搜索结果：Biophysics reports

Hackathons are intensive innovation-oriented events where participants work in teams to solve problems or create projects in as little as 24 or 48 hours. These events are common in startup culture, open source communities and mainstream industry. Here we examine how hackathons can be ported to academic teaching, specifically in computational biophysics. We propose hackathons as a teaching modality distinct from traditional courses and structured workshops. In particular, we suggest they can offer a low-stakes platform for students to overcome entry barriers to computational tools or to explore new topics, disciplines, and skills beyond their academic comfort zone. We tested this format in two computational biophysics hackathons on the Göttingen campus in 2023 and 2024, providing practical insights and a preliminary evaluation. To the best of our knowledge, the 2024 event is the first public hackathon dedicated to Biophysics. This paper explores the benefits of the hackathon format for teachers and researchers and provides guidelines for running a hackathon adapted to a teaching goal.

Purpose: We investigated the utilization of privacy-preserving, locally-deployed, open-source Large Language Models (LLMs) to extract diagnostic information from free-text cardiovascular magnetic resonance (CMR) reports. Materials and Methods: We evaluated nine open-source LLMs on their ability to identify diagnoses and classify patients into various cardiac diagnostic categories based on descriptive findings in 109 clinical CMR reports. Performance was quantified using standard classification metrics including accuracy, precision, recall, and F1 score. We also employed confusion matrices to examine patterns of misclassification across models. Results: Most open-source LLMs demonstrated exceptional performance in classifying reports into different diagnostic categories. Google's Gemma2 model achieved the highest average F1 score of 0.98, followed by Qwen2.5:32B and DeepseekR1-32B with F1 scores of 0.96 and 0.95, respectively. All other evaluated models attained average scores above 0.93, with Mistral and DeepseekR1-7B being the only exceptions. The top four LLMs outperformed our board-certified cardiologist (F1 score of 0.94) across all evaluation metrics in analyzing CMR reports.

British biophysics has a tradition of scientific invention and innovation, resulting in new technologies transforming biological insight, such as rapid DNA sequencing, super-resolution and label-free microscopy, high-throughput and single-molecule bio-sensing, and bio-inspired synthetic materials. Some advances were established through democratised platforms and many have biomedical success, a key example involving the SARS-CoV-2 spike protein during the COVID-19 pandemic. Here, three UK labs made crucial contributions revealing how the spike protein targets human cells, and how therapies of vaccines and neutralizing nanobodies work, enabled largely through biophysical innovations of cryo-electron microscopy. Here, we discuss leading-edge innovations which resulted from discovery-led British 'Physics of Life' research (capturing blends of physical-life sciences research in the UK including biophysics and biological physics) and have matured into wide-reaching sustainable commercial ventures enabling translational impact. We describe the biophysical science which led to these academic spinouts, presenting the scientific questions that were addressed through innovating new techniques

This is a provisional status report of biophysics activities in Africa. We start by highlighting the importance of biophysics research and development for every country's economy in the 21st century. Yet, the amount of biophysics activity in African countries varies between woefully little to nothing at all. We present a scope of biophysics research on the continent based on a pilot scientometrics study. We discuss a number of existing multinational programmes and infrastructure initiatives and propose a Pan African Professional Society for Biophysics. We emphasize the need for education, infrastructure and career development, and conclude with a list of suggested recommendations for expedited development of biophysics research on the continent.

This report is a serious call to scientists, innovators, investors, and policymakers to invest in the development of biophysics in Africa. The complex problems of our day demand multidisciplinary approaches, and biophysics offers training in much-needed multi- and cross-disciplinary thinking. Biophysics is a research field at the forefront of modern science because it provides a powerful scientific platform that addresses many of the critical challenges humanity faces today and in the future. It is a vital source of innovation for any country interested in developing a high-tech economy. However, there is woefully little biophysics educational and research activity in Africa, representing a critical gap that must be addressed with urgency. This report suggests key research areas that African biophysicists should focus on, identifies major challenges to growing biophysics in Africa, and underscores the high-priority needs that must be addressed.

With the growth of global maritime transportation, energy optimization has become crucial for reducing costs and ensuring operational efficiency. Shaft power is the mechanical power transmitted from the engine to the shaft and directly impacts fuel consumption, making its accurate prediction a paramount step in optimizing vessel performance. Power consumption is highly correlated with ship parameters such as speed and shaft rotation per minute, as well as weather and sea conditions. Frequent access to this operational data can improve prediction accuracy. However, obtaining high-quality sensor data is often infeasible and costly, making alternative sources such as noon reports a viable option. In this paper, we propose a transfer learning-based approach for predicting vessels shaft power, where a model is initially trained on high-frequency data from a vessel and then fine-tuned with low-frequency daily noon reports from other vessels. We tested our approach on sister vessels (identical dimensions and configurations), a similar vessel (slightly larger with a different engine), and a different vessel (distinct dimensions and configurations). The experiments showed that the mean abso

Biological molecules, like all active matter, use free energy to generate force and motion which drive them out of thermal equilibrium, and undergo inherent dynamic interconversion between metastable free energy states separated by levels barely higher than stochastic thermal energy fluctuations. Here, we explore the founding and emerging approaches of the field of single-molecule biophysics which, unlike traditional ensemble average approaches, enable the detection and manipulation of individual molecules and facilitate exploration of biomolecular heterogeneity and its impact on transitional molecular kinetics and underpinning molecular interactions. We discuss the ground-breaking technological innovations which scratch far beyond the surface into open questions of real physiology, that correlate orthogonal data types and interplay empirical measurement with theoretical and computational insights, many of which are enabling artificial matter to be designed inspired by biological systems. And finally, we examine how these insights are helping to develop new physics framed around biology.

Timely identification of issue reports reflecting software vulnerabilities is crucial, particularly for Internet-of-Things (IoT) where analysis is slower than non-IoT systems. While Machine Learning (ML) and Large Language Models (LLMs) detect vulnerability-indicating issues in non-IoT systems, their IoT use remains unexplored. We are the first to tackle this problem by proposing two approaches: (1) combining ML and LLMs with Natural Language Processing (NLP) techniques to detect vulnerability-indicating issues of 21 Eclipse IoT projects and (2) fine-tuning a pre-trained BERT Masked Language Model (MLM) on 11,000 GitHub issues for classifying \vul. Our best performance belongs to a Support Vector Machine (SVM) trained on BERT NLP features, achieving an Area Under the receiver operator characteristic Curve (AUC) of 0.65. The fine-tuned BERT achieves 0.26 accuracy, emphasizing the importance of exposing all data during training. Our contributions set the stage for accurately detecting IoT vulnerabilities from issue reports, similar to non-IoT systems.

Recent advances in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information. Integrating biophysics-informed regularisation is one effective way to change this situation, as it provides an prior regularisation for automated end-to-end learning. In this paper, we propose a novel approach that designs brain tumour growth Partial Differential Equation (PDE) models as a regularisation with deep learning, operational with any network model. Our method introduces tumour growth PDE models directly into the segmentation process, improving accuracy and robustness, especially in data-scarce scenarios. This system estimates tumour cell density using a periodic activation function. By effectively integrating this estimation with biophysical models, we achieve better capture of tumour characteristics. This approach not only aligns the segmentation closer to actual biological behaviour but also strengthens the model's performance under limited data conditions. We demonstrate the effectiveness of our framework through extensive

The future development of an AI scientist, a tool that is capable of integrating a variety of experimental data and generating testable hypotheses, holds immense potential. So far, bespoke machine learning models have been created to specialize in singular scientific tasks, but otherwise lack the flexibility of a general purpose model. Here, we show that a general purpose large language model, chatGPT 3.5-turbo, can be fine-tuned to learn the structural biophysics of DNA. We find that both fine-tuning models to return chain-of-thought responses and chaining together models fine-tuned for subtasks have an enhanced ability to analyze and design DNA sequences and their structures.

With many advancements in in silico biology in recent years, the paramount challenge is to translate the accumulated knowledge into exciting industry partnerships and clinical applications. Achieving models that characterize the link of molecular interactions to the activity and structure of a whole organ are termed multiscale biophysics. Historically, the pharmaceutical industry has worked well with in silico models by leveraging their prediction capabilities for drug testing. However, the needed higher fidelity and higher resolution of models for efficient prediction of pharmacological phenomenon dictates that in silico approaches must account for the verifiable multiscale biophysical phenomena, as a spatial and temporal dimension variation for different processes and models. The collection of different multiscale models for different tissues and organs can compose digital twin solutions towards becoming a service for researchers, clinicians, and drug developers. Our paper has two main goals: 1) To clarify to what extent detailed single- and multiscale modeling has been accomplished thus far, we provide a review on this topic focusing on the biophysics of epithelial, cardiac, and

In this project, we present a deep neural network (DNN)-based biophysics model that uses multi-scale and uniform topological and electrostatic features to predict protein properties, such as Coulomb energies or solvation energies. The topological features are generated using element-specific persistent homology (ESPH) on a selection of heavy atoms or carbon atoms. The electrostatic features are generated using a novel Cartesian treecode, which adds underlying electrostatic interactions to further improve the model prediction. These features are uniform in number for proteins of varying sizes; therefore, the widely available protein structure databases can be used to train the network. These features are also multi-scale, allowing users to balance resolution and computational cost. The optimal model trained on more than 17,000 proteins for predicting Coulomb energy achieves MSE of approximately 0.024, MAPE of 0.073 and $R^2$ of 0.976. Meanwhile, the optimal model trained on more than 4,000 proteins for predicting solvation energy achieves MSE of approximately 0.064, MAPE of 0.081, and $R^2$ of 0.926, showing the efficiency and fidelity of these features in representing the protein s

In 1984 Edward Witten proposed that an extremely dense form of matter composed of up, down, and strange quarks may be stable at zero pressure (Witten, 1984). Massive nuggets of such dense matter, if they exist, may pass through the Earth and be detectable by the seismic signals they generate (de Rujula and Glashow, 1984). With this motivation we investigated over 1 million seismic data reports to the U.S. Geological Survey for the years 1990-1993 not associated with epicentral sources. We report two results: (1) with an average of about 0.16 unassociated reports per minute after data cuts, we found a significant excess over statistical expectation for sets with ten or more reports in ten minutes; and (2) in spite of a very small a priori probability from random reports, we found one set of reports with arrival times and other features appropriate to signals from an epilinear source. This event has the properties predicted for the passage of a nugget of strange quark matter (SQM) through the earth, although there is no direct confirmation from other phenomenologies.

Fluorescence is one of the most widely used techniques in biological sciences. Its exceptional sensitivity and versatility make it a tool of first choice for quantitative studies in biophysics. The concept of phasors, originally introduced by Charles Steinmetz in the late 19th century for analyzing alternating current circuits, has since found applications across diverse disciplines, including fluorescence spectroscopy. The main idea behind fluorescence phasors was posited by Gregorio Weber in 1981. By analyzing the complementary nature of pulse and phase fluorometry data, he shows that two magnitudes -- denoted as $G$ and $S$ -- derived from the frequency-domain fluorescence measurements correspond to the real and imaginary part of the Fourier transform of the fluorescence intensity in the time domain. This review provides a historical perspective on how the concept of phasors originates and how it integrates into fluorescence spectroscopy. We discuss their fundamental algebraic properties, which enable intuitive model-free analysis of fluorescence data despite the complexity of the underlying phenomena. Some applications in biophysics illustrate the power of this approach in stud

Screening mammography is high volume, time sensitive, and documentation heavy. Radiologists must translate subtle visual findings into consistent BI-RADS assessments, breast density categories, and structured narrative reports. While recent Vision Language Models (VLMs) enable image-to-text reporting, many rely on closed cloud systems or tightly coupled architectures that limit privacy, reproducibility, and adaptability. We present MammoWise, a local multi-model pipeline that transforms open source VLMs into mammogram report generators and multi-task classifiers. MammoWise supports any Ollama-hosted VLM and mammography dataset, and enables zero-shot, few-shot, and Chain-of-Thought prompting, with optional multimodal Retrieval Augmented Generation (RAG) using a vector database for case-specific context. We evaluate MedGemma, LLaVA-Med, and Qwen2.5-VL on VinDr-Mammo and DMID datasets, assessing report quality (BERTScore, ROUGE-L), BI-RADS classification, breast density, and key findings. Report generation is consistently strong and improves with few-shot prompting and RAG. Classification is feasible but sensitive to model and dataset choice. Parameter-efficient fine-tuning (QLoRA) of

Improving the scientific literacy of non-scientists is an important goal, both because of the ever-increasing impact of science and technology on our lives, and because understanding science enriches our experience of the natural world. One route to improving scientific literacy is via general education undergraduate courses -- i.e. courses intended for students not majoring in the sciences or engineering -- which in many cases provide these students' last formal exposure to science. I describe here a course on biophysics for non-science-major undergraduates recently developed at the University of Oregon (Eugene, OR, USA). Biophysics, I claim, is a particularly useful vehicle for addressing scientific literacy. It involves important and general scientific concepts, demonstrates connections between basic science and tangible, familiar phenomena related to health and disease, and illustrates that scientific insights develop by applying tools and perspectives from disparate fields in creative ways. In addition, biophysics highlights the far-reaching impact of physics research. I describe the general design of this course, which spans both macroscopic and microscopic topics, and the sp

Here, we discuss a collection of cutting-edge techniques and applications in use today by some of the leading experts in the field of correlative approaches in single-molecule biophysics. A key difference in emphasis, compared with traditional single-molecule biophysics approaches detailed previously, is on the emphasis of the development and use of complex methods which explicitly combine multiple approaches to increase biological insights at the single-molecule level. These so-called correlative single-molecule biophysics methods rely on multiple, orthogonal tools and analysis, as opposed to any one single driving technique. Importantly, they span both in vivo and in vitro biological systems as well as the interfaces between theory and experiment in often highly integrated ways, very different to earlier traditional non-integrative approaches. The first applications of correlative single-molecule methods involved adaption of a range of different experimental technologies to the same biological sample whose measurements were synchronised. However, now we find a greater flora of integrated methods emerging that include approaches applied to different samples at different times and

Biophysics is a subject that is spread over many disciplines and transcends the skills and knowledge of the individual student. This makes it challenging both to teach and to learn. Educational materials are described to aid in teaching undergraduates biophysics in an interdisciplinary manner. Projects have been devised on topics that range from x-ray diffraction to the Hodgkin Huxley equations. They are team-based and encourage collaboration. The projects make extensive use of software written in Python/Scipy which can be modified to explore a large range of possible phenomena. The software can also be used in lectures and in the teaching of more traditional biophysics courses.

Deep learning based approaches are now widely used across biophysics to help automate a variety of tasks including image segmentation, feature selection, and deconvolution. However, the presence of multiple competing deep learning architectures, each with its own unique advantages and disadvantages, makes it challenging to select an architecture best suited for a specific application. As such, we present a comprehensive comparison of common models. Here, we focus on the task of segmentation assuming the typically small training dataset sizes available from biophysics experiments and compare the following four commonly used architectures: convolutional neural networks, U-Nets, vision transformers, and vision state space models. In doing so, we establish criteria for determining optimal conditions under which each model excels, thereby offering practical guidelines for researchers and practitioners in the field.