SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition.

It is projected that 10 million deaths could be attributed to drug-resistant bacteria infections in 2050. To address this concern, identifying new-generation antibiotics is an effective way. Antimicrobial peptides (AMPs), a class of innate immune effectors, have received significant attention for their capacity to eliminate drug-resistant pathogens, including viruses, bacteria, and fungi. Recent years have witnessed widespread applications of computational methods especially machine learning (ML) and deep learning (DL) for discovering AMPs. However, existing methods only use features including compositional, physiochemical, and structural properties of peptides, which cannot fully capture sequence information from AMPs. Here, we present SAMP, an ensemble random projection (RP) based computational model that leverages a new type of features called Proportionalized Split Amino Acid Composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. With this new feature set, SAMP captures the residue patterns like sorting signals at around both the N-terminus and the C-terminus, while also retaining the sequence order information from the middle peptide fragments. Benchmarking tests on different balanced and imbalanced datasets demonstrate that SAMP consistently outperforms existing state-of-the-art methods, such as iAMPpred and AMPScanner V2, in terms of accuracy, MCC, G-measure and F1-score. In addition, by leveraging an ensemble RP architecture, SAMP is scalable to processing large-scale AMP identification with further performance improvement, compared to those models without RP. To facilitate the use of SAMP, we have developed a Python package freely available at https://github.com/wan-mlab/SAMP.

Deciphering cell types by integrating scATAC-seq data with genome sequences.

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.

Impact of dust deposition on the growth of marine autotrophic and heterotrophic microorganisms: Evidence from the South China Sea.

Aeolian dust can provide nutrients for the ocean and affect the growth of phytoplankton. However, the impacts of dust deposition on autotrophic and heterotrophic microorganisms have rarely been studied. In this study, we conducted two microcosm experiments in the low-nutrient and low-chlorophyll environment of the South China Sea and found that dust did not stimulate the abundance of autotrophic and heterotrophic microorganisms. Our results show that dust contains most of the unreacted iron-bearing minerals, and thus provides limited bioavailable iron and nitrogen for bacterioplankton and phytoplankton growth. These results elucidate the overlooked impacts of the properties of the iron-bearing minerals in aeolian dust on microbial communities, which may play an important role in marine ecosystems and climate change.

Elevated nutrients and surface chlorophyll-α associated with natural methane seeps in the Haima cold seep area of the Qiongdongnan Basin, northern South China Sea.

Cold seeps are a significant source of methane to the ocean. However, nutrients and Chl-α in the euphotic layer overlying cold seeps have been poorly studied. Variations in Chl-α, nutrients, environmental parameters, and CH4 concentrations in the Haima cold seeps were analyzed. Results show that the overlying water exhibits a typical low nutrient and low Chl-α marine environment. Phosphate and Chl-α were significantly elevated, and the average SCM in cold seeps was much higher than that in control stations. Spearman correlation analysis indicated Chl-α in cold seep was positively correlated with salinity and negatively with nutrient and CH4 concentrations. It implied that the CH4 concentrations may promote the increase of Chl-α, and may be linked to CH4 plumes, bringing cold, nutrient-rich waters to the thermocline. However, due to the CH4 plumes hardly to track, more sampling is needed to determine the effects on Chl-α and phytoplankton in the euphotic layer.

The effects of age, sex, weight, and breed on canid methylomes.

Unlike genomes, which are static throughout the lifespan of an organism, DNA methylomes are dynamic. To study these dynamics, we developed quantitative models that measure the effect of multiple factors on DNA methylomes including, age, sex, weight, and genetics. We conducted our study in canids, which prove to be an ideal species to assess epigenetic moderators due to their extreme variability in size and well-characterized genetic structure. We collected buccal swabs from 217 canids (207 domestic dogs and 10 grey wolves) and used targeted bisulphite sequencing to measure methylomes. We also measured genotypes at over one thousand single nucleotide polymorphisms (SNPs). As expected, we found that DNA methylomes are strongly associated with age, enabling the construction of epigenetic clocks. However, we also identify novel associations between methylomes and sex, weight, and sterilization status, leading to accurate models that predict these factors. Methylomes are also affected by genetics, and we observe multiple associations between SNP loci and methylated CpGs. Finally, we show that several factors moderate the relationship between epigenetic ages and real ages, such as body weight, which increases epigenetic ageing. In conclusion, we demonstrate that the plasticity of DNA methylomes is impacted by myriad genetics and physiological factors, and that DNA methylation biomarkers are accurate predictors of age, sex and sterilization status.

Determining the double-bond positions of monounsaturated compounds in the alcohol fraction in seep carbonate.

The unsaturation patterns of molecular fossils are critical in distinguishing their biological precursors and diagenetic processes. However, questions regarding the determination of double-bond positions of unsaturated dialkyl glycerol ethers (DAGEs) in submarine hydrocarbon seep ecosystems remain unsolved. To address this problem, a protocol for dimethyl disulfide (DMDS) derivative analysis using gas chromatography (GC)-mass spectrometry was optimised. Herein, the double-bond positions of monounsaturated short-chain alcohols, monoalkyl glycerol ethers (MAGEs), and DAGEs in seep carbonates were analysed. Among these compounds, the double-bond positions of trimethylsilyl (TMS) derivatives of the monounsaturated DAGE-DMDS adducts were determined for the first time, with mass spectra characterized by molecular ions (M+·) and two major diagnostic ions (ω+ and Δ+) cleaved at the double bonds. For both the MAGEs and DAGEs, the double-bond positions of the monounsaturated n-C16:1-alkyl moieties were identified at ω5 and ω7. Compared to monounsaturated short-chain alcohols and MAGEs, both ionization efficiency and relative sensitivity for the TMS derivatives of DAGE-DMDS adducts were low as indicated by the high limit of detection at a signal-to-noise ratio of 3. In addition, the appropriate injection parameters and oven temperature program during GC analyses may be crucial in determining the double-bond positions within monounsaturated DAGEs.

Corrigendum: Active Anaerobic Archaeal Methanotrophs in Recently Emerged Cold Seeps of Northern South China Sea.

[This corrects the article DOI: 10.3389/fmicb.2020.612135.].

Three-dimensional multiscale fusion for porous media on microtomography images of different resolutions.

Accurately acquiring the three-dimensional (3D) image of a porous medium is an imperative issue for the prediction of multiple physical properties. Considering the inherent nature of the multiscale pores contained in porous media such as tight sandstones, to completely characterize the pore structure, one needs to scan the microstructure at different resolutions. Specifically, low-resolution (LR) images cover a larger field of view (FOV) of the sample, but are lacking small-scale features, whereas high-resolution (HR) images contain ample information, but sometimes only cover a limited FOV. To address this issue, we propose a method for fusing the spatial information from a two-dimensional (2D) HR image into a 3D LR image, and finally reconstructing an integrated 3D structure with added fine-scale features. In the fusion process, the large-scale structure depicted by the 3D LR image is fixed as background and the 2D image is utilized as training image to reconstruct a small-scale structure based on the background. To assess the performance of our method, we test it on a sandstone scanned with low and high resolutions. Statistical properties between the reconstructed image and the target are quantitatively compared. The comparison indicates that the proposed method enables an accurate fusion of the LR and HR images because the small-scale information is precisely reproduced within the large one.

Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks.

Digital rock imaging plays an important role in studying the microstructure and macroscopic properties of rocks, where microcomputed tomography (MCT) is widely used. Due to the inherent limitations of MCT, a balance should be made between the field of view (FOV) and resolution of rock MCT images-a large FOV at low resolution (LR) or a small FOV at high resolution (HR). However, large FOV and HR are both expected for reliable analysis results in practice. Super-resolution (SR) is an effective solution to break through the mutual restriction between the FOV and resolution of rock MCT images, for it can reconstruct an HR image from a LR observation. Most of the existing SR methods cannot produce satisfactory HR results on real-world rock MCT images. One of the main reasons for this is that paired images are usually needed to learn the relationship between LR and HR rock images. However, it is challenging to collect such a dataset in a real scenario. Meanwhile, the simulated datasets may be unable to accurately reflect the model in actual applications. To address these problems, we propose a cycle-consistent generative adversarial network (CycleGAN)-based SR approach for real-world rock MCT images, namely, SRCycleGAN. In the off-line training phase, a set of unpaired rock MCT images is used to train the proposed SRCycleGAN, which can model the mapping between rock MCT images at different resolutions. In the on-line testing phase, the resolution of the LR input is enhanced via the learned mapping by SRCycleGAN. Experimental results show that the proposed SRCycleGAN can greatly improve the quality of simulated and real-world rock MCT images. The HR images reconstructed by SRCycleGAN show good agreement with the targets in terms of both the visual quality and the statistical parameters, including the porosity, the local porosity distribution, the two-point correlation function, the lineal-path function, the two-point cluster function, the chord-length distribution function, and the pore size distribution. Large FOV and HR rock MCT images can be obtained with the help of SRCycleGAN. Hence, this work makes it possible to generate HR rock MCT images that exceed the limitations of imaging systems on FOV and resolution.

Active Anaerobic Archaeal Methanotrophs in Recently Emerged Cold Seeps of Northern South China Sea.

Cold seep ecosystems are developed from methane-rich fluids in organic rich continental slopes, which are the source of various dense microbial and faunal populations. Extensive studies have been conducted on microbial populations in this unique environment; most of them were based on DNA, which could not resolve the activity of extant organisms. In this study, RNA and DNA analyses were performed to evaluate the active archaeal and bacterial communities and their network correlations, particularly those participating in the methane cycle at three sites of newly developed cold seeps in the northern South China Sea (nSCS). The results showed that both archaeal and bacterial communities were significantly different at the RNA and DNA levels, revealing a higher abundance of methane-metabolizing archaea and sulfate-reducing bacteria in RNA sequencing libraries. Site ROV07-01, which exhibited extensive accumulation of deceased Calyptogena clam shells, was highly developed, and showed diverse and active anaerobic archaeal methanotrophs (ANME)-2a/b and sulfate-reducing bacteria from RNA libraries. Site ROV07-02, located near carbonate crusts with few clam shell debris, appeared to be poorly developed, less anaerobic and less active. Site ROV05-02, colonized by living Calyptogena clams, could likely be intermediary between ROV07-01 and ROV07-02, showing abundant ANME-2dI and sulfate-reducing bacteria in RNA libraries. The high-proportions of ANME-2dI, with respect to ANME-2dII in the site ROV07-01 was the first report from nSCS, which could be associated with recently developed cold seeps. Both ANME-2dI and ANME-2a/b showed close networked relationships with sulfate-reducing bacteria; however, they were not associated with the same microbial operational taxonomic units (OTUs). Based on the geochemical gradients and the megafaunal settlements as well as the niche specificities and syntrophic relationships, ANMEs appeared to change in community structure with the evolution of cold seeps, which may be associated with the heterogeneity of their geochemical processes. This study enriched our understanding of more active sulfate-dependent anaerobic oxidation of methane (AOM) in poorly developed and active cold seep sediments by contrasting DNA- and RNA-derived community structure and activity indicators.

Reconstruction of porous media from extremely limited information using conditional generative adversarial networks.

Porous media are ubiquitous in both nature and engineering applications. Therefore, their modeling and understanding is of vital importance. In contrast to direct acquisition of three-dimensional (3D) images of this type of medium, obtaining its subregion (s) such as 2D images or several small areas can be feasible. Therefore, reconstructing whole images from limited information is a primary technique in these types of cases. Given data in practice cannot generally be determined by users and may be incomplete or only partially informed, thus making existing reconstruction methods inaccurate or even ineffective. To overcome this shortcoming, in this study we propose a deep-learning-based framework for reconstructing full images from their much smaller subareas. In particular, conditional generative adversarial network is utilized to learn the mapping between the input (a partial image) and output (a full image). To ensure the reconstruction accuracy, two simple but effective objective functions are proposed and then coupled with the other two functions to jointly constrain the training procedure. Because of the inherent essence of this ill-posed problem, a Gaussian noise is introduced for producing reconstruction diversity, thus enabling the network to provide multiple candidate outputs. Our method is extensively tested on a variety of porous materials and validated by both visual inspection and quantitative comparison. It is shown to be accurate, stable, and even fast (∼0.08 s for a 128×128 image reconstruction). The proposed approach can be readily extended by, for example, incorporating user-defined conditional data and an arbitrary number of object functions into reconstruction, while being coupled with other reconstruction methods.

Dictionary optimization and constraint neighbor embedding-based dictionary mapping for superdimension reconstruction of porous media.

The three-dimensional (3D) structure of a digital core can be reconstructed from a single two-dimensional (2D) image via mathematical modeling. In classical mathematical modeling algorithms, such as multipoint geostatistics algorithms, the optimization of pattern sets (dictionaries) and the mapping problems are important issues. However, they have rarely been discussed thus far. Pattern set (dictionary)-related problems include the pattern set (dictionary) size problem and the one-to-many mapping problem in a pattern set (dictionary). The former directly affects the completeness of the dictionary, while the latter is manifested such that a single to-be-matched 2D patch has multiple matching patterns in the library and it is hence necessary to select these modes to establish an optimal mapping relationship. Whether the two above-mentioned problems can be properly resolved is directly related to the accuracy of the reconstruction results. Super-dimension reconstruction is a new 3D reconstruction method proposed by introducing the concepts of training dictionary, prior model, and mapping into the reconstruction of the digital core from the field of super-resolution reconstruction. In addition, mapping relationship extraction and dictionary building are also key issues in super-dimension reconstruction. Therefore, this paper discusses these common dictionary-related problems from the perspective of super-dimension dictionaries. We propose dictionary optimization using augmentation dictionaries and clustering based on the boundary features of the dictionary elements to improve the completeness and expand the expression ability of the dictionary. Furthermore, we propose constraint neighbor embedding-based dictionary mapping to establish a more reasonable dictionary mapping relationship for super-dimension reconstruction, and we solve the one-to-many mapping problem in the dictionary. Our experimental results show that the performance of the super-dimension dictionary can be improved by the above-mentioned algorithm. Thus, through the optimized dictionary structure and mapping relationship determined by the above-mentioned methods, the 2D patch to be reconstructed can match a more accurate 3D block in the dictionary. Consequently, the reconstruction precision is improved.

Improved multipoint statistics method for reconstructing three-dimensional porous media from a two-dimensional image via porosity matching.

Reconstructing a three-dimensional (3D) structure from a single two-dimensional training image (TI) is a challenging issue. Multiple-point statistics (MPS) is an effective method to solve this problem. However, in the traditional MPS method, errors occur while statistical features of reconstruction, such as porosity, connectivity, and structural properties, deviate from those of TI. Due to the MPS reconstruction mechanism that the voxel being reconstructed is dependent on the reconstructed voxel, it may cause error accumulation during simulations, which can easily lead to a significant difference between the real 3D structure and the reconstructed result. To reduce error accumulation and improve morphological similarity, an improved MPS method based on porosity matching is proposed. In the reconstruction, we search the matching pattern in the TI directly. Meanwhile, a multigrid approach is also applied to capture the large-scale structures of the TI. To demonstrate its superiority over the traditional MPS method, our method is tested on different sandstone samples from many aspects, including accuracy, stability, generalization, and flow characteristics. Experimental results show that the reconstruction results by the improved MPS method effectively match the CT sandstone samples in correlation functions, local porosity distribution, morphological parameters, and permeability.

Markov prior-based block-matching algorithm for superdimension reconstruction of porous media.

A superdimension reconstruction algorithm is used for the reconstruction of three-dimensional (3D) structures of a porous medium based on a single two-dimensional image. The algorithm borrows the concepts of "blocks," "learning," and "dictionary" from learning-based superresolution reconstruction and applies them to the 3D reconstruction of a porous medium. In the neighborhood-matching process of the conventional superdimension reconstruction algorithm, the Euclidean distance is used as a criterion, although it may not really reflect the structural correlation between adjacent blocks in an actual situation. Hence, in this study, regular items are adopted as prior knowledge in the reconstruction process, and a Markov prior-based block-matching algorithm for superdimension reconstruction is developed for more accurate reconstruction. The algorithm simultaneously takes into consideration the probabilistic relationship between the already reconstructed blocks in three different perpendicular directions (x, y, and z) and the block to be reconstructed, and the maximum value of the probability product of the blocks to be reconstructed (as found in the dictionary for the three directions) is adopted as the basis for the final block selection. Using this approach, the problem of an imprecise spatial structure caused by a point simulation can be overcome. The problem of artifacts in the reconstructed structure is also addressed through the addition of hard data and by neighborhood matching. To verify the improved reconstruction accuracy of the proposed method, the statistical and morphological features of the results from the proposed method and traditional superdimension reconstruction method are compared with those of the target system. The proposed superdimension reconstruction algorithm is confirmed to enable a more accurate reconstruction of the target system while also eliminating artifacts.

Evaluating the morphological completeness of a training image.

Understanding the three-dimensional (3D) stochastic structure of a porous medium is helpful for studying its physical properties. A 3D stochastic structure can be reconstructed from a two-dimensional (2D) training image (TI) using mathematical modeling. In order to predict what specific morphology belonging to a TI can be reconstructed at the 3D orthogonal slices by the method of 3D reconstruction, this paper begins by introducing the concept of orthogonal chords. After analyzing the relationship among TI morphology, orthogonal chords, and the 3D morphology of orthogonal slices, a theory for evaluating the morphological completeness of a TI is proposed for the cases of three orthogonal slices and of two orthogonal slices. The proposed theory is evaluated using four TIs of porous media that represent typical but distinct morphological types. The significance of this theoretical evaluation lies in two aspects: It allows special morphologies, for which the attributes of a TI can be reconstructed at a special orthogonal slice of a 3D structure, to be located and quantified, and it can guide the selection of an appropriate reconstruction method for a special TI.