1,721,141 research outputs found
Integrated RPA-CRISPR/Cas12a system towards Point-of-Care H. pylori detection
No full textThe rapidly advanced CRISPR/Cas biosensing technology provides unprecedent potential for the development of novel biosensing systems. It provides a new approach for realizing rapid, sensitivity and highly specific pathogen nucleic acid detection, with the capability to combine other technologies, including Polymerase Chain Reaction or isothermal amplifications. The detection of Helicobacter pylori (H. pylori), one of the most common human pathogens to cause various gastroduodenal diseases, has also been explored with the assistance of CRISPR/Cas systems. However, gaps still remain for the development of end-user friendly sensing systems.In this study, a combined RPA-CRISPR/Cas12a biosensing system has been established. It shown the capability to quantitively detect the presence of H. pylori genome DNA with 4 orders of magnitude linear range, and sensitivity of 1.4 copies/μL. The overall reaction can be done within 45 mins at room temperature, which eliminates the needs for heating instrumentation. In addition, with the addition of pullulan as a protective reagent, the potential of storing CRISPR/Cas12a system reagents by using a freeze-dry approach has also been demonstrated.Clinical Relevance-This study represents a novel exploration to applying CRISPR/Cas12a-based biosensing technology to the detection of pathogen DNA with improved potential for the development of Point-of-Care diagnostics. This critical aspect of our technology will contribute to address the newly emerged pathogenic threats and support public health systems.</p
Facing Off World Model Backbones: RNNs, Transformers, and S4
No full textWorld models are a fundamental component in model-based reinforcement learning (MBRL). To perform temporally extended and consistent simulations of the future in partially observable environments, world models need to possess long-term memory. However, state-of-the-art MBRL agents, such as Dreamer, predominantly employ recurrent neural networks (RNNs) as their world model backbone, which have limited memory capacity. In this paper, we seek to explore alternative world model backbones for improving long-term memory. In particular, we investigate the effectiveness of Transformers and Structured State Space Sequence (S4) models, motivated by their remarkable ability to capture long-range dependencies in low-dimensional sequences and their complementary strengths. We propose S4WM, the first world model compatible with parallelizable SSMs including S4 and its variants. By incorporating latent variable modeling, S4WM can efficiently generate high-dimensional image sequences through latent imagination. Furthermore, we extensively compare RNN-, Transformer-, and S4-based world models across four sets of environments, which we have tailored to assess crucial memory capabilities of world models, including long-term imagination, context-dependent recall, reward prediction, and memory-based reasoning. Our findings demonstrate that S4WM outperforms Transformer-based world models in terms of long-term memory, while exhibiting greater efficiency during training and imagination. These results pave the way for the development of stronger MBRL agents
Slot State Space Models
No full textRecent State Space Models (SSMs) such as S4, S5, and MAMBA have shown remarkable computational benefits in long-range temporal dependency modeling. However, in many sequence modeling problems, the underlying process is inherently modular and it is of interest to have inductive biases that mimic this modular structure. In this paper, we introduce SlotSSM, a novel framework for incorporating independent mechanisms into SSMs to preserve or encourage separation of information. Unlike conventional SSMs that maintain a monolithic state vector, SlotSSM maintains the state as a collection of multiple vectors called slots. Crucially, the state transitions are performed independently per slot with sparse interactions across slots implemented via the bottleneck of self-attention. In experiments, we evaluate our model in object-centric video understanding and video prediction tasks, which involve modeling multiple objects and their long-range temporal dependencies. We find that that our proposed design offers substantial performance gains over existing sequence modeling methods
RNA reporter based CRISPR/Cas12a biosensing platform for sensitive detection of circulating tumor DNA
No full textCRISPR/Cas biotechnology provides an exceptional platform for biosensor development. To date, the reported CRISPR/Cas biosensing systems have shown extraordinary performance for nucleic acids, small molecules, small proteins and microorganism detection. The CRISPR/Cas12a biosensing system, as a typical example, has been well established and applied for both nucleic acids and non-nucleic acids target detection. However, all established CRISPR/Cas12a biosensing systems are based on DNA reporters, which potentially limits further application.In this study, we established an RNA reporter based CRISPR/Cas12a biosensing system. A basic biosensing system was evaluated, and the limit of detection was found to be 1 nM. Afterwards, we optimized this biosensing system using both temperature and chemical enhancers. The final optimal biosensing system (with DTT & 37°C) shows fluorescence signal increased by a factor of ~10 compared with the basic system. The optimal biosensing system was further applied for the detection of circulating tumor DNA (ctDNA), which shows over 4 orders of magnitude detection range from 1pM to 25 nM, with the limit of detection of 1pM. This RNA reporter based CRISPR/Cas12a biosensing system provides an effective platform for nucleic acids quantification.Clinical Relevance-This research provides a novel approach for ctDNA diagnostics, which is an attractive biomarker for noninvasive monitoring of tumor growth, response, and spread.</p
Object-Centric Slot Diffusion
No full textThe recent success of transformer-based image generative models in object-centric learning highlights the importance of powerful image generators for handling complex scenes. However, despite the high expressiveness of diffusion models in image generation, their integration into object-centric learning remains largely unexplored in this domain. In this paper, we explore the feasibility and potential of integrating diffusion models into object-centric learning and investigate the pros and cons of this approach. We introduce Latent Slot Diffusion (LSD), a novel model that serves dual purposes: it is the first object-centric learning model to replace conventional slot decoders with a latent diffusion model conditioned on object slots, and it is also the first unsupervised compositional conditional diffusion model that operates without the need for supervised annotations like text. Through experiments on various object-centric tasks, including the first application of the FFHQ dataset in this field, we demonstrate that LSD significantly outperforms state-of-the-art transformer-based decoders, particularly in more complex scenes, and exhibits superior unsupervised compositional generation quality. In addition, we conduct a preliminary investigation into the integration of pre-trained diffusion models in LSD and demonstrate its effectiveness in real-world image segmentation and generation. Project page is available at https://latentslotdiffusion.github.i
Simple Hierarchical Planning with Diffusion
No full textDiffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long-horizon tasks. To overcome this, we introduce the Hierarchical Diffuser, a simple, fast, yet effective planning method combining the advantages of hierarchical and diffusion-based planning. Our model adopts a “jumpy” planning strategy at the high level, which allows it to have a larger receptive field but at a lower computational cost—a crucial factor for diffusion-based planning methods, as we have empirically verified. Additionally, the jumpy sub-goals guide our low-level planner, facilitating a fine-tuning stage and further improving our approach’s effectiveness. We conducted empirical evaluations on standard offline reinforcement learning benchmarks, demonstrating our method’s superior performance and efficiency in terms of training and planning speed compared to the non-hierarchical Diffuser as well as other hierarchical planning methods. Moreover, we explore our model’s generalization capability, particularly on how our method improves generalization capabilities on compositional out-of-distribution tasks
Paper-based lateral flow assay for the point-of-care detection of neurofilament light chain
No full textNeurofilament light chain (NF-L) is a protein found in neurons of the nervous system and is widely used as a biomarker for neurological disorders. However, the current methods for detecting NF-L levels are complicated, expensive, and require specialized equipment, making it challenging to implement in a point-of-care (POC) setting. In this study, we developed a gold nanoshell (AuNS)-assisted lateral flow assay (LFA) based test strip for the POC detection of NF-L at a low ng/mL level (8 ng/mL = 117.65 pM). The test strip is a simple, rapid, and cost-effective method for detecting NF-L, making it suitable for use in a POC setting for the diagnosis and treatment of various neurological disorders. With its ease of use and reliability, the paper-based LFA is a valuable tool for the diagnosis and management of neurological conditions.Clinical Relevance - The AuNS-assisted LFA test strip developed in this study offers a rapid, cost-effective, and simple method for detecting NF-L levels, making it of great interest to practicing clinicians for the diagnosis of various neurological diseases such as HIV-associated dementia (HID), Amyotrophic Lateral Sclerosis (ALS), and Creutzfeldt-Jakob disease (CJD).</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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