Essential genes identification model based on sequence feature map and graph convolutional neural network.

BACKGROUND: Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes. RESULTS: In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training. CONCLUSIONS: Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research.

Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.

DNA methylation takes on critical significance to the regulation of gene expression by affecting the stability of DNA and changing the structure of chromosomes. DNA methylation modification sites should be identified, which lays a solid basis for gaining more insights into their biological functions. Existing machine learning-based methods of predicting DNA methylation have not fully exploited the hidden multidimensional information in DNA gene sequences, such that the prediction accuracy of models is significantly limited. Besides, most models have been built in terms of a single methylation type. To address the above-mentioned issues, a deep learning-based method was proposed in this study for DNA methylation site prediction, termed the MEDCNN model. The MEDCNN model is capable of extracting feature information from gene sequences in three dimensions (i.e., positional information, biological information, and chemical information). Moreover, the proposed method employs a convolutional neural network model with double convolutional layers and double fully connected layers while iteratively updating the gradient descent algorithm using the cross-entropy loss function to increase the prediction accuracy of the model. Besides, the MEDCNN model can predict different types of DNA methylation sites. As indicated by the experimental results,the deep learning method based on coding from multiple dimensions outperformed single coding methods, and the MEDCNN model was highly applicable and outperformed existing models in predicting DNA methylation between different species. As revealed by the above-described findings, the MEDCNN model can be effective in predicting DNA methylation sites.

scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing.

BACKGROUND: Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS: Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS: We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.

Evaluation of a Series of Turnip Mosaic Virus Chimeric Clones Reveals Two Amino Acid Sites Critical for Systemic Infection in Chinese Cabbage.

Two infectious clones of turnip mosaic virus (TuMV), pKBC-1 and pKBC-8, with differential infectivity in Chinese cabbage (Brassica rapa subsp. pekinensis), were obtained. Both infected Nicotiana benthamiana systemically, inducing similar symptoms, whereas only virus KBC-8 infected Chinese cabbage systemically. To identify the determinants affecting infectivity on Chinese cabbage, chimeric clones were constructed by restriction fragment exchange between the parental clones and tested on several Chinese cabbage cultivars. Chimeric clones p1N8C and p8N1C demonstrated that the C-terminal portion of the polyprotein determines systemic infection of Chinese cabbage despite only three amino acid differences in this region, in the cylindrical inclusion (CI), viral protein genome-linked (VPg), and coat protein (CP). A second pair of hybrid constructs, pHindIII-1N8C and pHindIII-8N1C, failed to infect cultivars CR Victory and Jinseonnorang systemically, yet pHindIII-1N8C caused hypersensitive response-like lesions on inoculated leaves of these cultivars, and could systemically infect cultivars CR Chusarang and Jeongsang; this suggests that R genes effective against TuMV may exist in the first two cultivars but not the latter two. Constructs with single amino acid changes in both VPg (K2045E) and CP (Y3095H) failed to infect Chinese cabbage, implying that at least one of these two amino acid substitutions is essential for successful infection on Chinese cabbage. Successful infection by mutant KBC-8-CP-H and delayed infection with mutant HJY1-VPg-E following mutation or reversion suggested that VPg (2045K) is the residue required for infection of Chinese cabbage and involved in the interaction between VPg and eukaryotic initiation factor eIF(iso)4E, confirmed by yeast two-hybrid assay.

Parsing Netlists of Integrated Circuits from Images via Graph Attention Network.

A massive number of paper documents that include important information such as circuit schematics can be converted into digital documents by optical sensors like scanners or digital cameras. However, extracting the netlists of analog circuits from digital documents is an exceptionally challenging task. This process aids enterprises in digitizing paper-based circuit diagrams, enabling the reuse of analog circuit designs and the automatic generation of datasets required for intelligent design models in this domain. This paper introduces a bottom-up graph encoding model aimed at automatically parsing the circuit topology of analog integrated circuits from images. The model comprises an improved electronic component detection network based on the Swin Transformer, an algorithm for component port localization, and a graph encoding model. The objective of the detection network is to accurately identify component positions and types, followed by automatic dataset generation through port localization, and finally, utilizing the graph encoding model to predict potential connections between circuit components. To validate the model's performance, we annotated an electronic component detection dataset and a circuit diagram dataset, comprising 1200 and 3552 training samples, respectively. Detailed experimentation results demonstrate the superiority of our proposed enhanced algorithm over comparative algorithms across custom and public datasets. Furthermore, our proposed port localization algorithm significantly accelerates the annotation speed of circuit diagram datasets.

Monitoring of Single-Track Melting States Based on Photodiode Signal during Laser Powder Bed Fusion.

Single track is the basis for the melt pool modeling and physics work in laser powder bed fusion (LPBF). The melting state of a single track is closely related to defects such as porosity, lack of fusion, and balling, which have a significant impact on the mechanical properties of an LPBF-created part. To ensure the reliability of part quality and repeatability, process monitoring and feedback control are emerging to improve the melting states, which is becoming a hot topic in both the industrial and academic communities. In this research, a simple and low-cost off-axial photodiode signal monitoring system was established to monitor the melting pools of single tracks. Nine groups of single-track experiments with different process parameter combinations were carried out four times and then thirty-six LPBF tracks were obtained. The melting states were classified into three classes according to the morphologies of the tracks. A convolutional neural network (CNN) model was developed to extract the characteristics and identify the melting states. The raw one-dimensional photodiode signal data were converted into two-dimensional grayscale images. The average identification accuracy reached 95.81% and the computation time was 15 ms for each sample, which was promising for engineering applications. Compared with some classic deep learning models, the proposed CNN could distinguish the melting states with higher classification accuracy and efficiency. This work contributes to real-time multiple-sensor monitoring and feedback control.

Construction of full-length infectious cDNA clones of two Korean isolates of turnip mosaic virus breaking resistance in Brassica napus.

In this work, two new turnip mosaic virus (TuMV) strains (Canola-12 and Canola-14) overcoming resistance in canola (Brassica napus) were isolated from a B. napus sample that showed typical TuMV-like symptoms and was collected in the city of Gimcheon, South Korea, in 2020. The complete genome sequence was determined and an infectious clone was made for each isolate. Phylogenetic analysis indicated that the strains isolated from canola belonged to the World-B group. Both infectious clones, which used 35S and T7 promoters to drive expression, induced systemic symptoms in Nicotiana benthamiana and B. napus. To our knowledge, this is the first report of TuMV infecting B. napus in South Korea.

Construction of full-length infectious clones of turnip mosaic virus isolates infecting Perilla frutescens and genetic analysis of recently emerged strains in Korea.

Perilla is an annual herb with a unique aroma and taste that has been cultivated in Korea for hundreds of years. It has been widely cultivated in many Asian and European countries as a food and medicinal crop. Recently, several viruses have been reported to cause diseases in perilla in Korea, including turnip mosaic virus (TuMV), which is known as a brassica pathogen due to its significant damage to brassica crops. In this study, we determined the complete genome sequences of two new TuMV isolates originating from perilla in Korea. Full-length infectious cDNA clones of these two isolates were constructed, and their infectivity was tested by agroinfiltration of Nicotiana benthamiana and sap inoculation of Chinese cabbage and radish plants. In addition, we analyzed the phylogenetic relationship of six new Korean TuMV isolates to members of the four major groups. We also used RDP4 software to conduct recombination analysis of recent isolates from Korea, which provided new insight into the evolutionary relationships of Korean isolates of TuMV.

Reassortment of Infectious Clones of Radish Mosaic Virus Shows that Systemic Necrosis in Nicotiana benthamiana Is Determined by RNA1.

Three infectious clones of radish mosaic virus (RaMV) were generated from isolates collected in mainland Korea (RaMV-Gg) and Jeju Island (RaMV-Aa and RaMV-Bb). These isolates differed in sequences and pathogenicity. Examination of the wild-type isolates and reassortants between the genomic RNA1 and RNA2 of these three isolates revealed that severe symptoms were associated with RNA1 of isolates Aa or Gg causing systemic necrosis in Nicotiana benthamiana, or with RNA1 of isolate Bb for induction of veinal necrosis and severe mosaic symptoms in radish. Reverse transcription, followed by quantitative real-time PCR (Q-RT-PCR), results from infected N. benthamiana confirmed that viral RNA2 accumulation level was correlated to RaMV necrosis-inducing ability, and that the RNA2 accumulation level was mostly dependent on the origin of RNA1. However, in radish, Q-RT-PCR results showed more similar viral RNA2 accumulation levels regardless of the ability of the isolate to induce necrosis. Phylogenetic analysis of genomic RNAs sequence including previously characterized isolates from North America, Europe, and Asia suggest possible recombination within RNA1, while analysis of concatenated RNA1+RNA2 sequences indicates that reassortment of RNA1 and RNA2 has been more important in the evolution of RaMV isolates than recombination. Korean isolate Aa is a potential reassortant between isolates RaMV-J and RaMV-TW, while isolate Bb might have evolved from reassortment between isolates RaMV-CA and RaMV-J. The Korean isolates were shown to also be able to infect Chinese cabbage, raising concerns that RaMV may spread from radish fields to the Chinese cabbage crop in Korea, causing further economic losses.

Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder.

Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter-subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest-rest pair). Furthermore, high-level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high-order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions.

Functional network estimation using multigraph learning with application to brain maturation study.

Although most dramatic structural changes occur in the perinatal period, a growing body of evidences demonstrates that adolescence and early adulthood are also important for substantial neurodevelopment. We were thus motivated to explore brain development during puberty by evaluating functional connectivity network (FCN) differences between childhood and young adulthood using multi-paradigm task-based functional magnetic resonance imaging (fMRI) measurements. Different from conventional multigraph based FCN construction methods where the graph network was built independently for each modality/paradigm, we proposed a multigraph learning model in this work. It promises a better fitting to FCN construction by jointly estimating brain network from multi-paradigm fMRI time series, which may share common graph structures. To investigate the hub regions of the brain, we further conducted graph Fourier transform (GFT) to divide the fMRI BOLD time series of a node within the brain network into a range of frequencies. Then we identified the hub regions characterizing brain maturity through eigen-analysis of the low frequency components, which were believed to represent the organized structures shared by a large population. The proposed method was evaluated using both synthetic and real data, which demonstrated its effectiveness in extracting informative brain connectivity patterns. We detected 14 hub regions from the child group and 12 hub regions from the young adult group. We show the significance of these findings with a discussion of their functions and activation patterns as a function of age. In summary, our proposed method can extract brain connectivity network more accurately by considering the latent common structures between different fMRI paradigms, which are significant for both understanding brain development and recognizing population groups of different ages.

Refined measure of functional connectomes for improved identifiability and prediction.

Brain functional connectome analysis is commonly based on population-wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population-wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject-specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group-wise inferences and refined connectomes are indeed desirable for brain mapping.

A single nucleotide change in the overlapping MP and CP reading frames results in differences in symptoms caused by two isolates of Youcai mosaic virus.

Two isolates of Youcai mosaic virus (YoMV) were obtained, and their full-length genomic sequences were determined. Full-length infectious cDNA clones of each isolate were generated in which the viral sequence was under the control of dual T7 and 35S promoters for both in vitro transcript production and agro-infiltration. Comparison of the predicted amino acid sequences of the encoded proteins revealed only four differences between the isolates: three in the RNA-dependent RNA polymerase (RdRp) (V383I and M492I in the 125-kDa protein and T1245M in the 182-kDa protein); and one in the overlapping region of the movement protein (MP) and coat protein (CP) genes, affecting only the N-terminal domain of CP (CP M17T). One of the isolates caused severe symptoms in Nicotiana benthamiana plants, while the other caused only mild symptoms. In order to identify the amino acid residues associated with symptom severity, chimeric constructs were generated by combining parts of the two infectious YoMV clones, and the symptoms in infected plants were compared to those induced by the parental isolates. This allowed us to conclude that the M17T substitution in the N-terminal domain of CP was responsible for the difference in symptom severity. The M17T variation was found to be unique among characterized YoMV isolates. A difference in potential post-translational modification resulting from the presence of a predicted casein kinase II phosphorylation site only in the CP of isolate HK2 may be responsible for the symptom differences.

Sequence Variations Among 17 New Radish Isolates of Turnip mosaic virus Showing Differential Pathogenicity and Infectivity in Nicotiana benthamiana, Brassica rapa, and Raphanus sativus.

Infectious clones were generated from 17 new Korean radish isolates of Turnip mosaic virus (TuMV). Phylogenetic analysis indicated that all new isolates, and three previously characterized Korean radish isolates, belong to the basal-BR group (indicating that the pathotype can infect both Brassica and Raphanus spp.). Pairwise analysis revealed genomic nucleotide and polyprotein amino acid identities of >87.9 and >95.7%, respectively. Five clones (HJY1, HJY2, KIH2, BE, and prior isolate R007) had lower sequence identities than other isolates and produced mild symptoms in Nicotiana benthamiana. These isolates formed three distinct sequence classes (HJY1/HJY2/R007, KIH2, and BE), and several differential amino acid residues (in P1, P3, 6K2, and VPg) were present only in mild isolates HJY1, HJY2, and R007. The remaining isolates all induced systemic necrosis in N. benthamiana. Four mild isolates formed a phylogenetic subclade separate from another subclade including all of the necrosis-inducing isolates plus mild isolate KIH2. Symptom severity in radish and Chinese cabbage genotypes was not correlated with pathogenicity in N. benthamiana; indeed, Chinese cabbage cultivar Norang was not infected by any isolate, whereas Chinese cabbage cultivar Chusarang was uniformly susceptible. Four isolates were unable to infect radish cultivar Iljin, but no specific amino acid residues were correlated with avirulence. These results may lead to the identification of new resistance genes against TuMV.

A Turnip Mosaic Virus Determinant of Systemic Necrosis in Nicotiana benthamiana and a Novel Resistance-Breaking Determinant in Chinese Cabbage Identified from Chimeric Infectious Clones.

Infectious clones of Korean turnip mosaic virus (TuMV) isolates KIH1 and HJY1 share 88.1% genomic nucleotides and 96.4% polyprotein amino acid identity, and they induce systemic necrosis or mild mosaic, respectively, in Nicotiana benthamiana. Chimeric constructs between these isolates exchanged the 5', central, and 3' domains of KIH1 (K) and HJY1 (H), where the order of the letters indicates the origin of these domains. KIH1 and chimeras KHH and KKH induced systemic necrosis, whereas HJY1 and chimeras HHK, HKK, and HKH induced mild symptoms, indicating the determinant of necrosis to be within the 5' 3.9 kb of KIH1; amino acid identities of the included P1, Helper component protease, P3, 6K1, and cylindrical inclusion N-terminal domain were 90.06, 98.91, 93.80, 100, and 100%, respectively. Expression of P1 or P3 from a potato virus X vector yielded symptom differences only between P3 of KIH1 and HJY1, implicating a role for P3 in necrosis in N. benthamiana. Chimera KKH infected Brassica rapa var. pekinensis 'Norang', which was resistant to both KIH1 and HJY1, indicating that two separate TuMV determinants are required to overcome the resistance. Ability of diverse TuMV isolates, chimeras, and recombinants to overcome resistance in breeding lines may allow identification of novel resistance genes.

Blastic plasmacytoid dendritic cell neoplasm of the skin associated with myelodysplastic syndrome.

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a recently described, rare hematologic malignancy with prominent skin involvement. The prognosis of this disease is poor in most cases, with rapid progression despite chemotherapy administration. The first reported case of this disease was in 1994, and less than 200 cases worldwide have been described in the literature to date. Moreover coexistence of BPDCN and MDS is extremely rare. In this study, we describe a typical patient with BPDCN in China who presented with cutaneous involvement as the first manifestation associated with MDS; a brief review of literature is also given.

SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer.

Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy in predicting the structure of protein complexes. However, it cannot distinguish whether the input protein sequences can interact. Nonetheless, by analyzing the information in the models predicted by the AlphaFold Multimer, we propose a highly accurate method for predicting protein interactions. This study focuses on the use of deep neural networks, specifically to analyze protein complex structures predicted by the AlphaFold Multimer. By transforming atomic coordinates and utilizing sophisticated image-processing techniques, vital 3D structural details were extracted from protein complexes. Recognizing the significance of evaluating residue distances in protein interactions, this study leveraged image recognition approaches by integrating Densely Connected Convolutional Networks (DenseNet) and Deep Residual Network (ResNet) within 3D convolutional networks for protein 3D structure analysis. When benchmarked against leading protein-protein interaction prediction methods, such as SpeedPPI, D-script, DeepTrio, and PEPPI, our proposed method, named SpatialPPI, exhibited notable efficacy, emphasizing the promising role of 3D spatial processing in advancing the realm of structural biology. The SpatialPPI code is available at: https://github.com/ohuelab/SpatialPPI.

A deep learning model for DNA enhancer prediction based on nucleotide position aware feature encoding.

Enhancers, genomic DNA elements, regulate neighboring gene expression crucial for biological processes like cell differentiation and stress response. However, current machine learning methods for predicting DNA enhancers often underutilize hidden features in gene sequences, limiting model accuracy. Hence, this article proposes the PDCNN model, a deep learning-based enhancer prediction method. PDCNN extracts statistical nucleotide representations from gene sequences, discerning positional distribution information of nucleotides in modifier-like DNA sequences. With a convolutional neural network structure, PDCNN employs dual convolutional and fully connected layers. The cross-entropy loss function iteratively updates using a gradient descent algorithm, enhancing prediction accuracy. Model parameters are fine-tuned to select optimal combinations for training, achieving over 95% accuracy. Comparative analysis with traditional methods and existing models demonstrates PDCNN's robust feature extraction capability. It outperforms advanced machine learning methods in identifying DNA enhancers, presenting an effective method with broad implications for genomics, biology, and medical research.

Neutrophil extracellular traps facilitate cancer metastasis: cellular mechanisms and therapeutic strategies.

BACKGROUND: The formation of neutrophil extracellular traps (NETs) was initially discovered as a novel immune response against pathogens. Recent studies have also suggested that NETs play an important role in tumor progression. This review summarizes the cellular mechanisms by which NETs promote distant metastasis and discusses the possible clinical applications targeting NETs. METHOD: The relevant literature from PubMed and Google Scholar (2001-2021) have been reviewed for this article. RESULTS: The presence of NETs has been detected in various primary tumors and metastatic sites. NET-associated interactions have been observed throughout the different stages of metastasis, including initial tumor cell detachment, intravasation and extravasation, the survival of circulating tumor cells, the settlement and the growth of metastatic tumor cells. Several in vitro and in vivo studies proved that inhibiting NET formation resulted in anti-cancer effects. The biosafety and efficacy of some NET inhibitors have also been demonstrated in early phase clinical trials. CONCLUSIONS: Considering the role of NETs in tumor progression, NETs could be a promising diagnostic and therapeutic target for cancer management. However, current evidence is mostly derived from experimental models and as such more clinical studies are still needed to verify the clinical significance of NETs in oncological settings.