Microfluidic devices interfaced to matrix-assisted laser desorption/ionization mass spectrometry for proteomics

Microfluidic interfaces were developed for off-line matrix-assisted laser desorption/ionization mass spectrometry (MALDI). Microfluidic interfaces allow samples to be manipulated on-chip and deposited onto a MALDI target plate for analysis. For this research, microfluidic culturing devices and automated digestion and deposition microfluidic chip platforms were developed for the identification of proteins. The microfluidic chip components were fabricated on a poly(methyl methacrylate), PMMA, wafer using the hot embossing method and a molding tool with structures prepared via micromilling. One of the most important components of the chip system was a trypsin microreactor. An open channel microreactor was constructed in a 100 µm wide and 100 µm deep channel with a 4 cm effective channel length. This device integrated frequently repeated steps for MALDI-based proteomics such as digestion, mixing with a matrix solution, and depositing onto a MALDI target. The microreactor provided efficient digestion of proteins at a flow rate of 1 µL/min with a residence time of approximately 24 s in the reaction channel. An electrokinetically driven microreactor was also developed using a micropost structured chip for digestion. The micropost chip had a higher digestion efficiency due to the higher surface area-to-volume ratio in the channel. Also, the electrokinetic flow eliminated the need for an external pumping system and gave a flat flow profile in the microchannel. The post microreactor consisted of a 4 cm × 200 µm × 50 µm microfluidic channel with trypsin immobilized on an array of 50 µm in diameter micropost support structures with a 50 µm edge-to-edge inter-post spacing. This micropost reactor was also used for fingerprint analysis of whole bacterial cells. The entire tryptic digestion and deposition procedure for intact bacteria took about 1 min. A contact deposition solid-phase bioreactor coupled with MALDI-TOF MS allowed for low-volume fraction deposition with a smaller spot size and a higher local concentration of the analyte. A bacterial cell-culturing chip was constructed for growing cells on-chip followed by off-line MALDI analysis. Coupling MALDI-TOF MS whole cell analysis with microfluidic culturing resulted in more consistent spectra as well as reduction of the total processing time. The microfluidic cell culturing was performed in a PMMA chip with a polydimethylsiloxane (PDMS) cover to allow gas permeation into the culture channel, which contained a 2.1 μL volume active culture chamber. After incubation of E. coli in a microfluidic culture device at 37 ℃ for 24 h, the cultured cells were analyzed with MALDI MS. Also, a microfluidic cell culture device containing continuous perfusion of culture medium was developed using a polycarbonate membrane. This microfluidic culturing format was improved with a fluidic manifold and thermostatted microheaters. Fingerprint mass spectra distinguishing E. coli strains tested were obtained after a 6 h incubation time, which was shorter compared to the 24 h incubation time using conventional culturing techniques. In addition, an enhanced identification procedure for bacteria was achieved by integrating on-chip digestion of cultured bacteria

Impact of Sea Surface Temperature and Surface Air Temperature on Maximizing Typhoon Rainfall: Focusing on Typhoon Maemi in Korea

In this study, the effects of surface air temperature (SAT) and sea surface temperature (SST) changes on typhoon rainfall maximization are analysed. Based on the numerically reproduced Typhoon Maemi, this study tried to maximize the typhoon-induced rainfall by increasing the amount of saturated water vapour in the atmosphere and the amount of water vapour entering the typhoon. Using the Weather Research and Forecasting (WRF) model, which is one of the regional climate models (RCMs), the rainfall simulated by WRF while increasing the SAT and SST to various sizes at initial conditions and boundary conditions of the model was analysed. As a result of the simulated typhoon rainfall, the spatial distribution of total rainfall depth on the land due to the increase combination of SAT and SST showed a wide variety without showing a certain pattern. This is attributed to the geographical location of the Korean peninsula, which is a peninsula between the continent and the ocean. In other words, under certain conditions, typhoons may drop most of the rainfall on the southern sea of the peninsula before landing on the peninsula. For instance, the 6-hour duration maximum precipitation (MP) in Busan Metropolitan City was 472.1 mm when the SST increased by 2.0°C. However, when the SST increased by 4.0°C, the MP was found to be 395.3 mm, despite the further increase in SST. This indicates that the MP at a particular area and the increase in temperature do not have a linear relationship. Therefore, in order to maximize typhoon rainfall, it is necessary to repeat the numerical experiment on various conditions and search for the combination of SAT and SST increase which is most suitable for the target typhoon

Separable states to distribute entanglement

It was shown that two distant particles can be entangled by sending a third particle never entangled with the other two [T. S. Cubitt et al., Phys. Rev. Lett. 91, 037902 (2003)]. In this paper, we investigate a class of three-qubit separable states to distribute entanglement by the same way, and calculate the maximal amount of entanglement which two particles of separable states in the class can have after applying the way.Comment: 4 pages, no figures, Revised argumen

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization

Post-training quantization (PTQ) has been gaining popularity for the deployment of deep neural networks on resource-limited devices since unlike quantization-aware training, neither a full training dataset nor end-to-end training is required at all. As PTQ schemes based on reconstructing each layer or block output turn out to be effective to enhance quantized model performance, recent works have developed algorithms to devise and learn a new weight-rounding scheme so as to better reconstruct each layer or block output. In this work, we propose a simple yet effective new weight-rounding mechanism for PTQ, coined FlexRound, based on element-wise division instead of typical element-wise addition such that FlexRound enables jointly learning a common quantization grid size as well as a different scale for each pre-trained weight. Thanks to the reciprocal rule of derivatives induced by element-wise division, FlexRound is inherently able to exploit pre-trained weights when updating their corresponding scales, and thus, flexibly quantize pre-trained weights depending on their magnitudes. We empirically validate the efficacy of FlexRound on a wide range of models and tasks. To the best of our knowledge, our work is the first to carry out comprehensive experiments on not only image classification and natural language understanding but also natural language generation, assuming a per-tensor uniform PTQ setting. Moreover, we demonstrate, for the first time, that large language models can be efficiently quantized, with only a negligible impact on performance compared to half-precision baselines, achieved by reconstructing the output in a block-by-block manner.Comment: Accepted to ICML 202

Characterization of urinary cotinine in non-smoking residents in smoke-free homes in the Korean National Environmental Health Survey (KoNEHS)

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background The objectives of this study were to determine urinary cotinine concentrations in non-smoking residents of smoke-free homes and to establish the relationship of urinary cotinine with housing type and other socio-demographic and secondhand smoke (SHS) exposure factors. Methods We used data from the Korean National Environmental Health Survey I (2009–2011). The study included 814 non-smoking adult residents living in apartments, attached, and detached housing. Residents who lived with smokers were excluded. Urinary cotinine concentration was used as a biomarker for SHS exposure. The factors associated with urinary cotinine levels in non-smoking residents were determined using multivariate regression analysis. Results Urinary cotinine was detected in 88 % of the 814 non-smoking residents of smoke-free homes. The urinary cotinine concentrations of residents living in attached [1.18 ng/mg creatinine (Cr)] and detached housing (1.23 ng/mg Cr) were significantly higher than those of residents who lived in apartments (0.69 ng/mg Cr). Urinary cotinine concentrations were significantly higher in residents who were men, those with a household income ≤1000 USD/month, those who were former smokers with >1 year and ≤1 year of not smoking, and those who experienced SHS odor every day. In the multivariate regression analysis, housing type, sex, former smoking status, and frequency of experiencing SHS odor were associated with urinary cotinine concentrations (R 2 = 0.14). Conclusions The majority of non-smoking residents of smoke-free homes had detectable urinary cotinine. Housing type, sex, former smoking status, and frequency of experiencing SHS odor were predictors for urinary cotinine concentrations in the study participants

Inverse Design of Terahertz Nanoresonators through Physics-Informed Machine Learning

The rapid development of 6G communications using terahertz (THz) electromagnetic waves has created a demand for highly sensitive THz nanoresonators capable of detecting these waves. Among the potential candidates, THz nanogap loop arrays show promising characteristics but require significant computational resources for accurate simulation. This requirement arises because their unit cells are 10 times smaller than millimeter wavelengths, with nanogap regions that are 1,000,000 times smaller. To address this challenge, we propose a rapid inverse design method for terahertz nanoresonators using physics-informed machine learning, specifically employing double deep Q-learning combined with an analytical model of the THz nanogap loop array. Through approximately 200,000 iterations in about 39 hours on a middle-level personal computer (CPU: 3.40 GHz, 6 cores, 12 threads, RAM: 16 GB, GPU: NVIDIA GeForce GTX 1050), our approach successfully identifies the optimal structure, resulting in an experimental electric field enhancement of 32,000 at 0.2 THz, 300% stronger than previous achievements. By leveraging our analytical model-based approach, we significantly reduce the computational resources required, providing a viable alternative to the impractical numerical simulation-based inverse design that was previously impractical

Development of an Automated Digestion and Droplet Deposition Microfluidic Chip for MALDI-TOF MS

An automated proteolytic digestion bioreactor and droplet deposition system was constructed with a plastic microfluidic device for off-line interfacing to matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The microfluidic chips were fabricated in poly(methyl methacrylate) (PMMA), using a micromilling machine and incorporated a bioreactor, which was 100 μm wide, 100 μm deep, and possessed a 4 cm effective channel length (400 nL volume). The chip was operated by pressure-driven flow and mounted on a robotic fraction collector system. The PMMA bioreactor contained surface immobilized trypsin, which was covalently attached to the UV-modified PMMA surface using coupling reagents N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride (EDC) and hydroxysulfosuccinimide (sulfo-NHS). The digested peptides were mixed with a MALDI matrix on-chip and deposited as discrete spots on MALDI targets. The bioreactor provided efficient digestion of a test protein, cytochrome c, at a flow rate of 1 μL/min, producing a reaction time of ∼24 s to give adequate sequence coverage for protein identification. Other proteins were also evaluated using this solid-phase bioreactor. The efficiency of digestion was evaluated by monitoring the sequence coverage, which was 64%, 35%, 58%, and 47% for cytochrome c, bovine serum albumin (BSA), myoglobin, and phosphorylase b, respectively. © 2008 American Society for Mass Spectrometry