of similar length, but subfamilies each had their own motif structures. Both the ABC transporter and HMA gene families were highly conserved among subfamilies of flax and with those of Arabidopsis. While four types of gene duplication were observed at different frequencies, whole-genome or segmental duplications were the most frequent with 162 genes, followed by 29 dispersed, 14 tandem and 4 proximal duplications, suggesting that segmental duplications contributed the most to the expansion of both gene families in flax. The rates of non-synonymous to synonymous (Ka/Ks) mutations of paired duplicated genes were for the most part lower than one, indicative of a predominant purifying selection. Only five pairs of genes clearly exhibited positive selection with a Ka/Ks ratio greater than one. Gene ontology analyses suggested that most flax ABC transporter and HMA genes had a role in ATP binding, transport, catalytic activity, ATPase activity, and metal ion binding. The RNA-Seq analysis of eight different organs demonstrated diversified expression profiling patterns of the genes and revealed their functional or sub-functional conservation and neo-functionalization. Conclusion Characterization of the ABC transporter and HMA gene families will help in the functional analysis of candidate genes in flax and other crop species.

A pipeline network is an important transportation mode of natural gas, and different external factors will affect the development of natural gas scheduling plans to different degrees. However, the specific correlation between each external environmental factor and pipeline network scheduling decision is not clear at this stage. This paper developed a hybrid method with Pearson’s correlation coefficient and Spearman’s correlation coefficient to study the correlations between climate temperature, total gas supply, economic conditions, other energy consumption and natural gas pipeline scheduling plans. The results showed that the correlation between natural gas pipeline output and climate temperature is good, presenting a significance level of 5% and below; in contrast, the correlations with economic conditions and other factors are less significant but still reach a significance level of 10%. Meanwhile, taking energy consumption as the object of study, it was found that the correlation between natural gas consumption and electric energy, crude oil and crude coal is good, showing a significance level of 5% and below. Among them, there is a significant positive correlation between natural gas consumption and electric energy consumption, and between natural gas consumption and crude oil consumption, which reveals the synergistic effects within the energy system.

Surface oxidation is frequently encountered in powder metallurgy of metals and alloys, and it leads to a reduction in their electrical conductivities. Therefore, it is highly desired to remove the naturally occurring oxide layer from the particles surface and to prevent its subsequent formation. A new approach was proposed in this study, where copper particles were mixed with graphene oxide (GO) sheets in an aqueous solution containing dopamine (DA) molecules. It was expected that polymerization of the DA molecules on the surface of the copper particle could promote both a reduction of surface oxide layer and the adhesion of GO sheets to the particles surface. The powder system was then washed, heat-treated in inert atmosphere and compressed at room temperature to form compacts. Electron microscopy revealed nearly ideal dispersion of GO sheets within the copper matrix. X-ray photoelectron spectroscopy showed a shift from Cu 2+ to Cu + and metallic copper in the coated and heat-treated samples, and Raman spectroscopy pointed to the increased amount of sp 2 carbon as a result of the heat treatment. All DA/GO-coated and heat-treated compacts exhibited significantly higher electrical conductivity than those that have been made from pure copper powder or were not been heat-treated. Also, indentation measurements showed an increase in microhardness in samples with the shortest, 10 min, coating time and heat-treated at the highest, 600 °C, temperature.