Antimicrobial peptide identification using multi-scale convolutional network

Background: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results: In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions: Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN

Genomic Arrangement of Regulons in Bacterial Genomes

Regulons, as groups of transcriptionally co-regulated operons, are the basic units of cellular response systems in bacterial cells. While the concept has been long and widely used in bacterial studies since it was first proposed in 1964, very little is known about how its component operons are arranged in a bacterial genome. We present a computational study to elucidate of the organizational principles of regulons in a bacterial genome, based on the experimentally validated regulons of E. coli and B. subtilis. Our results indicate that (1) genomic locations of transcriptional factors (TFs) are under stronger evolutionary constraints than those of the operons they regulate so changing a TF’s genomic location will have larger impact to the bacterium than changing the genomic position of any of its target operons; (2) operons of regulons are generally not uniformly distributed in the genome but tend to form a few closely located clusters, which generally consist of genes working in the same metabolic pathways; and (3) the global arrangement of the component operons of all the regulons in a genome tends to minimize a simple scoring function, indicating that the global arrangement of regulons follows simple organizational principles.DOE of the US, BioEnergy Science Center grant (DE-PS02-06ER64304) NSF of the US, DEB-0830024

BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework

Motivation: Removal of batch effect between multiple datasets from different experimental platforms has become an urgent problem, since single-cell RNA sequencing (scRNA-seq) techniques developed rapidly. Although there have been some methods for this problem, most of them still face the challenge of under-correction or over-correction. Specifically, handling batch effect in highly nonlinear scRNA-seq data requires a more powerful model to address under-correction. In the meantime, some previous methods focus too much on removing difference between batches, which may disturb the biological signal heterogeneity of datasets generated from different experiments, thereby leading to over-correction. Results: In this article, we propose a novel multi-layer adaptation autoencoder with dual-channel framework to address the under-correction and over-correction problems in batch effect removal, which is called BERMAD and can achieve better results of scRNA-seq data integration and joint analysis. First, we design a multi-layer adaptation architecture to model distribution difference between batches from different feature granularities. The distribution matching on various layers of autoencoder with different feature dimensions can result in more accurate batch correction outcome. Second, we propose a dual-channel framework, where the deep autoencoder processing each single dataset is independently trained. Hence, the heterogeneous information that is not shared between different batches can be retained more completely, which can alleviate over-correction. Comprehensive experiments on multiple scRNA-seq datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art methods

Experimental Study of Filter Cake Cleanup by Acid/Water Jetting

The main purpose of acid/water jetting treatments currently applied in the field is to clean up the filter cake formed during the drilling process and perhaps further stimulate the wellbore by creating wormholes if acid jetting is used in carbonate formation. This purpose can be achieved for the reason that the filter cake on the borehole can be mechanically broken by the high speed jetting action, and additionally, if acid is used, some materials in the filter cake can be dissolved, which can facilitate the mechanical breaking action. The knowledge of jetting effectiveness under various conditions is crucial for the purpose of optimizing the treatment design. In order to investigate quantitatively the effectiveness of acid/water jetting for filter cake cleanup and wellbore productivity enhancement, laboratory experiments were carried out under conditions similar to those in the field. Filter cake was deposited on the face of a 4 inch diameter core and then water or 15% HCl were used for jetting treatment. The original permeability, the permeability right after the drill-in fluid damage, and the permeability after the jetting treatment were measured and compared. The effect of overbalance pressure during the jetting treatment was investigated. CT scan was carried out for those cores that may have wormholes after the acid jetting treatment. An analysis of the mechanism for filter cake removal and wormhole creating during acid jetting treatment was proposed. It is discovered that acid jetting can effectively remove the filter cake by penetrating and lifting it from beneath, and efficient wormhole creation can only happen when the overbalance pressure during the acid jetting treatment is above a certain value. Based on this study, several suggestions for field applications were made

Dynamic Reservoir Characterization Using Complex Grids Based on Streamline and Fast Marching Methods

Dynamic reservoir characterization of large three dimensional earth models has become an increasingly important topic in recent years. Conventional finite difference reservoir simulation may not always be the optimal choice in such applications. Alternative methods such as the streamline method and the Fast Marching Method (FMM) could be advantageous in many cases. A comprehensive study to extend these methods to more complex grids is both theoretically interesting and practically beneficial. The ability to use complex grids greatly increases the applicability of these methods, for example, to model complex geologic structures, horizontal and multilateral wellbores, and complex hydraulic/natural fractures. We present a comprehensive study of various velocity interpolation methods in polygons. These methods extend the widely used velocity interpolation algorithms, such as the Pollock’s algorithm, to more complex geometries such as perpendicular bisection (PEBI) grids, unstructured triangular grids and grids with local refinement. We analyze important issues such as local conservation, velocity continuity, and orders of interpolation. Based on our analysis, we recommend a lower order locally conservative method for the most robust and numerically efficient calculation of streamline trajectories on unstructured grids. The proposed method is then applied to generate streamline visualizations for various grids. Previous studies have demonstrated the use of the FMM and the diffusive time of flight for drainage volume visualization and pressure depletion estimation for un- conventional reservoirs. In the current study, we first extend the FMM to corner point grids and anisotropic permeabilities. We then propose a new formulation of the diffusivity equation using the diffusive time of flight τ as a spatial coordinate. This new τ -coordinate formulation reduces the problem from 3D to 1D in space. The basic formulation is extended by incorporating additional physical processes which are potentially important in shale gas reservoirs. The new formulation is validated by comparing with both analytical solution and traditional finite difference simulation. Our expectation is that the new formulation will become an efficient and versatile tool for pressure depletion and associated reservoir characterization applications

Self-assembly of Zein-based microcarrier system for colon-targeted oral drug delivery

The advances in pharmaceutical technology allow for the development of various region-selective delivery systems for oral administration to optimize local and systemic therapy. In this paper, micronization associated with a polymorph modification approach was proposed for improving the solubility of hydrophobic drugs for developing a Zein-based colon-targeted delivery system. A microcarrier based on self-assembled structures of Zein was fabricated via a built-in ultrasonic dialysis process, which displayed high payload of a model drug, indomethacin (Indo), with its optimal crystal form. The possible self-assembly mechanism of Zein/Indo forming porous structure in the ultrasonic dialysis process was attributed to the results of intra- and/or intermolecular interactions between Zein and Indo. The designed microspheres, Zein-Indo@PDA, with a surface coating of polydopamine (PDA) not only rendered them enhanced stability and mechanical resistance but also hindered the premature drug release at undesired sites. This innovative formulation design may offer better chances of colon-targeted release

dbAPIS: a database of anti-prokaryotic immune system genes

Anti-prokary otic immune sy stem (APIS) proteins, typically encoded b y phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, to xin-antito xin, CRISPR-Cas). A gro wing number of APIS genes ha v e been characterized and dispersed in the literature. Here w e de v eloped dbAPIS ( https:// bcb.unl.edu/ dbAPIS ), as the first literature curated data repository for experimentally verified APIS genes and their associated protein f amilies. T he k e y features of dbAPIS include: (i) e xperimentally v erified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted str uct ures, genomic context, sequence and str uct ural homologs from different microbiome / virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Mark o v 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 (e x cept f or anti-CRISPRs) that inhibit a v ariety of prokary otic defense systems (e.g. RM, TA, CB A SS , 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

Gradient-Guided Dynamic Efficient Adversarial Training

Adversarial training is arguably an effective but time-consuming way to train robust deep neural networks that can withstand strong adversarial attacks. As a response to the inefficiency, we propose the Dynamic Efficient Adversarial Training (DEAT), which gradually increases the adversarial iteration during training. Moreover, we theoretically reveal that the connection of the lower bound of Lipschitz constant of a given network and the magnitude of its partial derivative towards adversarial examples. Supported by this theoretical finding, we utilize the gradient's magnitude to quantify the effectiveness of adversarial training and determine the timing to adjust the training procedure. This magnitude based strategy is computational friendly and easy to implement. It is especially suited for DEAT and can also be transplanted into a wide range of adversarial training methods. Our post-investigation suggests that maintaining the quality of the training adversarial examples at a certain level is essential to achieve efficient adversarial training, which may shed some light on future studies.Comment: 14 pages, 8 figure