Search

In strawberry cultivation, precise disease management is crucial for maximizing yields and reducing unnecessary fungicide use. Traditional methods for measuring leaf wetness duration (LWD), a critical factor in assessing the risk of fungal diseases such as botrytis fruit rot and anthracnose, have been reliant on sensors with known limitations in accuracy and reliability and difficulties with calibrating. To overcome these limitations, this study introduced an innovative algorithm for leaf wetness detection systems employing high-resolution imaging and deep learning technologies, including convolutional neural networks (CNNs). Implemented at the University of Florida’s Plant Science Research and Education Unit (PSREU) in Citra, FL, USA, and expanded to three additional locations across Florida, USA, the system captured and analyzed images of a reference plate to accurately determine the wetness and, consequently, the LWD. The comparison of system outputs with manual observations across diverse environmental conditions demonstrated the enhanced accuracy and reliability of the artificial intelligence-driven approach. By integrating this system into the Strawberry Advisory System (SAS), this study provided an efficient solution to improve disease risk assessment and fungicide application strategies, promising significant economic benefits and sustainability advances in strawberry production.

ABSTRACTAntiangiogenic therapy is a recognized method for countering the immunosuppressive tumor microenvironment (TME) and improving anti-tumor immunity. PB101 is a glycosylated decoy receptor that binds to VEGF-A and PlGF with high affinity, based on the VEGFR1 backbone. Here, we elucidated PB101-induced remodeling of tumor angiogenesis and immunity, which enhances anti-PD-L1 immune checkpoint blockade. PB101 inhibited tumor growth by suppressing angiogenesis and enhancing CD8+ T cell infiltration into the tumors. PB101 induced robust reprogramming of antitumor immunity and activates intratumoral CD8+ T cells. Anti-tumor efficacy of PB101 is mostly dependent on CD8+ T cells and IFN-γ. PB101 reprograms tumor immunity in a manner distinct from that of the conventional VEGF decoy receptor, VEGF-trap. With its potent immune-modulating capability, PB101 synergizes with an anti-PD-L1, triggering strengthened antitumor immunity. Combining PB101 and anti-PD-L1 could establish durable protective immunity against tumor recurrence and metastasis. The findings of this study offer scientific rationales for further clinical development of PB101, particularly when used in combination with immune checkpoint inhibitors, as a potential treatment for advanced cancers.

Strawberries are an important and high-value crop in the United States. The total value of strawberry production in the United States was $3.42 billion in 2021. However, a major pest of strawberries, two-spotted spider mites (TSSM) (Tetranychus urticae Koch), can cause severe economic losses due to mites feeding on chlorophyll which interferes with photosynthesis. Predatory mites (Neoseiulus californicus and Phytoseiulus persimilis) can control TSSM populations and provide an alternative to conventional pesticides. Predatory mites are more friendly to the environment than pesticides. To monitor TSSM and predatory mite populations in the field, frequent sampling is required. However, manual counting of mites using a hand lens is challenging for a large field. Therefore, this study applied deep learning models to detect TSSM and predatory mites in strawberries from smartphone images. Three different smartphones (Motorola Moto E5 Play, OnePlus A5000, and iPhone XR) and a 25x macro lens were used to collect a total of 3,013 images, including 1,779 TSSM images and 1,234 predatory mite images. TSSM images were collected by the three smartphones, and only the OnePlus A5000 and iPhone XR were used to collect predatory mite images that only contained predatory mites or contained both predatory mites and TSSM in the same image. TSSM images were used to compare two deep learning models (YOLOv4 and Faster R-CNN Resnet50) and three different smartphone cameras. This study found that the average detection accuracy of YOLOv4 and Faster R-CNN was 0.935 and 0.863, respectively. Deep learning models achieved the best detection accuracy on the image collected by iPhone XR. The YOLOv4 model was trained to compare the detection performance of three different input image sizes (160 × 160, 320 × 320, 640 × 640 pixels) for TSSM and predatory mite detection. This study found that the mean average precision of the YOLOv4 increased as the image size increased, and the model trained with an image size of 640 × 640 pixels achieved the detection accuracy of 0.933 for TSSM and predatory mites. This study also found that the YOLOv4 could detect pests under different field illumination conditions. The smartphone image-based method can detect the TSSM and predatory mites quickly from images and with high detection accuracy, which can help growers use less time to monitor mite populations in the field.

The pedicled transverse rectus abdominis myocutaneous (TRAM) flap is often used during breast reconstruction. After completely elevating the flap, a medial tunnel is created to connect the dissection area to the mastectomy defect. This tunnel should typically accommodate four of the surgeon’s fingers, although this may vary depending on flap volume. Wider tunneling facilitates flap delivery, but can damage the breast and inframammary fold. Therefore, we introduce a technique to move the flap easily and safely through a small tunnel. During breast reconstruction using a pedicled TRAM flap, a tunnel was made in the mastectomy defect along the meridian of the breast. This tunnel accommodated a width of three of the surgeon’s fingers (6 cm). After completely elevating the flap, it was inserted into a funnel with an inner surface made of polymeric vinyl and a hydrophilic coating. The flap was propelled into the mastectomy defect via the funnel. Our novel technique for pedicled TRAM flap delivery using a funnel was easy and safe, and it is deemed to be suitable for delivering a pedicled TRAM flap.

Background & Aims: We elucidated the clinical and immunologic implications of serum IL-6 levels in patients with unresectable hepatocellular carcinoma (HCC) treated with atezolizumab and bevacizumab (Ate/Bev). Methods: We prospectively enrolled 165 patients with unresectable HCC (discovery cohort: 84 patients from three centres; validation cohort: 81 patients from one centre). Baseline blood samples were analysed using a flow cytometric bead array. The tumour immune microenvironment was analysed using RNA sequencing. Results: In the discovery cohort, clinical benefit 6 months (CB6m) was defined as complete or partial response, or stable disease for ≥6 months. Among various blood-based biomarkers, serum IL-6 levels were significantly higher in participants without CB6m than in those with CB6m (mean 11.56 vs. 5.05 pg/ml, p = 0.02). Using maximally selected rank statistics, the optimal cut-off value for high IL-6 was determined as 18.49 pg/ml, and 15.2% of participants were found to have high IL-6 levels at baseline. In both the discovery and validation cohorts, participants with high baseline IL-6 levels had a reduced response rate and worse progression-free and overall survival after Ate/Bev treatment compared with those with low baseline IL-6 levels. In multivariable Cox regression analysis, the clinical implications of high IL-6 levels persisted, even after adjusting for various confounding factors. Participants with high IL-6 levels showed reduced interferon-γ and tumour necrosis factor-α secretion from CD8+ T cells. Moreover, excess IL-6 suppressed cytokine production and proliferation of CD8+ T cells. Finally, participants with high IL-6 levels exhibited a non-T-cell-inflamed immunosuppressive tumour microenvironment. Conclusions: High baseline IL-6 levels can be associated with poor clinical outcomes and impaired T-cell function in patients with unresectable HCC after Ate/Bev treatment. Impact and implications: Although patients with hepatocellular carcinoma who respond to treatment with atezolizumab and bevacizumab exhibit favourable clinical outcomes, a fraction of these still experience primary resistance. We found that high baseline serum levels of IL-6 correlate with poor clinical outcomes and impaired T-cell response in patients with hepatocellular carcinoma treated with atezolizumab and bevacizumab.

Introduction: Atezolizumab and bevacizumab (Ate/Bev) combination has become the new first-line systemic therapy for unresectable hepatocellular carcinoma (HCC). Although several studies reported thyroid dysfunction after treatment with immune checkpoint inhibitors, the clinical and immunological significance of thyroid dysfunction in patients treated with Ate/Bev has not been comprehensively addressed. We aimed to comprehensively evaluate the clinical and immunological implications of thyroid dysfunction in unresectable HCC patients treated with Ate/Bev. Methods: We enrolled 208 patients with unresectable HCC treated with Ate/Bev from three Korean cancer centers. Thyroid adverse events (AEs) were reviewed, and cytokines and T cells in the blood samples were analyzed at baseline. For external validation, we analyzed clinical outcomes according to thyroid AEs in patients treated with Ate/Bev in the IMbrave150 study. Results: Forty-one (19.7%) out of 208 patients experienced thyroid dysfunction (hypothyroidism [17.3%] and thyrotoxicosis [5.8%]) after Ate/Bev treatment. Median time to onset of hypothyroidism and thyrotoxicosis after Ate/Bev treatment was 3.5 and 1.3 months, respectively. Patients with thyroid AEs demonstrated significantly better progression-free survival, overall survival, and objective response rate than those without thyroid AEs. These findings were still consistent even after adjusting for confounding factors. Furthermore, favorable survival outcomes in patients with thyroid AEs were also validated in a cohort of IMbrave150 patients. While patients with thyrotoxicosis showed a significantly lower level of baseline IL-6, those with hypothyroidism did not show significant differences in circulating cytokine levels and CD8+ T-cell fractions. Conclusions: A fraction of patients with HCC treated with Ate/Bev experienced thyroid dysfunction, and the development of thyroid AEs was associated with favorable clinical outcomes.

Cancer: Combination treatment targeting tumor blood vessels and immunity Combining anti-angiogenesis drugs that reduce the growth of blood vessels and immune checkpoint inhibitors that promote the activation of cancer-killing immune cells offers a promising new therapeutic regimen for patients with cancer. In a review article, a team led by Chan Kim and Hong Jae Chon from the CHA University School of Medicine in Seongnam, South Korea, discuss the molecular crosstalk between blood vessels and immune cells in the tumor microenvironment, a biological interconnectedness that provides a compelling rationale for the dual treatment strategy. The researchers summarize preclinical and clinical data demonstrating the potential of combining immunotherapy with treatment targeting blood vessel growth across a range of tumor types. These data have so far led to regulatory approvals for patients with cancers of the lung, kidney, liver and endometrium.

Strawberry is ranked third in the value of production of the crops in Florida, USA. Classifying strawberry maturity and monitoring strawberry growth status in the field is very critical for accurate strawberry yield prediction, efficient strawberry field management, and achieving the highest crop production. The traditional method of distinguishing strawberry maturity is based on either physical appearance or internal chemical substance content. However, the traditional method is time-consuming and costly. In this research, an automatic strawberry maturity classification system was developed for the rapid and accurate classification of different strawberry maturity stages. A state-of-the-art deep learning method, You Only Look Once (YOLOv3), which is good at small object detection, was trained and applied to detect strawberry flowers and strawberry fruit at different maturity stages. Two strawberry image acquisition methods, aerial imaging and near-ground imaging, were compared by using the same deep learning image processing method. As a result, three and seven strawberry maturity stages were classified for unmanned aerial vehicle (UAV) images and near-ground digital camera images, respectively. For UAV images, the highest mean average precision (mAP) of strawberry maturity classification was 0.88 for a test data set at 2 m, and the highest classification average precision (AP) was 0.93 for fully matured fruit. For near-ground digital camera images, the mAP of strawberry maturity classification was 0.89, and the highest classification AP was 0.94 for fully matured fruit as well. The result shows that YOLOv3 is an excellent approach for strawberry maturity classification on both image types.

Botrytis fruit rot and anthracnose are fungal diseases of strawberry. These diseases are a significant contributor to yield losses, requiring farmers to use fungicides frequently to prevent them. The proliferation of botrytis and anthracnose is directly linked to the duration of the presence of free water on the plant canopy, which is generally defined as leaf wetness duration (LWD). LWD is an important measure in determining the risk for these diseases to develop in the strawberry crop. By accurately measuring LWD, the risk of disease can be calculated more accurately, and specific fungicide application recommendations can be given to the farmers. This reduces the frequency with which fungicide is applied and ultimately reduces costs for farmers. There is no standard method to detect leaf wetness, but leaf wetness sensors are widely used for that purpose. These wetness sensors are difficult to calibrate and not very accurate, which reduces their reliability. The objective of this study was to find a better alternative to the commonly used leaf wetness sensors. This study implemented color and thermal imaging-based approaches as a solution to the problem of leaf wetness detection in strawberry plants. The proposed method used deep learning and computer vision techniques to detect leaf wetness from color and thermal images. The deep learning model was highly accurate in detecting wetness when compared with the visual observation of the images. It was also found that leaf wetness could be detected with a high degree of accuracy using deep learning with color images. In the future, using the findings of this study, a portable device can be developed to replace the commonly used wetness sensor with a more reliable imaging-based device.

The decline of natural pollinators necessitates the development of novel pollination technologies. In this work, we propose a drone-enabled autonomous pollination system (APS) that consists of five primary modules: environment sensing, flower perception, path planning, flight control, and pollination mechanisms. These modules are highly dependent upon each other, with each module relying on inputs from the other modules. In this paper, we focus on approaches to the flower perception, path planning, and flight control modules. First, we briefly introduce a flower perception method from our previous work to create a map of flower locations. With a map of flowers, APS path planning is defined as a variant of the Travelling Salesman Problem (TSP). Two path planning approaches are compared based on mixed-integer programming (MIP) and genetic algorithms (GA), respectively. The GA approach is chosen as the superior approach due to the vast computational savings with negligible loss of optimality. To accurately follow the generated path for pollination, we develop a convex optimization approach to the quadrotor flight control problem (QFCP). This approach solves two convex problems. The first problem is a convexified three degree-of-freedom QFCP. The solution to this problem is used as an initial guess to the second convex problem, which is a linearized six degree-of-freedom QFCP. It is found that changing the objective of the second convex problem to minimize the deviation from the initial guess provides improved physical feasibility and solutions similar to a general-purpose optimizer. The path planning and flight control approaches are then tested within a model predictive control (MPC) framework where significant computational savings and embedded adjustments to uncertainty are observed. Coupling the two modules together provides a simple demonstration of how the entire APS will operate in practice.

Renal cell carcinoma (RCC) is the most common type of kidney malignancy worldwide with Pembrolizumab and axitinib treatment (Pembro/Axi) amongst the most effective first-line immunotherapies for advanced RCC. However, it remains difficult to predict treatment response and early resistance. Therefore, we evaluated whether baseline serum interleukin-6 (IL-6) could be a predictive biomarker. Between November 2019 and December 2021, 58 patients with advanced RCC were enrolled, administered first-line Pembro/Axi, and baseline blood samples were analyzed using flow cytometry. The mean baseline serum IL-6 concentration was 8.6 pg/mL in responders and 84.1 pg/mL in patients with progressive disease. The IL-6 cut-off value was set at 6.5 pg/mL using time-dependent receiver operating characteristic curves, with 37.9% of patients having high baseline serum IL-6 levels and 62.1% having low levels. Objective response rates were 58.3% and 36.4% in low and high IL-6 groups, respectively. Overall survival and progression-free survival were longer in patients with low IL-6 levels than in those with high levels. High IL-6 levels were related to reduced interferon-γ and tumor necrosis factor-α production from CD8+ T cells. Overall, high baseline serum IL-6 levels were associated with worse survival outcomes and reduced T-cell responses in Pembro/Axi-treated advanced RCC patients.

The Strawberry Advisory System (SAS) is a tool developed to help Florida strawberry growers determine the risk of common fungal diseases and the need for fungicide applications. Leaf wetness duration (LWD) is one of the important parameters in SAS disease risk modeling. By accurately measuring the LWD, disease risk can be better assessed, leading to less fungicide use and more economic benefits to the farmers. This research aimed to develop and test a more accurate leaf wetness detection system than traditional leaf wetness sensors. In this research, a leaf wetness detection system was developed and tested using color imaging of a reference surface and a convolutional neural network (CNN), which is one of the artificial-intelligence-based learning methods. The system was placed at two separate field locations during the 2021–2022 strawberry-growing season. The results from the developed system were compared against manual observation to determine the accuracy of the system. It was found that the AI- and imaging-based system had high accuracy in detecting wetness on a reference surface. The developed system can be used in SAS for determining accurate disease risks and fungicide recommendations for strawberry production and allows the expansion of the system to multiple locations.

During laparoscopic surgery for colorectal or gastric cancers, locating the tumor for excision is difficult owing to it being obscured by mucous membranes. Therefore, a clip can be installed around the tumor, which can be located using a sensor. Most of the clip–detectors developed thus far can only detect tumors in either the colon or stomach and require a wire to connect the clip and detector. This study designs a clip and detector that can locate a tumor in the stomach and colon. The clip contains a neodymium magnet that generates a magnetic field, and the detector includes a Colpitts oscillator that allows magnetic coupling of the clip and detector. After installing the prepared clip at the tumor location, the detector is used to locate the clip. To test the clip and detector, we conducted animal experiments, during which four clips were installed in the colon and stomach of a mini pig. We succeeded in locating the clips within 2.17 and 3.14 s in the stomach and colon, respectively, which were shorter than the detection times reported in previous studies. The demand for laparoscopic surgery and endoscopes is predicted to increase owing to this method.

Background Peritoneal carcinomatosis is a fatal clinical presentation of colon cancer, characterized by unresponsiveness to conventional anticancer therapies, including immune checkpoint inhibitors. Here, we elucidated the immune-evasion mechanisms during the peritoneal carcinomatosis of colon cancer and developed a novel immunotherapy by activating the stimulator of interferon genes (STING) pathway.Methods We generated a syngeneic peritoneal carcinomatosis model of colon cancer. Mice were intraperitoneally treated with either STING agonist (MIW815, also known as ADU-S100) or PD-1 blockade or both. The tumor microenvironment was comprehensively analyzed using multiplexed immunofluorescence imaging, flow cytometry, and NanoString immune profiling.Results Intraperitoneal colon cancer cells induce a massive influx of immunosuppressive M2-like macrophages, upregulate immune checkpoints, and impair effector T cell functions during peritoneal dissemination; these collectively create a highly angiogenic and immunosuppressive milieu that is resistant to anti-PD-1 monotherapy. Intraperitoneal administration of a STING agonist suppressed aberrant angiogenesis, increased pericyte coverage, and normalized tumor vessels, thereby facilitating the infiltration of activated CD8+ T cells into peritoneal tumor nodules. Moreover, STING activation reprogramed tumor-associated macrophages toward the M1 phenotype. STING activation converted immunologically cold peritoneal tumors into T-cell-inflamed tumors in a type-I interferon-dependent manner. Lastly, the STING agonist synergistically cooperated with PD-1 and/or COX2 blockade to further suppress the peritoneal dissemination of colon cancer, resulting in complete eradication of tumor and ascites, and inducing durable antitumor immunity.Conclusions STING activation can normalize the peritoneal vascular and immune microenvironment, providing a rationale for a novel combination therapeutic strategy for peritoneal carcinomatosis in colon cancer.

Purpose: This study aimed to compare surgical revisions and balloon angioplasty after surgical thrombectomy on thrombosed dialysis access as a first event. Materials and Methods: Records of patients undergoing creation of arteriovenous grafts (AVGs) at the Gachon University Gil Medical Center between March 2008 and February 2016 were reviewed. Among them, patients who underwent treatment on first-time thrombotic occlusion after AVG creation were identified. Outcomes were primary, primary-assisted, and secondary patency. The patency was generated using the Kaplan-Meier method, and patency rates were compared by log-rank test. Results: A total of 59 de novo interventions (n=26, hybrid interventions; n=33, surgical revisions) for occlusive AVGs were identified. The estimated 1-year primary patency rates were 47% and 30% in the surgery and hybrid groups, respectively. The estimated primary patency rates were not different between the two groups (log-rank test, P=0.73). The Kaplan-Meier estimates of 6 and 12 months for primary-assisted patency rates were 68% and 57% in the surgery group and 56% and 56% in the hybrid group. The Kaplan-Meier estimates of 12 and 24 months secondary patency rates were 90% and 71% in the surgery group and 79% and 62% in the hybrid group. There were no differences in the estimated primary-assisted and secondary patency rates between the two groups. Conclusion: Our results showed no significant difference between the two groups in terms of primary patency (P=0.73), primary-assisted patency (P=0.85), and secondary patency (P=0.78). However, percutaneous transluminal angioplasty can give more therapeutic options for both surgeons and patients.

Kynurenine (Kyn) is a key inducer of an immunosuppressive tumor microenvironment (TME). Although indoleamine 2,3-dioxygenase (IDO)-selective inhibitors have been developed to suppress the Kyn pathway, the results were not satisfactory due to the presence of various opposing mechanisms. Here, we employed an orally administered novel Kyn pathway regulator to overcome the limitation of anti-tumor immune response. We identified a novel core structure that inhibited both IDO and TDO. An orally available lead compound, STB-C017 (designated hereafter as STB), effectively inhibited the enzymatic and cellular activity of IDO and TDO in vitro. Moreover, it potently suppressed Kyn levels in both the plasma and tumor in vivo. STB monotherapy increased the infiltration of CD8+ T cells into TME. In addition, STB reprogrammed the TME with widespread changes in immune-mediated gene signatures. Notably, STB-based combination immunotherapy elicited the most potent anti-tumor efficacy through concurrent treatment with immune checkpoint inhibitors, leading to complete tumor regression and long-term overall survival. Our study demonstrated that a novel Kyn pathway regulator derived using deep learning technology can activate T cell immunity and potentiate immune checkpoint blockade by overcoming an immunosuppressive TME.

Premise When plants are exposed to stress conditions, irreversible damage can occur, negatively impacting yields. It is therefore important to detect stress symptoms in plants, such as the accumulation of anthocyanin, as early as possible. Methods and Results Twenty‐two regression models in five color spaces were trained to develop a prediction model for plant anthocyanin levels from digital color imaging data. Of these, a quantile random forest regression model trained with standard red, green, blue (sRGB) color space data most accurately predicted the actual anthocyanin levels. This model was then used to noninvasively monitor the spatial and temporal accumulation of anthocyanin in Arabidopsis thaliana leaves. Conclusions The digital imaging–based nature of this protocol makes it a low‐cost and noninvasive method for the detection of plant stress. Applying a similar protocol to more economically viable crops could lead to the development of large‐scale, cost‐effective systems for monitoring plant health.

Objective Especially in emergency departments (EDs), a lack of internal medicine (IM) residents in charge causes difficulties in medical care and ED overcrowding. Thus, protocols without IM residents in EDs is needed. This study aimed to investigate changes in medical care when emergency medicine residents replaced the roles of IM residents. Methods This study was conducted at a single-site ED of a university medical center. The study group contained patients admitted to the IM department between September and December 2015, during which IM residents were absent in the ED. The control group contained patients admitted to the IM department between September and December 2014, during which IM residents were present in the ED. Changes in medical care between the presence and absence of IM residents in the ED were studied by comparing admission rates from the ED, length of ED stay, duration of hospitalization, and concordance of diagnoses between admission and discharge by the IM department. Results The study group contained 2,341 patients; the control group contained 2,215 patients. Admission rates from the ED increased by 53.4% (95% confidence interval [CI], P

The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000–2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R2 = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake.

FAM19A5 is a secretory protein that is predominantly expressed in the brain. Although the FAM19A5 gene has been found to be associated with neurological and/or psychiatric diseases, only limited information is available on its function in the brain. Using FAM19A5-LacZ knock-in mice, we determined the expression pattern of FAM19A5 in developing and adult brains and identified cell types that express FAM19A5 in naïve and traumatic brain injury (TBI)–induced brains. According to X-gal staining results, FAM19A5 is expressed in the ventricular zone and ganglionic eminence at a very early stage of brain development, suggesting its functions are related to the generation of neural stem cells and oligodendrocyte precursor cells (OPCs). In the later stages of developing embryos and in adult mice, FAM19A5 expression expanded broadly to particular regions of the brain, including layers 2/3 and 5 of the cortex, cornu amonis (CA) region of the hippocampus, and the corpus callosum. X-gal staining combined with immunostaining for a variety of cell-type markers revealed that FAM19A5 is expressed in many different cell types, including neurons, OPCs, astrocytes, and microglia; however, only some populations of these cell types produce FAM19A5. In a subpopulation of neuronal cells, TBI led to increased X-gal staining that extended to the nucleus, marked by slightly condensed content and increased heterochromatin formation along the nuclear border. Similarly, nuclear extension of X-gal staining occurred in a subpopulation of OPCs in the corpus callosum of the TBI-induced brain. Together, these results suggest that FAM19A5 plays a role in nervous system development from an early stage and increases its expression in response to pathological conditions in subsets of neurons and OPCs of the adult brain.

Protected cultivation systems such as greenhouses are becoming increasingly popular globally and have been adopted because of unpredictable climatic conditions and their ability to easily control micro- and macroenvironments. However, limitations such as hazardous work environments and shortages in labor are major concerns for agricultural production using these structures. This has led to the development and adoption of robotic systems. For the efficient use of robots in protected cultivation systems, we formulate the work efficiency problem and model a three-dimensional standard strawberry greenhouse to analyze the effectiveness of a strawberry-harvesting robot compared to different levels of human workforce (experienced, average, and beginner). Simulations are conducted using Quest software to compare the efficiency of different scenarios of robotics to humans. Different methods of improvement from battery capacity and charge rate to harvesting speed are investigated and optimal conditions are recommended. The average hourly production of the robot is about five times lower than that of skilled workers. However, robots are more productive due to their ability to work around the clock. Comparative analyses show that a reduction in harvesting time per strawberry from 3 to 1 s would result in an increase in daily production from 347.93 to 1021.30 kg. This would lead to a five-fold increase in comparison to present daily production. A 10% improvement in battery charge time would result in the battery capacity gaining two extra hours from the current 10 h and would cut the current 2 h needed for charge to 1 h. This paper proposes an operation process and suggestions for changes needed for improving the work efficiency of robots in a greenhouse. This could be extended to other crops and greenhouses.

Vascular smooth muscle cells (VSMCs) are the major cell type in the blood vessel walls, and their phenotypic modulation is a key cellular event driving vascular remodeling. Although high mobility group box-1 (HMGB1) plays a pivotal role in inflammatory processes after vascular injuries, the importance of the links between VSMCs, HMGB1 and vascular inflammation has not been clarified. To prove the hypothesis that VSMCs might be active players in vascular inflammation by secreting inflammatory cytokines, we investigated the proinflammatory effects of HMGB1 and its intermediary signaling pathways in VSMCs. When cultured human VSMCs were stimulated with HMGB1 (10–500 ng/ml), IL-1β production was markedly increased. HMGB1 also increased the expression of NLRP3 inflammasome components including NLRP3, ASC and caspase-1. Among these components, HMGB1-induced expressions of NLRP3 and caspase-1 were markedly attenuated in TLR2 siRNA-transfected cells, whereas ASC and caspase-1 expressions were reduced in RAGE-deficient cells. In TLR4-deficient cells, HMGB1-induced caspase-1 expression was significantly attenuated. Moreover, IL-1β production in HMGB1-stimulated cells was significantly reduced in cells transfected with caspase-1 siRNA as well as in cells treated with monoclonal antibodies or siRNAs for TLR2, TLR4 and RAGE. Overall, this study identified a pivotal role for NLRP3 inflammasome and its receptor signaling involved in the production of IL-1β in VSMCs stimulated with HMGB1. Thus, targeting HMGB1 signaling in VSMCs offers a promising therapeutic strategy for treating vascular remodeling diseases.

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields.

Pathologic fibrosis around silicone implants is problematic, and thus, these implants have been coated with a mixture of a biocompatible polymer and antifibrotic drug for sustained drug release to prevent fibrosis. However, a coating applied over an entire surface would be subject to mechanical instability as the implant would be severely crumpled for implant insertion. Therefore, in this work, we proposed localized, patterned coating dots, each composed of poly(lactic-co-glycolic acid) (PLGA) and tranilast, to be applied on the surface of silicone implants. The drug loaded in the pattern-coated implant herein was well retained after a cyclic tensile test. Due to the presence of PLGA in each coating dot, the tranilast could be released in a sustained manner for more than 14 days. When implanted in a subcutaneous pocket in living rats for 12 weeks, compared with the intact implant, the pattern-coated implant showed a decreased capsule thickness and collagen density, as well as less transforming growth factor-β (TGF-β) expression and fewer fibroblasts; importantly, these changes were similar between the surfaces with and without the coating dots. Therefore, we conclude that the pattern-coating strategy proposed in this study can still effectively prevent fibrosis by maintaining the physical stability of the coatings.

A neuroprotective role of autophagy mediates the degradation of β-amyloid peptide (Aβ) in Alzheimer's disease (AD). The previous study showed cilostazol modulates autophagy by increasing beclin1, Atg5 and LC3-II expressions, and depletes intracellular Aβ accumulation. This study elucidated the mechanisms through which cilostazol modulates the autophagic degradation of Aβ in neurons. In N2a cells, cilostazol (10-30 μM), significantly increased the expression of P-AMPKα (Thr 172) and downstream P-ACC (acetyl-CoA carboxylase) (Ser 79) as did resveratrol (SIRT1 activator), or AICAR (AMPK activator), which were blocked by KT5720, compound C (AMPK inhibitor), or sirtinol. Furthermore, phosphorylated-mTOR (Ser 2448) and phosphorylated-P70S6K (Thr 389) expressions were suppressed, and LC3-II levels were elevated in association with decreased P62/Sqstm1 by cilostazol. Cilostazol increased cathepsin B activity and decreased p62/SQSTM 1, consequently decreased accumulation of Aβ1-42 in the activated N2aSwe cells, and these results were blocked by sirtinol, compound C and bafilomycin A1 (autophagosome blocker), suggesting enhanced autophagosome formation by cilostazol. In SIRT1 gene-silenced N2a cells, cilostazol failed to increase the expressions of P-LKB1 (Ser 428) and P-AMPKα, which contrasted with its effect in negative control cells transfected with scrambled siRNA duplex. Further, N2a cells transfected with expression vectors encoding pcDNA SIRT1 showed increased P-AMPKα expression, which mimicked the effect of cilostazol in N2a cells; suggesting cilostazol-stimulated expressions of P-LKB1 and P-AMPKα were SIRT1-dependent. Unlike their effects in N2a cells, in HeLa cells, which lack LKB1, cilostazol and resveratrol did not elevate SIRT1 or P-AMPKα expression, indicating cilostazol and resveratrol-stimulated expressions of SIRT1 and P-AMPKα are LKB1-dependent. In conclusion, cilostazol upregulates autophagy by activating SIRT1-coupled P-LKB1/P-AMPKα and inhibiting mTOR activation, thereby decreasing Aβ accumulation.

Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.

Autophagy is a vital pathway for the removal of β-amyloid peptide (Aβ) and the aggregated proteins that cause Alzheimer's disease (AD). We previously found that cilostazol induced SIRT1 expression and its activity in neuronal cells, and thus, we hypothesized that cilostazol might stimulate clearances of Aβ and C-terminal APP fragment β subunit (APP-CTFβ) by up-regulating autophagy.When N2a cells were exposed to soluble Aβ1-42, protein levels of beclin-1, autophagy-related protein5 (Atg5), and SIRT1 decreased significantly. Pretreatment with cilostazol (10-30 μM) or resveratrol (20 μM) prevented these Aβ1-42 evoked suppressions. LC3-II (a marker of mammalian autophagy) levels were significantly increased by cilostazol, and this increase was reduced by 3-methyladenine. To evoke endogenous Aβ overproduction, N2aSwe cells (N2a cells stably expressing human APP containing the Swedish mutation) were cultured in medium with or without tetracycline (Tet+ for 48 h and then placed in Tet- condition). Aβ and APP-CTFβ expressions were increased after 12~24 h in Tet- condition, and these increased expressions were significantly reduced by pretreating cilostazol. Cilostazol-induced reductions in the expressions of Aβ and APP-CTFβ were blocked by bafilomycin A1 (a blocker of autophagosome to lysosome fusion). After knockdown of the SIRT1 gene (to ~40% in SIRT1 protein), cilostazol failed to elevate the expressions of beclin-1, Atg5, and LC3-II, indicating that cilostazol increases these expressions by up-regulating SIRT1. Further, decreased cell viability induced by Aβ was prevented by cilostazol, and this inhibition was reversed by 3-methyladenine, indicating that the protective effect of cilostazol against Aβ induced neurotoxicity is, in part, ascribable to the induction of autophagy. In conclusion, cilostazol modulates autophagy by increasing the activation of SIRT1, and thereby enhances Aβ clearance and increases cell viability.

A vision sensor was introduced and tested for early detection of citrus Huanglongbing (HLB). This disease is caused by the bacterium Candidatus Liberibacter asiaticus (CLas) and is transmitted by the Asian citrus psyllid. HLB is a devastating disease that has exerted a significant impact on citrus yield and quality in Florida. Unfortunately, no cure has been reported for HLB. Starch accumulates in HLB infected leaf chloroplasts, which causes the mottled blotchy green pattern. Starch rotates the polarization plane of light. A polarized imaging technique was used to detect the polarization-rotation caused by the hyper-accumulation of starch as a pre-symptomatic indication of HLB in young seedlings. Citrus seedlings were grown in a room with controlled conditions and exposed to intensive feeding by CLas-positive psyllids for eight weeks. A quantitative polymerase chain reaction was employed to confirm the HLB status of samples. Two datasets were acquired; the first created one month after the exposer to psyllids and the second two months later. The results showed that, with relatively unsophisticated imaging equipment, four levels of HLB infections could be detected with accuracies of 72%–81%. As expected, increasing the time interval between psyllid exposure and imaging increased the development of symptoms and, accordingly, improved the detection accuracy.

Three-dimensional (3D) reconstruction of a tree canopy is an important step in order to measure canopy geometry, such as height, width, volume, and leaf cover area. In this research, binocular stereo vision was used to recover the 3D information of the canopy. Multiple images were taken from different views around the target. The Structure-from-motion (SfM) method was employed to recover the camera calibration matrix for each image, and the corresponding 3D coordinates of the feature points were calculated and used to recover the camera calibration matrix. Through this method, a sparse projective reconstruction of the target was realized. Subsequently, a ball pivoting algorithm was used to do surface modeling to realize dense reconstruction. Finally, this dense reconstruction was transformed to metric reconstruction through ground truth points which were obtained from camera calibration of binocular stereo cameras. Four experiments were completed, one for a known geometric box, and the other three were: a croton plant with big leaves and salient features, a jalapeno pepper plant with median leaves, and a lemon tree with small leaves. A whole-view reconstruction of each target was realized. The comparison of the reconstructed box’s size with the real box’s size shows that the 3D reconstruction is in metric reconstruction.