CECT: Controllable Ensemble CNN and Transformer for COVID-19 Image Classification

Most computer vision models are developed based on either convolutional neural network (CNN) or transformer, while the former (latter) method captures local (global) features. To relieve model performance limitations due to the lack of global (local) features, we develop a novel classification network CECT by controllable ensemble CNN and transformer. CECT is composed of a convolutional encoder block, a transposed-convolutional decoder block, and a transformer classification block. Different from conventional CNN- or transformer-based methods, our CECT can capture features at both multi-local and global scales. Besides, the contribution of local features at different scales can be controlled with the proposed ensemble coefficients. We evaluate CECT on two public COVID-19 datasets and it outperforms existing state-of-the-art methods on all evaluation metrics. With remarkable feature capture ability, we believe CECT can be extended to other medical image classification scenarios as a diagnosis assistant.Comment: 17 pages, 4 figure

Recent Progress in Transformer-based Medical Image Analysis

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte

Origin and evolutionary history of Populus (Salicaceae): Further insights based on time divergence and biogeographic analysis

IntroductionPopulus (Salicaceae) species harbour rich biodiversity and are widely distributed throughout the Northern Hemisphere. However, the origin and biogeography of Populus remain poorly understood.MethodsWe infer the divergence times and the historical biogeography of the genus Populus through phylogenetic analysis of 34 chloroplast fragments based on a large sample.Results and DiscussionEurasia is the likely location of the early divergences of Salicaceae after the Cretaceous-Paleogene (K-Pg) mass extinction, followed by recurrent spread to the remainder of the Old World and the New World beginning in the Eocene; the extant Populus species began to diversity during the early Oligocene (approximately 27.24 Ma), climate changes during the Oligocene may have facilitated the diversification of modern poplar species; three separate lineages of Populus from Eurasia colonized North America in the Cenozoic via the Bering Land Bridges (BLB); We hypothesize that the present day disjunction in Populus can be explained by two scenarios: (i) Populus likely originated in Eurasia and subsequently colonized other regions, including North America; and (ii) the fact that the ancestor of the genus Populus that was once widely distributed in the Northern Hemisphere and eventually wiped out due to the higher extinction rates in North America, similar to the African Rand flora. We hypothesize that disparities in extinction across the evolutionary history of Populus in different regions shape the modern biogeography of Populus. Further studies with dense sampling and more evidence are required to test these hypotheses. Our research underscores the significance of combining phylogenetic analyses with biogeographic interpretations to enhance our knowledge of the origin, divergence, and distribution of biodiversity in temperate plant floras

Phylogeographic Analyses of a Widely Distributed Populus davidiana: Further Evidence for the Existence of Glacial Refugia of Cool‐Temperate Deciduous Trees in Northern East Asia

Despite several phylogeographic studies had provided evidence to support the existence of glacial refugia of cool‐temperate deciduous trees in northeast China, the species used in these studies were limited by the species ranges, which could not exclude the possibility that northern populations were the colonists from southern refugial populations during the last glacial maximum (LGM). Here, we estimated the nucleotide variation in Populus davidiana, a widespread species distributed in Eurasia. Three groups in northeast, central, and southwest China were constructed according to the simulation results from SAMOVA, composition of chloroplast haplotypes and structure results. We revealed that the northeast China had endemic haplotypes, the haplotypes and nucleotide diversity in northern regions were not lower than that in southern China, and this species has not experienced population expansion base on the estimation of Bayesian skyline plots. Ecological niche modeling (ENM) indicated that the northeast China had a high suitability score during the last glacial maximum. The combined evidence clearly demonstrated that northeastern and southwestern refugia were maintained across the current distributional range of P. davidiana during the LGM. The genetic differentiation between these two refugia might be mainly caused by differences of climate among these areas. The phylogeographic analyses of a widely distributed P. davidiana provided robust evidence to clarify the issue of refugia in northeast China, and these results are of great importance for understanding the influence of Quaternary glaciations on the distribution and evolution of species in East Asia

Global–Local Facial Fusion Based GAN Generated Fake Face Detection

Media content forgery is widely spread over the Internet and has raised severe societal concerns. With the development of deep learning, new technologies such as generative adversarial networks (GANs) and media forgery technology have already been utilized for politicians and celebrity forgery, which has a terrible impact on society. Existing GAN-generated face detection approaches rely on detecting image artifacts and the generated traces. However, these methods are model-specific, and the performance is deteriorated when faced with more complicated methods. What’s more, it is challenging to identify forgery images with perturbations such as JPEG compression, gamma correction, and other disturbances. In this paper, we propose a global–local facial fusion network, namely GLFNet, to fully exploit the local physiological and global receptive features. Specifically, GLFNet consists of two branches, i.e., the local region detection branch and the global detection branch. The former branch detects the forged traces from the facial parts, such as the iris and pupils. The latter branch adopts a residual connection to distinguish real images from fake ones. GLFNet obtains forged traces through various ways by combining physiological characteristics with deep learning. The method is stable with physiological properties when learning the deep learning features. As a result, it is more robust than the single-class detection methods. Experimental results on two benchmarks have demonstrated superiority and generalization compared with other methods

Semi-supervised classification of medical ultrasound images based on generative adversarial network

Medical ultrasound (US) is one of the most widely used imaging modalities in clinical practice. However, its use presents unique challenges such as variable imaging quality. Deep learning (DL) can be used as an advanced medical US image analysis tool, while the performance of the DL model is greatly limited by the scarcity of big datasets. Here, we develop semi-supervised classification enhancement (SSCE) structures by combining convolutional neural network (CNN) and generative adversarial network (GAN) to address the data shortage. A breast cancer dataset with 780 images is used as our base dataset. The results show that our SSCE structures obtain an accuracy of up to 97.9%, showing a maximum 21.6% improvement compared with utilizing CNN models alone and outperforming the previous methods using the same dataset by up to 23.9%. We believe our proposed state-of-the-art method can be regarded as a potential auxiliary tool for the diagnoses of medical US images.Comment: 13 pages, 7 figure

The complete chloroplast genome sequence of Xylosma congesta

We sequenced and analyzed the complete chloroplast genome of Xylosma congesta. The chloroplast genome was 156,520 bp in size, containing a large single-copy (84,404 bp) and a small single-copy region (16,474 bp) separated by two inverted repeat regions of 27,821 bp each. A total of 130 genes were annotated, including 86 protein-coding genes, 36 tRNA genes, and 8 rRNA genes. The overall GC content of the chloroplast genome was 36.7%. The phylogenomic analysis strongly supported the close relationship between X. congesta and Flacourtia indica

The complete chloroplast genome sequence of Flacourtia jangomas

In this study, the complete chloroplast genome sequence of Flacourtia jangomas was featured from Illumina pair-end sequencing. In F. jangomas, its length was 156,223 bp, consisting of a large single copy (LSC) region of 84,233 bp, two inverted repeat (IR) copies 27,649 bp and a small single copy (SSC) region of 16,693 bp. The entire GC contents were 37%, and in the LSC, SSC, and IR regions were 35, 31, and 42%, respectively. Flacourtia jangomas has 130 unique genes, including 86 protein-coding genes, 36 tRNA genes, and 8 rRNA genes. The chloroplast genomes of 12 species of Salicaceae and 1 species of Euphorbiaceae were used to construct the maximum-likelihood phylogenetic tree. It indicated that F. jangomas belongs to Salicaceae and is clustered with Flacourtia indica as sisters

The complete chloroplast genome sequence of Populus afghanica

The whole chloroplast genome sequence of Populus afghanica is characterized in this study. The size of cp genome of P. afghanica is 155,975 bp, containing a large single-copy region (LSC) of 84,223 bp, a small single-copy region (SSC) of 16,504 bp, and a pair of identical inverted repeat regions (IRa, b) of 27,624 bp. The overall GC content of the chloroplast genome is 36.8%. One hundred twenty-eight genes were annotated, including 81 protein-coding genes, 38 tRNA genes, and eight rRNA genes. The maximum-likelihood phylogenetic analysis with the reported chloroplast genomes showed that P. afghanica is sister to Populus lasiocarpa Oliv., implied a hybridization orign

Nonredox transformations of hematite-magnetite in mineralization process

The transformations of magnetite and hematite, which are main existing forms of iron oxides in nature, have been debated for many years. The transformation of magnetite and hematite in nature has generally been considered as a result of a redox reaction and linked to a specific oxidant or reductant. However, a nonredox reaction mechanism was proposed in recent years and it might be helpful in better understanding the complicated mineralization process. Ore textures caused by replacement of hematite and magnetite in natural environment was summarized in this paper. The nonredox reactions might exist in many different mineralization processes on the basis of evidences from studying on BIF and significances both in theoretical and practical aspects: one hand, it indicates that the presence of magnetite and hematite in geologic formations may not provide meaningful informaiton on the redox state of fluid; the other hand, it will provide new exploration strategies for hematite rich secondary ores, extending the target for orebodies to deep zones below the paleosurface