Development of a semi-anthropomorphic photoacoustic calcaneus phantom based on nano computed tomography and stereolithography 3D printing.

Osteoporosis is a major public health threat with significant physical, psychosocial, and financial consequences. The calcaneus bone has been used as a measurement site for risk prediction of osteoporosis by noninvasive quantitative ultrasound (QUS). By adding optical contrast to QUS, our previous studies indicate that a combination of photoacoustic (PA) and QUS, that is, PAQUS, provides a novel opportunity to assess the health of human calcaneus. Calibration of the PAQUS system is crucial to realize quantitative and repeatable measurements of the calcaneus. Therefore, a phantom which simulates the optical, ultrasound, and architectural properties of the human calcaneus, for PAQUS system calibration, is required. Additionally, a controllable phantom offers researchers a versatile framework for developing versatile structures, allowing more controlled assessment of how varying bone structures cause defined alterations in PA and QUS signals. In this work, we present the first semi-anthropomorphic calcaneus phantom for PAQUS. The phantom was developed based on nano computed-tomography (nano-CT) and stereolithography 3D printing, aiming to maximize accuracy in the approximation of both trabecular and cortical bone microstructures. Compared with the original digital input calcaneus model from a human cadaveric donor, the printed model achieved accuracies of 71.15% in total structure and 87.21% in bone volume fraction. Inorganic materials including synthetic blood, mineral oil, intralipid, and agar gel were used to model the substitutes of bone marrow and soft tissue, filling and covering the calcaneus phantom. The ultrasound and optical properties of this phantom were measured, and the results were consistent with those measured by a commercialized device and from previous in vivo studies. In addition, a short-term stability test was conducted for this phantom, demonstrating that the optical and ultrasound properties of the phantom were stable without significant variation over 1 month. This semi-anthropomorphic calcaneus phantom shows structural, ultrasound, and optical properties similar to those from a human calcaneus in vivo and, thereby, can serve as an effective source for equipment calibration and the comprehensive study of human patients.

Longitudinal Monitoring of Angiogenesis in a Murine Window Chamber Model In Vivo.

Angiogenesis induced by growth factor administration, which can augment the blood supply in regenerative applications, has drawn wide attention in medical research. Longitudinal monitoring of vascular structure and development in vivo is important for understanding and evaluating the dynamics of involved biological processes. In this work, a dual-modality imaging system consisting of photoacoustic microscopy (PAM) and optical coherence tomography (OCT) was applied for noninvasive in vivo imaging of angiogenesis in a murine model. Fibrin scaffolds, with and without basic fibroblast growth factor (bFGF), were implanted in a flexible imaging window and longitudinally observed over 9 days. Imaging was conducted at 3, 5, 7, and 9 days after implantation to monitor vascularization in and around the scaffold. Several morphometric parameters were derived from the PAM images, including vessel area density (VAD), total vessel length (TVL), and vessel mean diameter (VMD). On days 7 and 9, mice receiving bFGF-laden fibrin gels exhibited significantly larger VAD and TVL compared to mice with fibrin-only gels. In addition, VMD significantly decreased in +bFGF mice versus fibrin-only mice on days 7 and 9. Blood vessel density, evaluated using immunohistochemical staining of explanted gels and underlying tissue on day 9, corroborated the findings from the PAM images. Overall, the experimental results highlight the utility of a dual-modality imaging system in longitudinally monitoring of vasculature in vivo with high resolution and sensitivity, thereby providing an effective tool to study angiogenesis.

Data-driven monitoring for phase II clinical trial designs based on percentile event time test.

The goal of phase II clinical trials is to evaluate the therapeutic efficacy of a new drug. Some investigators want to use the time-to-event endpoint as the primary endpoint of the phase II study to see the improvement of the therapeutic efficacy of a new drug in median survival time. Recently, median event time test (METT) has been proposed to provide a simple and straightforward rule which compares the observed median survival time with the prespecified threshold. However, median survival time would not be observed during the trial if the drug performs well and indeed cures most patients or if the accrual rate is so fast. To address the issues in clinical practice, we first propose a percentile event time test (PETT), which generalizes METT to any percentile of the survival time, and develop data-driven monitoring for phase II clinical trial designs based on PETT. We evaluate the performance of the method through simulations and illustrate the proposed method with a trial example.

An Automatic 3-D Ultrasound and Photoacoustic Combined Imaging System for Human Inflammatory Arthritis.

Aiming at a point-of-care device for rheumatology clinics, we developed an automatic 3-D imaging system combining the emerging photoacoustic (PA) imaging with conventional Doppler ultrasound (US) for detecting human inflammatory arthritis. This system is based on a commercial-grade GE HealthCare (GEHC, Chicago, IL, USA) Vivid E95 US machine and a Universal Robot UR3 robotic arm. This system automatically locates the patient's finger joints from a photograph taken by an overhead camera powered by an automatic hand joint identification method, followed by the robotic arm moving the imaging probe to the targeted joint to scan and obtain 3-D PA and Doppler US images. The GEHC US machine was modified to enable high-speed, high-resolution PA imaging while maintaining the features available on the system. The commercial-grade image quality and the high sensitivity in detecting inflammation in peripheral joints via PA technology hold great potential to significantly benefit clinical care of inflammatory arthritis in a novel way.

Longitudinal volumetric assessment of inflammatory arthritis via photoacoustic imaging and Doppler ultrasound imaging.

Aiming at clinical translation, we developed an automatic 3D imaging system combining the emerging photoacoustic imaging with conventional Doppler ultrasound for detecting inflammatory arthritis. This system was built with a GE HealthCare (GEHC) Vivid™ E95 ultrasound system and a Universal Robot UR3 robotic arm. In this work, the performance of this system was examined with a longitudinal study utilizing a clinically relevant adjuvant induced arthritis (AIA) murine model. After adjuvant injection, daily imaging of the rat ankle joints was conducted until joint inflammation was obvious based on visual inspection. Processed imaging results and statistical analyses indicated that both the hyperemia (enhanced blood volume) detected by photoacoustic imaging and the enhanced blood flow detected by Doppler ultrasound reflected the progress of joint inflammation. However, photoacoustic imaging, by leveraging the highly sensitive optical contrast, detected inflammation earlier than Doppler ultrasound, and also showed changes that are more statistically significant. This side-by-side comparison between photoacoustic imaging and Doppler ultrasound using the same commercial grade GEHC ultrasound machine demonstrates the advantage and potential value of the emerging photoacoustic imaging for rheumatology clinical care of arthritis.

SwinOCSR: end-to-end optical chemical structure recognition using a Swin Transformer.

Optical chemical structure recognition from scientific publications is essential for rediscovering a chemical structure. It is an extremely challenging problem, and current rule-based and deep-learning methods cannot achieve satisfactory recognition rates. Herein, we propose SwinOCSR, an end-to-end model based on a Swin Transformer. This model uses the Swin Transformer as the backbone to extract image features and introduces Transformer models to convert chemical information from publications into DeepSMILES. A novel chemical structure dataset was constructed to train and verify our method. Our proposed Swin Transformer-based model was extensively tested against the backbone of existing publicly available deep learning methods. The experimental results show that our model significantly outperforms the compared methods, demonstrating the model's effectiveness. Moreover, we used a focal loss to address the token imbalance problem in the text representation of the chemical structure diagram, and our model achieved an accuracy of 98.58%.

Determination of geographic origins and types of Lindera aggregata samples using a portable short-wave infrared hyperspectral imager.

A portable short-wavelength infrared microscope hyperspectral imager (SMHI) combined with machine learning algorithms for the purpose of classifying geographical origins as well as root types of Lindera aggregata is developed. The spectral range of the SMHI system is 1090-1820 nm (5500-9100 cm-1) with spectral and spatial resolutions of 4 nm and 27.3 µm, respectively. Utilizing PCA-RF algorithms, the geographic origin of tuberous roots and leaves from five different origins were classified with accuracies of 97.5% and 97.8%, respectively. In addition, spatial identification of tuberous root and taproot tubers in a mixed sample was done with an accuracy of 98.98%. The accuracy of origin classification and spatial identification are high enough which indicate the significant potential of applying SMHI system into the non-invasive spatial mapping and rapid quality assessment of medicinal herbs.

Incoherent broadband cavity-enhanced absorption spectroscopy for sensitive measurement of nutrients and microalgae.

Incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) can achieve sensitive measurements at trace concentrations for liquid phase marine samples. The IBBCEAS system consists of a cavity-enhancement module (CEM) and a transmission hyperspectral module (THM). The CEM has cavity-enhancement factors up to 78 at 550 nm. Measurements were obtained over a wide wavelength range (420-640 nm) with a halogen lamp, and the optical cavity was formed by two concave highly reflective mirrors (R=0.99). The minimum detectable absorption coefficient αmin of 7.3×10-7cm-1 at 550 nm corresponds to a limit of detection for nutrients of 780 pM. The spectral resolution of the THM is 3 nm in the wavelength range of 400 to 750 nm. We performed the IBBCEAS measurements for biological and chemical substances, including nutrients, microalgae, and Cy5 dye. The concentrations of nutrients in a deionized water environment and artificial seawater environment were measured at nanomolar levels; the concentration of microalgae phaeocystis was detected with 3.46×104/mL, and fluorescence substances such as Cy5 dye could be measured at 0.03 mg/L. Experimental results show that the IBBCEAS system has the capability for sensitive measurements of biological and chemical substances and has strong potential forin situ ecological marine environmental monitoring function.

A chromosome-level genome assembly of an alpine plant Crucihimalaya lasiocarpa provides insights into high-altitude adaptation.

It remains largely unknown how plants adapt to high-altitude habitats. Crucihimalaya (Brassicaceae) is an alpine genus occurring in the Qinghai-Tibet Plateau characterized by cold temperatures and strong ultraviolet radiation. Here, we generated a chromosome-level genome for C. lasiocarpa with a total size of 255.8 Mb and a scaffold N50 size of 31.9 Mb. We first examined the karyotype origin of this species and found that the karyotype of five chromosomes resembled the ancestral karyotype of the Brassicaceae family, while the other three showed strong chromosomal structural variations. In combination with the rough genome sequence of another congener (C. himalaica), we found that the significantly expanded gene families and positively selected genes involved in alpine adaptation have occurred since the origin of this genus. Our new findings provide valuable information for the chromosomal karyotype evolution of Brassicaceae and investigations of high-altitude environment adaptation of the genus.

Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae.

We propose a confocal hyperspectral microscopic imager (CHMI) that can measure both transmission and fluorescent spectra of individual microalgae, as well as obtain classical transmission images and corresponding fluorescent hyperspectral images with a high signal-to-noise ratio. Thus, the system can realize precise identification, classification, and location of microalgae in a free or symbiosis state. The CHMI works in a staring state, with two imaging modes, a confocal fluorescence hyperspectral imaging (CFHI) mode and a transmission hyperspectral imaging (THI) mode. The imaging modes share the main light path, and thus obtained fluorescence and transmission hyperspectral images have point-to-point correspondence. In the CFHI mode, a confocal technology to eliminate image blurring caused by interference of axial points is included. The CHMI has excellent performance with spectral and spatial resolutions of 3 nm and 2 µm, respectively (using a 10× microscope objective magnification). To demonstrate the capacity and versatility of the CHMI, we report on demonstration experiments on four species of microalgae in free form as well as three species of jellyfish with symbiotic microalgae. In the microalgae species classification experiments, transmission and fluorescence spectra collected by the CHMI were preprocessed using principal component analysis (PCA), and a support vector machine (SVM) model or deep learning was then used for classification. The accuracy of the SVM model and deep learning method to distinguish one species of individual microalgae from another was found to be 96.25% and 98.34%, respectively. Also, the ability of the CHMI to analyze the concentration, species, and distribution differences of symbiotic microalgae in symbionts is furthermore demonstrated.

Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager.

A dual-mode microscopic hyperspectral imager (DMHI) combined with a machine learning algorithm for the purpose of classifying origins and varieties of Tetrastigma hemsleyanum (T. hemsleyanum) was developed. By switching the illumination source, the DMHI can operate in reflection imaging and fluorescence detection modes. The DMHI system has excellent performance with spatial and spectral resolutions of 27.8 µm and 3 nm, respectively. To verify the capability of the DMHI system, a series of classification experiments of T. hemsleyanum were conducted. Captured hyperspectral datasets were analyzed using principal component analysis (PCA) for dimensional reduction, and a support vector machine (SVM) model was used for classification. In reflection microscopic hyperspectral imaging (RMHI) mode, the classification accuracies of T. hemsleyanum origins and varieties were 96.3% and 97.3%, respectively, while in fluorescence microscopic hyperspectral imaging (FMHI) mode, the classification accuracies were 97.3% and 100%, respectively. Combining datasets in dual mode, excellent predictions of origin and variety were realized by the trained model, both with a 97.5% accuracy on a newly measured test set. The results show that the DMHI system is capable of T. hemsleyanum origin and variety classification, and has the potential for non-invasive detection and rapid quality assessment of various kinds of medicinal herbs.

Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning.

A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm, respectively. We performed hyperspectral imaging (HSI) of three species of microalgae to verify their absorption characteristics. Transmission spectra were analyzed using principal component analysis (PCA) and peak ratio algorithms for dimensionality reduction and feature extraction, and a support vector machine (SVM) model was used for classification. The average accuracy, sensitivity and specificity to distinguish one species from the other two species were found to be 94.4%, 94.4% and 97.2%, respectively. A species identification experiment for a group of mixed microalgae in solution demonstrates the usability of the classification method. Using a random forest (RF) model, the growth stage in a phaeocystis growth cycle cultivated under laboratory conditions was predicted with an accuracy of 98.1%, indicating the feasibility to evaluate the growth state of microalgae through their transmission spectra. Experimental results show that the THMI system has the capability for classification, identification and growth stage estimation of microalgae, with strong potential for in-situ marine environmental monitoring and early warning detection applications.

Light-Sheet Microscopy for Surface Topography Measurements and Quantitative Analysis.

A novel light-sheet microscopy (LSM) system that uses the laser triangulation method to quantitatively calculate and analyze the surface topography of opaque samples is discussed. A spatial resolution of at least 10 µm in z-direction, 10 µm in x-direction and 25 µm in y-direction with a large field-of-view (FOV) is achieved. A set of sample measurements that verify the system's functionality in various applications are presented. The system has a simple mechanical structure, such that the spatial resolution is easily improved by replacement of the objective, and a linear calibration formula, which enables convenient system calibration. As implemented, the system has strong potential for, e.g., industrial sample line inspections, however, since the method utilizes reflected/scattered light, it also has the potential for three-dimensional analysis of translucent and layered structures.