: The pericellular matrix (PCM), a thin coating surrounding nearly all mammalian cells, plays a critical role in many cell-surface phenomena. In osteocytes, the PCM is believed to control both “outside-in” (mechanosensing) and “inside-out” (signaling molecule transport) processes. However, the osteocytic PCM is challenging to study in situ because it is thin (∼100 nm) and enclosed in mineralized matrix. To this end, we recently developed a novel tracer velocimetry approach that combined fluorescence recovery after photobleaching (FRAP) imaging with hydrodynamic modeling to quantify the osteocytic PCM in young murine bone. In this study, we applied the technique to older mice expressing or deficient for perlecan/HSPG2, a large heparan-sulfate proteoglycan normally secreted in osteocytic PCM. The objectives were (1) to characterize transport within an altered PCM; (2) to test the sensitivity of our approach in detecting the PCM alterations; and (3) to dissect the roles of the PCM in osteocyte mechanosensing. We found that: (1) solute transport increases in the perlecan-deficient (hypomorphic [Hypo]) mice compared with control mice; (2) PCM fiber density decreases with aging and perlecan deficiency; (3) osteocytes in the Hypo bones are predicted to experience higher shear stress (+34%), but decreased fluid drag force (−35%) under 3-N peak tibial loading; and (4) when subjected to tibial loading in a preliminary in vivo experiment, the Hypo mice did not respond to the anabolic stimuli as the CTL mice did. These findings support the hypothesis that the PCM fibers act as osteocyte's sensing antennae, regulating load-induced cellular stimulations and thus bone's sensitivity and in vivo bone adaptation. If this hypothesis is further confirmed, osteocytic PCM could be new targets to develop osteoporosis treatments by modulating bone's intrinsic sensitivity to mechanical loading and be used to design patient-specific exercise regimens to promote bone formation. © 2014 American Society for Bone and Mineral Research.

In recent years, machine vision systems (MVS) with convolutional neural networks (CNN) for precision spraying have been increasingly investigated due to their robust performance in plant detection. However, the high computational requirement of CNNs makes them slow to be adopted in field operations, especially in unstructured working environments such as broadcast-seeded fields. In this study, we developed a modular precision sprayer by distributing the high computational load of CNN among parallel low-cost and low-power vision computing devices. The sprayer utilized a custom precision spraying algorithm based on SSD-MobileNetV1 running on a Jetson Nano 4 GB. The model achieved 76% ${\mathit{mAP}}^{0.5}$ at 19 fps for weed and soybean detection in a broadcast-seeded field. Further, the sprayer targeted all weed samples and exhibited up to 48.89% spray volume reduction with a typical walking speed up to 3.0 km/h, which was three times faster than similar systems with known targeting performance. With these results, the study demonstrated that CNN-based precision spraying in a complex broadcast-seeded field can achieve increased velocity at high accuracy without needing powerful and expensive computational hardware using modular designs.

In recent years, Convolutional Neural Network (CNN) has become an attractive method to recognize and localize plant species in unstructured agricultural environments. However, developed systems suffer from unoptimized combinations of the CNN model, computer hardware, camera configuration, and travel velocity to prevent missed detections. Missed detection occurs if the camera does not capture a plant due to slow inferencing speed or fast travel velocity. Furthermore, modularity was less focused on Machine Vision System (MVS) development. However, having a modular MVS can reduce the effort in development as it will allow scalability and reusability. This study proposes the derived parameter, called overlapping rate ($r_o$), or the ratio of the camera field of view ($S$) and inferencing speed ($fps$) to the travel velocity ($\stackrel{\rightharpoonup }{v}$) to theoretically predict the plant detection rate ($r_d$) of an MVS and aid in developing a CNN-based vision module. Using performance from existing MVS, the values of $r_o$ at different combinations of inferencing speeds (2.4 to 22 fps) and travel velocity (0.1 to 2.5 m/s) at 0.5 m field of view were calculated. The results showed that missed detections occurred when $r_o$ was less than 1. Comparing the theoretical detection rate ($r_{d,th}$) to the simulated detection rate ($r_{d,sim}$) showed that $r_{d,th}$ had a 20% margin of error in predicting plant detection rate at very low travel distances (<1 m), but there was no margin of error when travel distance was sufficient to complete a detection pattern cycle (≥10 m). The simulation results also showed that increasing $S$ or having multiple vision modules reduced missed detection by increasing the allowable ${\stackrel{\rightharpoonup }{v}}_{max}$. This number of needed vision modules was equal to rounding up the inverse of $r_o$. Finally, a vision module that utilized SSD MobileNetV1 with an average effective inferencing speed of 16 fps was simulated, developed, and tested. Results showed that the $r_{d,th}$ and $r_{d,sim}$ had no margin of error in predicting $r_{actual}$ of the vision module at the tested travel velocities (0.1 to 0.3 m/s). Thus, the results of this study showed that $r_o$ can be used to predict $r_d$ and optimize the design of a CNN-based vision-equipped robot for plant detections in agricultural field operations with no margin of error at sufficient travel distance.

Ultraviolet disinfection has been proven to be effective for surface sanitation. Traditional ultraviolet disinfection systems generate omnidirectional radiation, which introduces safety concerns regarding human exposure. Large scale disinfection must be performed without humans present, which limits the time efficiency of disinfection. We propose and experimentally demonstrate a targeted ultraviolet disinfection system using a combination of robotics, lasers, and deep learning. The system uses a laser-galvo and a camera mounted on a two-axis gimbal running a custom deep learning algorithm. This allows ultraviolet radiation to be applied to any surface in the room where it is mounted, and the algorithm ensures that the laser targets the desired surfaces avoids others such as humans. Both the laser-galvo and the deep learning algorithm were tested for targeted disinfection.