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Complex oligonucleotide (oligo) libraries are essential materials for diverse applications in synthetic biology, pharmaceutical production, nanotechnology and DNA-based data storage. However, the error rates in synthesizing complex oligo libraries can be substantial, leading to increment in cost and labor for the applications. As most synthesis errors arise from faulty insertions and deletions, we developed a length-based method with single-base resolution for purification of complex libraries containing oligos of identical or different lengths. Our method—purification of multiplex oligonucleotide libraries by synthesis and selection—can be performed either step-by-step manually or using a next-generation sequencer. When applied to a digital data-encoded library containing oligos of identical length, the method increased the purity of full-length oligos from 83% to 97%. We also show that libraries encoding the complementarity-determining region H3 with three different lengths (with an empirically achieved diversity >10 6 ) can be simultaneously purified in one pot, increasing the in-frame oligo fraction from 49.6% to 83.5%. Accurate oligonucleotide libraries are produced by synthesis and selection.

In this study, we investigate appropriate machine learning methods for predicting agricultural commodity prices. Since environmental factors including weather affect price fluctuations of agricultural commodities, we constructed a multivariate time series dataset combining wholesale prices of four agricultural commodities in South Korea, six weather variables, and week numbers. We adopted two prominent prediction methods based on recurrent neural networks (RNN) and graph neural networks (GNN): one is the stacked long short-term memory, and the other consists of two GNN-based methods, the spectral temporal graph neural network (StemGNN) and the temporal graph convolutional network. Also, we utilized a univariate prediction model as a control to evaluate the effectiveness of the multivariate approach for predicting agricultural commodity prices. In this investigation, we applied five different smoothing time window lengths to evaluate the effect of mitigating short-term fluctuations on the predictive performance of the models. The experimental results showed that the mitigation of short-term fluctuations had a greater impact on improving the performance of multivariate prediction models compared to the univariate prediction model. Among the multivariate prediction models, the GNN-based network outperformed the RNN-based network. In view of the trained model, we analyzed the main weather variables affecting agricultural commodity prices by utilizing the adjacency weight matrices in the self-attention mechanism of StemGNN.

DNA-based data storage has attracted attention because of its higher physical density of the data and longer retention time than those of conventional digital data storage. However, previous DNA-based data storage lacked index features and the data quality of storage after a single access is not preserved, obstructing its industrial use. Here, DNA micro-disks, quick response (QR)-coded micro-sized disks that harbour data-encoded DNA molecules for the efficient management of DNA-based data storage, are proposed. The two major features that previous DNA-based data storage studies could not achieve are demonstrated. One feature is accessing data items efficiently by indexing the data-encoded DNA library. Another is achieving write-once-read-many (WORM) memory through the immobilization of DNA molecules on the disk and their enrichment through in situ DNA production. Through these features, the reliability of DNA-based data storage was increased by allowing multiple accession of data-encoded DNA without data loss. Competing Interest Statement Yeongjae Choi, Hyung Jong Bae, Taehoon Ryu, Suk-Heung Song, Seoju Kim, Hyeli Kim, Wook Park and Sunghoon Kwon are inventors of a patent application for the method described in this paper. The remaining authors declare no conflict of interest.

The diagnosis of severe acute respiratory syndrome 2 (SARS-CoV-2) infection by quantitative PCR with reverse transcription (RT–qPCR) typically involves bulky instrumentation in centralized laboratories and an assay time of 1–2 h. Here, we show that SARS-CoV-2 RNA can be detected in 17 min via a portable device integrating reverse transcription, fast thermocycling (via plasmonic heating through magneto-plasmonic nanoparticles) and in situ fluorescence detection following magnetic clearance of the nanoparticles. The device correctly classified all nasopharyngeal, oropharyngeal and sputum samples from 75 patients with COVID-19 and 75 healthy controls, with good concordance in fluorescence intensity with standard RT–qPCR (Pearson coefficients > 0.7 for the N1 , N2 and RPP30 genes). Fast, portable and automated nucleic acid detection should facilitate testing at the point of care. A portable device integrating reverse transcription, fast thermocycling and in situ fluorescence detection accurately detects SARS-CoV-2 RNA in patient samples in 17 min.

Among physical stimulation modalities, magnetism has clear advantages, such as deep penetration and untethered interventions in biological subjects. However, some of the working principles and effectiveness of existing magnetic neurostimulation approaches have been challenged, leaving questions to be answered. Here we introduce m-Torquer, a magnetic toolkit that mimics magnetoreception in nature. It comprises a nanoscale magnetic torque actuator and a circular magnet array, which deliver piconewton-scale forces to cells over a working range of ~70 cm. With m-Torquer, stimulation of neurons expressing bona fide mechanosensitive ion channel Piezo1 enables consistent and reproducible neuromodulation in freely moving mice. With its long working distance and cellular targeting capability, m-Torquer provides versatility in its use, which can range from single cells to in vivo systems, with the potential application in large animals such as primates. A magnetic torque actuator has been developed and is capable of modulation of neurons expressing the mechanosensitive ion channel, Piezo1, resulting in long-distance control of locomotion of mice.

Photolithography is the prevalent microfabrication technology. It needs to meet resolution and yield demands at a cost that makes it economically viable. However, conventional far-field photolithography has reached the diffraction limit, which imposes complex optics and short-wavelength beam source to achieve high resolution at the expense of cost efficiency. Here, we present a cost-effective near-field optical printing approach that uses metal patterns embedded in a flexible elastomer photomask with mechanical robustness. This technique generates sub-diffraction patterns that are smaller than 1/10 th of the wavelength of the incoming light. It can be integrated into existing hardware and standard mercury lamp, and used for a variety of surfaces, such as curved, rough and defect surfaces. This method offers a higher resolution than common light-based printing systems, while enabling parallel-writing. We anticipate that it will be widely used in academic and industrial productions. Photolithography is an established microfabrication technique but commonly uses costly shortwavelength light sources to achieve high resolution. Here the authors use metal patterns embedded in a flexible elastomer photomask with mechanical robustness for generation of subdiffraction patterns as a cost effective near-field optical printing approach.

Current soft neural probes are still operated by bulky, rigid electronics mounted to a body, which deteriorate the integrity of the device to biological systems and restrict the free behavior of a subject. We report a soft, conformable neural interface system that can monitor the single-unit activities of neurons with long-term stability. The system implements soft neural probes in the brain, and their subsidiary electronics which are directly printed on the cranial surface. The high-resolution printing of liquid metals forms soft neural probes with a cellular-scale diameter and adaptable lengths. Also, the printing of liquid metal-based circuits and interconnections along the curvature of the cranium enables the conformal integration of electronics to the body, and the cranial circuit delivers neural signals to a smartphone wirelessly. In the in-vivo studies using mice, the system demonstrates long-term recording (33 weeks) of neural activities in arbitrary brain regions. In T-maze behavioral tests, the system shows the behavior-induced activation of neurons in multiple brain regions. Neural systems are often bulky and heavy. Here, the authors produce a conformable neural interface for mice using liquid metals directly printed on the skull that can monitor neural activities with long-term stability.

In light of the growing safety concerns associated with lithium-ion batteries integrated in electric automobiles, there is an escalating need to explore the mechanical behavior of lithium-ion batteries under diverse loading conditions using finite element simulations. In this paper, a homogenized finite element model was proposed to predict the material behavior of pouch lithium-ion cells using finite element simulations under various loading conditions. This homogenized model consisted of four layers of solid elements wrapped by shell elements, and it was made to consider especially the porosity and the pressure sensitivity of a pouch cell. The material properties of this homogenized model were calibrated based on the out-of-plane compression, in-plane confined compression, and 3-point bending test data for a single cell. The simulations using the model exhibited good agreement with the experimental load-displacement data not only for a single cell but also for two stacked cells. Additionally, the effects of the pouch and the vacuum on the mechanical behavior were examined.

To the best of our knowledge, few studies have utilized cold plasma to improve soybean protein extraction yield and the functional properties of soybean protein. In this study, we aimed to assess the benefits of remote plasma treatments on soybean with respect to the utilization of soybean protein. This study involved two different sample forms (whole and crushed beans), two different plasma chemistry modes (ozone and nitrogen oxides [NOx = NO + NO2]), and a novel pressure-swing reactor. Crushed soybeans were significantly affected by NOx-mode plasma treatment. Crushed soybeans treated with NOx-mode plasma had the best outcomes, wherein the protein extraction yield increased from 31.64% in the control to 37.90% after plasma treatment. The water binding capacity (205.50%) and oil absorption capacity (267.67%) of plasma-treated soybeans increased to 190.88% and 246.23 % of the control, respectively. The emulsifying activity and emulsion stability slightly increased compared to those of the control. The secondary structure and surface hydrophobicity were altered. The remote plasma treatment of crushed soybeans increased soybean protein extraction yield compared to plasma-treated whole beans as well as untreated beans and altered the structural and physicochemical properties of soybean proteins.

CRISPR/Cas systems offer a powerful sensing mechanism to transduce sequence‐specific information into amplified analytical signals. However, performing multiplexed CRISPR/Cas assays remains challenging and often requires complex approaches for multiplexed assays. Here, a hydrogel‐based CRISPR/Cas12 system termed CLAMP (Cas‐Loaded Annotated Micro‐Particles) is described. The approach compartmentalizes the CRISPR/Cas reaction in spatially‐encoded hydrogel microparticles (HMPs). Each HMP is identifiable by its face code and becomes fluorescent when target DNA is present. The assay is further streamlined by capturing HMPs inside a microfluidic device; the captured particles are then automatically recognized by a machine‐learning algorithm. The CLAMP assay is fast, highly sensitive (attomolar detection limits with preamplification), and capable of multiplexing in a single‐pot assay. As a proof‐of‐concept clinical application, CLAMP is applied to detect nucleic acid targets of human papillomavirus in cervical brushing samples. A new strategy is demonstrated for multiplexed CRISPR/Cas assays. In this approach, each Cas reaction takes place inside a spatially‐encoded hydrogel microparticle (HMP). A machine‐learning (ML) algorithm then identifies coded HMPs and quantifies their intended molecular targets. The method is scalable to detect many targets in a single‐pot format, facilitating high‐throughput diagnoses.