Four deep learning frameworks consisting of Yolov5m and Yolov5m combined with ResNet50, ResNet-101, and EfficientNet-B0, respectively, are proposed for classifying tomato fruit on the vine into three categories: ripe, immature, and damaged. For a training dataset consisting of 4500 images and a training process with 200 epochs, a batch size of 128, and an image size of 224 × 224 pixels, the prediction accuracy for ripe and immature tomatoes is found to be 100% when combining Yolo5m with ResNet-101. Meanwhile, the prediction accuracy for damaged tomatoes is 94% when using Yolo5m with the Efficient-B0 model. The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. Thus, all four frameworks have the potential for tomato fruit classification in automated tomato fruit harvesting applications in agriculture.

In this study, we extend on the three parameter analysis approach of utilizing a noninvasive dual-liquid–crystal-based polarization-resolved second harmonic generation (SHG) microscopy to facilitate the quantitative characterization of collagen types I and II in fracture healing tissues. The SHG images under various linear and circular polarization states are analyzed and quantified in terms of the peptide pitch angle (PA), SHG-circular dichroism (CD), and anisotropy parameter (AP). The results show that the collagen PA has a value of 49.26° after 2 weeks of fracture healing (collagen type II domination) and 49.05° after 4 weeks (collagen type I domination). Moreover, the SHG-CD and AP values of the different collagen types differ by 0.05. The change tendencies of the extracted PA, SHG-CD, and AP parameters over the healing time are consistent with the collagen properties of healthy nonfractured bone. Thus, the feasibility of the proposed dual-liquid–crystal-based polarization-SHG method for differentiating between collagen types I and II in bone fracture healing tissue is confirmed.

The farming industry is facing the major challenge of intensive and inefficient harvesting labors. Thus, an efficient and automated fruit harvesting system is required. In this study, three object classification models based on Yolov5m integrated with BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs), respectively, are proposed for the automatic detection of tomato fruit. The various models were trained using 1508 normalized images containing three classes of cherry tomatoes, namely ripe, immature, and damaged. The detection accuracy for the three classes was found to be 94%, 95%, and 96%, respectively, for the modified Yolov5m + BoTNet model. The model thus appeared to provide a promising basis for the further development of automated harvesting systems for tomato fruit.

A graphene-based surface plasmon resonance (SPR) prism coupler sensor is proposed for the rapid detection of immunoglobulin G (IgG) antibodies. The feasibility of the proposed sensor is demonstrated by measuring the IgG concentration in phantom mouse and human serum solutions over the range of 0–250 ng/mL. The results show that the circular dichroism and principal fast axis angle of linear birefringence increase in line with increases in IgG concentration over the considered range. Moreover, the proposed device has a resolution of 5–10 ng/mL and a response time of less than three minutes. In general, the sensor provides a promising approach for IgG detection and has significant potential for rapid infectious viral disease testing applications.

Collagen of type I (Col I) and type II (Col II) are critical for cartilage and connective tissues in the human body, and several diseases may alter their properties. Assessing the identification and quantification of fibrillar collagen without biomarkers is a challenge. Advancements in non-invasive polarization-resolved second-harmonic generation (PSHG) microscopy have provided a method for the non-destructive investigation of collagen molecular level properties. Here we explored an alternative polarization modulated approach, dual-LC PSHG, that is based on two liquid crystal devices (Liquid crystal polarization rotators, LPRs) operating simultaneously with a laser scanning SHG microscope. We demonstrated that this more accessible technology allows the quick and accurate generation of any desired linear and circular polarization state without any mechanical parts. This study demonstrates that this method can aid in improving the ability to quantify the characteristics of both types of collagen, including pitch angle, anisotropy, and circular dichroism analysis. Using this approach, we estimated the effective pitch angle for Col I and Col II to be 49.7° and 51.6°, respectively. The effective peptide pitch angle for Col II gel was first estimated and is similar to the value obtained for Col I gel in the previous studies. Additionally, the difference of the anisotropy parameter of both collagen type gels was assessed to be 0.293, which reflects the different type molecular fibril assembly. Further, our work suggests a potential method for monitoring and differentiating different collagen types in biological tissues, especially cartilage or connective tissue.

Collagen is the most abundant extracellular matrix (ECM) protein in the human body by weight and is critical for various tissues, for it functions as both a structural component and a signaling molecule operating via biomolecular interactions. Collagen constitutes 30% of ECM proteins and interacts with a diverse range of biomolecules, including proteins, sugars, proteoglycans, polyphenols, and drugs.¹ A number of studies have investigated the influence of ECM mechanics on several diseases, such as cancer, corneal diseases, osteoarthritis, connective tissue diseases, autoimmune disorders, and cardiovascular diseases,² showing fibrous changes occurring in the microenvironment of tissues at the molecular level. Several studies have demonstrated that collagen protein molecules can covalently add sugar molecules not only through a natural enzyme-catalyzed reaction but also by a nonenzymatic reaction called glycation. Recent studies have indicated that the nonenzymatic glycation of collagen occurs with the accumulation of collagen in the ECM, with age and/or diabetes, leading to alteration of tissue mechanics and cell function. The end products of these reactions are known to be permanent. Therefore, the effects of nonenzymatic cross-linking on collagen are important factors to be studied in modern biomaterial science because they affect the properties of tissues or biologically derived scaffolds and play an essential role in diseases. Effects of nonenzymatic cross-linking are also implicated in the pathology associated with diabetes, atherosclerosis, and Alzheimer’s disease.^{3 – 6} Yuen et al. reported the effect of diabetes-induced glycation of the ECM on cell function by modeling the glycation reaction based on collagen treated with methylglyoxal for elucidating the development of fibrosis in diabetes.⁷ Collagen glycation was thus shown to enhance intercellular adhesion formation and cell migration over collagen to generate myofibroblasts.