Biological Factor Regulatory Neural Network

Genes are fundamental for analyzing biological systems and many recent works proposed to utilize gene expression for various biological tasks by deep learning models. Despite their promising performance, it is hard for deep neural networks to provide biological insights for humans due to their black-box nature. Recently, some works integrated biological knowledge with neural networks to improve the transparency and performance of their models. However, these methods can only incorporate partial biological knowledge, leading to suboptimal performance. In this paper, we propose the Biological Factor Regulatory Neural Network (BFReg-NN), a generic framework to model relations among biological factors in cell systems. BFReg-NN starts from gene expression data and is capable of merging most existing biological knowledge into the model, including the regulatory relations among genes or proteins (e.g., gene regulatory networks (GRN), protein-protein interaction networks (PPI)) and the hierarchical relations among genes, proteins and pathways (e.g., several genes/proteins are contained in a pathway). Moreover, BFReg-NN also has the ability to provide new biologically meaningful insights because of its white-box characteristics. Experimental results on different gene expression-based tasks verify the superiority of BFReg-NN compared with baselines. Our case studies also show that the key insights found by BFReg-NN are consistent with the biological literature

Top-Down Structure and Device Fabrication using \u3ci\u3eIn Situ\u3c/i\u3e Nanomachining

We demonstrate the potential of an alternative tool for the fabrication of nanoscale structures and devices. A nanoindenter integrated with an atomic force microscope is shown to be a powerful machine tool for cutting precise length nanowires or nanobelts and for manipulating the shortened wires. We also demonstrate its utility in cutting grooves and fabricating dents (or periodic arrays of dents) in ZnSnanobelts. This approach permits the direct mechanical machining of nanodevices that are supported on a substrate without the inherent complications of e beam or photolithography

Effect of Tensile Offset Angles on Micro/Nanoscale Tensile Testing

For one-dimensional (1D) structures such as tubes, wires, and beams, tensile testing is a simple and reliable methodology for measuring their mechanical properties. The tensile offset angle effect on mechanical property measurement has long been ignored. In this study, theoretical and finite-element analysis(FEA) models for analyzing the tensile offset angle effect have been established. It is found that longitudinal stress decreases with increasing offset angles. The theoretically calculated elastic modulus relative errors reach 4.45% at the offset angle of 10°, whereas the experimentally measured elastic modulus relative errors are 45.4% at the offset angle of 15°. The difference in elastic modulus relative errors between the theoreticalanalysis and the experimental results is discussed with reference to the sensing system in the experimental instrumentation. To accurately measure the mechanical properties using the tensile testing technique, perfect alignment with a zero or small offset angle less than 5° is needed. A calibration methodology for aligning specimens has been developed

Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.Comment: Accepted by AAAI-202

Tobacco Mosaic Virus Templated Synthesis of One Dimensional Inorganic–Polymer Hybrid Fibres

Inorganic–polymer hybrid nanofibres were prepared by using a rod-like tobacco mosaic virus (TMV) as a template. With tetraethylorthosilicate (TEOS) as a precursor, long silica-coated TMVfibres were formed via a head-to-tail assembly, which showed a substantial increase of the elastic modulus. Furthermore, homogenous titania–TMV hybrid fibres could be prepared using polyaniline-coated TMV fibres as a template, which were used to form a composite film that was able to sense liquefied petroleum gases

Tobacco Mosaic Virus Templated Synthesis of One Dimensional Inorganic-Polymer Hybrid Fibres

Inorganic–polymer hybrid nanofibres were prepared by using a rod-like tobacco mosaic virus (TMV) as a template. With tetraethylorthosilicate (TEOS) as a precursor, long silica-coated TMVfibres were formed via a head-to-tail assembly, which showed a substantial increase of the elastic modulus. Furthermore, homogenous titania–TMV hybrid fibres could be prepared using polyaniline-coated TMV fibres as a template, which were used to form a composite film that was able to sense liquefied petroleum gases

On-line detection of spherical sensor for inrush current detection

With the exploration and demand for the field of marine, underwater vehicles are used in deep water widely, however, there is a lack of research about underwater vehicles which are applied in shallow wary water. When underwater vehicles working in shallow wary water, they will be affected by the inrush current effect of shallow waters, so the research of underwater vehicles about anti current control becomes a meaningful item. But due to the high cost of underwater work, simulation and analysis to the inrush current online detection mechanism first, to determine the effects of surge phenomenon of underwater vehicles. Electromotor drives propeller to generate the flow, the detection mechanism is subjected to the impact of all directions. For some mechanical analysis of the inrush current online detection mechanism and analyzing the influence on the water inrush current when it moved in this paper, so we can reduce the effects of water inrush current on underwater vehicles in the subsequent experiments

Field Scanner Design for MUSTANG of the Green Bank Telescope

MUSTANG is a bolometer camera for the Green Bank Telescope (GBT) working at a frequency of 90 GHz. The detector has a field of view of 40 arcseconds. To cancel out random emission change from atmosphere and other sources, requires a fast scanning reflecting system with a few arcminute ranges. In this paper, the aberrations of an off-axis system are reviewed. The condition for an optimized system is provided. In an optimized system, as additional image transfer mirrors are introduced, new aberrations of the off-axis system may be reintroduced, resulting in a limited field of view. In this paper, different scanning mirror arrangements for the GBT system are analyzed through the ray tracing analysis. These include using the subreflector as the scanning mirror, chopping a flat mirror and transferring image with an ellipse mirror, and chopping a flat mirror and transferring image with a pair of face-to-face paraboloid mirrors. The system analysis shows that chopping a flat mirror and using a well aligned pair of paraboloids can generate the required field of view for the MUSTUNG detector system, while other systems all suffer from larger off-axis aberrations added by the system modification. The spot diagrams of the well aligned pair of paraboloids produced is only about one Airy disk size within a scanning angle of about 3 arcmin.Comment: 7 pages, 9 figure

Urinary biomarkers associated with podocyte injury in lupus nephritis

The most prevalent and devastating form of organ damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN). LN is characterized by glomerular injury, inflammation, cell proliferation, and necrosis, leading to podocyte injury and tubular epithelial cell damage. Assays for urine biomarkers have demonstrated significant promise in the early detection of LN, evaluation of disease activity, and tracking of reaction to therapy. This is because they are non-invasive, allow for frequent monitoring and easy self-collection, transport and storage. Podocyte injury is believed to be a essential factor in LN. The extent and type of podocyte injury could be connected to the severity of proteinuria, making podocyte-derived cellular debris and injury-related urinary proteins potential markers for the diagnosis and monitoring of LN. This article focuses on studies examining urinary biomarkers associated with podocyte injury in LN, offering fresh perspectives on the application of biomarkers in the early detection and management of LN

Novel and Potent Acetylcholinesterase Inhibitors for the Treatment of Alzheimer’s Disease from Natural (±)-7,8-Dihydroxy-3-methyl-isochroman-4-one

Alzheimer’s disease (AD) is a neurodegenerative disease that causes memory and cognitive decline as well as behavioral problems. It is a progressive and well recognized complex disease; therefore, it is very urgent to develop novel and effective anti-AD drugs. In this study, a series of novel isochroman-4-one derivatives from natural (±)-7,8-dihydroxy-3-methyl-isochroman-4-one [(±)-XJP] were designed and synthesized, and their anti-AD potential was evaluated. Among them, compound 10a [(Z)-3-acetyl-1-benzyl-4-((6,7-dimethoxy-4-oxoisochroman-3-ylidene)methyl)pyridin-1-ium bromide] possessed potent anti-acetylcholinesterase (AChE) activity as well as modest antioxidant activity. Further molecular modeling and kinetic investigations revealed that compound 10a was a dual-binding inhibitor that binds to both catalytic anionic site (CAS) and peripheral anionic site (PAS) of the enzyme AChE. In addition, compound 10a exhibited low cytotoxicity and moderate anti-Aβ aggregation efficacy. Moreover, the in silico screening suggested that these compounds could pass across the blood–brain barrier with high penetration. These findings show that compound 10a was a promising lead from a natural product with potent AChE inhibitory activity and deserves to be further developed for the prevention and treatment of AD