Detection of streptavidin using liquid crystal based whispering gallery mode microbubble

Protein is a complex chemical substance essential for human survival. Traditional protein detection methods, such as colorimetry, electrochemical analysis, and enzyme-linked immunosorbent assays, have shown good specificity and accuracy for the protein detection. However, all these methods require specialized instruments, and the detection procedures are laborious and time-consuming. As a result, a rapid, sensitive, label-free protein detection method is urgently needed. Herein, we have developed an ultra-sensitive biosensor for the detection of low-concentration protein molecules, employing liquid crystal (LC)-amplified optofluidic resonator. Since the orientations of LCs highly depend on the surface biomolecular binding processes, LCs can be employed to realize the extremely sensitive detection of biomolecules. Immobilized protein molecules interfere with the orientation of LCs by reducing the vertical anchoring force from the alignment layer in which the spectral wavelength shift was monitored as a sensing parameter. A biosensing platform based on an LC-amplified optofluidic whispering gallery mode (WGM) resonator was designed and studied accordingly. Due to the simultaneous interaction of the WGM and the LCs in the optofluidic resonator, the changes caused by the injection of protein molecules will be amplified, resulting in a shift in the resonance wavelength. Total wavelength shifts scale proportionally to the molecular concentrations of the protein within a certain range. The detection limit for streptavidin (SA) can reach as low as the femtometer level, which is significantly higher than the detection limit in the classic polarized optical microscope (POM) method visible with the naked eye. In addition to SA, the LC-based optofluidic resonator can also be applied to detect a variety of protein molecules. Our study demonstrates that LC-amplified optofluidic resonator can provide a novel solution for ultrasensitive real-time characterization of biosensing and biomolecular interactions

Model-enhanced Vector Index

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions

Label-free protein quantitation using liquid crystal-enhanced optofluidic biosensor

Protein detection plays an important role in the medical research. Liquid crystals (LCs), as a class of sensitive materials, exhibit a promising ability in the biosensing field. Herein, we exploited an ultrasensitive biosensor for protein detection, employing the whispering-gallery-mode (WGM) from the LC-amplified optofluidic micro-resonator. The biomolecules can trigger both light-matter interactions and the orientation transitions of LC molecules. The WGM spectral wavelength shift was recorded as the sensing indicator, and a detection limit of 15 fM for bovine serum albumin (BSA) was achieved. Our LC-amplified optofluidic biosensor provides a new solution for the ultrasensitive, real-time, and stable biological detection

Fracture behavior and self-sharpening mechanisms of polycrystalline cubic boron nitride in grinding based on cohesive element method

Unlike monocrystalline cubic boron nitride (CBN), polycrystalline CBN (PCBN) shows not only higher fracture resistance induced by tool-workpiece interaction but also better self-sharpening capability; therefore, efforts have been devoted to the study of PCBN applications in manufacturing engineering. Most of the studies, however, remain qualitative due to difficulties in experimental observations and theoretical modeling and provide limited in-depth understanding of the self-sharpening behavior/mechanism. To fill this research gap, the present study investigates the self-sharpening process of PCBN abrasives in grinding and analyzes the macro-scale fracture behavior and highly localized micro-scale crack propagation in detail. The widely employed finite element (FE) method, together with the classic Voronoi diagram and cohesive element technique, is used considering the pronounced success of FE applications in polycrystalline material modeling. Grinding trials with careful observation of the PCBN abrasive morphologies are performed to validate the proposed method. The self-sharpening details, including fracture morphology, grinding force, strain energy, and damage dissipation energy, are studied. The effects of maximum grain cut depths (MGCDs) and grinding speeds on the PCBN fracture behavior are discussed, and their optimum ranges for preferable PCBN self-sharpening performance are suggested

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.National Natural Science Foundation of China [32022078]; National Key R&D Program of China [2022YFF1000900]; Local Innovative and Research Teams Project of Guangdong Province [2019BT02N630]; China Agriculture Research System [CARS-35]. Funding for open access charge: National Natural Science Foundation of China [32022078].Peer reviewe

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve