Engineering Conditional Guide RNAs for Cell-Selective Regulation of CRISPR/Cas9

CRISPR/Cas9 is a versatile platform for implementing diverse modes of genetic perturbation such as gene silencing, induction, deletion, or replacement. This technology is popularly used in developmental biology to probe genetic circuitry via constitutive gene knockdown. Global gene silencing could introduce artifacts in the study of developmental regulatory pathways, and this motivates the development of cell-selective gene editing. Our lab has recently created conditional guide RNAs (cgRNA) that enable CRISPR/Cas9 systems to silence a desired gene Y conditioned on the detection of an RNA transcript X inside of a cell. cgRNA systems were discovered via insertion and deletion mutations that systematically explored the structure function of the guide RNA. Nucleic acid engineering software (NUPACK) was used to generate orthogonal libraries of cgRNA molecules that executed both ON → OFF logic (conditional inactivation by an RNA trigger) and OFF → ON logic (conditional activation by an RNA trigger). A dCas9-based RFP silencing assay in bacteria was developed and used to show these cgRNA sequences were functional and could detect short exogenous trigger sequences in an orthogonal and doseresponsive manner. Subsequent studies on cgRNA structure and function enabled us to engineer next-generation systems that have fewer constraints on the trigger sequence or structure. These next-generation cgRNAs were tested against short synthetic mRNA transcripts, truncated sub-sequences of endogenous mRNAs, and full-length endogenous mRNAs. Synthetic mRNA transcripts were used to study the effect of protein translation on trigger RNA binding. cgRNAs were capable of detecting synthetic sequences embedded in the 3′ UTR of fluorescent protein mRNAs. cgRNAs could also detect short synthetic mRNAs or truncated subsequences from endogenous mRNAs. However, the detection of native full-length endogenous mRNAs remained challenging because we cannot reliably predict the local structure of sub-sequences within a long RNA transcript. High-throughput cgRNAscreening may prove necessary for finding accessible binding sites onmRNA transcripts. Nevertheless, cgRNA functionalities could be useful in developmental biology by enabling precision perturbation of regulatory events, linking guide RNA activity to an RNA marker X correlated to a specific cell type or temporal expression pattern. This work opens the possibility for future applications such as cell-selective gene therapies.</p

Iridium-Catalyzed Silylation of C-H Bonds in Unactivated Arenes: A Sterically Encumbered Phenanthroline Ligand Accelerates Catalysis.

We report a new system for the silylation of aryl C-H bonds. The combination of [Ir(cod)(OMe)]2 and 2,9-Me2-phenanthroline (2,9-Me2-phen) catalyzes the silylation of arenes at lower temperatures and with faster rates than those reported previously, when the hydrogen byproduct is removed, and with high functional group tolerance and regioselectivity. Inhibition of reactions by the H2 byproduct is shown to limit the silylation of aryl C-H bonds in the presence of the most active catalysts, thereby masking their high activity. Analysis of initial rates uncovered the high reactivity of the catalyst containing the sterically hindered 2,9-Me2-phen ligand but accompanying rapid inhibition by hydrogen. With this catalyst, under a flow of nitrogen to remove hydrogen, electron-rich arenes, including those containing sensitive functional groups, undergo silylation in high yield for the first time, and arenes that underwent silylation with prior catalysts react over much shorter times with lower catalyst loadings. The synthetic value of this methodology is demonstrated by the preparation of key intermediates in the synthesis of medicinally important compounds in concise sequences comprising silylation and functionalization. Mechanistic studies demonstrate that the cleavage of the aryl C-H bond is reversible and that the higher rates observed with the 2,9-Me2-phen ligand are due to a more thermodynamically favorable oxidative addition of aryl C-H bonds

Collaborative Neural Rendering using Anime Character Sheets

Drawing images of characters with desired poses is an essential but laborious task in anime production. Assisting artists to create is a research hotspot in recent years. In this paper, we present the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images (AKA Character Sheets). In general, the diverse hairstyles and garments of anime characters defies the employment of universal body models like SMPL, which fits in most nude human shapes. To overcome this, CoNR uses a compact and easy-to-obtain landmark encoding to avoid creating a unified UV mapping in the pipeline. In addition, the performance of CoNR can be significantly improved when referring to multiple reference images, thanks to feature space cross-view warping in a carefully designed neural network. Moreover, we have collected a character sheet dataset containing over 700,000 hand-drawn and synthesized images of diverse poses to facilitate research in this area. Our code and demo are available at https://github.com/megvii-research/IJCAI2023-CoNR.Comment: The first three authors contribute equally. In the Arts and Creativity Track of IJCAI202

Microwave Dielectric Properties of (1-x)Ba_(3.75)Nd_(9.5)Cr_(0.25)Nb_(0.25)Ti_(17.5)O_(54)-x NdAlO_3 Ceramics

This study presents the microwave dielectric properties calculation of (1-x)Ba_(3.75)Nd_(9.5)Cr_(0.25)Nb_(0.25)Ti_(17.5)O_(54)–xNdAlO_3 ceramics where x denotes the volume molar fraction. From X-ray diffraction results, the solid solution limit is calculated to be about 0.76, where it forms a single BaNd_2Ti_4O_(12) phase in Region I (0≤x<0.76), and both BaNd_2Ti_4O_(12) and NdAlO_3 coexist in Region II (0.76≤x<1). The solid solution limit is confirmed by independently calculating it from the dielectric constant data. There is less than 4% deviation between the measured dielectric constant (εr) and the one calculated from the Maxwell-Wagner formula. The total quality factor (Q) remains almost constant in Region I and increases rapidly with the volume molar fraction of NdAlO_3 in Region II. The measured Q×f, where f is the resonant frequency, is also consistent with the calculated value in both regions. The temperature coefficient at the resonant frequency is −1.4 ppm/°C, which agrees well with the calculated value of 0 ppm/°C. In addition, we observed a close correlation between the bulk density and the phase evolution

Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection

Fabric defect segmentation is integral to textile quality control. Despite this, the scarcity of high-quality annotated data and the diversity of fabric defects present significant challenges to the application of deep learning in this field. These factors limit the generalization and segmentation performance of existing models, impeding their ability to handle the complexity of diverse fabric types and defects. To overcome these obstacles, this study introduces an innovative method to infuse specialized knowledge of fabric defects into the Segment Anything Model (SAM), a large-scale visual model. By introducing and training a unique set of fabric defect-related parameters, this approach seamlessly integrates domain-specific knowledge into SAM without the need for extensive modifications to the pre-existing model parameters. The revamped SAM model leverages generalized image understanding learned from large-scale natural image datasets while incorporating fabric defect-specific knowledge, ensuring its proficiency in fabric defect segmentation tasks. The experimental results reveal a significant improvement in the model's segmentation performance, attributable to this novel amalgamation of generic and fabric-specific knowledge. When benchmarking against popular existing segmentation models across three datasets, our proposed model demonstrates a substantial leap in performance. Its impressive results in cross-dataset comparisons and few-shot learning experiments further demonstrate its potential for practical applications in textile quality control.Comment: 13 pages,4 figures, 3 table

A workflow for accurate metabarcoding using nanopore MinION sequencing

1. Metabarcoding has become a common approach to the rapid identification of the species composition in a mixed sample. The majority of studies use established short‐read high‐throughput sequencing platforms. The Oxford Nanopore MinION™, a portable sequencing platform, represents a low‐cost alternative allowing researchers to generate sequence data in the field. However, a major drawback is the high raw read error rate that can range from 10% to 22%. 2. To test if the MinION™ represents a viable alternative to other sequencing platforms we used rolling circle amplification (RCA) to generate full‐length consensus DNA barcodes for a bulk mock sample of 50 aquatic invertebrate species with at least 15% genetic distance to each other. By applying two different laboratory protocols, we generated two MinION™ runs that were used to build error‐corrected consensus sequences. A newly developed Python pipeline, ASHURE, was used for data processing, consensus building, clustering, and taxonomic assignment of the resulting reads. 3. Our pipeline achieved median accuracies of up to 99.3% for long concatemeric reads (>45 barcodes) and successfully identified all 50 species in the mock community. The use of RCA was integral for increasing consensus accuracy but was also the most time‐consuming step of the laboratory workflow. Most concatemeric reads were skewed towards a shorter read length range with a median read length of up to 1262bp. 4. Our study demonstrates that Nanopore sequencing can be used for metabarcoding, but exploration of other isothermal amplification procedures to improve consensus accuracy is recommended

Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution

The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual quality of videos, by simultaneously performing video frame interpolation (VFI) and video super-resolution (VSR). However, facing the challenge of the additional temporal dimension and scale inconsistency, most existing STVSR methods are complex and inflexible in dynamically modeling different motion amplitudes. In this work, we find that choosing an appropriate processing scale achieves remarkable benefits in flow-based feature propagation. We propose a novel Scale-Adaptive Feature Aggregation (SAFA) network that adaptively selects sub-networks with different processing scales for individual samples. Experiments on four public STVSR benchmarks demonstrate that SAFA achieves state-of-the-art performance. Our SAFA network outperforms recent state-of-the-art methods such as TMNet and VideoINR by an average improvement of over 0.5dB on PSNR, while requiring less than half the number of parameters and only 1/3 computational costs.Comment: WACV2024, 16 page