Rapid multiple protein sequence search by parallel and heterogeneous computation.

MOTIVATION: Protein sequence database search and multiple sequence alignment generation is a fundamental task in many bioinformatics analyses. As the data volume of sequences continues to grow rapidly, there is an increasing need for efficient and scalable multiple sequence query algorithms for super-large databases without expensive time and computational costs. RESULTS: We introduce Chorus, a novel protein sequence query system that leverages parallel model and heterogeneous computation architecture to enable users to query thousands of protein sequences concurrently against large protein databases on a desktop workstation. Chorus achieves over 100× speedup over BLASTP without sacrificing sensitivity. We demonstrate the utility of Chorus through a case study of analyzing a ∼1.5-TB large-scale metagenomic datasets for novel CRISPR-Cas protein discovery within 30 min. AVAILABILITY AND IMPLEMENTATION: Chorus is open-source and its code repository is available at https://github.com/Bio-Acc/Chorus.

Efficient self-attention mechanism and structural distilling model for Alzheimer's disease diagnosis.

Structural magnetic resonance imaging (sMRI) is commonly used for the identification of Alzheimer's disease because of its keen insight into atrophy-induced changes in brain structure. Current mainstream convolutional neural network-based deep learning methods ignore the long-term dependencies between voxels; thus, it is challenging to learn the global features of sMRI data. In this study, an advanced deep learning architecture called Brain Informer (BraInf) was developed based on an efficient self-attention mechanism. The proposed model integrates representation learning, feature distilling, and classifier modeling into a unified framework. First, the proposed model uses a multihead ProbSparse self-attention block for representation learning. This self-attention mechanism selects the first ⌊lnN⌋ elements that can represent the overall features from the perspective of probability sparsity, which significantly reduces computational cost. Subsequently, a structural distilling block is proposed that applies the concept of patch merging to the distilling operation. The block reduces the size of the three-dimensional tensor and further lowers the memory cost while preserving the original data as much as possible. Thus, there was a significant improvement in the space complexity. Finally, the feature vector was projected into the classification target space for disease prediction. The effectiveness of the proposed model was validated using the Alzheimer's Disease Neuroimaging Initiative dataset. The model achieved 97.97% and 91.89% accuracy on Alzheimer's disease and mild cognitive impairment classification tasks, respectively. The experimental results also demonstrate that the proposed framework outperforms several state-of-the-art methods.

Stretchable, Stable, and Degradable Silk Fibroin Enabled by Mesoscopic Doping for Finger Motion Triggered Color/Transmittance Adjustment.

As a kind of biocompatible material with long history, silk fibroin is one of the ideal platforms for on-skin and implantable electronic devices, especially for self-powered systems. In this work, to solve the intrinsic brittleness as well as poor chemical stability of pure silk fibroin film, mesoscopic doping of regenerated silk fibroin is introduced to promote the secondary structure transformation, resulting in huge improvement in mechanical flexibility (∼250% stretchable and 1000 bending cycles) and chemical stability (endure 100 °C and 3-11 pH). Based on such doped silk film (SF), a flexible, stretchable and fully bioabsorbable triboelectric nanogenerator (TENG) is developed to harvest biomechanical energy in vitro or in vivo for intelligent wireless communication, for example, such TENG can be attached on the fingers to intelligently control the electrochromic function of rearview mirrors, in which the transmittance can be easily adjusted by changing contact force or area. This robust TENG shows great potential application in intelligent vehicle, smart home and health care systems.

From Molecular Reconstruction of Mesoscopic Functional Conductive Silk Fibrous Materials to Remote Respiration Monitoring.

Turning insulating silk fibroin materials into conductive ones turns out to be the essential step toward achieving active silk flexible electronics. This work aims to acquire electrically conductive biocompatible fibers of regenerated Bombyx mori silk fibroin (SF) materials based on carbon nanotubes (CNTs) templated nucleation reconstruction of silk fibroin networks. The electronical conductivity of the reconstructed mesoscopic functional fibers can be tuned by the density of the incorporated CNTs. It follows that the hybrid fibers experience an abrupt increase in conductivity when exceeding the percolation threshold of CNTs >35 wt%, which leads to the highest conductivity of 638.9 S m-1 among organic-carbon-based hybrid fibers, and 8 times higher than the best available materials of the similar types. In addition, the silk-CNT mesoscopic hybrid materials achieve some new functionalities, i.e., humidity-responsive conductivity, which is attributed to the coupling of the humidity inducing cyclic contraction of SFs and the conductivity of CNTs. The silk-CNT materials, as a type of biocompatible electronic functional fibrous material for pressure and electric response humidity sensing, are further fabricated into a smart facial mask to implement respiration condition monitoring for remote diagnosis and medication.

All-Textile Electronic Skin Enabled by Highly Elastic Spacer Fabric and Conductive Fibers.

Electronic fabrics that combine traditional fabric with intelligent functionalities have attracted increasing attention. Here an all-fabric pressure sensor with a wireless battery-free monitoring system was successfully fabricated, where a 3D penetrated fabric sandwiched between two highly conductive fabric electrodes acts as a dielectric layer. Thanks to the good elastic recovery of the spacer fabric, the capacitance pressure sensor exhibits a high sensitivity of 0.283 KPa-1 with a fast response time and good cycling stability (≥20 000). Water-soluble poly(vinyl alcohol) template-assisted silver nanofibers were constructed on the high-roughness fabric surface to achieve high conductivity (0.33 Ω/sq), remarkable mechanical robustness, and good biocompatibility with human skin. In addition, the coplanar fabric sensor arrays were successfully designed and fabricated to spatially map resolved pressure information. More importantly, the gas-permeable fabrics can be stuck on the skin for wireless real-time pressure detection through a fiber inductor coil with a resonant frequency shift sensitivity of 6.8 MHz/kPa. Our all-fabric sensor is more suitable for textile technology compared with traditional pressure sensors and exhibited wide potential applications in the field of intelligent fabric for electronic skin.