Editing Techniques Research Articles

Overview of Marker-assisted Selection in Animal Breeding

Marker-assisted selection (MAS) in animal breeding has undergone transformative advancements, reshaping genetic improvement and reproductive traits in livestock and expanding into new domains. From its inception in the late 20th century to current developments, MAS has evolved dynamically through the integration of genomic technologies, sophisticated statistical models, and innovative gene editing techniques. These advancements have significantly enhanced the precision and impact of MAS, revolutionizing genetic improvement and reproductive performance. By synergizing MAS with cutting-edge technologies, the collective aim is to optimize reproductive performance and genetic potential, fostering a sustainable and productive future for livestock breeding and beyond. The genetic markers, typically DNA sequences or genes linked to specific traits, revolutionized animal breeding by enabling the identification of animals carrying favorable genetic variants without the need for resource-intensive phenotypic evaluations. Marker-assisted selection in reproduction also laid the foundation for genomic selection, an advanced approach utilizing genome-wide markers to estimate breeding values and predict genetic potential. This comprehensive review encapsulates the journey of MAS, emphasizing its historical significance, current advancements, and promising future trajectories, showcasing its pivotal role in shaping the landscape of animal breeding and reproductive science.

Targeting Viral Transcription for HIV Cure Strategies.

Combination antiretroviral therapy (ART) suppresses viral replication to undetectable levels, reduces mortality and morbidity, and improves the quality of life of people living with HIV (PWH). However, ART cannot cure HIV infection because it is unable to eliminate latently infected cells. HIV latency may be regulated by different HIV transcription mechanisms, such as blocks to initiation, elongation, and post-transcriptional processes. Several latency-reversing (LRA) and -promoting agents (LPA) have been investigated in clinical trials aiming to eliminate or reduce the HIV reservoir. However, none of these trials has shown a conclusive impact on the HIV reservoir. Here, we review the cellular and viral factors that regulate HIV-1 transcription, the potential pharmacological targets and genetic and epigenetic editing techniques that have been or might be evaluated to disrupt HIV-1 latency, the role of miRNA in post-transcriptional regulation of HIV-1, and the differences between the mechanisms regulating HIV-1 and HIV-2 expression.

Marker-Assisted Breeding Techniques for the Development of Gluten-Free Wheat Varieties: A Comprehensive Review

Ingestion of gluten proteins from wheat, barley and rye poses a significant health risk. The sole treatment for various disease is a lifelong adherence to a strict gluten-free diet. Conventional breeding methods have limitations in producing wheat varieties that maintain baking quality due to the complexity of the wheat genome and the multitude of gluten genes. This review explores the use of RNA interference (RNAi) silencing and CRISPR/Cas9 gene editing techniques. Efficient screening and selection processes for identifying lines with reduced celiac disease epitopes at both the DNA and protein levels, as well as maintaining baking quality, are discussed. The integration of gene editing for the production of wheat products holds significant potential in meeting the growing demand for safer dietary options while ensuring compliance with regulatory standards and addressing the complex challenges associated with disease management. Marker-assisted selection (MAS) offers several advantages over conventional selection methods in plant breeding, including timesaving, cost-effectiveness and goal-oriented outcomes. This review aims to delineate various molecular markers, encompassing sequence-tagged sites (STS), simple sequence repeats (SSR), genotyping by sequencing (GBS), single nucleotide polymorphism (SNP) arrays, exome capture, Competitive Allele Specific PCR (KASP), cleaved amplified polymorphic sequences (CAPS), semi-thermal asymmetric reverse PCR (STARP), and genotyping by target sequencing (GBTS). Additionally, we compile quantitative trait loci (QTL)/genes and their associated markers, which hold potential utility in MAS applications. The rapid advancement of wheat genomics is poised to expedite marker development, QTL mapping, gene cloning, and wheat breeding processes.

Materializing Magic: How the Witches of Instagram Make the Invisible Visible Through Digital Photography and Editing Techniques

Recently, contemporary witchcraft has become increasingly subject to commodification, with many practitioners criticized for their pursuit of the “witch aesthetic” on social media. With Instagram feeds featuring carefully staged photographs of tools, materials, and spaces related to the craft, these “Insta-witches” or “#witchesofinstagram” often use photo editing applications like Photoshop to digitally manipulate these images in order to enhance their magical or otherworldly dimensions. In this article, I argue that the excessive use of magical “stuff,” along with the staging and editing of images shared on social media does not have to be superficial or devoid of meaning. Rather, I explore how these Witches of Instagram participate in what anthropologist Jennifer Deger refers to as “thick photography”—a process of image creation and alteration that gives rise to multilayered stories capable of extending beyond the bounds of the ordinary. In this context, photography foregrounds the intuitive, imaginative, and embodied ways of knowing central to the practice of magic, making visible that which may otherwise remain invisible. These digital images thus become relationally constituted material surfaces where networks of bodies, objects and energies are visualized, revealing the porous boundaries between categories such as human and nonhuman, internal and external, material and immaterial, and mundane and magical.

Pigs in Transplantation Research and Their Potential as Sources of Organs in Clinical Xenotransplantation.

The pig has long been used as a research animal and has now gained importance as a potential source of organs for clinical xenotransplantation. When an organ from a wild-type (i. e., genetically unmodified) pig is transplanted into an immunosuppressed nonhuman primate, a vigorous host immune response causes hyperacute rejection (within minutes or hours). This response has been largely overcome by 1) extensive gene editing of the organ-source pig and 2) the administration to the recipient of novel immunosuppressive therapy based on blockade of the CD40/CD154 T cell costimulation pathway. Gene editing has consisted of 1) deletion of expression of the 3 known carbohydrate xenoantigens against which humans have natural (preformed) antibodies and 2) the introduction of human 'protective' genes. The combination of gene editing and novel immunosuppressive therapy has extended life-supporting pig kidney graft survival to greater than 1 y and of pig heart survival to up to 9 mo. This review briefly describes the techniques of gene editing, the potential risks of transfer of porcine endogenous retroviruses with the organ, and the need for breeding and housing of donor pigs under biosecure conditions.

The SpRY Cas9 variant release the PAM sequence constraint for genome editing in the model plant Physcomitrium patens

Genome editing via CRISPR/Cas has enabled targeted genetic modifications in various species, including plants. The requirement for specific protospacer-adjacent motifs (PAMs) near the target gene, as seen with Cas nucleases like SpCas9, limits its application. PAMless SpCas9 variants, designed with a relaxed PAM requirement, have widened targeting options. However, these so-call PAMless SpCas9 still show variation of editing efficiency depending on the PAM and their efficiency lags behind the native SpCas9. Here we assess the potential of a PAMless SpCas9 variant for genome editing in the model plant Physcomitrium patens. For this purpose, we developed a SpRYCas9i variant, where expression was optimized, and tested its editing efficiency using the APT as a reporter gene. We show that the near PAMless SpRYCas9i effectively recognizes specific PAMs in P. patens that are not or poorly recognized by the native SpCas9. Pattern of mutations found using the SpRYCas9i are similar to the ones found with the SpCas9 and we could not detect off-target activity for the sgRNAs tested in this study. These findings contribute to advancing versatile genome editing techniques in plants.

Biotechnological Innovations in Sustainable Food Production

This review article explores the critical role of biotechnology in addressing the global food security crisis aggravated by the COVID-19 pandemic and Eastern European conflict. It starts by examining the profound impacts of these events on food supply chains and pricing, supported by relevant statistical evidence. The study then discusses agricultural biotechnology, comparing conventional and sustainable agriculture, with an emphasis on genome editing and modification techniques such as Zinc Finger Nucleases (ZFNs), Oligonucleotide-directed mutagenesis (ODM), and CRISPR. The application of RNA interference in agriculture and microbial biotechnology in promoting soil health and sustainable food production is discussed. Furthermore, the review shifts focus to the utilization of microbial biotechnology for soil health enhancement and sustainable food production, encompassing the development of pest-resistant and drought-tolerant crops and biotechnological methods for optimizing water use efficiency. Additionally, it examines biotechnology's applications in food processing, including enhancing nutritional content and improving shelf life and safety. The European Green Deal is analyzed, particularly its influence on agriculture through strategies like Farm to Fork, Biodiversity, and Circular Economy. Finally, the review concludes by addressing policy considerations, ethical challenges, and the necessity of international cooperation in biotechnology research, exemplified by projects like the Golden Rice and Heat-Tolerant Maize for Asia (HTMA), highlighting the multidisciplinary nature and global significance of biotechnological innovations in ensuring sustainable food production.

Leveraging the sugarcane CRISPR/Cas9 technique for genetic improvement of non-cultivated grasses.

Under changing climatic scenarios, grassland conservation and development have become imperative to impart functional sustainability to their ecosystem services. These goals could be effectively and efficiently achieved with targeted genetic improvement of native grass species. To the best of our literature search, very scant research findings are available pertaining to gene editing of non-cultivated grass species (switch grass, wild sugarcane, Prairie cordgrass, Bermuda grass, Chinese silver grass, etc.) prevalent in natural and semi-natural grasslands. Thus, to explore this novel research aspect, this study purposes that gene editing techniques employed for improvement of cultivated grasses especially sugarcane might be used for non-cultivated grasses as well. Our hypothesis behind suggesting sugarcane as a model crop for genetic improvement of non-cultivated grasses is the intricacy of gene editing owing to polyploidy and aneuploidy compared to other cultivated grasses (rice, wheat, barley, maize, etc.). Another reason is that genome editing protocols in sugarcane (x = 10-13) have been developed and optimized, taking into consideration the high level of genetic redundancy. Thus, as per our knowledge, this review is the first study that objectively evaluates the concept and functioning of the CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 technique in sugarcane regarding high versatility, target specificity, efficiency, design simplicity, and multiplexing capacity in order to explore novel research perspectives for gene editing of non-cultivated grasses against biotic and abiotic stresses. Additionally, pronounced challenges confronting sugarcane gene editing have resulted in the development of different variants (Cas9, Cas12a, Cas12b, and SpRY) of the CRISPR tool, whose technicalities have also been critically assessed. Moreover, different limitations of this technique that could emerge during gene editing of non-cultivated grass species have also been highlighted.

Cross-platform adaptation of algorithmic editing techniques

The research relevance is determined by the rapid development of technology and the growing need for efficient data processing on various platforms. The study aimed to address methods that would enable the use of data editing algorithms on various operating systems and hardware platforms. A methodology was developed for studying cross-platform adaptation technologies, including cross-compilation, virtualisation, the use of universal libraries and Application Programming Interface, as well as methods for testing and optimising algorithm performance. The study addressed various approaches to implementing cross-platform compatibility, including the use of cross-compilation, virtualisation and containerisation. The main technical challenges are managing resources, optimising performance and ensuring compatibility with hardware from different platforms. The principles included selecting the most appropriate technology for the task at hand, considering performance and security requirements, and ensuring effective integration of existing systems and infrastructure. The workflows are focused on creating modular and extensible solutions that can easily adapt to changes in the technological environment and user requirements. In the context of this study, artificial intelligence software plays a key role in improving the efficiency and accuracy of data processing across different platforms. The results showed that artificial intelligence software can automate various stages of the video and audio editing process. Artificial intelligence is used to analyse large amounts of content data, such as video files, images and audio recordings. The study determined that artificial intelligence is increasingly relevant in various aspects of movie production. Artificial intelligence can analyse scripts, predict their potential success and suggest improvements using data from previous films and their commercial success

Precise Structural and Dynamical Details in Zeolites Revealed by Coupling-Edited 1H-17O Double Resonance NMR Spectroscopy.

Despite the extensive industrial and research interests in zeolites, their intrinsic catalytic nature is not fully understood due to the complexity of the hydroxyl-aluminum moieties. 17O NMR would provide irreplaceable opportunities for much-needed fine structural determination given the ubiquitous presence of oxygen atoms in nearly all species; however, the low sensitivity and quadrupolar nature of oxygen-17 make its NMR spectroscopic elucidation challenging. Here, we show that state-of-the-art double resonance solid-state NMR techniques have been combined with spectral editing methods based on scalar (through-bond) and dipolar (through-space) couplings, which allowed us to address the subtle protonic structures in zeolites. Notably, the often-neglected and undesired second-order quadrupolar-dipolar cross-term interaction ("2nd-QD interaction") can actually be exploited and can help gain invaluable information. Eventually, a comprehensive set of 1H-17O/1H-27Al double resonance NMR with J-/D-coupling spectral editing techniques have been designed in this work and enabled us to reveal atomic-scale precise structural and dynamical details in zeolites including: 1) The jump rate of the bridging acid site (BAS) proton is relatively low, i.e., far less than 100 s-1 at room temperature. 2) The Al-OH groups with 1H chemical shift at 2.6-2.8 ppm, at least for nonseverely dealuminated H-ZSM-5 catalysts, exhibit a rigid bridging environment similar to that of BAS. 3) The Si-OH groups at 2.0 ppm are not hydrogen bonded and undergo fast cone-rotational motion. The results in this study predict the 2nd-QD interaction to be universal for any rigid -17O-H environment, such as those in metal oxide surfaces or biomaterials.

BARET: Balanced Attention Based Real Image Editing Driven by Target-Text Inversion

Image editing approaches with diffusion models have been rapidly developed, yet their applicability are subject to requirements such as specific editing types (e.g., foreground or background object editing, style transfer), multiple conditions (e.g., mask, sketch, caption), and time consuming fine-tuning of diffusion models. For alleviating these limitations and realizing efficient real image editing, we propose a novel editing technique that only requires an input image and target text for various editing types including non-rigid edits without fine-tuning diffusion model. Our method contains three novelties: (I) Target-text Inversion Schedule (TTIS) is designed to fine-tune the input target text embedding to achieve fast image reconstruction without image caption and acceleration of convergence. (II) Progressive Transition Scheme applies progressive linear interpolation between target text embedding and its fine-tuned version to generate transition embedding for maintaining non-rigid editing capability. (III) Balanced Attention Module (BAM) balances the tradeoff between textual description and image semantics. By the means of combining self-attention map from reconstruction process and cross-attention map from transition process, the guidance of target text embeddings in diffusion process is optimized. In order to demonstrate editing capability, effectiveness and efficiency of the proposed BARET, we have conducted extensive qualitative and quantitative experiments. Moreover, results derived from user study and ablation study further prove the superiority over other methods.

PMET: Precise Model Editing in a Transformer

Model editing techniques modify a minor proportion of knowledge in Large Language Models (LLMs) at a relatively low cost, which have demonstrated notable success. Existing methods assume Transformer Layer (TL) hidden states are values of key-value memories of the Feed-Forward Network (FFN). They usually optimize the TL hidden states to memorize target knowledge and use it to update the weights of the FFN in LLMs. However, the information flow of TL hidden states comes from three parts: Multi-Head Self-Attention (MHSA), FFN, and residual connections. Existing methods neglect the fact that the TL hidden states contains information not specifically required for FFN. Consequently, the performance of model editing decreases. To achieve more precise model editing, we analyze hidden states of MHSA and FFN, finding that MHSA encodes certain general knowledge extraction patterns. This implies that MHSA weights do not require updating when new knowledge is introduced. Based on above findings, we introduce PMET, which simultaneously optimizes Transformer Component (TC, namely MHSA and FFN) hidden states, while only using the optimized TC hidden states of FFN to precisely update FFN weights. Our experiments demonstrate that PMET exhibits state-of-the-art performance on both the \textsc{counterfact} and zsRE datasets. Our ablation experiments substantiate the effectiveness of our enhancements, further reinforcing the finding that the MHSA encodes certain general knowledge extraction patterns and indicating its storage of a small amount of factual knowledge. Our code is available at \url{https://github.com/xpq-tech/PMET}.

TexFit: Text-Driven Fashion Image Editing with Diffusion Models

Fashion image editing aims to edit an input image to obtain richer or distinct visual clothing matching effects. Existing global fashion image editing methods are difficult to achieve rich outfit combination effects while local fashion image editing is more in line with the needs of diverse and personalized outfit matching. The local editing techniques typically depend on text and auxiliary modalities (e.g., human poses, human keypoints, garment sketches, etc.) for image manipulation, where the auxiliary modalities essentially assist in locating the editing region. Since these auxiliary modalities usually involve additional efforts in practical application scenarios, text-driven fashion image editing shows high flexibility. In this paper, we propose TexFit, a Text-driven Fashion image Editing method using diffusion models, which performs the local image editing only with the easily accessible text. Our approach employs a text-based editing region location module to predict precise editing region in the fashion image. Then, we take the predicted region as the generation condition of diffusion models together with the text prompt to achieve precise local editing of fashion images while keeping the rest part intact. In addition, previous fashion datasets usually focus on global description, lacking local descriptive information that can guide the precise local editing. Therefore, we develop a new DFMM-Spotlight dataset by using region extraction and attribute combination strategies. It focuses locally on clothes and accessories, enabling local editing with text input. Experimental results on the DFMM-Spotlight dataset demonstrate the effectiveness of our model. Code and Datasets are available at https://texfit.github.io/.

Abstract 537: Preclinical PD-1 resistance models: Crucial tools for unveiling PD-1 resistance mechanisms and advancing novel treatment strategies

Abstract In the cancer-immunity cycle, the immune checkpoint PD-1 and its ligand PD-L1 collaborate to facilitate tumor immune evasion and promote tumor progression by thwarting immunity-induced apoptosis. Immune checkpoint inhibitors disrupt this alliance by preventing PD-1 from binding to PD-L1, thereby reactivating the patient's immune system. While anti-PD-1/PD-L1 therapy has demonstrated remarkable efficacy in treating solid tumors, durable responses are observed in only a minority of patients. The majority of patients do not experience the full benefits of anti-PD-1/PD-L1 therapy, and a significant proportion of initial responders eventually develop acquired resistance. Thus, the establishment of reliable preclinical PD-1 resistance models is of paramount importance for uncovering resistance mechanisms and devising novel treatment strategies. Common mechanisms contributing to PD-1/PD-L1 resistance include the absence of appropriate tumor antigens, inactivation of tumor surface MHC molecules, aberrant IFNγ signaling pathways, and the presence of an immunosuppressive tumor microenvironment. We have successfully generated multiple drug resistance models at both cellular and animal levels, offering valuable tools for the investigation of diverse drug resistance mechanisms. For instance, our animal model of drug resistance, induced by long-term treatment with the PD-1 antibody (Keytruda), simulates the clinical scenario of acquired resistance due to prolonged medication. Through gene editing techniques, we have created a drug-resistant cell line (CT26-B2M KO) by disrupting the B2M gene in the CT26 cell line, thereby mimicking a tumor model with B2M functional deficiency in mice. Additionally, we have compiled efficacy data from various naturally occurring resistant models, which serve as valuable resources for studying primary resistance or intricate resistance mechanisms. In summary, preclinical PD-1 resistance models serve as crucial tools for researchers to dissect the intricacies of PD-1 resistance mechanisms and offer essential guidance for the development of innovative treatment strategies. Citation Format: Yini Zhu, Yuan Fang, Huiyi Wang, Jun Xing, Lu Yang, Jing Zhao, Xiang Gao, Cunxiang Ju. Preclinical PD-1 resistance models: Crucial tools for unveiling PD-1 resistance mechanisms and advancing novel treatment strategies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 537.

A knowledge distillation based deep learning framework for cropped images detection in spatial domain

Cropping an image is a common image editing technique that aims to find viewpoints with suitable image composition. It is also a frequently used post-processing technique to reduce the evidence of tampering in an image. Detecting cropped images poses a significant challenge in the field of digital image forensics, as the distortions introduced by image cropping are often imperceptible to the human eye. Although deep neural networks achieve state-of-the-art performance, due to their ability to encode large-scale data and handle billions of model parameters. However, due to their high computational complexity and substantial storage requirements, it is difficult to deploy these large deep learning models on resource-constrained devices such as mobile phones and embedded systems. To address this issue, we propose a lightweight deep learning framework for cropping detection in spatial domain, based on knowledge distillation. Initially, we constructed four datasets containing a total of 60,000 images cropped using various tools. We then used Efficient-Net-B0, pre-trained on ImageNet with significant surgical adjustments, as the teacher model, which makes it more robust and faster to converge in this downstream task. The model was trained on 20,000 cropped and uncropped images from our own dataset, and we then applied its knowledge to a more compact model called the student model. Finally, we selected the best-performing lightweight model as the final prediction model, with a testing accuracy of 98.44% on the test dataset, which outperforms other methods. Extensive experiments demonstrate that our proposed model, distilled from Efficient-Net-B0, achieves state-of-the-art performance in terms of detection accuracy, training parameters, and FLOPs, outperforming existing methods in detecting cropped images.

Droplets for Gene Editing Using CRISPR-Cas9 and Clonal Selection Improvement Using Hydrogels.

Gene editing tools have triggered a revolutionary transformation in the realms of cellular and molecular physiology, serving as a fundamental cornerstone for the evolution of disease models and assays in cell culture reactions, marked by various enhancements. Concurrently, microfluidics has emerged over recent decades as a versatile technology capable of elevating performance and reducing costs in daily experiments across diverse scientific disciplines, with a pronounced impact on cell biology. The amalgamation of these groundbreaking techniques holds the potential to amplify the generation of stable cell lines and the production of extracellular matrix hydrogels. These hydrogels, assuming a pivotal role in isolating cells at the single-cell level, facilitate a myriad of analyses. This study presents a novel method that seamlessly integrates CRISPR-Cas9 gene editing techniques with single-cell isolation methods in induced pluripotent stem cell (hiPSC) lines, utilizing the combined power of droplets and hydrogels. This innovative approach is designed to optimize clonal selection, thereby concurrently reducing costs and the time required for generating a stable genetically modified cell line. By bridging the advancements in gene editing and microfluidic technologies, our approach not only holds significant promise for the development of disease models and assays but also addresses the crucial need for efficient single-cell isolation. This integration contributes to streamlining processes, making it a transformative method with implications for enhancing the efficiency and cost-effectiveness of stable cell line generation. As we navigate the intersection of gene editing and microfluidics, our study marks a significant stride toward innovative methodologies in the dynamic landscape of cellular and molecular physiology research.

CRISPR-M: Predicting sgRNA off-target effect using a multi-view deep learning network.

Using the CRISPR-Cas9 system to perform base substitutions at the target site is a typical technique for genome editing with the potential for applications in gene therapy and agricultural productivity. When the CRISPR-Cas9 system uses guide RNA to direct the Cas9 endonuclease to the target site, it may misdirect it to a potential off-target site, resulting in an unintended genome editing. Although several computational methods have been proposed to predict off-target effects, there is still room for improvement in the off-target effect prediction capability. In this paper, we present an effective approach called CRISPR-M with a new encoding scheme and a novel multi-view deep learning model to predict the sgRNA off-target effects for target sites containing indels and mismatches. CRISPR-M takes advantage of convolutional neural networks and bidirectional long short-term memory recurrent neural networks to construct a three-branch network towards multi-views. Compared with existing methods, CRISPR-M demonstrates significant performance advantages running on real-world datasets. Furthermore, experimental analysis of CRISPR-M under multiple metrics reveals its capability to extract features and validates its superiority on sgRNA off-target effect predictions.

Time perception in film viewing: A modulation of scene's duration estimates as a function of film editing

Filmmakers and editors have empirically developed techniques to ensure the spatiotemporal continuity of a film's narration. In terms of time, editing techniques (e.g., elliptical, overlapping, or cut minimization) allow for the manipulation of the perceived duration of events as they unfold on screen. More specifically, a scene can be edited to be time compressed, expanded, or real-time in terms of its perceived duration. Despite the consistent application of these techniques in filmmaking, their perceptual outcomes have not been experimentally validated. Given that viewing a film is experienced as a precise simulation of the physical world, the use of cinematic material to examine aspects of time perception allows for experimentation with high ecological validity, while filmmakers gain more insight on how empirically developed techniques influence viewers' time percept. Here, we investigated how such time manipulation techniques of an action affect a scene's perceived duration. Specifically, we presented videos depicting different actions (e.g., a woman talking on the phone), edited according to the techniques applied for temporal manipulation and asked participants to make verbal estimations of the presented scenes' perceived durations. Analysis of data revealed that the duration of expanded scenes was significantly overestimated as compared to that of compressed and real-time scenes, as was the duration of real-time scenes as compared to that of compressed scenes. Therefore, our results validate the empirical techniques applied for the modulation of a scene's perceived duration. We also found interactions on time estimates of scene type and editing technique as a function of the characteristics and the action of the scene presented. Thus, these findings add to the discussion that the content and characteristics of a scene, along with the editing technique applied, can also modulate perceived duration. Our findings are discussed by considering current timing frameworks, as well as attentional saliency algorithms measuring the visual saliency of the presented stimuli.

CRISPR-Cas9 in basic and translational aspects of cancer therapy.

The discovery of gene editing techniques has opened a new era within the field of biology and enabled scientists to manipulate nucleic acid molecules. CRISPR-Cas9 genome engineering has revolutionized this achievement by successful targeting the DNA molecule and editing its sequence. Since genomic changes are the basis of the birth and growth of many tumors, CRISPR-Cas9 method has been successfully applied to identify and manipulate the genes which are involved in initiating and driving some neoplastic processes. By review of the existing literature on application of CRISPR-Cas9 in cancer, different databases, such as PubMed and Google Scholar, we started data collection for "CRISPR-Cas9", "Genome Editing", "Cancer", "Solid tumors", "Hematologic malignancy" "Immunotherapy", "Diagnosis", "Drug resistance" phrases. Clinicaltrials.gov, a resource that provides access to information on clinical trials, was also searched in this review. We have defined the basics of this technology and then mentioned some clinical and preclinical studies using this technology in the treatment of a variety of solid tumors as well as hematologic neoplasms. Finally, we described the progress made by this technology in boosting immune-mediated cell therapy in oncology, such as CAR-T cells, CAR-NK cells, and CAR-M cells. CRISPR-Cas9 system revolutionized the therapeutic strategies in some solid malignant tumors and leukemia through targeting the key genes involved in the pathogenesis of these cancers.

Advanced Technologies in Radiation Research.

The U.S. Government is committed to maintaining a robust research program that supports a portfolio of scientific experts who are investigating the biological effects of radiation exposure. On August 17 and 18, 2023, the Radiation and Nuclear Countermeasures Program, within the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), partnered with the National Cancer Institute, NIH, the National Aeronautics and Space Administration, and the Radiation Injury Treatment Network to convene a workshop titled, Advanced Technologies in Radiation Research (ATRR), which focused on the use of advanced technologies under development or in current use to accelerate radiation research. This meeting report provides a comprehensive overview of the research presented at the workshop, which included an assembly of subject matter experts from government, industry, and academia. Topics discussed during the workshop included assessments of acute and delayed effects of radiation exposure using modalities such as clustered regularly interspaced short palindromic repeats (CRISPR) - based gene editing, tissue chips, advanced computing, artificial intelligence, and immersive imaging techniques. These approaches are being applied to develop products to diagnose and treat radiation injury to the bone marrow, skin, lung, and gastrointestinal tract, among other tissues. The overarching goal of the workshop was to provide an opportunity for the radiation research community to come together to assess the technological landscape through sharing of data, methodologies, and challenges, followed by a guided discussion with all participants. Ultimately, the organizers hope that the radiation research community will benefit from the workshop and seek solutions to scientific questions that remain unaddressed. Understanding existing research gaps and harnessing new or re-imagined tools and methods will allow for the design of studies to advance medical products along the critical path to U.S. Food and Drug Administration approval.