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关键词： Health sciences   Natural sciences   Computer science  

摘要： With advancements in large language models, artificial intelligence (AI) is undergoing a paradigm shift where AI models can be repurposed with minimal effort across various downstream tasks. This provides great promise in learning generally useful representations from biomedical corpora, at scale, which would empower AI solutions in healthcare and biomedical research. Nonetheless, our understanding of how they work, when they fail, and what they are capable of remains underexplored due to their emergent properties. Consequently, there is a need to comprehensively examine the use of language models in biomedicine. This review aims to summarize existing studies of language models in biomedicine and identify topics ripe for future research, along with the technical and analytical challenges w.r.t. interpretability. We expect this review to help researchers and practitioners better understand the landscape of language models in biomedicine and what methods are available to enhance the interpretability of their models.

关键词： Physics   Biological sciences   Computer science  

摘要： Cell therapy using proliferating human hepatocytes (ProliHHs) is an effective treatment approach for advanced liver diseases. However, rapid and accurate identification of high -quality ProliHHs from different donors is challenging due to individual heterogeneity. Here, we developed a machine learning framework to integrate single -cell Raman spectroscopy from multiple donors and identify different stages of ProliHHs. A repository of more than 14,000 Raman spectra, consisting of primary human hepatocytes (PHHs) and different passages of ProliHHs from six donors, was generated. Using a sliding window algorithm, potential biomarkers distinguishing the different cell stages were identified through differential analysis. Leveraging machine learning models, accurate classification of cell stages was achieved in both within -donor and cross -donor prediction tasks. Furthermore, the study assessed the relationship between donor and cell numbers and its impact on prediction accuracy, facilitating improved quality control design. A similar workflow can also be extended to encompass other cell types.

关键词： Health sciences   Physics   Computer science   Materials science  

摘要： In the smart era, big data analysis based on sensor units is important in intelligent motion. In this study, a dance sports and injury monitoring system (DIMS) based on a recyclable flexible triboelectric nanogenerator (RF-TENG) sensor module, a data processing hardware module, and an upper computer intelligent analysis module are developed to promote intelligent motion. The resultant RF-TENG exhibits an ultrafast response time of 17 ms, coupled with robust stability demonstrated over 4200 operational cycles, with 6% variation in output voltage. The DIMS enables immersive training by providing visual feedback on sports status and interacting with virtual games. Combined with machine learning (K -nearest neighbor), good classification results are achieved for ground -jumping techniques. In addition, it shows some potential in sports injury prediction (i.e., ankle sprains, knee hyperextension). Overall, the sensing system designed in this study has broad prospects for future applications in intelligent motion and healthcare.

关键词： Health sciences   Biological sciences   Computer science  

摘要： Virtually all cells use energy -driven, ion -specific membrane pumps to maintain large transmembrane gradients of Na + , K + , Cl - , Mg ++ , and Ca ++ , but the corresponding evolutionary benefit remains unclear. We propose that these gradients enable a dynamic and versatile biological system that acquires, analyzes, and responds to environmental information. We hypothesize that environmental signals are transmitted into the cell by ion fluxes along pre-existing gradients through gated ion -specific membrane channels. The consequent changes in cytoplasmic ion concentration can generate a local response or orchestrate global/ regional cellular dynamics through wire -like ion fluxes along pre-existing and self -assembling cytoskeleton to engage the endoplasmic reticulum, mitochondria, and nucleus.

摘要： In the philosophy of science, humans are seen as thinking creatures and can use their minds. Computer science is part of science where knowledge is first tested for truth through the scientific method. Philosophy and technology have a close relationship. Philosophy is the basis of thinking that gives birth to science that will continue to develop and produce products in the form of technology. Then humans use technology to make it easier to achieve goals. Currently, the problem is also in manually identifying the types of medicinal plants. There are difficulties in distinguishing types of medicinal plants due to the similarity of shape and color in the background for developing knowledge by utilizing machine learning and computer vision. The philosophy of science became a foundation for the development of computer science. The result is carried out using three machine learning methods, namely decision tree, KNN, and neural network, which are philosophical concepts connected with human life principles. By carrying out a systematic and structured scientific method and good truth testing in accordance with the philosophical perspective, it is found that the accuracy of the neural network method is 94%, the accuracy of the KNN method is 77.9%, and the accuracy of the DT method is 65.1%. Based on the evaluation of recall performance, precision, and F1 score, it is known that the neural network method can be used to identify medicinal plants.

关键词： Computer science   Fluidics   Physics  

摘要： Droplet generation technology has become increasingly important in a wide range of applications, including biotechnology and chemical synthesis. T-junction channels are commonly used for droplet generation due to their integration capability of a larger number of droplet generators in a compact space. In this study, a finite element analysis (FEA) approach is employed to simulate droplet production and its dynamic regimes in a T-junction configuration and collect data for post-processing analysis. Next, image analysis was performed to calculate the droplet length and determine the droplet generation regime. Furthermore, machine learning (ML) and deep learning (DL) algorithms were applied to estimate outputs through examination of input parameters within the simulation range. At the end, a graphical user interface (GUI) was developed for estimation of the droplet characteristics based on inputs, enabling the users to preselect their designs with comparable microfluidic configurations within the studied range. © 2024 The Author(s)

关键词： Computer science   Artificial intelligence   Engineering  

摘要： Datasets collected under different sensors, viewpoints, or weather conditions cause different domains. Models trained on domain A applied to tasks of domain B result in low performance. To overcome the domain shift, we propose an unsupervised pedestrian detection method that utilizes CycleGAN to establish an intermediate domain and transform a large gap domain -shift problem into two feature alignment subtasks with small gaps. The intermediate domain trained with labels from domain A, after two rounds of feature alignment using adversarial learning, can facilitate effective detection in domain B. To further enhance the training quality of intermediate domain models, Image Quality Assessment (IQA) is incorporated. The experimental results evaluated on Citypersons, KITTI, and BDD100K show that MR of 24.58%, 33.66%, 28.27%, and 28.25% were achieved in four cross -domain scenarios. Compared with typical pedestrian detection models, our proposed method can better overcome the domain -shift problem and achieve competitive results.

摘要： Liver disorders in humans do not see age, all age groups from young to old can be affected by liver disorders. Liver disorders can be medically broken down into 3 more specific types, namely hepatitis C, fibrosis and cirrhosis. Hepatitis C is generally caused by a virus, fibrosis is caused by genetic factors and cirrhosis is caused by the emergence of tissue in the liver. To make a specific diagnosis of this type of liver disorder, it is necessary to conduct research related to prediction of human liver function disorders using machine learning methods. There are many machine learning methods, and on this occasion the researcher is also trying to find the philosophy of computer science from this research which will look for evidence of the truth of the performance of each machine learning method that makes predictions in this study. The end result is axiological philosophy of science from the analysis of the truth of the performance of this study which states that the performance of the neural network has a greater accuracy of 94.4% and is followed by other methods such as nave Bayes 81.3%, kNN 93.6%, and SVM 94.1%.

关键词： Natural sciences   Physics   Computer science  

摘要： Efficient solution of physical boundary value problems (BVPs) remains a challenging task demanded in many applications. Conventional numerical methods require time-consuming domain discretization and solving techniques that have limited throughput capabilities. Here, we present an efficient data -driven DNN approach to non -iterative solving arbitrary 2D linear elastic BVPs. Our results show that a U -Netbased surrogate model trained on a representative set of reference FDM solutions can accurately emulate linear elastic material behavior with manifold applications in deformable modeling and simulation.

关键词： Natural sciences   Chemistry   Computer science  

摘要： This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given experimental variables, leveraging the human -like reasoning and inference capabilities of LLMs, using selective catalytic reduction of NO x with NH 3 as a case study. We implement the chain of thought (CoT) concept to formulate logical steps for uncovering connections within the data, introducing an "Ordered-and-Structured"CoT (OSCoT) prompting strategy. We compare the OSCoT strategy with the more conventional "One-Pot"CoT (OPCoT) approach and with human experts. We demonstrate that GPT-4, equipped with this new OSCoT prompting strategy, outperforms the other two settings and accurately predicts experimental outcomes and provides intuitive reasoning for its