Special issue “Photoacoustic imaging: microscopy, tomography, and their recent applications in biomedicine” in visual computation for industry, biomedicine, and art

Photoacoustic(PA)imaging is a promising non-invasive and non-ionizing biomedical imaging modality that emerged in recent years.The articles presented in this special issue describe some of newest progress in this field.We are extremely grateful to all contributing authors.The first part of the issue covers new laser source devel-opment,including fiber lasers and laser diodes.The sec-ond part is dedicated to improving the image resolution through chronic cranial window techniques,virtual-point concept,fast polygon scanning,and Fabry Perot sensing.The third part shows the basic principles of photoacous-tic/ultrasound imaging and its applications.

An overview of Web3 technology:Infrastructure,applications,and popularity

Web3,the next generation of the Internet,represents a decentralized and democratized web.Although it has garnered significant public interest and found numerous real-world applications,there is a limited understanding of people’s perceptions and experiences with Web3.In this study,we conducted an empirical study to investigate the categories of Web3 applications and their popularity,as well as the potential challenges and opportunities within this emerging landscape.Our research was carried out in two phases.In the first phase,we analyzed 200 popular Web3 projects associated with 10 leading Web3 venture capital firms.In the second phase,we collected and examined code-related data from GitHub and market-related data from blockchain browsers(e.g.,Etherscan)for these projects.Our analysis revealed that the Web3 ecosystem can be categorized into two groups,i.e.,Web3 infrastructure and Web3 applications,with each consisting of several subcategories or subdomains.We also gained insights into the popularity of these Web3 projects at both the code and market levels and pointed out the challenges in the Web3 ecosystem at the system,developer,and user levels,as well as the opportunities it presents.Our findings contribute to a better understanding of Web3 for researchers and developers,which in turn promotes further exploration and advancement in this innovative field.

Collagen fiber anisotropy characterization by polarized photoacoustic imaging for just-in-time quantitative evaluation of burn severity

Just-in-time burn severity assessment plays a vital role in burn treatment and care.However,it is still difficult to quantitatively and promptly evaluate burn severity by existing medical imaging methods via initial burn depth measurement since burn wounds are usually dynamically developed.As an elastic skeleton of skin,the degree of conformational changes of collagen fibers caused by overheating can reflect the burn severity in a timelier manner.Herein,the polarized photoacoustic technique(PPAT)for just-in-time quantitative evaluation of burn severity via collagen fiber anisotropy assessment is proposed.First,phantom experiments demonstrate the ability of PPAT for deep imaging in a transport mean free path and accurately quantify changes in microstructural order by thermal damage.Then,the Pearson correlation coefficient of the PPAT in assessing burn severity is shown to be up to 0.95,validated by burn skin samples.The PPAT provides a just-in-time quantitative strategy for burn severity evaluation.

Automated GUI widgets classification

1 Introduction Graphical User Interface(GUI)widgets classification entails classifying widgets into their appropriate domain-specific types(e.g.,CheckBox and EditText)[1,2].The widgets classification is essential as it supports several software engineering tasks,such as GUI design and testing[1,3].The ability to obtain better widget classification performance has become one of the keys to the success of these tasks.Researchers in recent years have proposed many techniques for improving widget classification performance[1,2,4].For example,Moran et al.[1]proposed a deep learning technique to classify GUI widgets into their domain-specific type.The authors used the deep learning algorithm,a Convolutional Neural Network(CNN)architecture,to classify the GUI widgets.Chen et al.[2]proposed combining text-based and non-text-based models to improve the overall performance of GUI widget detection while classifying the widgets with the ResNet50 model.

Automatic test report augmentation to assist crowdsourced testing

In crowdsourced mobile application testing, workers are often inexperienced in and unfamiliar with software testing. Meanwhile, workers edit test reports in descriptive natural language on mobile devices. Thus, these test reports generally lack important details and challenge developers in understanding the bugs. To improve the quality of inspected test reports, we issue a new problem of test report augmentation by leveraging the additional useful information contained in duplicate test reports. In this paper, we propose a new framework named test report augmentation framework (TRAF) towards resolving the problem. First, natural language processing (NLP) techniques are adopted to preprocess the crowdsourced test reports. Then, three strategies are proposed to augment the environments, inputs, and descriptions of the inspected test reports, respectively. Finally, we visualize the augmented test reports to help developers distinguish the added information. To evaluate TRAF, we conduct experiments over five industrial datasets with 757 crowdsourced test reports. Experimental results show that TRAF can recommend relevant inputs to augment the inspected test reports with 98.49% in terms of NDCG and 88.65% in terms of precision on average, and identify valuable sentences from the descriptions of duplicates to augment the inspected test reports with 83.58% in terms of precision, 77.76% in terms of recall, and 78.72% in terms of F-measure on average. Meanwhile, empirical evaluation also demonstrates that augmented test reports can help developers understand and fix bugs better.

Multifunctional layered black phosphorene-based nanoplatform for disease diagnosis and treatment:a review

As an outstanding two-dimensional material,black phosphorene,has attracted significant attention in the biomedicine field due to its large surface area,strong optical absorption,distinct bioactivity,excellent biocompatibility,and high biodegradability.In this review,the preparation and properties of black phosphorene are summarized first.Thereafter,black phosphorene-based multifunctional platforms employed for the diagnosis and treatment of diseases,including cancer,bone injuries,brain diseases,progressive oxidative diseases,and kidney injury,are reviewed in detail.This review provides a better understanding of the exciting properties of black phosphorene,such as its high drug-loading efficiency,photothermal conversion capability,high'O2 generation efficiency,and high electrical conductivity,as well as how these properties can be exploited in biomedicine.Finally,the research perspectives of black phosphorene are discussed.

Optical-resolution photo acoustic microscopy continually monitors macrophages activities of acute inflammation in vivo

Photoacoustic imaging has been developed to image the immune study at the macro scale.Macrophages play diverse roles in the acute response to infection and tissue repair.However,macrophages activities in acute inflammation at the microscopic level still remain challenging.In this work,we proposed optical-resolution photoacoustic microscopy to promptly monitor the labeled macrophages activities in normal and inflammatory groups.The result showed that many labeled macrophages emerged around the vessels firstly,then exuded into tissues,and finally disappeared in the inflammatory group injected with labeled macrophages.In summary,our method allows us to exactly image and track the immune cells of inflammatory diseases.

Optical recording of brain functions based on voltage-sensitive dyes

To better understand the spatial distribution of brain functions,we need to monitor and analyze neuronal activities.Electrophysiological technique has provided an important method for the exploration of some neural circuits.However,this method cannot simultaneously detect the activities of nerve cell groups.Therefore,methods that can monitor the spatial distribution of neuronal population activity are demanded to explore brain functions.Voltage-sensitive dyes(VSDs)shift their absorption or emission optical signals in response to different membrane potentials,allowing assessing the global electrical state of neurons.Optical recording technique coupled with VSDs is a promising method to monitor the brain functions by detecting optical signal changes.This review focuses on the fast and slow responses of VSDs to membrane potential changes and optical recordings utilized in the central nervous system.In this review,we attempt to show how VSDs and optical recordings can be used to obtain brain functional monitoring at high spatial and temporal resolution.Understanding of brain functions will not only greatly improve the cognition of information transmission of complex neural network,but also provide new methods of treating brain diseases such as Parkinson’s and Alzheimer’s diseases.