Gallbladder microbial species and host bile acids biosynthesis linked to cholesterol gallstone comparing to pigment individuals.

Gallstones are crystalline deposits in the gallbladder that are traditionally classified as cholesterol, pigment, or mixed stones based on their composition. Microbiota and host metabolism variances among the different types of gallstones remain largely unclear. Here, the bile and gallstone microbial species spectra of 29 subjects with gallstone disease (GSD, 24 cholesterol and 5 pigment) were revealed by type IIB restriction site-associated DNA microbiome sequencing (2bRAD-M). Among them (21 subjects: 18 cholesterol and 3 pigment), plasma samples were subjected to liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics. The microbiome yielded 896 species comprising 882 bacteria, 13 fungi, and 1 archaeon. Microbial profiling revealed significant enrichment of Cutibacterium acnes and Microbacterium sp005774735 in gallstone and Agrobacterium pusense and Enterovirga sp013044135 in the bile of cholesterol GSD subjects. The metabolome revealed 2296 metabolites, in which malvidin 3-(6''-malonylglucoside), 2-Methylpropyl glucosinolate, and ergothioneine were markedly enriched in cholesterol GSD subjects. Metabolite set enrichment analysis (MSEA) demonstrated enriched bile acids biosynthesis in individuals with cholesterol GSD. Overall, the multi-omics analysis revealed that microbiota and host metabolism interaction perturbations differ depending on the disease type. Perturbed gallstone type-related microbiota may contribute to unbalanced bile acids metabolism in the gallbladder and host, representing a potential early diagnostic marker and therapeutic target for GSD.

Effect of the combined binding of topotecan and catechin/protocatechuic acid to a pH-sensitive DNA tetrahedron on release and cytotoxicity: Spectroscopic and calorimetric studies.

The therapeutic efficacy of chemotherapy drugs can be effectively improved through the dual effects of their combination with natural polyphenols and the delivery of targeted DNA nanostructures. In this work, the interactions of topotecan (TPT), (+)-catechin (CAT), or protocatechuic acid (PCA) with a pH-sensitive DNA tetrahedron (MUC1-TD) in the binary and ternary systems at pHs 5.0 and 7.4 were investigated by fluorescence spectroscopy and calorimetry. The intercalative binding mode of TPT/CAT/PC to MUC1-TD was confirmed, and their affinity was ranked in the order of PCA > CAT > TPT. The effects of the pH-sensitivity of MUC1-TD and different molecular structures of CAT and PCA on the loading, release, and cytotoxicity of TPT were discussed. The weakened interaction under acidic conditions and the co-loading of CAT/PCA, especially PCA, improved the release of TPT loaded by MUC1-TD. The targeting of MUC1-TD and the synergistic effect with CAT/PCA, especially CAT, enhanced the cytotoxicity of TPT on A549 cells. For L02 cells, the protective effect of CAT/PCA reduced the damage caused by TPT. The single or combined TPT loaded by MUC1-TD was mainly concentrated in the nucleus of A549 cells. This work will provide key information for the combined application of TPT and CAT/PCA loaded by DNA nanostructures to improve chemotherapy efficacy and reduce side effects.

Killing capacity analysis of tumor-infiltrating cytotoxic lymphocytes and impact on lymph node metastasis in differentiated papillary carcinoma of thyroid with the BRAF V600E mutation.

BACKGROUND: Cytotoxic lymphocytes (CLs) express potent toxins, including perforin (P) and granzyme-B (G), which brings about target cell death. The purpose of this study was to evaluate the killing capacity of tumor-infiltrating CLs by means of P and G analysis, and explore the association with lymph node metastasis in papillary carcinoma of thyroid (PTC) without Hashimoto's thyroiditis (HT). METHODS: Infiltration of lymphocytes in PTC was observed in frozen sections. Both fresh tumor tissues and paracancerous tissues with lymphocyte infiltration were collected and prepared into a single cell suspension. Flow cytometry was used to detect the percentages of CD3+P+, CD3+G+, CD8+P+, and CD8+G+ T lymphocytes (TLs) and CD16-CD56+P+ and CD16-CD56+G+ natural killer (NK) cells. Finally, we investigated differential expression of P and G in NK cells and cytotoxic T lymphocytes (CTLs) in paired tumor tissues (group T, n = 44) and paracancerous tissues (group N, n = 44) from patients with PTC with the BRAF V600E mutation. Furthermore, patients were divided into two groups according to whether cervical central lymph node metastasis (CCLNM) existed: group A (with lymph node metastases, n = 27) and group B (with nonlymph node metastases, n = 17). Patients were also divided into three groups according to the total number of positive CCLNM: group B, group C (with low-level lymph node metastases, less than 5, n = 17) and group D (with high-level lymph node metastases, no less than 5, n = 10). RESULTS: The percentage of CD3+P+ CTLs was significantly higher in group N than in group T (P < 0.05). The percentage of CD8+G+ CTLs was significantly higher in group T than in group N (P < 0.05). The percentages of CD3+G+, CD16-CD56+P+and CD16-CD56+G+ NK cells showed no significant difference in either group T or group N (P > 0.05). The percentages of CD3+P+ CTLs in group A and group C were significantly higher in the paracancerous tissue than in the tumor tissue (P < 0.05). The percentages of CD8+G+ CTLs in group A and group C were significantly higher in the tumor tissues than in the paracancerous tissues (P < 0.05). The percentage of CD16-CD56+G+ NK cells in group D was significantly higher in the tumor tissues than in the paracancerous tissues (P < 0.05). CONCLUSIONS: The killing capacity of infiltrating CLs in PTC differed between tumor tissues and paracancerous tissues. In cases with CCLNM, higher expression of CD16-CD56+G+ NK cells in tumor tissues may be associated with a high risk of lymph node metastasis.

Nanoemulsions Stable against Ostwald Ripening.

Ostwald ripening, the dominant mechanism of droplet size growth for an O/W nanoemulsion at high surfactant concentrations, depends on micelles in the water phase and high aqueous solubility of oil, especially for spontaneously formed nanoemulsions. In our study, O/W nanoemulsions were formed spontaneously by mixing a water phase with an oil phase containing fatty alcohol polyoxypropylene polyoxyethylene ether (APE). By monitoring periodically the droplet size of the nanoemulsions via dynamic light scattering, we demonstrated that the formed O/W nanoemulsions are stable against Ostwald ripening, i.e., droplet growth. In contrast, the nanoemulsion droplets grew with the addition of micelles, demonstrating the pivotal role of the presence of micelles in the water phase in the occurrence of Ostwald ripening. The influence of the initial phase of APE, the oil or water phase in which APE is present, on the micelle formation is discussed by the partition coefficient and interfacial adsorption of APE between the oil and water phase using a surface and interfacial tensiometer. In addition, the spontaneously formed O/W nanoemulsion, which is stable against Ostwald ripening, can be used as a nanocarrier for the delivery of water-insoluble pesticides. These results provide a novel approach for the preparation of stable nanoemulsions and contribute to elucidating the mechanism of instability of nanoemulsions.

Carbohydrate-active enzyme annotation in microbiomes using dbCAN.

CAZymes or carbohydrate-active enzymes are critically important for human gut health, lignocellulose degradation, global carbon recycling, soil health, and plant disease. We developed dbCAN as a web server in 2012 and actively maintain it for automated CAZyme annotation. Considering data privacy and scalability, we provide run_dbcan as a standalone software package since 2018 to allow users perform more secure and scalable CAZyme annotation on their local servers. Here, we offer a comprehensive computational protocol on automated CAZyme annotation of microbiome sequencing data, covering everything from short read pre-processing to data visualization of CAZyme and glycan substrate occurrence and abundance in multiple samples. Using a real-world metagenomic sequencing dataset, this protocol describes commands for dataset and software preparation, metagenome assembly, gene prediction, CAZyme prediction, CAZyme gene cluster (CGC) prediction, glycan substrate prediction, and data visualization. The expected results include publication-quality plots for the abundance of CAZymes, CGCs, and substrates from multiple CAZyme annotation routes (individual sample assembly, co-assembly, and assembly-free). For the individual sample assembly route, this protocol takes ∼33h on a Linux computer with 40 CPUs, while other routes will be faster. This protocol does not require programming experience from users, but it does assume a familiarity with the Linux command-line interface and the ability to run Python scripts in the terminal. The target audience includes the tens of thousands of microbiome researchers who routinely use our web server. This protocol will encourage them to perform more secure, rapid, and scalable CAZyme annotation on their local computer servers.

dbAPIS: a database of anti-prokaryotic immune system genes.

Anti-prokaryotic immune system (APIS) proteins, typically encoded by phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, toxin-antitoxin, CRISPR-Cas). A growing number of APIS genes have been characterized and dispersed in the literature. Here we developed dbAPIS (https://bcb.unl.edu/dbAPIS), as the first literature curated data repository for experimentally verified APIS genes and their associated protein families. The key features of dbAPIS include: (i) experimentally verified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted structures, genomic context, sequence and structural homologs from different microbiome/virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Markov models (HMMs); (iii) user-friendly web interface for data browsing by the inhibited immune system types or by the hosts, and functions for searching and batch downloading of pre-computed data; (iv) Inclusion of all types of APIS proteins (except for anti-CRISPRs) that inhibit a variety of prokaryotic defense systems (e.g. RM, TA, CBASS, Thoeris, Gabija). The current release of dbAPIS contains 41 verified APIS proteins and ∼4400 sequence homologs of 92 families and 38 clans. dbAPIS will facilitate the discovery of novel anti-defense genes and genomic islands in phages, by providing a user-friendly data repository and a web resource for an easy homology search against known APIS proteins.

Exploration of street space architectural color measurement based on street view big data and deep learning-A case study of Jiefang North Road Street in Tianjin.

Urban space architectural color is the first feature to be perceived in a complex vision beyond shape, texture and material, and plays an important role in the expression of urban territory, humanity and style. However, because of the difficulty of color measurement, the study of architectural color in street space has been difficult to achieve large-scale and fine development. The measurement of architectural color in urban space has received attention from many disciplines. With the development and promotion of information technology, the maturity of street view big data and deep learning technology has provided ideas for the research of street architectural color measurement. Based on this background, this study explores a highly efficient and large-scale method for determining architectural colors in urban space based on deep learning technology and street view big data, with street space architectural colors as the research object. We conducted empirical research in Jiefang North Road, Tianjin. We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. Based on K-Means clustering model, we identified the colors of the architectural elements in the street view. The accuracy of the building color measurement results was cross-sectionally verified by means of a questionnaire survey. The validation results show that the method is feasible for the study of architectural colors in street space. Finally, the overall coordination, sequence continuity, and primary and secondary hierarchy of architectural colors of Jiefang North Road in Tianjin were analyzed. The results show that the measurement model can realize the intuitive expression of architectural color information, and also can assist designers in the analysis of architectural color in street space with the guidance of color characteristics. The method helps managers, planners and even the general public to summarize the characteristics of color and dig out problems, and is of great significance in the assessment and transformation of the color quality of the street space environment.

Salient Object Detection in Optical Remote Sensing Images Driven by Transformer.

Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG and ResNet. Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm. In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm. Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions. D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism. SWSAM discards the direction-aware part of D-SWSAM to focus on localizing salient objects in the highest-level features. KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features. Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/GeleNet.

Learning to Immunize Images for Tamper Localization and Self-Recovery.

Digital images are vulnerable to nefarious tampering attacks such as content addition or removal that severely alter the original meaning. It is somehow like a person without protection that is open to various kinds of viruses. Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation, so that the protected images are immune to the viruses in that the tampered contents can be auto-recovered. This paper presents Imuge+, an enhanced scheme for image immunization. By observing the invertible relationship between image immunization and the corresponding self-recovery, we employ an invertible neural network to jointly learn image immunization and recovery respectively in the forward and backward pass. We also introduce an efficient attack layer that involves both malicious tamper and benign image post-processing, where a novel distillation-based JPEG simulator is proposed for improved JPEG robustness. Our method achieves promising results in real-world tests where experiments show accurate tamper localization as well as high-fidelity content recovery. Additionally, we show superior performance on tamper localization compared to state-of-the-art schemes based on passive forensics.

Thermodynamic and cellular studies of doxorubicin/daunorubicin loaded by a DNA tetrahedron for diagnostic imaging, chemotherapy, and gene therapy.

The combined diagnostic imaging, chemotherapy, and gene therapy based on DNA nanocarriers can reduce the toxic side effects and overcome multidrug resistance (MDR). In this study, we designed an antisense oligonucleotides (ASOs)-linked DNA tetrahedron (ASOs-TD). The detection limit of ASOs-TD for MDR1 mRNA was 0.05 µM. By using fluorescence spectroscopy and isothermal titration calorimetry (ITC), the interactions between doxorubicin (DOX) /daunorubicin (DAU) and ASOs-TD were investigated. The number of binding sites (n), binding constant (Ka), entropy change (ΔSo), enthalpy change (ΔHo) and Gibbs free energy change (ΔGo) were obtained. The intercalation of DOX/DAU with ASOs-TD was demonstrated by differential scanning calorimetry (DSC) and quenching researches of potassium ferricyanide K4[Fe(CN)6]. The in vitro release rate of DOX/DAU loaded in ASOs-TD was accelerated by deoxyribonuclease I (DNase I). In vitro cytotoxicity proved the good gene therapy effect of ASOs-TD and the increased cytotoxicity of DOX/DAU to MCF-7/ADR cells. The results of confocal laser scanning microscope (CLSM) suggested that ASOs-TD could effectively identify drug-resistant cells due to its good imaging ability for MDR1 mRNA. This work offers theoretical significance for overcoming MDR using DNA nanostructures which combine diagnostic imaging, chemotherapy, and gene therapy.

Adaptive 3D Mesh Steganography Based on Feature-Preserving Distortion.

Current 3D mesh steganography algorithms relying on geometric modification are prone to detection by steganalyzers. In traditional steganography, adaptive steganography has proven to be an efficient means of enhancing steganography security. Taking inspiration from this, we propose a highly adaptive embedding algorithm, guided by the principle of minimizing a carefully crafted distortion through efficient steganography codes. Specifically, we tailor a payload-limited embedding optimization problem for 3D settings and devise a feature-preserving distortion (FPD) to measure the impact of message embedding. The distortion takes on an additive form and is defined as a weighted difference of the effective steganalytic subfeatures utilized by the current 3D steganalyzers. With practicality in mind, we refine the distortion to enhance robustness and computational efficiency. By minimizing the FPD, our algorithm can preserve mesh features to a considerable extent, including steganalytic and geometric features, while achieving a high embedding capacity. During the practical embedding phase, we employ the Q-layered syndrome trellis code (STC). However, calculating the bit modification probability (BMP) for each layer of the Q-layered STC, given the variation of Q, can be cumbersome. To address this issue, we design a universal and automatic approach for the BMP calculation. The experimental results demonstrate that our algorithm achieves state-of-the-art performance in countering 3D steganalysis.

dbCAN3: automated carbohydrate-active enzyme and substrate annotation.

Carbohydrate active enzymes (CAZymes) are made by various organisms for complex carbohydrate metabolism. Genome mining of CAZymes has become a routine data analysis in (meta-)genome projects, owing to the importance of CAZymes in bioenergy, microbiome, nutrition, agriculture, and global carbon recycling. In 2012, dbCAN was provided as an online web server for automated CAZyme annotation. dbCAN2 (https://bcb.unl.edu/dbCAN2) was further developed in 2018 as a meta server to combine multiple tools for improved CAZyme annotation. dbCAN2 also included CGC-Finder, a tool for identifying CAZyme gene clusters (CGCs) in (meta-)genomes. We have updated the meta server to dbCAN3 with the following new functions and components: (i) dbCAN-sub as a profile Hidden Markov Model database (HMMdb) for substrate prediction at the CAZyme subfamily level; (ii) searching against experimentally characterized polysaccharide utilization loci (PULs) with known glycan substates of the dbCAN-PUL database for substrate prediction at the CGC level; (iii) a majority voting method to consider all CAZymes with substrate predicted from dbCAN-sub for substrate prediction at the CGC level; (iv) improved data browsing and visualization of substrate prediction results on the website. In summary, dbCAN3 not only inherits all the functions of dbCAN2, but also integrates three new methods for glycan substrate prediction.

Landscape in the gallbladder mycobiome and bacteriome of patients undergoing cholelithiasis with chronic cholecystitis.

Gallstone disease (GSD) is associated with changes in the gut and gallbladder bacterial composition, but there is limited information on the role of the fungal community (mycobiome) in disease development. This study aimed to characterize the gallbladder mycobiome profiles and their interactions with bacteriome in GSD. A total of 136 bile and gallstone samples (34 paired for bacteriome, and 33 paired and extra 2 bile samples for mycobiome) were obtained from calculi patients with chronic cholecystitis. Bile and gallstone bacteriome and mycobiome were profiled by 16S and internal transcribed spacer (ITS) rRNA gene sequencing, respectively. Gallbladder bacteriome, mycobiome, and interkingdom and intrakingdom interactions were compared between bile and gallstone. In general, microbial diversity was higher in bile than in gallstone, and distinct microbial community structures were observed among them. Deep Sea Euryarchaeotic Group, Rhodobacteraceae, and Rhodobacterales were microbial biomarkers of bile, while Clostridiales and Eubacterium coprostanoligenes were biomarkers of gallstone. Five fungal taxa, including Colletotrichum, Colletotrichum sublineola, and Epicoccum, were enriched in gallstone. Further ecologic analyses revealed that intensive transkingdom correlations between fungi and bacteria and intrakingdom correlations within them observed in gallstone were significantly decreased in bile. Large and complex fungal communities inhabit the gallbladder of patients with GSD. Gallstone, compared with bile, is characterized by significantly altered bacterial taxonomic composition and strengthened bacterial-bacterial, fungal-fungal, and bacterial-fungal correlations in the gallbladder of patients with GSD.

Spectroscopic, calorimetric and cytotoxicity studies on the combined binding of daunorubicin and acridine orange to a DNA tetrahedron.

Chemotherapy-phototherapy (CTPT) combination drugs co-loaded by targeted DNA nanostructures can achieve controlled drug delivery, reduce toxic side effects and overcome multidrug resistance. Herein, we constructed and characterized a DNA tetrahedral nanostructure (MUC1-TD) linked with the targeting aptamer MUC1. The interaction of daunorubicin (DAU)/acridine orange (AO) alone and in combination with MUC1-TD and the influence of the interaction on the cytotoxicity of the drugs were evaluated. Potassium ferrocyanide quenching analysis and DNA melting temperature assays were used to demonstrate the intercalative binding of DAU/AO to MUC1-TD. The interactions of DAU and/or AO with MUC1-TD were analyzed by fluorescence spectroscopy and differential scanning calorimetry. The number of binding sites, binding constant, entropy and enthalpy changes of the binding process were obtained. The binding strength and binding sites of DAU were higher than those of AO. The presence of AO in the ternary system weakened the binding of DAU to MUC1-TD. In vitro cytotoxicity studies demonstrated that the loading of MUC1-TD augmented the inhibitory effects of DAU and AO and the synergistic cytotoxic effects of DAU + AO on MCF-7 cells and MCF-7/ADR cells. Cell uptake studies showed that the loading of MUC1-TD was beneficial in promoting the apoptosis of MCF-7/ADR cells due to its enhanced targeting to the nucleus. This study has important guiding significance for the combined application of DAU and AO co-loaded by DNA nanostructures to overcome multidrug resistance.

Precise Control of Crystallization and Phase-Transition with Green Anti-Solvent in Wide-Bandgap Perovskite Solar Cells with Open-Circuit Voltage Exceeding 1.25 V.

Wide-bandgap perovskite solar cells (PSCs) have attracted a lot of attention due to their application in tandem solar cells. However, the open-circuit voltage (VOC ) of wide-bandgap PSCs is dramatically limited by high defect density existing at the interface and bulk of the perovskite film. Here, an anti-solvent optimized adduct to control perovskite crystallization strategy that reduces nonradiative recombination and minimizes VOC deficit is proposed. Specifically, an organic solvent with similar dipole moment, isopropanol (IPA) is added into ethyl acetate (EA) anti-solvent, which is beneficial to form PbI2 adducts with better crystalline orientation and direct formation of α-phase perovskite. As a result, EA-IPA (7-1) based 1.67 eV PSCs deliver a power conversion efficiency of 20.06% and a VOC of 1.255 V, which is one of the remarkable values for wide-bandgap around 1.67 eV. The findings provide an effective strategy for controlling crystallization to reduce defect density in PSCs.

Screening of phosphorus solubilizing microorganisms in cold environment and their effects on the growth of Brassica napus.

To screen out phosphorus solubilizing strains that can adapt to cold climate in Qinghai Province, Bacillus mucilaginosus, B. megaterium, B. cereus, Streptomyces violovariabilis, S. cinnamofuscus, and S. flavoagglomeratus were screened with solid plate medium as the primary and liquid medium as the secondary screening, with calcium phosphate, lecithin, and phytic acid as the single source of phosphorus. By comprehensively comparing the size of phosphate solubilizing circle in the solid plate medium and the soluble phosphorus content in the liquid medium, three strains of phosphate solubilizing bacteria with good phosphate solubilizing effects were screened, S. violovariabilis, S. cinnamofuscus, and B. mucilaginosus. The three phosphate solubilizing bacteria were made into liquid ino-culants, and the small rapeseed pot experiment was carried out with two soils with different fertilities in a cold climate in September. Compared with the control, plant height, fresh weight, root length, and root weight of rapes in high-fertility cultivated soil increased by 35.5%, 191.0%, 26.2%, and 282.7%, while plant phosphorus absorption, total phosphorus and available phosphorus contents in the rhizosphere soil increased by 968.9%, 5.1%, and 2.1%, respectively. In low-fertility soil, plant height and fresh weight was increased by 45.8% and 61.3%, root length and weight was decreased by 2.6% and 4.4%, while plant phosphorus absorption and the contents of total P and available P in rhizosphere soil were increased by 91.5 %, 29.1%, and 213.7%, respectively. The effect of the other two inoculants treatments was less significant than S. violovariabilis. Therefore, S. violovariabilis was the phosphate solubilizing strain suitable for the cold climate in Qinghai.

JPEG Steganography With Content Similarity Evaluation.

Content similarity is a representative property of natural images, for example, similar regions, which is utilized by modern steganalysis. Existing JPEG steganographic methods mainly focus on the complexity of content but ignore content similarity. This article investigates content similarity to improve the undetectability of JPEG steganography. Specifically, the content similarity of DCT blocks and the 64 parallel channels is used to design the distortion function. Given a JPEG image, initial embedding costs are assigned for quantized DCT coefficients using an appropriate algorithm among the existing distortion functions. Then, the similarities of blocks and channels are used to update the initial embedding costs, respectively. After combination, the final distortion function can be obtained. Using syndrome trellis coding (STC), which achieves minimal embedding distortion with respect to a given distortion function, secret data are embedded into the cover image with a final distortion function. Experimental results show that our scheme achieves better undetectability than current state-of-the-art JPEG steganographic methods.

Maximum Block Energy Guided Robust Subspace Clustering.

Subspace clustering is useful for clustering data points according to the underlying subspaces. Many methods have been presented in recent years, among which Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR) and Least Squares Regression clustering (LSR) are three representative methods. These approaches achieve good results by assuming the structure of errors as a prior and removing errors in the original input space by modeling them in their objective functions. In this paper, we propose a novel method from an energy perspective to eliminate errors in the projected space rather than the input space. Since the block diagonal property can lead to correct clustering, we measure the correctness in terms of a block in the projected space with an energy function. A correct block corresponds to the subset of columns with the maximal energy. The energy of a block is defined based on the unary column, pairwise and high-order similarity of columns for each block. We relax the energy function of a block and approximate it by a constrained homogenous function. Moreover, we propose an efficient iterative algorithm to remove errors in the projected space. Both theoretical analysis and experiments show the superiority of our method over existing solutions to the clustering problem, especially when noise exists.

Consistency-Induced Multiview Subspace Clustering.

Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.

dbCAN-seq update: CAZyme gene clusters and substrates in microbiomes.

Carbohydrate Active EnZymes (CAZymes) are significantly important for microbial communities to thrive in carbohydrate rich environments such as animal guts, agricultural soils, forest floors, and ocean sediments. Since 2017, microbiome sequencing and assembly have produced numerous metagenome assembled genomes (MAGs). We have updated our dbCAN-seq database (https://bcb.unl.edu/dbCAN_seq) to include the following new data and features: (i) ∼498 000 CAZymes and ∼169 000 CAZyme gene clusters (CGCs) from 9421 MAGs of four ecological (human gut, human oral, cow rumen, and marine) environments; (ii) Glycan substrates for 41 447 (24.54%) CGCs inferred by two novel approaches (dbCAN-PUL homology search and eCAMI subfamily majority voting) (the two approaches agreed on 4183 CGCs for substrate assignments); (iii) A redesigned CGC page to include the graphical display of CGC gene compositions, the alignment of query CGC and subject PUL (polysaccharide utilization loci) of dbCAN-PUL, and the eCAMI subfamily table to support the predicted substrates; (iv) A statistics page to organize all the data for easy CGC access according to substrates and taxonomic phyla; and (v) A batch download page. In summary, this updated dbCAN-seq database highlights glycan substrates predicted for CGCs from microbiomes. Future work will implement the substrate prediction function in our dbCAN2 web server.