MOSAIC enables in situ saturation mutagenesis of genes and CRISPR prime editing guide RNA optimization in human cells.

CRISPR prime editing offers unprecedented versatility and precision for the installation of genetic edits in situ . Here we describe the development and characterization of the Multiplexing Of Site-specific Alterations for In situ Characterization ( MOSAIC ) method, which leverages a non-viral PCR-based prime editing method to enable rapid installation of thousands of defined edits in pooled fashion. We show that MOSAIC can be applied to perform in situ saturation mutagenesis screens of: (1) the BCR-ABL1 fusion gene, successfully identifying known and potentially new imatinib drug-resistance variants; and (2) the IRF1 untranslated region (UTR), re-confirming non-coding regulatory elements involved in transcriptional initiation. Furthermore, we deployed MOSAIC to enable high-throughput, pooled screening of hundreds of systematically designed prime editing guide RNA ( pegRNA ) constructs for a large series of different genomic loci. This rapid screening of >18,000 pegRNA designs identified optimized pegRNAs for 89 different genomic target modifications and revealed the lack of simple predictive rules for pegRNA design, reinforcing the need for experimental optimization now greatly simplified and enabled by MOSAIC. We envision that MOSAIC will accelerate and facilitate the application of CRISPR prime editing for a wide range of high-throughput screens in human and other cell systems.

CRISPR PERSIST-On enables heritable and fine-tunable human gene activation.

Current technologies for upregulation of endogenous genes use targeted artificial transcriptional activators but stable gene activation requires persistent expression of these synthetic factors. Although general "hit-and-run" strategies exist for inducing long-term silencing of endogenous genes using targeted artificial transcriptional repressors, to our knowledge no equivalent approach for gene activation has been described to date. Here we show stable gene activation can be achieved by harnessing endogenous transcription factors ( EndoTF s) that are normally expressed in human cells. Specifically, EndoTFs can be recruited to activate endogenous human genes of interest by using CRISPR-based gene editing to introduce EndoTF DNA binding motifs into a target gene promoter. This Precision Editing of Regulatory Sequences to Induce Stable Transcription-On ( PERSIST-On ) approach results in stable long-term gene activation, which we show is durable for at least five months. Using a high-throughput CRISPR prime editing pooled screening method, we also show that the magnitude of gene activation can be finely tuned either by using binding sites for different EndoTF or by introducing specific mutations within such sites. Our results delineate a generalizable framework for using PERSIST-On to induce heritable and fine-tunable gene activation in a hit-and-run fashion, thereby enabling a wide range of research and therapeutic applications that require long-term upregulation of a target gene.

Transurethral En Bloc Resection Versus Standard Resection of Bladder Tumour: A Randomised, Multicentre, Phase 3 Trial.

BACKGROUND AND OBJECTIVE: Conventionally, standard resection (SR) is performed by resecting the bladder tumour in a piecemeal manner. En bloc resection of the bladder tumour (ERBT) has been proposed as an alternative technique in treating non-muscle-invasive bladder cancer (NMIBC). The objective of this study is to investigate whether ERBT could improve the 1-yr recurrence rate of NMIBC, as compared with SR. METHODS: A multicentre, randomised, phase 3 trial was conducted in Hong Kong. Adults with bladder tumour(s) of ≤ 3cm were enrolled from April 2017 to December 2020, and followed up until 1 yr after surgery. Patients were randomly assigned to receive either ERBT or SR in a 1:1 ratio. The primary outcome was 1-yr recurrence rate. A modified intention-to-treat analysis on patients with histologically confirmed NMIBC was performed. The main secondary outcomes included detrusor muscle sampling rate, operative time, hospital stay, 30-d complications, any residual or upstaging of disease upon second-look transurethral resection, and 1-yr progression rate. KEY FINDINGS AND LIMITATIONS: A total of 350 patients underwent randomisation, and 276 patients were histologically confirmed to have NMIBC. At 1 yr, 31 patients in the ERBT group and 46 in the SR group developed recurrence; the Kaplan-Meier estimate of 1- yr recurrence rates were 29% (95% confidence interval, 18-37) in the ERBT group and 38% (95% confidence interval, 28-46) in the SR group (p = 0.007). Upon a subgroup analysis, patients with 1-3 cm tumour, single tumour, Ta disease, or intermediate-risk NMIBC had a significant benefit from ERBT. None of the patients in the ERBT group and three patients in the SR group developed progression to muscle-invasive bladder cancer; the Kaplan-Meier estimates of 1-yr progression rates were 0% in the ERBT group and 2.6% (95% confidence interval, 0-5.5) in the SR group (p = 0.065). The median operative time was 28 min (interquartile range, 20-45) in the ERBT group and 22 min (interquartile range, 15-30) in the SR group (p < 0.001). All other secondary outcomes were similar in the two groups. CONCLUSIONS AND CLINICAL IMPLICATIONS: In patients with NMIBC of ≤ 3cm, ERBT resulted in a significant reduction in the 1-yr recurrence rate when compared with SR. The study results support ERBT as the first-line surgical treatment for patients with bladder tumours of≤ 3cm.

Deep Learning Methods for Calibrated Photometric Stereo and Beyond.

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models.

Landmark Localization From Medical Images With Generative Distribution Prior.

In medical image analysis, anatomical landmarks usually contain strong prior knowledge of their structural information. In this paper, we propose to promote medical landmark localization by modeling the underlying landmark distribution via normalizing flows. Specifically, we introduce the flow-based landmark distribution prior as a learnable objective function into a regression-based landmark localization framework. Moreover, we employ an integral operation to make the mapping from heatmaps to coordinates differentiable to further enhance heatmap-based localization with the learned distribution prior. Our proposed Normalizing Flow-based Distribution Prior (NFDP) employs a straightforward backbone and non-problem-tailored architecture (i.e., ResNet18), which delivers high-fidelity outputs across three X-ray-based landmark localization datasets. Remarkably, the proposed NFDP can do the job with minimal additional computational burden as the normalizing flows module is detached from the framework on inferencing. As compared to existing techniques, our proposed NFDP provides a superior balance between prediction accuracy and inference speed, making it a highly efficient and effective approach. The source code of this paper is available at https://github.com/jacksonhzx95/NFDP.

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing.

Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um.

Potencial da comunidade lusófona de Toronto para a criação de uma rede de apoio social para o câncer de mama* / Toronto's Portuguese-speaking community potential for creating a social support -networks for breast cancer

Estudo etnográfico que explorou ideias sobre a possibilidade de criação de redes sociais de apoio para o câncer de mama na comunidade lusófona em Toronto, (Canadá). Dezenove homens e mulheres das comunidades angolana, brasileira e portuguesa discutiram sobre a criação de uma rede de apoio social com foco em condições facilitadoras e dificultadoras para seu estabelecimento. Os componentes fundamentais para essa criação foram: a desmistificação do câncer de mama e sua prevenção, ênfase na educação em saúde, mobilização de voluntários e apoio social direto às mulheres que vivem com câncer de mama. Os fatores facilitadores potenciais foram a participação de mulheres mais idosas como líderes sociais e a mobilização de escolas e instituições religiosas. As barreiras percebidas foram: a crença de que câncer de mama é uma doença feminina, falta de conhecimento sobre sua cura/reabilitação, como também uma sensibilidade limitada ao câncer. As redes de apoio social devem considerar as diversas necessidades culturais e tangíveis das comunidades, assim como serviços de apoio social mais informais.

An ethnographic study explored ideas about the possibility of creating social support networks for breast cancer within the Portuguese- speaking community in Toronto (Canada). Nineteen men and women from Angolan, Brazilian and Portuguese communities informed about a social support network with a focus on enabling versus challenging conditions for its construction. The fundamental components in creating social support networks were: the demystification of breast cancer and its prevention, emphasis on health education, mobilizing volunteers and direct social support to women living with breast cancer. The potential enabling factors were the participation of older women as social leaders, and the utilization of schools and religious institutions. Perceived barriers were: breast cancer believed to be women's disease, lack of knowledge about its cure/ rehabilitation, as well as a limited sensitivity to cancer. Social support networks should consider the communities' diverse cultural and tangible needs, as well as more informal social support services.