MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases

Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks like medical question answering (QA). Moreover, they tend to function as "black-boxes," making it challenging to modify their behavior. Addressing this, our study delves into model editing utilizing in-context learning, aiming to improve LLM responses without the need for fine-tuning or retraining. Specifically, we propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then we incorporate them into the query prompt for the LLM. Focusing on medical QA using the MedQA-SMILE dataset, we evaluate the impact of different retrieval models and the number of facts provided to the LLM. Notably, our edited Vicuna model exhibited an accuracy improvement from 44.46% to 48.54%. This work underscores the potential of model editing to enhance LLM performance, offering a practical approach to mitigate the challenges of black-box LLMs.Comment: 6 page

A new approach for obtaining rapid uniformity in rice (Oryza sativa L.) via a 3x x 2x cross

A triploid (2n = 3x = 36) rice plant was obtained by screening a twin seedling population in which each seed germinated to two or three sprouts that were then crossed with diploid plants. One diploid plant was chosen among the various F1 progenies and developed into an F 2 population via self-pollination. Compared with the control variety Shanyou 63, this F 2 population had a stable agronomical performance in field trials, as confirmed by the F-test. The stability of the F 2 population was further substantiated by molecular analysis with simple sequence repeat markers. Specifically, of 160 markers assayed, 37 (covering all 12 chromosomes) were polymorphic between the parental lines. Testing the F 1 hybrid individually with these markers showed that each PCR product had only a single band instead of two bands from each parent. The bands were identical to either maternal (23 markers) or paternal (eight markers) bands or distinct from both parents (six markers). The amplified bands of all 60 randomly selected F 2 plants were uniform and identical to those of the F 1 hybrid. These results suggest that the F 1 plant is a non-segregating hybrid and that a stable F 2 population was obtained. This novel system provides an efficient means for shortening the cycle of hybrid rice seed production

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure

Seismic Impedance Inversion Using a Joint Deep Learning Model Based on Convolutional Neural Network and Transformer

Seismic impedance is an important factor in characterizing reservoirs, so accurate seismic impedance inversion is significant in seismic exploration. However, achieving high-resolution impedance inversion has remained a complex problem due to challenges related to the unknown seismic wavelet and the frequency band limitations of the observed data. In recent years, deep learning methods such as convolutional neural network (CNN) have been successfully applied to the field of seismic impedance inversion, which can obtain higher resolution results compared with traditional inversion methods. However, limited by the size of the local receptive field, CNN is not conducive to extracting global information. In contrast, a transformer can efficiently extract long-range dependencies but relies entirely on the self-attention mechanism to compute correlations between data, which requires a lot of training data. Therefore, this article proposes a joint deep learning model based on CNN and a transformer for impedance inversion. Among them, CNN and transformer are used to learn local and global information in the data, respectively, and feature fusion through residual connection, which can improve the feature representation capability of neural networks. In addition, we train a CNN-based forward operator that can introduce information from unlabeled data into the network training to enhance the network's generalization ability and improve the stability of the inversion. Experimental results in the SEAM model and field data show that the method can predict impedance effectively and with better accuracy than classical constrained sparse spike inversion and conventional deep learning methods

Their Applications in Nano-Fabrication

b

Nanofibre optic force transducers with sub-piconewton resolution via near-field plasmon-dielectric interactions.

Ultrasensitive nanomechanical instruments, including the atomic force microscope (AFM)1-4 and optical and magnetic tweezers5-8, have helped shed new light on the complex mechanical environments of biological processes. However, it is difficult to scale down the size of these instruments due to their feedback mechanisms9, which, if overcome, would enable high-density nanomechanical probing inside materials. A variety of molecular force probes including mechanophores10, quantum dots11, fluorescent pairs12,13 and molecular rotors14-16 have been designed to measure intracellular stresses; however, fluorescence-based techniques can have short operating times due to photo-instability and it is still challenging to quantify the forces with high spatial and mechanical resolution. Here, we develop a compact nanofibre optic force transducer (NOFT) that utilizes strong near-field plasmon-dielectric interactions to measure local forces with a sensitivity of <200 fN. The NOFT system is tested by monitoring bacterial motion and heart-cell beating as well as detecting infrasound power in solution

Nanofibre optic force transducers with sub-piconewton resolution via near-field plasmon–dielectric interactions

Ultrasensitive nanomechanical instruments, including the atomic force microscope (AFM)(1-4) and optical and magnetic tweezers(5-8), have helped shed new light on the complex mechanical environments of biological processes. However, it is difficult to scale down the size of these instruments due to their feedback mechanisms9, which, if overcome, would enable high-density nanomechanical probing inside materials. A variety of molecular force probes including mechanophores(10), quantum dots(11), fluorescent pairs(12,13) and molecular rotors(14-16) have been designed to measure intracellular stresses; however, fluorescence-based techniques can have short operating times due to photo-instability and it is still challenging to quantify the forces with high spatial and mechanical resolution. Here, we develop a compact nanofibre optic force transducer (NOFT) that utilizes strong near-field plasmon-dielectric interactions to measure local forces with a sensitivity of <200 fN. The NOFT system is tested by monitoring bacterial motion and heart-cell beating as well as detecting infrasound power in solution.National Science Foundation [ECCS 1150952, ECCS-1542148]; University of California, Office of the President [UC-LFRP 12-LR-238415]; California Institute of Regenerative Medicine [RT3-07899]; National Institutes of Health [R01EB021857]; National Institute on Aging of National Institutes of Health [AG028709]6 month embargo; Published online: 15 May 2017This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Nanofibre optic force transducers with sub-piconewton resolution via near-field plasmon-dielectric interactions.

Ultrasensitive nanomechanical instruments, including the atomic force microscope (AFM)1-4 and optical and magnetic tweezers5-8, have helped shed new light on the complex mechanical environments of biological processes. However, it is difficult to scale down the size of these instruments due to their feedback mechanisms9, which, if overcome, would enable high-density nanomechanical probing inside materials. A variety of molecular force probes including mechanophores10, quantum dots11, fluorescent pairs12,13 and molecular rotors14-16 have been designed to measure intracellular stresses; however, fluorescence-based techniques can have short operating times due to photo-instability and it is still challenging to quantify the forces with high spatial and mechanical resolution. Here, we develop a compact nanofibre optic force transducer (NOFT) that utilizes strong near-field plasmon-dielectric interactions to measure local forces with a sensitivity of &lt;200 fN. The NOFT system is tested by monitoring bacterial motion and heart-cell beating as well as detecting infrasound power in solution

Rapid 3D bioprinting of a multicellular model recapitulating pterygium microenvironment.

Pterygium is an ocular surface disorder with high prevalence that can lead to vision impairment. As a pathological outgrowth of conjunctiva, pterygium involves neovascularization and chronic inflammation. Here, we developed a 3D multicellular in vitro pterygium model using a digital light processing (DLP)-based 3D bioprinting platform with human conjunctival stem cells (hCjSCs). A novel feeder-free culture system was adopted and efficiently expanded the primary hCjSCs with homogeneity, stemness and differentiation potency. The DLP-based 3D bioprinting method was able to fabricate hydrogel scaffolds that support the viability and biological integrity of the encapsulated hCjSCs. The bioprinted 3D pterygium model consisted of hCjSCs, immune cells, and vascular cells to recapitulate the disease microenvironment. Transcriptomic analysis using RNA sequencing (RNA-seq) identified a distinct profile correlated to inflammation response, angiogenesis, and epithelial mesenchymal transition in the bioprinted 3D pterygium model. In addition, the pterygium signatures and disease relevance of the bioprinted model were validated with the public RNA-seq data from patient-derived pterygium tissues. By integrating the stem cell technology with 3D bioprinting, this is the first reported 3D in vitro disease model for pterygium that can be utilized for future studies towards personalized medicine and drug screening