TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis

Multi-modal skin lesion diagnosis (MSLD) has achieved remarkable success by modern computer-aided diagnosis technology based on deep convolutions. However, the information aggregation across modalities in MSLD remains challenging due to severity unaligned spatial resolution (dermoscopic image and clinical image) and heterogeneous data (dermoscopic image and patients' meta-data). Limited by the intrinsic local attention, most recent MSLD pipelines using pure convolutions struggle to capture representative features in shallow layers, thus the fusion across different modalities is usually done at the end of the pipelines, even at the last layer, leading to an insufficient information aggregation. To tackle the issue, we introduce a pure transformer-based method, which we refer to as ``Throughout Fusion Transformer (TFormer)", for sufficient information intergration in MSLD. Different from the existing approaches with convolutions, the proposed network leverages transformer as feature extraction backbone, bringing more representative shallow features. We then carefully design a stack of dual-branch hierarchical multi-modal transformer (HMT) blocks to fuse information across different image modalities in a stage-by-stage way. With the aggregated information of image modalities, a multi-modal transformer post-fusion (MTP) block is designed to integrate features across image and non-image data. Such a strategy that information of the image modalities is firstly fused then the heterogeneous ones enables us to better divide and conquer the two major challenges while ensuring inter-modality dynamics are effectively modeled. Experiments conducted on the public Derm7pt dataset validate the superiority of the proposed method. Our TFormer outperforms other state-of-the-art methods. Ablation experiments also suggest the effectiveness of our designs

Dense Pixel-to-Pixel Harmonization via Continuous Image Representation

High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on processing low-resolution (LR) images. Some recent works resort to combining with color-to-color transformations but are either limited to certain resolutions or heavily depend on hand-crafted image filters. In this work, we explore leveraging the implicit neural representation (INR) and propose a novel image Harmonization method based on Implicit neural Networks (HINet), which to the best of our knowledge, is the first dense pixel-to-pixel method applicable to HR images without any hand-crafted filter design. Inspired by the Retinex theory, we decouple the MLPs into two parts to respectively capture the content and environment of composite images. A Low-Resolution Image Prior (LRIP) network is designed to alleviate the Boundary Inconsistency problem, and we also propose new designs for the training and inference process. Extensive experiments have demonstrated the effectiveness of our method compared with state-of-the-art methods. Furthermore, some interesting and practical applications of the proposed method are explored. Our code is available at https://github.com/WindVChen/INR-Harmonization.Comment: Accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification

Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achieving significant performance, these SCL-based methods focus more on head classes, yet ignoring the utilization of information in tail classes. In this paper, we propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally. For information enhancement, we design a hybrid-proxy model to generate class-dependent proxies and propose a cycle update strategy for parameters optimization. A balanced-hybrid-proxy loss is designed to exploit relations between samples and proxies with different classes treated equally. Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into account, we further present a balanced-weighted cross-entropy loss following curriculum learning schedule. Experimental results on the classification of imbalanced skin lesion data have demonstrated the superiority and effectiveness of our method

CS-SELEX Generates High-Affinity ssDNA Aptamers as Molecular Probes for Hepatitis C Virus Envelope Glycoprotein E2

Currently, the development of effective diagnostic reagents as well as treatments against Hepatitis C virus (HCV) remains a high priority. In this study, we have described the development of an alive cell surface -Systematic Evolution of Ligands by Exponential Enrichment (CS-SELEX) technique and screened the functional ssDNA aptamers that specifically bound to HCV envelope surface glycoprotein E2. Through 13 rounds of selection, the CS-SELEX generated high-affinity ssDNA aptamers, and the selected ssDNA aptamer ZE2 demonstrated the highest specificity and affinity to E2-positive cells. HCV particles could be specifically captured and diagnosed using the aptamer ZE2. A good correlation was observed in HCV patients between HCV E2 antigen-aptamer assay and assays for HCV RNA quantities or HCV antibody detection. Moreover, the selected aptamers, especially ZE2, could competitively inhibit E2 protein binding to CD81, an important HCV receptor, and significantly block HCV cell culture (HCVcc) infection of human hepatocytes (Huh7.5.1) in vitro. Our data demonstrate that the newly selected ssDNA aptamers, especially aptamer ZE2, hold great promise for developing new molecular probes, as an early diagnostic reagent for HCV surface antigen, or a therapeutic drug specifically for HCV

Efficient One-Step Fusion PCR Based on Dual-Asymmetric Primers and Two-Step Annealing

Gene splicing by fusion PCR is a versatile and widely used methodology, especially in synthetic biology. We here describe a rapid method for splicing two fragments by one-round fusion PCR with a dual-asymmetric primers and two-step annealing (ODT) method. During the process, the asymmetric intermediate fragments were generated in the early stage. Thereafter, they were hybridized in the subsequent cycles to serve as template for the target full-length product. The process parameters such as primer ratio, elongation temperature and cycle numbers were optimized. In addition, the fusion products produced with this method were successfully applied in seamless genome editing. The fusion of two fragments by this method takes less than 0.5 day. The method is expected to facilitate various kinds of complex genetic engineering projects with enhanced efficiency

A novel C-terminal protein degron identified in bacterial aldehyde decarbonylases using directed enzyme evolution

Metabolic engineers have successfully synthesized alkanes, the bulk component of gasoline, using microbial cell factories as a sustainable alternative to petroleum-based fuels. Aldehyde decarbonylases (AD), enzymes which transform acyl aldehydes into alkanes, have been identified as the bottleneck in these alkane producing pathways. Previous studies demonstrated degradation of AD in E. coli cells via unknown molecular mechanism. Here, we present the discovery of a degradation tag (degron) in AD from Prochlorococcus marinus. AD variants were generated by random mutation using error-prone PCR, transferred into E. coli, and grown in chemostat culture with 2g/L hexanal to select for positive mutations. A short C-terminal sequence of AD from P. marinus was proven to be an intact degron by fusing to fluorescent proteins. Statistical analysis of C-terminal sequences of 371 non-redundant ADs from bacteria revealed a conserved sequence in this region, which was proven to be an effective degron. We also showed that ATP-dependent proteases clpAP and lon are responsible for the degradation of AD degron tagged protein. Furthermore, our results indicate that the AD degron caused 91.4% of green fluorescent protein (GFP) degradation when fused to its C-terminus, whereas its elimination in AD enhanced alkane production in vivo. Thus, our work demonstrated the presence of a protein degron tag in bacterial ADs, thereby facilitating further improvements in AD-based alkane production pathways. Please click Additional Files below to see the full abstract

Thermodynamic and kinetic study of CO2 adsorption/desorptionon amine-functionalized sorbents

473-482The thermodynamic and kinetic characteristics of CO2 adsorption of SBA-16 loaded with pentaethylenehexamine (PEHA) have been investigated using adsorption column system. The Langmuir isotherm model fitts the CO2 adsorption isotherms well, and the average isosteric heat of adsorption is 59.6 kJ/mol, indicating that the CO2 adsorption on PEHA-loaded SBA-16 is chemisorption. The Avrami fractional dynamics model is very suitable for illustrating the adsorption behaviour of CO2 adsorption, and the results of kinetic analysis show that increasing the partial pressure of CO2 or the working temperature is beneficial to the adsorption of CO2. Three desorption methods were evaluatedto achieve the optimal desorption method. The results show that VTSA and steam stripping method are effective methods for industrial CO2 desorption. Steam stripping may be more suitable for plants that already have low-cost steam. The activation energy Ea of CO2 adsorption/desorption is calculated using Arrhenius equation. The activation energy Ea of CO2 adsorption/desorption was calculated using the Arrhenius equation. The results show that the absolute value of Ea (adsorption) decreases with the increase of CO2 partial pressure. In addition, the Ea value of vacuum rotary regeneration method and steam stripping method is smaller than the Ea value of temperature swing regeneration

PCR-Based Seamless Genome Editing with High Efficiency and Fidelity in <i>Escherichia coli</i>

Efficiency and fidelity are the key obstacles for genome editing toolboxes. In the present study, a PCR-based tandem repeat assisted genome editing (TRAGE) method with high efficiency and fidelity was developed. The design of TRAGE is based on the mechanism of repair of spontaneous double-strand breakage (DSB) via replication fork reactivation. First, cat-sacB cassette flanked by tandem repeat sequence was integrated into target site in chromosome assisted by Red enzymes. Then, for the excision of the cat-sacB cassette, only subculturing is needed. The developed method was successfully applied for seamlessly deleting, substituting and inserting targeted genes using PCR products. The effects of different manipulations including sucrose addition time, subculture times in LB with sucrose and stages of inoculation on the efficiency were investigated. With our recommended procedure, seamless excision of cat-sacB cassette can be realized in 48 h efficiently. We believe that the developed method has great potential for seamless genome editing in E. coli