Limits to the three domains of life: lessons from community assembly along an Antarctic salinity gradient.

Extremophiles exist among all three domains of life; however, physiological mechanisms for surviving harsh environmental conditions differ among Bacteria, Archaea and Eukarya. Consequently, we expect that domain-specific variation of diversity and community assembly patterns exist along environmental gradients in extreme environments. We investigated inter-domain community compositional differences along a high-elevation salinity gradient in the McMurdo Dry Valleys, Antarctica. Conductivity for 24 soil samples collected along the gradient ranged widely from 50 to 8355 µS cm-1. Taxonomic richness varied among domains, with a total of 359 bacterial, 2 archaeal, 56 fungal, and 69 non-fungal eukaryotic operational taxonomic units (OTUs). Richness for bacteria, archaea, fungi, and non-fungal eukaryotes declined with increasing conductivity (all P < 0.05). Principal coordinate ordination analysis (PCoA) revealed significant (ANOSIM R = 0.97) groupings of low/high salinity bacterial OTUs, while OTUs from other domains were not significantly clustered. Bacterial beta diversity was unimodally distributed along the gradient and had a nested structure driven by species losses, whereas in fungi and non-fungal eukaryotes beta diversity declined monotonically without strong evidence of nestedness. Thus, while increased salinity acts as a stressor in all domains, the mechanisms driving community assembly along the gradient differ substantially between the domains.

Microbial community analysis of pH 4 thermal springs in Yellowstone National Park.

The pH of the majority of thermal springs in Yellowstone National Park (YNP) is from 1 to 3 and 6 to 10; relatively few springs (~5%) have a pH range of 4-5. We used 16S rRNA gene pyrosequencing to investigate microbial communities sampled from four pH 4 thermal springs collected from four regions of YNP that differed in their fluid temperature and geochemistry. Our results revealed that the composition of bacterial communities varied among the sites, despite sharing similar pH values. The taxonomic composition and metabolic functional potential of the site with the lowest temperature (55 °C), a thermal spring from the Seven Mile Hole (SMH) area, were further investigated using shotgun metagenome sequencing. The taxonomic classification, based on 372 Mbp of unassembled metagenomic reads, indicated that this community included a high proportion of Chloroflexi, Bacteroidetes, Proteobacteria, and Firmicutes. Functional comparison with other YNP thermal spring metagenomes indicated that the SMH metagenome was enriched in genes related to energy production and conversion, transcription, and carbohydrate transport. Analysis of genes involved in nitrogen metabolism revealed assimilatory and dissimilatory nitrate reduction pathways, whereas the majority of genes involved in sulfur metabolism were related to the reduction of sulfate to adenylylsulfate, sulfite, and H2S. Given that pH 4 thermal springs are relatively less common in YNP and thermal areas worldwide, they may harbor novel microbiota and the communities that inhabit them deserve further investigation.

Contrastive learning with token projection for Omicron pneumonia identification from few-shot chest CT images.

Introduction: Deep learning-based methods can promote and save critical time for the diagnosis of pneumonia from computed tomography (CT) images of the chest, where the methods usually rely on large amounts of labeled data to learn good visual representations. However, medical images are difficult to obtain and need to be labeled by professional radiologists. Methods: To address this issue, a novel contrastive learning model with token projection, namely CoTP, is proposed for improving the diagnostic quality of few-shot chest CT images. Specifically, (1) we utilize solely unlabeled data for fitting CoTP, along with a small number of labeled samples for fine-tuning, (2) we present a new Omicron dataset and modify the data augmentation strategy, i.e., random Poisson noise perturbation for the CT interpretation task, and (3) token projection is utilized to further improve the quality of the global visual representations. Results: The ResNet50 pre-trained by CoTP attained accuracy (ACC) of 92.35%, sensitivity (SEN) of 92.96%, precision (PRE) of 91.54%, and the area under the receiver-operating characteristics curve (AUC) of 98.90% on the presented Omicron dataset. On the contrary, the ResNet50 without pre-training achieved ACC, SEN, PRE, and AUC of 77.61, 77.90, 76.69, and 85.66%, respectively. Conclusion: Extensive experiments reveal that a model pre-trained by CoTP greatly outperforms that without pre-training. The CoTP can improve the efficacy of diagnosis and reduce the heavy workload of radiologists for screening of Omicron pneumonia.

MC-DC: An MLP-CNN Based Dual-path Complementary Network for Medical Image Segmentation.

BACKGROUND: Fusing the CNN and Transformer in the encoder has recently achieved outstanding performance in medical image segmentation. However, two obvious limitations require addressing: (1) The utilization of Transformer leads to heavy parameters, and its intricate structure demands ample data and resources for training, and (2) most previous research had predominantly focused on enhancing the performance of the feature encoder, with little emphasis placed on the design of the feature decoder. METHODS: To this end, we propose a novel MLP-CNN based dual-path complementary (MC-DC) network for medical image segmentation, which replaces the complex Transformer with a cost-effective Multi-Layer Perceptron (MLP). Specifically, a dual-path complementary (DPC) module is designed to effectively fuse multi-level features from MLP and CNN. To respectively reconstruct global and local information, the dual-path decoder is proposed which is mainly composed of cross-scale global feature fusion (CS-GF) module and cross-scale local feature fusion (CS-LF) module. Moreover, we leverage a simple and efficient segmentation mask feature fusion (SMFF) module to merge the segmentation outcomes generated by the dual-path decoder. RESULTS: Comprehensive experiments were performed on three typical medical image segmentation tasks. For skin lesions segmentation, our MC-DC network achieved 91.69% Dice and 9.52mm ASSD on the ISIC2018 dataset. In addition, the 91.6% Dice and 94.4% Dice were respectively obtained on the Kvasir-SEG dataset and CVC-ClinicDB dataset for polyp segmentation. Moreover, we also conducted experiments on the private COVID-DS36 dataset for lung lesion segmentation. Our MC-DC has achieved 87.6% [87.1%, 88.1%], and 92.3% [91.8%, 92.7%] on ground-glass opacity, interstitial infiltration, and lung consolidation, respectively. CONCLUSIONS: The experimental results indicate that the proposed MC-DC network exhibits exceptional generalization capability and surpasses other state-of-the-art methods in higher results and lower computational complexity.

Local attention and long-distance interaction of rPPG for deepfake detection.

With the development of generative models, abused Deepfakes have aroused public concerns. As a defense mechanism, face forgery detection methods have been intensively studied. Remote photoplethysmography (rPPG) technology extract heartbeat signal from recorded videos by examining the subtle changes in skin color caused by cardiac activity. Since the face forgery process inevitably disrupts the periodic changes in facial color, rPPG signal proves to be a powerful biological indicator for Deepfake detection. Motivated by the key observation that rPPG signals produce unique rhythmic patterns in terms of different manipulation methods, we regard Deepfake detection also as a source detection task. The Multi-scale Spatial-Temporal PPG map is adopted to further exploit heartbeat signal from multiple facial regions. Moreover, to capture both spatial and temporal inconsistencies, we propose a two-stage network consisting of a Mask-Guided Local Attention module (MLA) to capture unique local patterns of PPG maps, and a Temporal Transformer to interact features of adjacent PPG maps in long distance. Abundant experiments on FaceForensics + + and Celeb-DF datasets prove the superiority of our method over all other rPPG-based approaches. Visualization also demonstrates the effectiveness of the proposed method.

FERGCN: facial expression recognition based on graph convolution network.

Due to the problems of occlusion, pose change, illumination change, and image blur in the wild facial expression dataset, it is a challenging computer vision problem to recognize facial expressions in a complex environment. To solve this problem, this paper proposes a deep neural network called facial expression recognition based on graph convolution network (FERGCN), which can effectively extract expression information from the face in a complex environment. The proposed FERGCN includes three essential parts. First, a feature extraction module is designed to obtain the global feature vectors from convolutional neural networks branch with triplet attention and the local feature vectors from key point-guided attention branch. Then, the proposed graph convolutional network uses the correlation between global features and local features to enhance the expression information of the non-occluded part, based on the topology graph of key points. Furthermore, the graph-matching module uses the similarity between images to enhance the network's ability to distinguish different expressions. Results on public datasets show that our FERGCN can effectively recognize facial expressions in real environment, with RAF-DB of 88.23%, SFEW of 56.15% and AffectNet of 62.03%.

A multimodal transformer to fuse images and metadata for skin disease classification.

Skin disease cases are rising in prevalence, and the diagnosis of skin diseases is always a challenging task in the clinic. Utilizing deep learning to diagnose skin diseases could help to meet these challenges. In this study, a novel neural network is proposed for the classification of skin diseases. Since the datasets for the research consist of skin disease images and clinical metadata, we propose a novel multimodal Transformer, which consists of two encoders for both images and metadata and one decoder to fuse the multimodal information. In the proposed network, a suitable Vision Transformer (ViT) model is utilized as the backbone to extract image deep features. As for metadata, they are regarded as labels and a new Soft Label Encoder (SLE) is designed to embed them. Furthermore, in the decoder part, a novel Mutual Attention (MA) block is proposed to better fuse image features and metadata features. To evaluate the model's effectiveness, extensive experiments have been conducted on the private skin disease dataset and the benchmark dataset ISIC 2018. Compared with state-of-the-art methods, the proposed model shows better performance and represents an advancement in skin disease diagnosis.

Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN.

Chest X-ray (CXR) is a medical imaging technology that is common and economical to use in clinical. Recently, coronavirus (COVID-19) has spread worldwide, and the second wave is rebounding strongly now with the coming winter that has a detrimental effect on the global economy and health. To make pre-diagnosis of COVID-19 as soon as possible, and reduce the work pressure of medical staff, making use of deep learning networks to detect positive CXR images of infected patients is a critical step. However, there are complex edge structures and rich texture details in the CXR images susceptible to noise that can interfere with the diagnosis of the machines and the doctors. Therefore, in this paper, we proposed a novel multi-resolution parallel residual CNN (named MPR-CNN) for CXR images denoising and special application for COVID-19 which can improve the image quality. The core of MPR-CNN consists of several essential modules. (a) Multi-resolution parallel convolution streams are utilized for extracting more reliable spatial and semantic information in multi-scale features. (b) Efficient channel and spatial attention can let the network focus more on texture details in CXR images with fewer parameters. (c) The adaptive multi-resolution feature fusion method based on attention is utilized to improve the expression of the network. On the whole, MPR-CNN can simultaneously retain spatial information in the shallow layers with high resolution and semantic information in the deep layers with low resolution. Comprehensive experiments demonstrate that our MPR-CNN can better retain the texture structure details in CXR images. Additionally, extensive experiments show that our MPR-CNN has a positive impact on CXR images classification and detection of COVID-19 cases from denoised CXR images.

Patterns of bacterial biodiversity in the glacial meltwater streams of the McMurdo Dry Valleys, Antarctica.

Microbial consortia dominate glacial meltwater streams from polar regions, including the McMurdo Dry Valleys (MDV), where they thrive under physiologically stressful conditions. In this study, we examined microbial mat types and sediments found in 12 hydrologically diverse streams to describe the community diversity and composition within and across sites. Sequencing of the 16S rRNA gene from 129 samples revealed ∼24 000 operational taxonomic units (<97% DNA similarity), making streams the most biodiverse habitat in the MDV. Principal coordinate analyses revealed significant but weak clustering by mat type across all streams (ANOSIM R-statistic = 0.28) but stronger clustering within streams (ANOSIM R-statistic from 0.28 to 0.94). Significant relationships (P < 0.05) were found between bacterial diversity and mat ash-free dry mass, suggesting that diversity is related to the hydrologic regimes of the various streams, which are predictive of mat biomass. However, correlations between stream chemistry and community members were weak, possibly reflecting the importance of internal processes and hydrologic conditions. Collectively, these results suggest that localized conditions dictate bacterial community composition of the same mat types and sediments from different streams, and while MDV streams are hotspots of biodiversity in an otherwise depauperate landscape, controls on community structure are complex and site specific.

Expression and purification of recombinant hemoglobin in Escherichia coli.

BACKGROUND: Recombinant DNA technologies have played a pivotal role in the elucidation of structure-function relationships in hemoglobin (Hb) and other globin proteins. Here we describe the development of a plasmid expression system to synthesize recombinant Hbs in Escherichia coli, and we describe a protocol for expressing Hbs with low intrinsic solubilities. Since the α- and ß-chain Hbs of different species span a broad range of solubilities, experimental protocols that have been optimized for expressing recombinant human HbA may often prove unsuitable for the recombinant expression of wildtype and mutant Hbs of other species. METHODOLOGY/PRINCIPAL FINDINGS: As a test case for our expression system, we produced recombinant Hbs of the deer mouse (Peromyscus maniculatus), a species that has been the subject of research on mechanisms of Hb adaptation to hypoxia. By experimentally assessing the combined effects of induction temperature, induction time and E. coli expression strain on the solubility of recombinant deer mouse Hbs, we identified combinations of expression conditions that greatly enhanced the yield of recombinant protein and which also increased the efficiency of post-translational modifications. CONCLUSION/SIGNIFICANCE: Our protocol should prove useful for the experimental study of recombinant Hbs in many non-human animals. One of the chief advantages of our protocol is that we can express soluble recombinant Hb without co-expressing molecular chaperones, and without the need for additional reconstitution or heme-incorporation steps. Moreover, our plasmid construct contains a combination of unique restriction sites that allows us to produce recombinant Hbs with different α- and ß-chain subunit combinations by means of cassette mutagenesis.