Improved automatic segmentation of brain metastasis gross tumor volume in computed tomography images for radiotherapy: a position attention module for U-Net architecture.

Background: Brain metastases present significant challenges in radiotherapy due to the need for precise tumor delineation. Traditional methods often lack the efficiency and accuracy required for optimal treatment planning. This paper proposes an improved U-Net model that uses a position attention module (PAM) for automated segmentation of gross tumor volumes (GTVs) in computed tomography (CT) simulation images of patients with brain metastases to improve the efficiency and accuracy of radiotherapy planning and segmentation. Methods: We retrospectively collected CT simulation imaging datasets of patients with brain metastases from two centers, which were designated as the training and external validation datasets. The U-Net architecture was enhanced by incorporating a PAM into the transition layer, which improved the automated segmentation capability of the U-Net model. With cross-entropy loss employed as the loss function, the samples from the training dataset underwent training. The model's segmentation performance on the external validation dataset was assessed using metrics including the Dice similarity coefficient (DSC), intersection over union (IoU), accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and Hausdorff distance (HD). Results: The proposed automated segmentation model demonstrated promising performance on the external validation dataset, achieving a DSC of 0.753±0.172. In terms of evaluation metrics (including the DSC, IoU, accuracy, sensitivity, MCC, and HD), the model outperformed the standard U-Net, which had a DSC of 0.691±0.142. The proposed model produced segmentation results that were closer to the ground truth and could reveal more detailed features of brain metastases. Conclusions: The PAM-improved U-Net model offers considerable advantages in the automated segmentation of the GTV in CT simulation images for patients with brain metastases. Its superior performance in comparison with the standard U-Net model supports its potential for streamlining and improving the accuracy of radiotherapy. With its ability to produce segmentation results consistent with the ground truth, the proposed model holds promise for clinical adoption and provides a reference for radiation oncologists to make more informed GTV segmentation decisions.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Advances in research and application of artificial intelligence and radiomic predictive models based on intracranial aneurysm images.

Intracranial aneurysm is a high-risk disease, with imaging playing a crucial role in their diagnosis and treatment. The rapid advancement of artificial intelligence in imaging technology holds promise for the development of AI-based radiomics predictive models. These models could potentially enable the automatic detection and diagnosis of intracranial aneurysms, assess their status, and predict outcomes, thereby assisting in the creation of personalized treatment plans. In addition, these techniques could improve diagnostic efficiency for physicians and patient prognoses. This article aims to review the progress of artificial intelligence radiomics in the study of intracranial aneurysms, addressing the challenges faced and future prospects, in hopes of introducing new ideas for the precise diagnosis and treatment of intracranial aneurysms.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.

Multi-omics sequencing is expected to become clinically routine within the next decade and transform clinical care. However, there is a paucity of viable and interpretable genome-wide modeling methods that can facilitate rational selection of patients for tailored intervention. Here we develop an integral genomic signature-based method called iGenSig-Rx as a white-box tool for modeling therapeutic response based on clinical trial datasets with improved cross-dataset applicability and tolerance to sequencing bias. This method leverages high-dimensional redundant genomic features to address the challenges of cross-dataset modeling, a concept similar to the use of redundant steel rods to reinforce the pillars of a building. Using genomic datasets for HER2 targeted therapies, the iGenSig-Rx model demonstrates stable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We expect that iGenSig-Rx as a class of biologically interpretable multi-omics modeling methods will have broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx. NOTE: the Github website will be released upon publication and the R package is available for review through google drive: https://drive.google.com/drive/folders/1KgecmUoon9-h2Dg1rPCyEGFPOp28Ols3?usp=sharing.

Spatiotemporal dynamics of self-generated imagery reveal a reverse cortical hierarchy from cue-induced imagery.

Visual imagery allows for the construction of rich internal experience in our mental world. However, it has remained poorly understood how imagery experience derives volitionally as opposed to being cue driven. Here, using electroencephalography and functional magnetic resonance imaging, we systematically investigate the spatiotemporal dynamics of self-generated imagery by having participants volitionally imagining one of the orientations from a learned pool. We contrast self-generated imagery with cue-induced imagery, where participants imagined line orientations based on associative cues acquired previously. Our results reveal overlapping neural signatures of cue-induced and self-generated imagery. Yet, these neural signatures display substantially differential sensitivities to the two types of imagery: self-generated imagery is supported by an enhanced involvement of the anterior cortex in representing imagery contents. By contrast, cue-induced imagery is supported by enhanced imagery representations in the posterior visual cortex. These results jointly support a reverse cortical hierarchy in generating and maintaining imagery contents in self-generated versus externally cued imagery.

Proteins released into the plant apoplast by the obligate parasitic protist Albugo selectively repress phyllosphere-associated bacteria.

Biotic and abiotic interactions shape natural microbial communities. The mechanisms behind microbe-microbe interactions, particularly those protein based, are not well understood. We hypothesize that released proteins with antimicrobial activity are a powerful and highly specific toolset to shape and defend plant niches. We have studied Albugo candida, an obligate plant parasite from the protist Oomycota phylum, for its potential to modulate the growth of bacteria through release of antimicrobial proteins into the apoplast. Amplicon sequencing and network analysis of Albugo-infected and uninfected wild Arabidopsis thaliana samples revealed an abundance of negative correlations between Albugo and other phyllosphere microbes. Analysis of the apoplastic proteome of Albugo-colonized leaves combined with machine learning predictors enabled the selection of antimicrobial candidates for heterologous expression and study of their inhibitory function. We found for three candidate proteins selective antimicrobial activity against Gram-positive bacteria isolated from A. thaliana and demonstrate that these inhibited bacteria are precisely important for the stability of the community structure. We could ascribe the antibacterial activity of the candidates to intrinsically disordered regions and positively correlate it with their net charge. This is the first report of protist proteins with antimicrobial activity under apoplastic conditions that therefore are potential biocontrol tools for targeted manipulation of the microbiome.

Learning Representative Features by Deep Attention Network for 3D Point Cloud Registration.

Three-dimensional point cloud registration, which aims to find the transformation that best aligns two point clouds, is a widely studied problem in computer vision with a wide spectrum of applications, such as underground mining. Many learning-based approaches have been developed and have demonstrated their effectiveness for point cloud registration. Particularly, attention-based models have achieved outstanding performance due to the extra contextual information captured by attention mechanisms. To avoid the high computation cost brought by attention mechanisms, an encoder-decoder framework is often employed to hierarchically extract the features where the attention module is only applied in the middle. This leads to the compromised effectiveness of the attention module. To tackle this issue, we propose a novel model with the attention layers embedded in both the encoder and decoder stages. In our model, the self-attentional layers are applied in the encoder to consider the relationship between points inside each point cloud, while the decoder utilizes cross-attentional layers to enrich features with contextual information. Extensive experiments conducted on public datasets prove that our model is able to achieve quality results on a registration task.

Survey on Exact kNN Queries over High-Dimensional Data Space.

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from data mining, recommendation system and Internet of Things, to Industry 4.0 framework applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been helpful for intrusion detection systems and fault detection. Due to the importance of kNN queries, many algorithms have been proposed in the literature, for both static and dynamic data. In this paper, we focus on exact kNN queries and present a comprehensive survey of exact kNN queries. In particular, we study two fundamental types of exact kNN queries: the kNN Search queries and the kNN Join queries. Our survey focuses on exact approaches over high-dimensional data space, which covers 20 kNN Search methods and 9 kNN Join methods. To the best of our knowledge, this is the first work of a comprehensive survey of exact kNN queries over high-dimensional datasets. We specifically categorise the algorithms based on indexing strategies, data and space partitioning strategies, clustering techniques and the computing paradigm. We provide useful insights for the evolution of approaches based on the various categorisation factors, as well as the possibility of further expansion. Lastly, we discuss some open challenges and future research directions.

WheatCENet: A Database for Comparative Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors.

Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed co-expression networks for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named WheatCENet. WheatCENet is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. Functional annotations like pathways, gene families, protein-protein interactions, microRNAs (miRNAs), and several lines of epigenome data are integrated into this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using WheatCENet, we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION 1 (WAPO1) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CCWWWWWWGG (CArG) specifically in the promoter region of WAPO-A1, suggesting that neofunctionalization of the WAPO-A1 gene affects spikelet development in hexaploid wheat. WheatCENet is useful for investigating co-expression networks and conducting other analyses, and thus facilitates comparative and functional genomic studies in wheat. WheatCENet is freely available at http://bioinformatics.cpolar.cn/WheatCENet and http://bioinformatics.cau.edu.cn/WheatCENet.

De novo genome assembly of the medicinal plant Gentiana macrophylla provides insights into the genomic evolution and biosynthesis of iridoids.

Gentiana macrophylla is a perennial herb in the Gentianaceae family, whose dried roots are used in traditional Chinese medicine. Here, we assembled a chromosome-level genome of G. macrophylla using a combination of Nanopore, Illumina, and Hi-C scaffolding approaches. The final genome size was ~1.79 Gb (contig N50 = 720.804 kb), and 98.89% of the genome sequences were anchored on 13 pseudochromosomes (scaffold N50 = 122.73 Mb). The genome contained 55,337 protein-coding genes, and 73.47% of the assemblies were repetitive sequences. Genome evolution analysis indicated that G. macrophylla underwent two rounds of whole-genome duplication after the core eudicot γ genome triplication event. We further identified candidate genes related to the biosynthesis of iridoids, and the corresponding gene families mostly expanded in G. macrophylla. In addition, we found that root-specific genes are enriched in pathways involved in defense responses, which may greatly improve the biological adaptability of G. macrophylla. Phylogenomic analyses showed a sister relationship of asterids and rosids, and all Gentianales species formed a monophyletic group. Our study contributes to the understanding of genome evolution and active component biosynthesis in G. macrophylla and provides important genomic resource for the genetic improvement and breeding of G. macrophylla.

A recent burst of gene duplications in Triticeae.

Gene duplication provides raw genetic materials for evolution and potentially novel genes for crop improvement. The two seminal genomic studies of Aegilops tauschii both mentioned the large number of genes independently duplicated in recent years, but the duplication mechanism and the evolutionary significance of these gene duplicates have not yet been investigated. Here, we found that a recent burst of gene duplications (hereafter abbreviated as the RBGD) has probably occurred in all sequenced Triticeae species. Further investigations of the characteristics of the gene duplicates and their flanking sequences suggested that transposable element (TE) activity may have been involved in generating the RBGD. We also characterized the duplication timing, retention pattern, diversification, and expression of the duplicates following the evolution of Triticeae. Multiple subgenome-specific comparisons of the duplicated gene pairs clearly supported extensive differential regulation and related functional diversity among such pairs in the three subgenomes of bread wheat. Moreover, several duplicated genes from the RBGD have evolved into key factors that influence important agronomic traits of wheat. Our results provide insights into a unique source of gene duplicates in Triticeae species, which has increased the gene dosage together with the two polyploidization events in the evolutionary history of wheat.

Inferring Species Compositions of Complex Fungal Communities from Long- and Short-Read Sequence Data.

The kingdom Fungi is highly diverse in morphology and ecosystem function. Yet fungi are challenging to characterize as they can be difficult to culture and morphologically indistinct. Overall, their description and analysis lag far behind other microbes such as bacteria. Classification of species via high-throughput sequencing is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. With the rapid development of sequencing technologies, however, standardized procedures for taxonomic assignment of long sequence reads have not yet been well established. Focusing on nanopore sequencing technology, we compared classification and community composition analysis pipelines using shotgun and amplicon sequencing data generated from mock communities comprising 43 fungal species. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against a fungal-specific database. During the assessment of classification algorithms, we found that applying cutoffs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without major data loss. We also generated draft genome assemblies for three fungal species from nanopore data which were absent from genome databases. Our study improves sequence-based classification and estimation of relative sequence abundance using real fungal community data and provides a practical guide for the design of metagenomics analyses focusing on fungi. IMPORTANCE Our study is unique in that it provides an in-depth comparative study of a real-life complex fungal community analyzed with multiple long- and short-read sequencing approaches. These technologies and their application are currently of great interest to diverse biologists as they seek to characterize the community compositions of microbiomes. Although great progress has been made on bacterial community compositions, microbial eukaryotes such as fungi clearly lag behind. Our study provides a detailed breakdown of strategies to improve species identification with immediate relevance to real-world studies. We find that real-life data sets do not always behave as expected, distinct from reports based on simulated data sets.

Physical separation of haplotypes in dikaryons allows benchmarking of phasing accuracy in Nanopore and HiFi assemblies with Hi-C data.

BACKGROUND: Most animals and plants have more than one set of chromosomes and package these haplotypes into a single nucleus within each cell. In contrast, many fungal species carry multiple haploid nuclei per cell. Rust fungi are such species with two nuclei (karyons) that contain a full set of haploid chromosomes each. The physical separation of haplotypes in dikaryons means that, unlike in diploids, Hi-C chromatin contacts between haplotypes are false-positive signals. RESULTS: We generate the first chromosome-scale, fully-phased assembly for the dikaryotic leaf rust fungus Puccinia triticina and compare Nanopore MinION and PacBio HiFi sequence-based assemblies. We show that false-positive Hi-C contacts between haplotypes are predominantly caused by phase switches rather than by collapsed regions or Hi-C read mis-mappings. We introduce a method for phasing of dikaryotic genomes into the two haplotypes using Hi-C contact graphs, including a phase switch correction step. In the HiFi assembly, relatively few phase switches occur, and these are predominantly located at haplotig boundaries and can be readily corrected. In contrast, phase switches are widespread throughout the Nanopore assembly. We show that haploid genome read coverage of 30-40 times using HiFi sequencing is required for phasing of the leaf rust genome, with 0.7% heterozygosity, and that HiFi sequencing resolves genomic regions with low heterozygosity that are otherwise collapsed in the Nanopore assembly. CONCLUSIONS: This first Hi-C based phasing pipeline for dikaryons and comparison of long-read sequencing technologies will inform future genome assembly and haplotype phasing projects in other non-haploid organisms.

The complete chloroplast genome of Nymphaea thermarum (Nymphaeaceae) from Rwanda, Africa.

Nymphaea thermarum is classified in the Nymphaeaceae, and is the smallest water lily in the world. It has been extinct its native environment and needs urgent protection. Here, we report and characterize the complete chloroplast genome of N. thermarum. The total length of the chloroplast genome is 159,849 bp and the GC content is 39.2% (A: 30.1%, C: 20.0%, G: 19.2%, T: 30.8%). The chloroplast genome consists of 8 rRNA, 37 tRNA, and 85 protein-coding genes. Phylogenetic analysis of N. thermarum fully resolved this taxon in a clade with Nymphaea capensis. The chloroplast genome of N. thermarum provides scientific guidance for its conservation genetics and also contributes genome resources for the phylogenetic relationship of Nymphaea.

Precise Control of Radial Catenane Synthesis via Clipping and Pumping.

An unprecedented molecular pumping cassette was designed and implemented for the construction of molecular necklaces, that is, radial [n]catenanes. The mechanism was fully confirmed on a model [2]pseudorotaxane, and the novel clipping-followed-by-pumping strategy was used to prepare a series of [n]catenanes (n = 2-5). A pair of [3]catenane diastereomers sequentially threaded with two different wheels was also accomplished. The success of utilizing molecular pumping to construct molecular necklaces offers new insights into complex molecular architectures and expands the application of molecular machines in synthesis.

New insights into the dispersion history and adaptive evolution of taxon Aegilops tauschii in China.

Aegilops tauschii, the wild progenitor of wheat D-genome and a valuable germplasm for wheat improvement, has a wide natural distribution from eastern Turkey to China. However, the phylogenetic relationship and dispersion history of Ae. tauschii in China has not been scientifically clarified. In this study, we genotyped 208 accessions (with 104 in China) using ddRAD sequencing and 55K SNP array, and classified the population into six sublineages. Three possible spreading routes or events were identified, resulting in specific distribution patterns, with four sublineages found in Xinjiang, one in Qinghai, two in Shaanxi and one in Henan. We also established the correlation of SNP-based, karyotype-based and spike-morphology-based techniques to demonstrate the internal classification of Ae. tauschii, and developed consensus dataset with 1245 putative accessions by merging data previously published. Our analysis suggested that eight inter-lineage accessions could be assigned to the putative Lineage 3 and these accessions would help to conserve the genetic diversity of the species. By developing the consensus phylogenetic relationships of Ae. tauschii, our work validated the hypothesis on the dispersal history of Ae. tauschii in China, and contributed to the efficient and comprehensive germplasm-mining of the species.

Whole-Genome Sequencing of Acer catalpifolium Reveals Evolutionary History of Endangered Species.

Acer catalpifolium is an endangered species restricted to remote localities of West China. Understanding the genomic content and evolution of A. catalpifolium is essential to conservation efforts of this rare and ecologically valuable plant. Here, we report a high-quality genome of A. catalpifolium consisting of ∼654 Mbp and ∼35,132 protein-coding genes. We detected 969 positively selected genes in two Acer genomes compared with four other eudicots, 65 of which were transcription factors. We hypothesize that these positively selected mutations in transcription factors might affect their function and thus contribute to A. catalpifolium's decline-type population. We also identified 179 significantly expanded gene families compared with 12 other eudicots, some of which are involved in stress responses, such as the FRS-FRF family. We inferred that A. catalpifolium has experienced gene family expansions to cope with environmental stress in its evolutionary history. Finally, 109 candidate genes encoding key enzymes in the lignin biosynthesis pathway were identified in A. catalpifolium; of particular note were the large range and high copy number of cinnamyl alcohol dehydrogenase genes. The chromosome-level genome of A. catalpifolium presented here may serve as a fundamental genomic resource for better understanding endangered Acer species, informing future conservation efforts.

Whole genome based insights into the phylogeny and evolution of the Juglandaceae.

BACKGROUND: The walnut family (Juglandaceae) contains commercially important woody trees commonly called walnut, wingnut, pecan and hickory. Phylogenetic relationships and diversification within the Juglandaceae are classic and hot scientific topics that have been elucidated by recent fossil, morphological, molecular, and (paleo) environmental data. Further resolution of relationships among and within genera is still needed and can be achieved by analysis of the variation of chloroplast, mtDNA, and nuclear genomes. RESULTS: We reconstructed the backbone phylogenetic relationships of Juglandaceae using organelle and nuclear genome data from 27 species. The divergence time of Juglandaceae was estimated to be 78.7 Mya. The major lineages diversified in warm and dry habitats during the mid-Paleocene and early Eocene. The plastid, mitochondrial, and nuclear phylogenetic analyses all revealed three subfamilies, i.e., Juglandoideae, Engelhardioideae, Rhoipteleoideae. Five genera of Juglandoideae were strongly supported. Juglandaceae were estimated to have originated during the late Cretaceous, while Juglandoideae were estimated to have originated during the Paleocene, with evidence for rapid diversification events during several glacial and geological periods. The phylogenetic analyses of organelle sequences and nuclear genome yielded highly supported incongruence positions for J. cinerea, J. hopeiensis, and Platycarya strobilacea. Winged fruit were the ancestral condition in the Juglandoideae, but adaptation to novel dispersal and regeneration regimes after the Cretaceous-Paleogene boundary led to the independent evolution of zoochory among several genera of the Juglandaceae. CONCLUSIONS: A fully resolved, strongly supported, time-calibrated phylogenetic tree of Juglandaceae can provide an important framework for studying classification, diversification, biogeography, and comparative genomics of plant lineages. Our addition of new, annotated whole chloroplast genomic sequences and identification of their variability informs the study of their evolution in walnuts (Juglandaceae).

Insights into angiosperm evolution, floral development and chemical biosynthesis from the Aristolochia fimbriata genome.

Aristolochia, a genus in the magnoliid order Piperales, has been famous for centuries for its highly specialized flowers and wide medicinal applications. Here, we present a new, high-quality genome sequence of Aristolochia fimbriata, a species that, similar to Amborella trichopoda, lacks further whole-genome duplications since the origin of extant angiosperms. As such, the A. fimbriata genome is an excellent reference for inferences of angiosperm genome evolution, enabling detection of two novel whole-genome duplications in Piperales and dating of previously reported whole-genome duplications in other magnoliids. Genomic comparisons between A. fimbriata and other angiosperms facilitated the identification of ancient genomic rearrangements suggesting the placement of magnoliids as sister to monocots, whereas phylogenetic inferences based on sequence data we compiled yielded ambiguous relationships. By identifying associated homologues and investigating their evolutionary histories and expression patterns, we revealed highly conserved floral developmental genes and their distinct downstream regulatory network that may contribute to the complex flower morphology in A. fimbriata. Finally, we elucidated the genetic basis underlying the biosynthesis of terpenoids and aristolochic acids in A. fimbriata.