Sports complexity must be investigated at competitions; therefore, non-invasive methods are essential. In this context, computer vision, image processing, and machine learning techniques can be useful in designing a non-invasive system for data acquisition that identifies players’ positions in official basketball matches. Here, we propose and evaluate a novel video-based framework to perform automatic 3D localization of multiple basketball players. The introduced framework comprises two parts. The first stage is player detection, which aims to identify players’ heads at the camera image level. This stage is based on background segmentation and on classification performed by an artificial neural network. The second stage is related to 3D reconstruction of the player positions from the images provided by the different cameras used in the acquisition. This task is tackled by formulating a constrained combinatorial optimization problem that minimizes the re-projection error while maximizing the number of detections in the formulated 3D localization problem. Recent advances in sports science have been made possible due to the development of appropriate technology. For instance, computer-aided systems can be applied in several sports to obtain both high and low-level data about the performance of a player or team. In basketball, a typical example of low-level data is the position of a player on the court. Knowing the players’ positions reveals important information because it can be used to compute higher-order data related to technical and physical activities as well as tactical analysis. As claimed in the sports science literature , sports The proposed framework, summarized in Image acquisition is the first step required for player detection . To accoIn order to illustrate part of the player detection process, we provide in The Gaussian Mixture-based Background/Foreground Segmentation algorithm was used to do this . With thThe next step is to identify the players’ heads. To do that, the first task is to estimate the contour of a player in the binary foreground image . Due to A classification into \head” or \non-head” is the ultimate goal of player detection; therefore, the candidate points were classified by a multilayer perceptron neural network, which had previously been trained. The features used were the Histogram of Oriented Gradients, HOG  using a The centers of the circles obtained are considered as the candidate points, however, these points are not necessarily heads – they could represent a raised arm , N1, theThe chosen architecture was a multilayer perceptron feed-forward network with 10 hidden neurons in one hidden layer. The neural network was trained with a back-propagation algorithm . To traiA prerequisite task for a 3D reconstruction of a given point is to calibrate the cameras. The camera calibration aimed to estimate the parameters of each camera so we could later transform the image coordinates of the player reference point (the head in this paper) to the global coordinates associated with the court dimensions. After correcting for image distortion, a direct linear transform, DLT , was adohttp://www.kwon3d.com/theory/dlt/dlt.html. The absolute re-projection errors of the calibration points that appeared at least twice  to estimate the eleven DLT parameters. Eleven unknown variables require eleven or more equations, which means that a minimum of six pairs of points between the image 155 and the real measurements were required (because a point pair provides two equations) . The parAfter obtaining the parameters, it was then possible to reconstruct the X, Y, and Z coordinates in court space from at least two pairs of camera image coordinates.The goal of the proposed 3D reconstruction process is to estimate the 3D localization of all the players in the scene. The process is built upon a constrained combinatorial optimization problem. The underlying problem-to assign the points detected to a true player-can be modeled by the following assignment matrix:p,l is a binary (decision) variable that takes a value of 1 when an indexed image point from player head detection p is related to a labeled player l; otherwise, it takes a value of 0. To locate a given player in court space, a reconstruction must be performed by considering only the points that represent the given player. The problem here is that we do not know the labels of the detected points; in other words, the associations between detected points and players are unknown. A possible solution to that problem would be to test all possible combinations, searching for the combination that minimizes the re-projection error. Unfortunately, that is a combinatorial optimization problem that can be extremely costly in terms of computation. Moreover, an additional complication is that the number of players in the scene is unknown. Thus, to estimate the assignment matrix A, we propose a constructive greedy solution that initially locates a single player. Having located this first player, the image points related to the head of the located player are dropped out of the next interaction. This heuristic drastically decreases the number of required calculations. The 3D localization of a new player stops when no more feasible solutions are available. If there are remaining player head image points that have not been assigned yet, the method takes a priori information into account  to locate the last players on the court.where ALet us detail our approach  presentsand, (5) the maximization of the number of the assigned points, which is given by the following:The rationale behind this cost function comes from the notion that greater numbers of designated image points allow for better approximations of the players’ localizations. Of course, this is not the case for outlier points, which require additional constraints to prevent their designation. Note that the cost functions expressed in Equations (4) and (5) are in conflict because a larger number of designated players increases the re-projection error. In view of this fact, we propose to merge these cost functions into a single function, as follows:For the greedy solution, we solve the 1st round to obtain the location of the 1st player , 8 and tThe examples can be seen in Constraint (7) means that for every point p, only one player l can be assigned. Constraint (8) means that for every player, the number of points assigned must be equal to or less than the number of cameras. Constraint (9) means that for every player, the number of points assigned must be equal or greater than two (this is required for 3D reconstruction). Finally, Constraint (10) sets the maximum re-projection error in terms of pixel tolerance, and Constraint (11) imposes the head height limits. Having estimated the assignment matrix A, the reconstruction of the 3D position of each player can be obtained by solving a set of algebraic equations, as shown in As already mentioned, due to the limited number of cameras along with the requirement of having at least two points from different views for 3D reconstruction, some detected points are not assigned as players and, thus, are not located in court space. In these cases, we used a priori information to solve a DLT with a fixed mean height. The first player localized is shown by a blue circle, the second by a green circle, and the third by a red circle. These are the top optimizations. For this example, 3 players were 3D localized by optimization and 3 players was located using a priori information (the remaining points detected only in camera 1-up left) at a position near the middle of the court (the white/red diamond). For a better understanding of results presented below, the results section will be divided into 3 subsections, as named: Neural Network Classification Performance, Players Detection Evaluation and Player Localization Evaluation.The performance obtained by the neural network for the 30,009 samples in Game 1 is illustrated in Each confusion matrix depicts the occurrences of true classification (\head” classified as \head” or \non-head” classified as \non-head”), false positive classification (\non-head” classified as \head”), and false negative classification (\head” classified as \non-head”) for one of the subsets or for all subsets together. A classification was considered positive only if the head appears centralized in the square region where the HOG features were computed  performed using the DVideo  system  run by aThe results of 10,164 detections were as follows: for cameras 1, 2, and 3, respectively, the true detection rates were 78.9, 68.9, and 79.8%; the false positive rates were 2, 1.2, and 5%; and the misdetection rates were 19.1, 29.9, and 19.7%. For just the true detections, the root mean squared error (RMSE) found was 6.59 pixels.Error computation of player localization by optimization were accounted for only the players present in at least two cameras visualization inside of interest area . The RMSE of 0.16 m in plane court (axis X and Y), and RMSE of 0.18 m in space court reference . For the remaining (917 samples) point issues that were not assigned , the errors are shown an RMSE of 0.30 m in plane court (axis X and Y) and 0.33 m in space court . The processing time required to assign the players’ locations by combinatorial optimization grows exponentially according to the number of player heads detected. The computation time required was computed for the preliminary Matlab code  and is depicted in The framework presented in the previous section and its performance are discussed in this section in more detail. The steps of player detection and localization are inherent to video-based tracking methods and play an important role in tracking. Therefore, we will discuss how tracking methods reported both in the literature and our proposal address player detection and localization and how other methods determine the players’ positions compared with our approach. Systems for data acquisition in sports must be feasible and reliable; therefore, a complete and automatic solution to measuring the players’ positions on the basketball court cannot be achieved using the knowledge from only a single research field. Thus, it was necessary to integrate tools from different fields for our approach to successfully localize the players. Searching for high-interest objects in player tracking works by integrating methods from several fields. We observed that such integrations corroborate the choice of going beyond the frontiers of knowledge in any one field. Some integration examples in the literature include image processing mixed with graph representations , AdaBoosA video-based framework for automatic 3D localization of multiple basketball players was described in the context of official games. Player detection was based on image processing techniques and – considering the complexity of basketball games – the classification problem presented satisfactory results. The classification procedure was essential to properly reject head candidate points .A combinatorial optimization problem was solved with a greedy heuristic and provided satisfactory results in accurately determining both the number of players in a scene and their positions. Knowing the player’s positions in 3D relative to the court is crucial for basketball performance analysis due to the nature of the sport. This work helps to further systems development aiming to acquire 3D player position data during competitions, and the application can be extended to other indoor team sports in which a vertical component is relevant.All the datasets generated and analyzed for this study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.LMo, LD, and MM: writing-original draft preparation. LMo, LD, MM, AJ, and LMe: investigation, data analysis, and conceptualization. LMo, LD, MM, and LMe: funding acquisition.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A conservative forkhead (FH) domain and a transactivation (FoxO-TAD) domain were identified. Abalone foxo-specific siRNA  was injected to investigate the functions of foxo on glucose metabolism. Knockdown of foxo inhibited expression of phosphoenolpyruvate carboxykinase (pepck) and significantly increased expressions of hexokinase (hk) and pyruvate kinase (pk), but it failed to inhibit the relative mRNA level of glucose-6-phosphatase (g6pase). Then, a 100-day feeding trial was conducted to investigate the response of foxo and glucose metabolism in abalone fed with 1.57% , 3.82%  and 6.72%  of dietary lipid, respectively. The insulin-signaling pathway (AKT) was depressed and FoxO was activated by the HFD, but it did not inhibit glycolysis (hk) or improved gluconeogenesis significantly (pepck and g6pase). At the same time, impaired hepatopancreas glycogen storage raised hemolymph glucose levels. In conclusion, abalone foxo can be regulated by dietary lipid and can regulate gluconeogenesis or glycolysis in response to changes of dietary lipid levels, in which glycogen metabolism plays an important role.The forkhead box O (FoxO) subfamily is a member of the forkhead transcription factor family. It has regulation functions in glucose metabolism in mammals and fish. In the present study, a gene of the  FoxO proteins have multipurpose effects in animal systems, including functions related to cell survival, anti-oxidative stress, autophagy, and especially glucose metabolism [pepck) and glucose-6-phosphatase (g6pase) [gk) and pyruvate kinase (pk), and then inhibit glycolysis and glucose utilization [Forkhead box O (FoxO) proteins are members of the O subfamily of the forkhead transcription factor family . There itabolism ,3. Contig6pase) . FoxO6 ppase . Fo(g6pase) . On the lization ,9.foxo1 in turbot was isolated and characterized. Knockdown of foxo1 in primary hepatocytes inhibited the gene expression of g6pase1 and cpepck, but the expression of gk was not significantly increased [Ctenopharyngodon Idella, [Epinephelus fuscoguttatus ♀×E. lanceolatus ♂, [foxo1 had effects on adipocyte differentiation and lipolysis. Genes of foxo1, foxo3, foxo4, and foxo6 in channel catfish Ictalurus punctatus were also characterized. Furthermore, it was shown that these four foxo genes were significantly up-regulated in channel catfish after Edwardsiella ictaluri infection. It was suggested that foxo genes could play important roles in responses to bacterial infection [In fish, ncreased . Previou Idella,  and hybrlatus ♂,  found thnfection .+ and sirtuins are helpful in keeping FoxO proteins active to promote hepatic glucose production. Inversely, in fed states, the activities of FoxO proteins are inhibited in the mouse liver [Drosophila homolog of FoxO transcription factors [foxo1 was promoted compared to the normal-diet group and played a key role in mediating insulin resistance (IR) [Anabas testudineus, with long-term palmitate feeding [Oncorhynchus mykiss, hepatocytes cultured with higher concentrations of oleic acid, phosphorylation of FoxO1 was found to increase [As major targets of insulin action, transcriptional activities of FoxO proteins are suppressed by the insulin/PI3K/AKT signal pathway . In respse liver . Excess  factors . In highnce (IR) . IR also feeding . But in increase . The abofoxo homologous have been identified in many species of mollusc, including the Pacific oyster, Crassostrea gigas [Lottia gigantea [Crassostrea hongkongensis [Sinonovacula constricta [foxo in the S. constricta [foxo may be a gene related to growth traits and it may possess potential in the breeding-group selection of S. constricta. There is little research about the functions of foxo on glucose metabolism or other pathways of nutrition metabolism in molluscs.In aquatic invertebrates, ea gigas , the owlgigantea , the Honongensis , and thenstricta . Five sinstricta . All of Haliotis discus hannai, is one of the precious species of mariculture molluscs in China. It is popular for its nutritional and medicinal value. Mai et al. [Abalone, i et al.  suggestei et al. , abaloneAll animal care and handling procedures in this study were approved by the Animal Care Committee of Ocean University of China.Experimental abalones were obtained from a local seafood market in Qingdao, Shandong province, China. They were kept in the lucifugal seawater at 10–18 °C with continuous aeration, and fed with seaweed once daily for two weeks. After that, six abalone (body weight: 20.0 ± 0.1 g) were anesthetized by 5% of ethyl alcohol and used for isolation of tissues including the hepatopancreas, muscle, intestines, mantle, and gills. Samples were cleaned using saline solution and immediately frozen in liquid nitrogen, and then transferred to −80°C for the subsequent analysis.® RT reagent kit with gDNA Eraser .Total RNA in the hepatopancreas was extracted using Tissue Total RNA Isolation Kit . The purity and concentration of total RNA were measured with a Nanodrop 2000  and the ratio of 260:280 was 1.8–2.0. The integrity of total RNA was tested by agarose gel electrophoresis. Complementary DNA (cDNA) was synthesized using PrimeScriptfoxo was searched from our transcript database of abalone (unpublished data). To amplify and confirm the complete CDS of abalone foxo, primers of CDS (foxo-F 5′ ATTACATCGCAGATTGGAG 3′ and foxo-R 5′ GACCGACAACCTCCCTGAT 3′) were designed using Primer Premier 5 and synthesized . The PCR amplification was composed of 2 μL (200 ng/μL) of cDNA as template, 2 μL of each primer, 2 μL of 2× Taq Plus Master Mix , and ddH2O added to 50 μL. PCR was performed at 95 °C for 5 min then 35 cycles of 95 °C for 15 s, 59 °C for 15 s, 72 °C for 2 min, and 72 °C for 5 min. The products of PCR were separated by 1.2% agarose gel and the target band was purified using DNA Gel Extraction Kit . The purified DNA was then ligated into pEASY-T1 vector  for sequencing.The sequence of abalone foxo was aligned using the BlastX algorithm at the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/blast (accessed on 28 October 2020)) and similarity with other foxo gene sequences of different organisms was detected. The predicted protein sequence was deduced by the Expert Protein Analysis System (expasy) translate tool (https://web.expasy.org/translate/ (accessed on 28 October 2020)). The theoretical isoelectric point and molecular weight of FoxO protein were calculated by the expasy (https://web.expasy.org/cgi-bin/compute_pi/pi_tool(accessed on 28 October 2020)). The conserved domain in the amino acid sequence was predicted by the Simple Modular Architecture Research Tool (SMART: http://www.smart.emblheidelbergde/ (accessed on 28 October 2020)), and a phylogeny tree of neighbor-joining type was constructed using MAGE 7.0 software after multiple alignment of FoxO proteins using the ClustalW program in BioEdit software.The cDNA sequence of foxo gene was investigated in the hepatopancreas, muscle, intestine, mantle, and gills by Quantitative real-time PCR (qPCR) using β-actin as reference gene based on the study of Cheng et al. [Tissues of six healthy abalones were isolated. The relative mRNA level of the g et al. .foxo-specific siRNAs (http://biodev.extra.cea.fr/DSIR/DSIR.html (accessed on 28 October 2020)). T7 RNAi Transcription Kit  was used to synthesize siRNAs. The scrambled siRNA (siRNA-NC) was also synthesized according to the sequences in a previous study [2O (100 μL) and injected into the column muscle of abalone. There were six abalone in every group. In the control group, abalone were injected with 100 μL of RNase-free H2O. Then, 24 h after injection, the hepatopancreas of each abalone was collected to determine the interference efficiency by -qPCR. Afterwards, the group injected with the most-efficient siRNA was selected to detect the downstream genes expression.Three abalone c siRNAs  were desus study . The siR® II Q RT SuperMix for qPCR (+gDNA wiper)  and then diluted three times with DEPC-treated water. The resulting products were used as template for amplification. qPCR was performed in a quantitative thermal cycle .Total RNA was extracted from tissues, with the exception of the hepatopancreas, using TRIzol reagent  and the Tissue Total RNA Isolation Kit . The quality of total RNA was tested by the same procedure as mentioned above in foxo was detected to determine its tissue distribution in abalone. In the foxo-interfering test, target genes included foxo, hexokinase (hk), pk, pepck, and g6pase. The reaction was in a total volume of 15 μL , 0.3 μL (10μM) of each primer, 1 μL of cDNA, and 5.9 μL of DEPC-treated water) and underwent the following process: 95 °C for 2 min, followed by 40 cycles of 95 °C for 10 s, 58 °C for 10 s, and 72 °C for 20 s. Gene expression levels were rectified to β-actin mRNA levels according to the previous study [−ΔΔ method. All the primers used in this study were previously tested for amplification efficiency. The sequences of all primers used in the present study are shown in Gene expression of us study  and relaThe experimental diets were prepared using fish meal and soy protein concentration as the main protein sources, fish oil and soybean oil as the main lipid sources. Three isonitrogenous (30% protein) experimental diets were formulated to contain 1.57% , 3.82% , and 6.72%  of lipid, respectively Table S1g, 10 min, 4 °C). And then the plasma was collected and stored in −20 °C. Abalone hepatopancreas were cleaned using saline solution and immediately frozen in liquid nitrogen, and then transferred to −80 °C for subsequent analysis.A 100-day feeding trial was performed in the natural sea area of Pingtan, Fujian Province, China. Abalone were obtained from a local commercial farm and acclimatized to the experimental environment for 14 days. Then a completely random design was adopted to assign abalone (body weight: 10.98 ± 0.05 g) into three groups and fed with the three experimental diets, respectively. There were three replicates per group, and 60 abalones per replicate (sea cage). Abalones were fed once every 2 days. The feces and uneaten diets were removed to maintain water quality. During the feeding trial, water temperature ranged from 15 to 28 °C, pH 7.9–8.3, and the dissolved oxygen was not less than 6 mg/L. At the end of the feeding trial, the hemolymph and hepatopancreas of abalone were sampled after being fasted for three days and anesthetized by 5% ethyl alcohol. The hemolymph samples were collected and centrifuged  for 1% supernatant. Then 20 and 30 μL of supernatant was taken, respectively, for PK and HK activity analysis. In the PEPCK and G6PDH determination, 0.1 g of hepatopancreas tissue and 1 mL of extracting solution were, respectively, added into a tube to homogenize and centrifuged as mentioned above. Then 50 μL and 10 μL of supernatant was taken, respectively, for activity analysis.The glucose, insulin, leptin, and adiponectin contents in hemolymph were analyzed. The glycogen content and activities of PK, HK, PEPCK, and glucose6-phosphate dehydrogenase (G6PDH) in the hepatopancreas were analyzed. The insulin, leptin, and adiponectin contents were determined using a double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) (shellfish-specific). The kit for leptin (YX-120516S) was purchased from Sino Best Bio, Shanghai, China. Kits for insulin (ml601411) and adiponectin (ml208360) were purchased from MLbio, Shanghai, China. In the ELISA procedure, the standard 50 μL with different concentrations was added to the standard wells to make a standard curve. Then 10 μL of hemolymph sample and 40 μL of diluent were added into testing wells. The blank well was empty. Then, horseradish peroxidase (HRP)-labeled antibody (100 μL) was added into each standard well and sample well. The ELISA plate was incubated and thoroughly washed. The substrate TMB was used to develop color and the absorbance at 450 nm was determined to calculate hormone concentration. The content of hemolymph glucose (A154-1-1) and glycogen (A043-1-1), and activities of PK (A076-1-1), HK (A077-3-1), and PEPCK (A131-1-1) in the hepatopancreas were determined using commercial kits from Nanjing Jiancheng Bioengineering Institute, Nanjing, China. The kit for activity of G6PDH (BC0265) was from Solarbio, Beijing, China. For glucose determination, 10 μL of hemolymph sample was oxidized and colored by glucose oxidase and peroxidase. Then absorbance at 505 nm was determined. To analyze glycogen, 50 mg of hepatopancreas samples was put into the test tube and 150 μL of hydrolysate was added. Then the tubes were put into a boiling water bath for 20min and then diluted to 1%. Anthrone colorimetry was used for color development and concentration determination. For PK and HK determination, 0.05 g of hepatopancreas tissue was weighed and 450 μL of normal saline was added. The tissue was homogenized on ice and centrifuged , and then protein samples were diluted to 1.5 μg/μL with RIPA buffer (high). Protein samples (15 μL protein per lane) were separated by SDS-PAGE and transferred to 0.45 μm PVDF membrane  for Western blot analysis. The membrane was blocked with 5% nonfat milk in TBST buffer  for 1 h at room temperature. After being washed by TBST three times, the membrane was incubated with primary antibody overnight at 60r/min, 4 °C before horseradish peroxidase (HRP)-conjugated secondary antibodies were added and incubated for 1h at room temperature. The membrane was visualized using ECL reagents . The primary antibodies are as follows: antibodies against reduced glyceraldehyde-phosphate dehydrogenase (GAPDH) , FoxO , protein kinase B (AKT) , phospho-AKT (Thr308) (Affinity Biosciences Cat#AF3262),) and phospho-FoxO (Ser319) . The Western bands were quantified using ImageJ software.Tissues of hepatopancreas were homogenized in RIPA buffer (high)  with protease and phosphatase inhibitor cocktails  on ice. After 10 min, the homogenate was cleared by centrifugation at 12,000× T-test was used to compare with the control group and one-way analysis of variance (ANOVA) was used for the tissue distribution of foxo and the effects of different dietary lipid levels. Significant differences among groups were examined by Tukey’s multiple range test .Data are expressed as the mean ± S.E and were analyzed in SPSS 25.0. For efficiency of siRNA interference, the foxo gene was cloned from abalone and was predicted to encode 641 amino acids (aa) (GenBank Accession No: MN864138). The molecular weight of speculative protein was 69.84 KDa, and the theoretical isoelectric point was 5.23. Abalone FoxO contains a conservative forkhead (FH) domain (64–153aa) and a transactivation (FoxO-TAD) domain (590–622 aa) (Lottia gigantea (BAQ19211.1) and 47% with Crassostrea gigas (XP_011414359.1). The phylogenetic tree (The –622 aa) . The restic tree  was constic tree .foxo was detected in the hepatopancreas, muscle, intestines, mantle, and gills  significantly decreased, while mRNA levels of glucose-6-phosphatase (g6pase) was significantly higher than that in the control group (p < 0.05) (hk) (pk) (p < 0.05).At the 24th hour after injection of siRNA-335, the mRNA levels of phosphoenolpyruvate carboxykinase ( < 0.05) B,C. Both05) (hk) D and pyrhk) (pk) E had sigp < 0.05), while there was no significant difference between the other two groups (p > 0.05). The content of glycogen in the hepatopancreas (p < 0.05). The contents of leptin (p < 0.05). The level of adiponectin in hemolymph (p < 0.05). There was no significant difference in the contents of insulin (p > 0.05).The data are shown in pancreas B declinef leptin C in the emolymph D was hig insulin E among tp < 0.05) (p < 0.05) (p < 0.05) (p < 0.05), but there was no significant difference between the MFD and the HFD groups (p > 0.05)  A. Activi < 0.05) B. The MF < 0.05) C. Activi > 0.05) D.foxo (pepck (g6pase (p < 0.05) and slightly increased in the HFD group (p > 0.05). The transcriptional levels of hk (pk (p < 0.05) and hk had the lowest transcriptional levels in the MFD group (p < 0.05). No significant difference was present on gene transcriptional levels of tk (g6pdh (p > 0.05). The gene expression of glut1 (p < 0.05). In the MFD group, the relative mRNA level of pyg (p < 0.05) and gene expression of gsk-3β (p < 0.05). The transcriptional level of pgc1-α (p < 0.05). At the same time, protein levels were detected (p < 0.05). The LFD group showed the highest level of phosphorylation of FoxO (p-FoxO) (p < 0.05). The protein level of AKT increased in the MFD group and then decreased in the HFD group, but there was no significant difference (p < 0.05).The relative mRNA levels of foxo A in the o (pepck B and g6p (g6pase C increasls of hk D and pk f hk (pk E were sils of tk F and g6pk (g6pdh G among aof glut1 H increasl of pyg I was lowf gsk-3β J elevatef pgc1-α K showed detected A. The prdetected B increas(p-FoxO) C  and Pacific oyster g6pase2 (42%) [g6pase2 was also increased significantly after knockdown of foxo1 in primary hepatocytes [foxo resulted in increase in glycolytic gene expression but failed to reduce gluconeogenesis in the hepatopancreas of abalone.In mice, FoxO proteins have a synergistic effect on liver glucose production. Compared with knocking out FoxO1 alone, knocking out FoxO1/3/4 enhances higher glucose tolerance and insulin sensitivity ,36. FoxOogenesis . Previouogenesis . FoxO progenesis ,39. In te2 (42%) . And in atocytes . This sufoxo on glucose metabolism in abalone under the stress of high dietary lipid levels.Carbohydrates and lipids are two important energy sources in body and are related closely to the regulation of metabolic homeostasis. A high-carbohydrate diet might lead to a high level of plasma insulin, and further deposition of glycogen and lipids in liver and muscle. Meanwhile, it results in the dysregulation of glucose homeostasis ,42. HoweMegalobrama amblycephala [Oreochromis niloticus [Oncorhynchus mykiss [In the HFD group of the present study, the hemolymph glucose level in abalone increased, which was consistent with levels in the blunt snout bream, ycephala , tilapiailoticus , and rais mykiss . The cons mykiss ,57. Adips mykiss ,61,62,63s mykiss ,65,66. Igk) in blunt snout bream Megalobrama amblycephala with a high level of blood glucose [hk and pk were elevated compared to the MFD group. Meanwhile, the protein level of FoxO was lowest and its phosphorylation level increased. As the dietary lipid content decreased, in the present study, activities of G6PDH in the LFD group increased. G6PDH is a rate-limiting enzyme in the pentose phosphate pathway that produces NADPH for biosynthesis of fatty acids and cholesterol [pepck and g6pase) in abalone was not clear. Previous studies showed that FoxO also interacts with glucocorticoids, PGC1-α, and some factors in gluconeogenesis [pepck and g6pase. However, in mammals, there is a paradox in the regulation of PGC-1 by high-fat diet. Gu et al. [pgc1-α was significantly elevated in rats fed with a high-fat diet. In the meanwhile, activity of FoxO1 and relative mRNA levels of pepck and g6pase were higher than those in the control group. These results indicated that the regulation mechanism of HFD on the FoxO/PGC1-α pathway and gluconeogenesis in abalone are not clear and remain to be discovered.Compared with the MFD group, down-regulation of FoxO protein expression level and up-regulation of phosphorylation of FoxO indicated that the transcriptional activity of FoxO was inhibited in the LFD group. In the HFD group, however, protein expression of FoxO was significantly increased, and the two glycolytic enzymes (HK and PK) had higher gene expressions and activities than those in the MFD group. Similarly, a high-fat diet promoted gene expression of glucokinase  expressed and secreted by adipose tissue interfere with the phosphorylation of insulin receptor or insulin-receptor substrate ,83,84. Wtic mice . Liver gtic mice . Impairegsk3-β) and content raised the glucose level in hemolymph. Therefore, insulin signaling (AKT) bifurcates at FoxO and GSK3-β to regulate gluconeogenesis and glycogen metabolism in abalone. Underlying mechanisms of the synergistic effect of FoxO and other transcription factors as well as their effects on glucose metabolism under different nutrition status require further study.In conclusion, the activity of FoxO and hemolymph glucose level in abalone were elevated by high dietary lipid levels. Meanwhile, gene expression and activities of the two key enzymes (PEPCK and G6Pase) in gluconeogenesis did not increase, but under high lipid levels, impaired hepatopancreas glycogen storage (increased


Data and services are available anywhere at any time thanks to the Internet and mobile devices. Nowadays, there are new ways of representing data through trendy technologies such as augmented reality (AR), which extends our perception of reality through the addition of a virtual layer on top of real-time images. The great potential of unmanned aerial vehicles (UAVs) for carrying out routine and professional tasks has encouraged their use in the creation of several services, such as package delivery or industrial maintenance. Unfortunately, drone piloting is difficult to learn and requires specific training. Since regular training is performed with virtual simulations, we decided to propose a multiplatform cloud-hosted solution based in Web AR for drone training and usability testing. This solution defines a configurable trajectory through virtual elements represented over barcode markers placed on a real environment. The main goal is to provide an inclusive and accessible training solution which could be used by anyone who wants to learn how to pilot or test research related to UAV control. For this paper, we reviewed drones, AR, and human–drone interaction (HDI) to propose an architecture and implement a prototype, which was built using a Raspberry Pi 3, a camera, and barcode markers. The validation was conducted using several test scenarios. The results show that a real-time AR experience for drone pilot training and usability testing is achievable through web technologies. Some of the advantages of this approach, compared to traditional methods, are its high availability by using the web and other ubiquitous devices; the minimization of technophobia related to crashes; and the development of cost-effective alternatives to train pilots and make the testing phase easier for drone researchers and developers through trendy technologies. The great advantage of unmanned aerial vehicle (UAV) usage includes the exploration of locations that are difficult to access and data gathering from an aerial perspective, which makes it useful in different areas and scenarios. The countless benefits and potential of drones are strongly related to their capacity to perform tasks that are difficult, dull, and dangerous , which iThis study is motivated by the need to simplify and improve drone pilot training and usability testing, thus decreasing technophobia and the exclusion that still exist regarding trendy technologies like UAVs and AR. The great majority of training alternatives for drone pilots are performed in entirely virtual simulators, which detaches people from the feeling of piloting a real drone with a real controller in a real environment. This motivates the combination of UAVs and AR to create training experiences that enhance the real world with virtual elements.The control of UAVs and their flight dynamics tend to be difficult for new users to understand, especially for those who are not used to traditional remote controllers (RCs).Traditional AR experiences based on surface detection are limited to enthusiasts, since high-end hardware, such as HoloLens or Magic Leap 1, is required to run them. In the case of mobile devices, the oftware Development Kits (SDKs) for AR development are compatible with a specific list of devices which tend to be expensive, consequently reducing the number of users that can benefit from the technology in their daily lives.For UAV usability testing, developers usually set up simple scenarios and trajectories made of real obstacles. Every time a new trajectory is needed, the real obstacles  must be moved. Though higher obstacles bring added value to tests of aerial maneuvers, it can be difficult and dangerous to change an obstacle every time a new trajectory is required, for instance, when it is placed on the top of a high wall.New users are intimidated and afraid to learn drone piloting in environments with real obstacles due to the fear of colliding and crashing , which rWe identified and addressed the following issues:These issues can be addressed through a dedicated solution that relies on web and AR technologies to create intuitive training experiences that can be represented in a browser, which are therefore available for a great number of users. The use of AR per se allows for the creation of different virtual training activities based on real environments and of digital obstacles which make it impossible to crash the UAVs. Because of these potential advantages, we propose a multiplatform cloud-hosted solution based on cost-effective technologies for UAV pilot training and usability testing. This solution includes the easy configuration of virtual sequences to build circuits with progressive levels of difficulty and introduces users to the control and behavior of drones. These circuits are intended to be used together with real-time control alternatives to create dynamic and interactive UAV piloting practice sessions. During the training session, the system uses AR to create a trajectory based on a reactive scenario, which is defined by a set of printed markers, placed in the real world. The drone’s camera is streamed in real time to a browser application that recognizes and interprets the markers that represent visual cues, made of 3D models or textual hints, creating a predefined virtual circuit. This means that the markers, which are physical objects, are placed in a specific position in the environment, either on the floor, walls, or ceiling, whereas their meaning is represented virtually and changes according to the configuration performed by the user. This way, through the placement of some markers around a training facility, the proposed system is able to generate several trajectories that require easier or harder tasks to be performed by the pilot, without the need for changing the obstacle in the real world. The resultant system is a data-driven controller that depends on a dynamic environment, which can change according to the placement of the markers around the training facility, and the digital system configuration, which can change the trajectory without moving the markers . This waConcerning technophobia, the placement of virtual obstacles around an open area can help decrease the fear of crashing the drone since it is impossible to collide with them. The training sessions become similar to the virtual UAV simulations, though in this case in a real environment, with a real RC and a real drone.With this study, we intend to answer the following research question:Research Question: “Can AR be used in a multiplatform solution, which is highly available and adapted for neophytes, to provide a way for anyone to learn and improve their UAV piloting skills, as well as to simplify the construction of scenarios to assess UAV research?”To answer this question, we used the following methodology: First, we studied the basics of drone flight and piloting and the topic of AR, with examples related to drones, as well as Web AR. Then we conducted a review of studies related to applications combining UAVs with XR technologies and applications for UAV pilot training and assessment. Based on this knowledge, we designed an architecture to support an AR solution that can be used for UAV pilot training and testing UAV-related solutions. To validate it, we built a functional prototype that underwent different test scenarios, leading to improvements in the final system. The aim of this paper is to propose a solution that uses AR to potentiate UAV pilot training and the creation of scenarios for usability testing.A reduction of the difficulty and intimidation involved in the process of learning how to pilot a UAV, using AR to provide context and feedback about the real environment;A reduction of the fear of crashing in a real environment through the use of AR to display virtual obstacles;A reduction of the effort, danger, and level of difficulty in the creation of flight circuits through the use of AR to display virtual obstacles;The application of Web AR technologies to create achievable multiplatform, highly available, cost-effective, and easy to configure AR experiences that work in real time;A combination of human–drone interaction (HDI) and AR to create an inclusive solution that potentiates pilot training and the execution of usability testing for anyone;The creation of real-world training courses with the real-time simulation of dynamic virtual obstacles through Web AR;Help for future research about UAV control in the assessment phase through the fast montage of aerial test scenarios consisting of AR markers.This study and the proposed solution present the following contributions:This proposal considers two target user groups, pilots and researchers. With this proposal, neophyte pilots get a new tool to boost their learning process through a controlled experience with improved contexts and feedback provided by AR. At the same time, this proposal can reduce the level of complexity and effort for researchers who have to build personalized test scenarios in real environments. After placing the markers, the solution allows the user to create countless configurations and, therefore, different circuits.The contributions of this proposal mitigate the factors that drastically reduce the engagement of common users in UAV pilot activities. About the technology itself, the use of Web AR reduces the limitations found in traditional AR solutions that usually require high-end devices and promotes the application of the proposed solution in several platforms. This also promotes user inclusion, since more personal devices can run the solution. This concept that considers new strategies for feedback and contexts using AR during the control of a UAV can potentiate the user experience and help pilots to read the real environment better with the addition of virtual elements.If someone buys a drone and wants to train their skills with specific trajectories or environments cluttered with obstacles, they can print and place the markers and use this solution with their real drone. The cost of this approach is minimal compared to a reliable virtual simulation based on expensive VR systems like the HTC Vive or Valve Index . Additionally, this solution can ease the acceptance of UAVs by technologically illiterate people or nonenthusiasts through their motivation to learn to pilot real drones without the fear of collisions, since obstacles are virtual in this solution. To the best of our knowledge, there are no proposals considering the development of a Web AR solution that takes advantage of visual markers to create virtual obstacle courses and integrate a UAV test framework or a dynamic hint-based circuit to train UAV piloting skills.These contributions show the feasibility of combining HDI and AR to build a cost-effective training solution using visual markers, which opens many possibilities for future applications that can help boost multimedia experiences from an aerial perspective.Improvement of training sessions in terms of practice and engagement;Use of trendy technologies to create interactive environments and flight trajectory feedback;Autonomous creation of configurable training scenarios for UAV pilots;Simulation of obstacles to train real scenarios without the possibility of crashing;Opportunity of using of low-range  personal devices to run the experience.The proposed solution brings the following advantages besides its contributions:The piloted UAV must be equipped with a camera to stream its aerial perspective;The experience requires a device with a screen working as a GCS, besides the control interface.As for disadvantages, we find the following, which comply with the requirements for the proposed system to work:Even though, in this study, we consider the solution to be used to train pilot skills for different UAVs, we focus the proposal and validation on drones.The document’s structure is as follows: This research work explores ways to improve the way users interact with UAVs, whether they are neophyte pilots or researchers concerned about control improvements. Therefore, this section provides an overview of human–drone interaction (HDI) that summarizes the process of flight operation; an overview of augmented reality as a trendy technology that can enhance the feedback of the experience with virtual feedback; and a selection of studies that combine the previous concepts. Finally, we present the lessons learned during the research.UAVs can be useful in many fields and tasks. For instance, drones are used in surveillance to monitor road traffic in cities  or crowdWith regard to UAV flight operation, there are manual, semi-autonomous, and autonomous vehicles . Only thUAVs can reach areas of difficult terrestrial access and sometimes there are cases where the pilot cannot monitor the position of the controlled aircraft, due to line-of-sight obstructions . To solve this problem, UAVs stream their camera’s video to the control equipment for the pilots to safely fly them. The previous approach describes an exocentric perspective, which allows pilots to see what drones see through a video transmission displayed on the screen of the GCS  headset), opening the possibility to create immersive piloting AR experiences. On the other hand, the traditional approach is based on an egocentric perspective, where the pilot monitors the drone flight by looking directly at it . CompariAR is a trendy technology that can enhance the experience of controlling a UAV with new ways of providing a digital context and feedback over the representation of a real scenario. To understand the use of AR, this section summarizes its current state and provides examples of its application, approaches the way to create AR experiences and the technology required, and analyzes Web AR as an alternative compatible with several platforms.In July 2016, AR was introduced to the general public by the mobile video game Pokémon GO . This tyThe groundbreaking part of an AR experience is not to place a virtual representation over the real world, but to identify a surface or a position to which the respective graphics can be anchored. This can be achieved through RGB-D (Red Green Blue-Depth) sensors, such as Microsoft’s Kinect or common RGB cameras, which are embedded in most mobile devices. However, the latter has limitations created by the environment, as Google lists in its AR Design Guidelines  . The useThere are pattern-based markers, which depend on trained images, and barcodes that rely on a matrix that represents a number. To minimize the problems related to light conditions, the markers can be built with a more nonreflective material, instead of using a printed marker that easily creates light artifacts due to its reflectivity. After the comparison between five different black materials, which is represented in Besides surface detection and marker identification, it is also possible to create AR experiences based on Global Positioning System (GPS), such as Yelp’s Monocle, which displays the location of restaurants near the user. Another example is Media Markt, a technology company that uses image recognition to display a virtual 3D model of the products over the store catalog .To represent AR and MR experiences, there are see-through and pass-through technologies , with thBesides game development for standard VR equipment  it is also possible to create both VR and AR experiences through the frameworks of game engines like Unity and Unreal Engine with specialized SDKs  . HoweverThe Web AR concept relies on web technologies to run an AR experience in a browser application, supposedly making it compatible with every device with enough computing capabilities . There aThis technology is the subject of research in different areas. In , the autThe documents reviewed in this section were selected according to the results delivered by Elsevier’s Scopus citation database. The search query submitted to the database form was “TITLE ((uav OR drone OR quadcopter) AND (((control* OR pilot* OR simulat*) AND ) OR )) AND PUBYEAR > 2015”, which returned 246 articles. These results were analyzed and, from the total, we selected 16 papers that were relevant for the scope of this research work. The following sections present and discuss these papers.Beginning with VR, Mangina et al.  proposedRegarding AR, in , ManzoliLiu et al.  proposedAs for MR, Llasag et al.  studied Bruzzone et al.  presenteThe related work shows that researchers are interested in obtaining the best from immersive reality to apply in different UAV case scenarios. One of the main goals is to combine a real UAV with a virtual replica of a specific scenario or activity in an application that enhances the perspective of the pilot over the task in hand in real time, improving the pilot’s awareness of the location of the drone and, therefore, the precision of the control. AR and MR are used to add a layer of graphical information over the real world that can be about the UAV or the environment captured by its camera. The review of the literature provides relevant insights for our current work, regarding how UAVs and the reality spectrum are combined, as well as the training methodologies and the approaches used to evaluate them. This is an interesting combination due to the aerial view that is now easily achievable and used to obtain new perspectives, either for cultural  or professional purposes . Another relevant observation is found in , a paperAs part of this study, we intend to propose a solution that implements a new concept that relies on the use of AR to bring new contexts and feedback to pilots and UAV researchers. The goal is to improve pilot training and decrease the effort needed to prepare a test scenario in real environments, with real UAVs, but with virtual obstacles and goals instead. There are few papers in the literature that combine the real and the virtual worlds through AR, or MR, and web technologies, and to the best of our knowledge, there is no AR application for UAVs with the objective of creating virtual courses over the real world to train UAV pilots or help researchers or developers assess their prototypes. These arguments present a great opportunity and support for the development of a solution that exploits AR and web technologies to improve the perspective of the pilot over the control of a UAV.There are problems in the control of UAVs that affect both neophyte pilots and researchers. Some of them relate to the difficult learning process and others to the preparation of a scenario to practice or assess research work related to HDI. Considering these problems, the current paper proposes a solution that intends to improve the engagement of anyone in UAV piloting and decrease the complexity and effort found in the creation of a personalized flight scenario. This section presents the description of the proposal, the architecture to support it, and the implementation of the solution.This proposal is the first step of a complete solution to help both piloting novices and developers or researchers working with UAVs. This work is not about the development of a control solution. Instead, it proposes a solution to complement a control session in real time, where the environment is real, but the obstacles or trajectory indicators are virtual.Therefore, this solution focuses on two topics. One of them is to facilitate the process of learning how to fly manual and semi-autonomous drones or improving piloting skills. The other is to create an easy-to-use obstacle tool to test the research and production of UAV solutions, including new controllers or intelligent systems for autonomous flight tasks, such as obstacle avoidance or flight path calculation. We propose a system to generate virtual courses, made of virtual points of interest (POIs), which can be adapted to constitute activities resembling a common UAV obstacle course, a guided tour, or a treasure hunt activity. This system can act as a step-by-step method for introducing new commands and maneuvers to the users, or simply to produce hybrid courses  for researchers and developers to perform evaluation metrics. The POIs of the courses are identified through AR markers, which are printed and placed over a predesigned scenario. The user is guided through a sequence of markers ensured by dynamic representations that are virtually placed over each marker, according to the specific circuit. The use of markers and their respective detection allow for the system to keep track of the progress of the pilot. For instance, the pattern of each marker represents an integer value that is used as an identification number, which estimates the location of the aircraft, according to the configuration of the sequence and the detection event. This detection event can also be used to count flight times, such as how long a pilot takes to arrive at a specific marker or to finish the circuit. This approach facilitates the time-counting process and makes it unnecessary to integrate location or detection technologies, such as GPS or proximity sensors, both in the drone and in the real POIs or obstacles, if relevant. With the same scenario and markers, it becomes possible to create several different trajectories. The solution can configure dynamic sequences based on each marker’s identification number to define levels of progressive difficulty. The proposed approach can digitally change the circuit , without changing the placement of the markers. The same would not happen with real objects. Moreover, the system can change the interpretation of each marker to comply with different sequential challenges. For instance, in a training activity inspired by a treasure hunt, a marker that in a certain circuit was the last POI, in another one can act as an intermediate step of the trajectory and suggest that the pilot move right to find the next POI. Briefly, the virtual course application is represented by the feedback loop in Although the two modes converge as training activities, they were developed considering different needs. Whilst the mode inspired by a treasure hunt was considered for users to learn different sequences of piloting tasks through hint-based trajectories, the simple obstacle course was thought of as an improvement for developers that want to test the usability of their UAV solutions with the same conditions and define a low-cost scenario with printed markers. With these approaches, we aim for a solution that can help the participants improve their control skills, in terms of dexterity and keeping track of the direction the drone is facing. In a general way, as Whilst the treasure hunt presents arrows pointing the way, so that the users can follow a trajectory, the obstacle course presents one or more types of obstacles , so that the users must dodge the hazards to avoid a virtual crash. In the case of the treasure hunt activity, the goals are to learn the basic flight mechanics and improve maneuverability and the capacity to follow well-defined paths as fast as possible, whilst the goals of obstacle courses are to improve reaction time and the decision capacities of the user to plan a new trajectory.The architecture of this solution is composed of the user, a drone with a camera, and a computer connected through the Internet to a cloud platform, as presented in This architecture represents a solution to be used as a new data-driven feedback layer over the control layer. The integration of the solution in the control layer and its parameters is not described in detail, as the focus of this work is the validation of an AR solution to potentiate the control, and therefore training activities. A proposal for the architecture that describes the integration between the controller and the proposed solution is illustrated in There are implementation differences depending on the system modes. Whilst in an obstacle course every marker is configured as an obstacle, the treasure hunt mode follows a sequence that is updated in real time as the UAV approaches a marker that corresponds to the correct next index of the current sequence. The logic for the implementation of the treasure hunt mode is presented in There are four states for the markers: Invalid, Validated, Current and Next. Each time a marker in the Next state is detected, the previous marker changes its state to Validated. This marker is defined as Current and the next one is defined as Next. This way, each marker must have references to the previous and the next ones. The remaining markers are identified as Invalid. The meaning of each state is summarized in Besides, a marker must know the direction of the next one, to display an arrow for the user to follow correctly. The direction is represented with an integer value, from 0 to 7, which corresponds to the cardinal directions: north, northeast, east, southeast, south, southwest, west, and northwest. The negative values indicate that there are no previous/next markers. Each marker is also classified as first, default, or last to describe their role in the level. This represents different graphics on the start and finish markers to determine if the level is about to start or end, based on their representation.The prototype built in this study follows the proposal and its implementation to validate the concerns of this paper, which are to provide a way to improve piloting skills and to assess UAV prototypes created by investigators or developers. Therefore, this section includes the description of the prototype and its evaluation.The control of a real UAV is not included in the developed prototype, since the focus of this work is the development of a solution complementary to the control, based on the enhancement of real-world images captured in real time with virtual elements.The solution developed in this study uses web-based technologies, so it can be used in the largest number of compatible platforms possible, with the unique implementation of a cloud-hosted web system. Every device with a web browser and a camera should be capable of running this prototype, unlike AR development platforms, such as ARCore or Vuforia, which require devices with special support. To simulate the operation of a modularly built UAV with an open flight controller, we prototyped a portable system made of a Raspberry Pi 3 model B small computer, powered by a power bank, which streams the video feed of its Raspberry Pi camera through Wi-Fi to the real-time browser application, in order to create a pass-through AR experience. A computer is connected to the same local Wi-Fi network as the Raspberry Pi and the video stream is transmitted through WebRTC, which enables real-time communication with the camera of the drone through a web browser. To build the browser application, we used HTML5 to create the GUI, and JavaScript to implement the functionalities. The libraries AR.js  and A-FrThe treasure hunt circuit is made of four models that describe the status of each POI. A green check model shows that the POIs were already visited in the correct order, whilst a blue arrow is shown in the current marker to point in the direction of the next one. When the player visits a new marker in the wrong order, a red cross is presented. Finally, the last marker presents a checkered flag to show the user that it is the end of the trajectory. The obstacle course was implemented considering a sequence of markers distributed parallel to the ground to create an obstacle course made of street cones. The goal is to define a flight path for the pilot to accomplish, as in The development of the prototype was divided into iterations that helped improve the result. The assessment was separated into two phases. In the first phase, the solution was tested to validate the implementation logic and the success of the intended functionalities. Then, the performance of the prototype itself was evaluated to measure the quality of the experience built with Web AR. This section presents the two assessment phases and their results.The validation of this solution began with the development of a virtual simulation of the proposed solution using the Unity game engine . The objTo evaluate the operation of the proposed solution, we created several distinct scenarios with 6 barcode markers, in which the first step was to build the sequence and define the level with the following order: 2, 6, 3, 5, 1, 4. The first scenario corresponds to a 2 by 3 grid, displayed on a single wall, as shown in To assess the developed prototype, we designed and performed three different tests. In the first one, the objective was to verify how the marker detection technology behaves with different distances, perspectives, and camera specifications. The second one was intended to understand how fast a user could react to the representation of a virtual element, from the moment the solution detects a marker. The third evaluates, with users, the improvements that AR visual hints can bring to UAV training circuits relative to the perception of the trajectory.Test 1—Detection of the marker according to different distances, perspectives, and camerasFirst, to evaluate the suitability of the technology used in the implementation, we defined three test scenarios based on a marker, measuring 18.5 cm by 18.5 cm, printed on an A4 page. In the first scenario, the marker was fixed to a wall, perpendicular to the ground, in an outdoor scenario with cloudy weather. The second one was similar, but the marker was fixed onto the wall of an indoor garage with standard incandescent lights, during the night. In the third one, the marker that was fixed on the ground was overflown by a drone with its camera pointing down. These tests were used to assess the efficiency of the detection with different distances and inclinations.This experiment had four sets of seven iterations with three different devices: a low-mid range smartphone Xiaomi MI A2, an out-of-the-box DJI Phantom 4 drone , and a sThe smartphone and the drone captured pictures with 9 MP. Every iteration consisted of taking 10 pictures from different angles and positions, with the defined distances of 1 m, 2.5 m, 5 m, 7.5 m, 10 m, 12.5 m, and 15 m. In the case of the aerial drone footage, the distance was measured with the DJI GCS app, DJI GO 4. So, the first scenario resulted in 140 pictures, 70 captured with the Raspberry Pi and 70 with the smartphone. The second scenario resulted in 70 pictures, captured with the Raspberry Pi, and the third scenario in 70 pictures, captured with the DJI drone. These pictures were loaded to the Web AR application to validate the detection and the results are represented in These results show the percentage of successes for each iteration case. In the case of 1 m apart, every iteration was successful, except for a picture captured by the Raspberry Pi in the garage, which presented a shadow crossing the marker. In the case of 2.5 m apart, every iteration was successful. In the case of 5 m apart, only the pictures captured by the smartphone were fully successful. In the case of the pictures captured by the Raspberry Pi outside and in the garage, the system did not detect the marker in 1 of them, both presenting a success rate of 90%. The drone presented the worst results, with only 2 detections out of 10. In the case of 7.5 m apart, both the Raspberry Pi outside and the smartphone presented a 50% success rate, whilst the Raspberry Pi in the garage shows better results, with 7 detections out of 10. The drone did not provide any detection with an altitude of 7.5 m. In the case of 10 m apart, the detection only happened in 2 pictures of the smartphone. In the cases of 12.5 m and 15 m, there was no detection. The smartphone samples were the only ones that could detect AR markers up to 10 m, whilst the samples from the DJI Phantom drone could only detect the AR markers up to 5 m. Additionally, the smartphone presented the best results for detection with the longest distance , with 2 In this context and according to the performance of the detection system, the results are interpreted as follows. The true positives (TPs) are the samples with detected AR markers; the true negatives (TNs) are the samples without AR markers and no detection; the false positives (FPs) are the samples without AR markers and detection; and the false negatives (FNs) are the samples with undetected AR markers. Among the iterations, there are no samples without AR markers, which means that there are no TNs or FPs. These circumstances influence the precision and recall. The precision value is 1 for every iteration because there are no false positive samples, since it considers the TP samples relative to all the positive ones . On the other hand, the recall corresponds to the normalized success percentage that describes the results of the 10 samples for each distance, so the higher the recall, the better the performance of the system. This value considers the TP values relative to the total of relevant samples . These values are represented in By analyzing the pictures in which the marker was not detected, we realize that shadows that partially change the color of the marker can negatively affect its segmentation and the subsequent detection. The solution is intended to work according to the frames obtained from a video feed, preferably of 30 frames per second. This means that in a second, the algorithm can receive 30 images to process, and some can be blurred or present light artifacts, but there should also be good frames with a clean capture of the marker.The solution works from different angles and distances, which can go up to 10 m with the smartphone. This supports the development of a cost-effective AR experience for UAVs, which can be used for pilot training or the assessment of related solutions in a dynamic virtual environment, and that saves the user from spending money on physical obstacles for flying courses. Additionally, the success of the solution will depend on the quality of the camera and size of the marker. In this case, for cost-effective equipment, the identification of the marker is achievable from 7.5 m, which suggests that with a better camera and bigger marker, the solution would work from a longer distance.Test 2—Measurement of the user reaction time after the detection of a markerNext, to evaluate if the use of the solution was plausible in real time, we defined a test scenario consisting of a simple AR web application, a laptop, and a printed marker. This test was designed to measure how long a user would take to react after the system identified a marker and displayed the respective virtual element.In the web application, we included the basic AR functionalities required in the development of the solution, which are the detection of a marker and the representation of a 3D model. In this application, we added a button to register timing in the event of a click. The time interval (∆t) considered for the measurements starts with the event triggered by the AR.js library when a marker is found and ends with the click of the register button, as in the following Equation (1). Each timing includes the delay from the mouse input.The web application was deployed locally, since the implementation of the system followed the pure front-end approach, which means that the AR part of the solution always runs on the client device. So, for these measurements, it is not relevant if the solution is deployed locally or whether it is cloud hosted. The experiment was performed in daylight conditions, using a laptop, its webcam, and a barcode marker. With the application running, we passed the marker in front of the webcam and clicked on the register button when the 3D model was presented. The samples were collected during the daytime, on a sunny day, in a room filled with natural sunlight. From the 108 registered samples, we discarded eight outliers that resulted from input errors, resulting in 100 samples, presented in The average reaction time for the 100 samples was 332.5 ms, as represented in Test 3—User testing to validate AR hints as an improvement in a simulated circuitTo validate the proposed training solution with users, we defined two test scenarios based on the same environment, presented in the virtual simulation created in Unity, where a quadcopter can be fully piloted through a keyboard or a gamepad, which is similar to the real UAV controllers . The simThis assessment intends to validate if the addition of dynamic AR visual hints with the POIs of an unknown trajectory improves to the flight times. With that purpose, this test considers the times registered for the first time the participants complete the two configured circuits, that correspond to the two test scenarios, represented by levels 1 and 2 in the simulation. The first level corresponds to a circuit made of 26 POIs, which create a trajectory of 443.56 m. The second level corresponds to a circuit made of 26 POIs as well, which creates a trajectory of 432.42 m.In both levels, the participants must detect the markers in the correct order, which is represented with text above the markers, in this case, from 1 to 26. When a participant detects a marker, the simulation checks if it is part of the defined trajectory, as the next POI to visit, or not. These two situations trigger an audio feedback that represents success and failure. Additionally, when the participants detect the first marker, the stopwatch available in the simulation starts counting. When the participants arrive at the 26th marker, the recorded time is presented on the screen in the format “00:00:00:000”, showing hours, minutes, seconds, and milliseconds. The data were converted to minutes and rounded to three decimal places, to facilitate their presentation and interpretation.The difference between the two levels is the absence or presence of the visual hints. Level one only presents the markers with a numeral indicator on top, representing the order of the trajectory. As the solution proposed in this paper, level 2 simulates the addition of AR visual hints when the drone gets close to the markers, as presented in As for the simulated quadcopter, it flies with a maximum speed of 6 m/s, either when moving along its horizontal plane or increasing and decreasing its altitude, and it rotates 90 degrees per second along its vertical axis, to perform turns leftwards and rightwards.This evaluation was performed by 44 participants, 31 men and 13 women, with ages from 18 to 58. After the experience, each participant answered a Google Forms survey to upload the results, which are presented in The third sample had the highest values , whilst the sample with the lowest values, besides the control sample, was sample 33 . There are 5 samples that can be identified as outliers, which correspond to users with less dexterity and less experience in control interfaces . Samples 13  and 35  describe the results from two participants that gave up from level 1, due to not being able to find the marker identified by the number 4, then finished level 2 and returned to finish level 1, failing to comply with the test rules. The average from level 1 is 4.061 min , whilst the average from level 2 is 2.052 min . The comparison of the average values shows that the participants were 1.979 times faster traveling the trajectory of level 2, which includes the simulation of the AR visual hints. From the 44 participants, 5 reported that having to search for the trajectory in level 1 was annoying, but then enjoyed flying in level 2, following the AR visual hints. Additionally, 8 participants complained about the controls not being intuitive, which is a common feeling among users that are not used to the piloting of UAVs.The results greatly support the use of AR visual hints to improve the performance of the participants learning to fly along a trajectory. The addition of the visual hints  in level 2 substantially decreased the time required to finish a similar trajectory, within the same environment configuration.This study resulted in the proposal of a solution that enables a novel AR application for UAV pilot training and assessment, considering the remote control of the vehicle from a non immersive exocentric point of view. With this solution, the user can decide how to control a UAV based on the images captured by its camera, augmented with virtual content, and displayed on a screen. Using Web AR technologies to develop the prototype, it was possible to create a cloud-hosted AR experience theoretically compatible with all devices supporting a camera and a web browser. With this solution, it is possible to create several digital scenarios in the same real-world environment, each one with challenging paths, new symbology, and tasks. All of this can be used for learning and to improve UAV piloting skills, as well as to help developers and researchers create the required test environments. This study can help mitigate some of the problems found in HDI resulting from the technophobia related to crashes and the difficult and dangerous process of creating test scenarios with obstacles placed in high places. In the end, the proposal helps pilots and researchers and provides an inclusive solution to improve the engagement of any type of user, from neophytes to enthusiasts, in UAV-related activities. This proposal is particularly useful for researchers because it greatly improves the approach of creating countless trajectories for different test scenarios after a single placement of AR markers in a real environment, which can be configured with a new meaning.This proposal, focusing on UAV control, can also be applied in other areas, such as navigation for outdoor digital guided tours. This novel approach presents advantages over the traditional methods of AR and UAV training, which is usually performed in entirely virtual simulators. One of them is to pilot real UAVs with real controllers, which delivers an authentic experience instead of a simulation. This solution, based on trendy and emergent technologies, is easily available thanks to its multiplatform approach. It is cost-effective and establishes an easy-to-use solution to potentiate user training through an AR representation of dynamic courses made of visual markers. The use of virtual obstacles reduces the fear of crashing the aircraft, which can improve the engagement of novice users. These characteristics intend to potentiate the engagement of the users and, therefore, help in the mitigation of the exclusion that still exists in trendy technologies, related to technophobia issues or approaches limited to expensive equipment that is usually not available for the common user.The solution was implemented and the resulting prototype underwent three different tests. The first test allowed us to validate the marker detection technology in different environments, with different light conditions and with different cameras. The results confirmed the capacity to detect a marker from a distance of 10 m, though shorter distances had greater success, even from different perspectives. As the second test illustrated, the solution allows users to take a third of a second on average to react to a stimulus of a virtual element represented over a marker after its detection. These results validate the use of this solution in real time without jeopardizing the experience of training with a real drone in a real environment that is enhanced with virtual elements through Web AR technologies. This enhancement is verified with the results of the third test, based on a virtual simulation experienced by 44 participants, that showed a significant decrease in the time required to fly along a circuit when using AR visual hints.As part of future work, the prototype should be improved and integrated into out-of-the-box drones, such as the DJI Phantom and other DIY vehicles included in this study. Another future task should be to perform more user-experience studies in real scenarios and real UAVs to understand the impact of the proposed solution with realistic characteristics, to validate the results obtained in Test 3 through the simulation. A study should be conducted with a group of subjects with different levels of UAV piloting skills to determine their individual improvement rate, based on how long they take to finish several AR trajectories, which can be measured through the already developed marker detection system. Additionally, a questionnaire should be completed by the participants of the test experience to evaluate the quality and usability of the solution through the Likert scale.


Escherichia coli, at concentrations suitable for human use. Much research into finding alternative enzymes to NfnB has resulted in the identification of the Xenobiotic reductases, XenA and XenB, which have been shown in the literature to reduce environmentally polluting nitro‐compounds. In this study, they were assessed for their potential use in cancer prodrug therapy strategies. Both proteins were cloned into the pET28a+ expression vector to give the genetically modified proteins XenA‐his and XenB‐his, of which only XenB‐his was active when tested with CB1954. XenB‐his was further modified to include a cysteine‐tag to facilitate direct immobilization on to a gold surface for future magnetic nanoparticle DEPT (MNDEPT) treatments and was named XenB‐cys. When tested using high‐performance liquid chromatography (HPLC), XenB‐his and XenB‐cys both demonstrated a preference for reducing CB1954 at the 4‐nitro position. Furthermore, XenB‐his and XenB‐cys successfully induced cell death in SK‐OV‐3 cells when combined with CB1954. This led to XenB‐cys being identified as a promising candidate for use in future MNDEPT treatments.Directed enzyme prodrug therapy (DEPT) is a cancer chemotherapy strategy in which bacterial enzymes are delivered to a cancer site before prodrug administration, resulting in prodrug activation at the cancer site and more localized treatment. A major limitation to DEPT is the poor effectiveness of the most studied enzyme for the CB1954 prodrug, NfnB from  Both recombinant XebB‐his and XenB‐cys successfully induced cell death in SK‐OV‐3 cells when combined with the nitroaromatic prodrug CB1954. This led to XenB‐cys being identified as a promising candidate for use in cancer prodrug therapy treatments. This was then followed by a final hold at 4°C. The PCR products were purified as previously described  was performed using t4 DNA ligase  at 16°C overnight. To confirm successful ligation between the gene of interest and the plasmid vector, a PCR based on the T7 promoter and T7 terminator sequences, which flank the gene insert region contained within the vector, was performed using Taq DNA Polymerase Master Mix . To amplify the recombinant plasmids, competent E. coli DH5α cells (200 µl) were transformed with a plasmid (~10 µl) and incubated on agar plates containing Kanamycin (50 µg/ml). Kanamycin is used to select for bacterial colonies containing the pET28a+ plasmid with the KanR gene.After being purified, ligation between the PCR products and a pET28a2.2xenA and xenB genes into E. coli Rosetta pLysS  was done following the method described previously by Ball, Thompson, Anderson, Gwenin, and Gwenin  containing CB1954 (20 µM) was added. Next, medium (50 µl) containing the purified enzyme was added and after 4 h, the medium was removed, and cells were replenished with complete DMEM (100 µl). After 48 h, 20 µl of MTT (5 mg/ml) was added to each well and incubated at 37°C for 4 h. The formazan crystals that formed were dissolved in 100 µl of DMSO, after removing the media carefully, and the absorbance was read at 570 nm in a Thermo Scientific Varioscan 96‐well plate microplate reader.Cell viability experiments were conducted using an MTT assay Mosmann, , followi33.1xenA and xenB genes were successfully amplified using PCR, purified, and inserted into the pET28a+ expression vector, which adds an N‐terminal histidine tag (his‐tag) for ease of protein purification. The recombinant plasmids containing the xenA and xenB genes were sequenced verified to confirm their identity before they were used in other experiments.Both the 3.2P. putida XenA and XenB cloned into the pET28a+ vector. The XenB‐cys, however, was genetically modified using PCR to contain an additional cysteine‐tag (designed by our research group) . XenA‐his and XenB‐his are the native While NTR genes express a homodimeric flavoprotein  in a similar manner  and a lower efficiency in its reaction with the prodrug compared to XenB‐his (Kcat/Km = 0.0039 μM−1 S−1 compared to 0.0049 μM−1 S−1 for XenB‐his) despite having a much higher affinity for the prodrug (Km = 457 μM compared to 1690 μM for XenB‐his). When compared to the kinetic data observed when testing NfnB‐his with the CB1954 prodrug, it is evident that both XenB‐his and XenB‐cys demonstrate a superior affinity for the prodrug as they display a much lower value for Km (Km = 4064 μM for NfnB‐his). However, both XenB‐his and XenB‐cys were shown to be less efficient in their reaction with CB1954 than NfnB‐his as they were shown to have lower values of Kcat/Km (Kcat/Km = 0.0062 μM−1 s−1 for NfnB‐his). The lower Km values produced when using XenB‐his and XenB‐cys compared to NfnB‐his is a result of clinical significance as the maximum tolerated dose of CB1954 in humans is less than 10 μM CB1954  and XenB‐cys (6:94) were both extremely similar to one another, and in both cases, the enzyme demonstrated a preference for reducing CB1954 at the 4‐position to give the 4‐NHOH as the major product of the reaction. This is a result of clinical significance as the 4‐NHOH derivative of CB1954 has been shown in the literature to be further reduced by intracellular thioesters such as Acetyl Coenzyme A to form a compound that can cross‐link DNA  cause cell death in Sk‐OV‐3 ovarian cancer cells when reacted with the prodrug.The major aim of this research was to clone and express genetically modified Xenobiotic reductases from 2 position and induced increased cell death in the ovarian cancer cell line SK‐OV‐3.All of the aims were achieved and XenB‐cys identified as a promising candidate for MNDEPT. Despite the poor activity of XenA‐his leading to this enzyme being dropped from the study, XenB‐his and subsequently XenB‐cys have been shown to reduce the CB1954 prodrug. Both proteins showed a preference for reduction at the more desirable 4‐NO2 position was a significant result because intracellularly the 4‐NHOH derivative of CB1954 is further reduced by thioesters to a DNA crosslinking species which is highly cytotoxic  than the 4‐NHOH product. In the case of XenB‐his and XenB‐cys, a negligible amount of 2‐NH2 was obtained, which indicated that little‐to‐no 2‐hydroxylamine had been formed and that both proteins reduced CB1954 almost exclusively at the 4‐NHOH position. It should be noted that while reduction at the 4‐NO2 position is more favorable in terms of generating a more potent DNA‐crosslinking product, literature has suggested that there may also be negatives to using enzymes that favor 4‐NO2 reduction over 2‐NO2 reduction because of the significantly higher bystander effect of the products formed after CB1954 is reduced at the 2‐NO2 position  to see if this leads to any loss of activity with the CB1954 prodrug or a change in the performance in cell viability assays.In conclusion, two Xenobiotic reductases were identified, cloned, expressed, and tested with the CB1954 prodrug. Unfortunately, the XenA‐his reductase was shown not to reduce the CB1954 prodrug at detectable levels, even though it was capable of reducing nitro‐glycerin. Together with other literature, it was thus hypothesized that this enzyme was unable to reduce nitroaromatic compounds. XenB‐his and subsequently XenB‐cys were shown to be able to readily reduce CB1954, primarily at the 4‐NONone declared.Patrick Ball: Formal analysis ; writing – original draft (lead). Jennifer Halliwell: Formal analysis ; writing – review & editing (supporting). Simon Anderson: Formal analysis (supporting); writing – review & editing (supporting). Vanessa Gwenin: Writing – review & editing (supporting). Christopher Gwenin: Conceptualization ; formal analysis ; funding acquisition ; supervision (lead); writing – review & editing (supporting).None required.


Rationale: Aberrant androgen receptor (AR) signaling via full-length AR (AR-FL) and constitutively active AR variant 7 (AR-V7) plays a key role in the development of castration-resistant prostate cancer (CRPC) and resistance to hormone therapies. Simultaneous targeting of AR-FL and AR-V7 may be a promising strategy to overcome resistance to hormone therapy. This study aimed to identify novel drug candidates co-targeting AR-FL and AR-V7 activities and elucidate their molecular mechanism of anti-CRPC activities.Methods: Using a CRPC cell-based reporter assay system, we screened a small library of antimalarial agents to explore the possibility of repositioning them for CRPC treatment and identified bruceantin (BCT) as a potent anti-CRPC drug candidate. A series of cell-based, molecular, biochemical, and in vivo approaches were performed to evaluate the therapeutic potential and molecular mechanism of BCT in CRPC. These approaches include reporter gene assays, cell proliferation, RNA-seq, qRT-PCR, mouse xenografts, co-immunoprecipitation, GST pull-down, immobilized BCT pull-down, molecular modeling, and bioinformatic analyses.Results: We identified BCT as a highly potent inhibitor co-targeting AR-FL and AR-V7 activity. BCT inhibits the transcriptional activity of AR-FL/AR-V7 and downregulates their target genes in CRPC cells. In addition, BCT efficiently suppresses tumor growth and metastasis of CRPC cells. Mechanistically, BCT disrupts the interaction of HSP90 with AR-FL/AR-V7 by directly binding to HSP90 and inhibits HSP90 chaperone function, leading to degradation of AR-FL/AR-V7 through the ubiquitin-proteasome system. Clinically, HSP90 expression is upregulated and correlated with AR/AR-V7 levels in CRPC.Conclusion: Our findings suggest that BCT could serve as a promising therapeutic candidate against CRPC and highlight the potential benefit of targeting AR-FL/AR-V7-HSP90 axis to overcome resistance caused by aberrant AR-FL/AR-V7 signaling. C4-2B-MDVR cells were kindly provided by Dr. Christopher Evans (UC Davis). Cells were tested regularly for mycoplasma contamination by qPCR and authenticated by STR analysis. 22RV1, C4-2B, C4-2B-MDVR, and LNCaP were grown in RPMI 1640 supplemented with 10% fetal bovine serum (FBS). DU145, PC3, VCaP, and 293T cells were cultured in DMEM with 10% FBS. RWPE-1 cells were cultured in KSFM containing 5 ng/mL of EGF and 25 μg/mL of bovine pituitary extract. For hormone starvation, cells were cultured in RPMI supplemented with 5% dextran-coated charcoal (DCC)-treated FBS for 72 h prior to vehicle, dihydrotestosterone (DHT), or inhibitor treatment.22RV1 cells cultured in DCC-FBS medium were transfected with MMTV-LUC and pRL-SV40 (Promega) using Lipofectamine 3000 (ThermoFisher Scientific) and plated into 96-well plates. After 48 h transfection, cells were treated with vehicle, 20 nM DHT, compounds , or 20 nM DHT with compounds for 24 h and then lysed for dual luciferase reporter assays (Promega). Compounds screened in this study are listed in 22RV1 cells were cultured in full medium  and treated with DMSO or 10 nM BCT for 24 h. Total RNAs were purified using RNeasy Plus Mini Kit (Qiagen). RNA-seq libraries from 1 μg of total RNA were prepared using TruSeq RNA Library Prep Kit v2 (Illumina) according to the manufacturer's instructions. The RNA-seq libraries were sequenced as 100 bp paired-end reads on an Illumina HiSeq 2500 with HiSeq PE Rapid Cluster Kit v2 and HiSeq Rapid SBS Kit v2 (Illumina), generating an average of ~120 million reads per sample. RNA-seq reads were filtered and trimmed using Trimmomatic and were aligned to the human reference genome (hg19/GRCh37) using RNA-seq read mapper HISAT2 (v2.1.0). Mapped reads were assembled using StringTie (v1.3.4d), and gene expression levels were calculated as fragments per kilobase of transcript per million mapped reads (FPKM). Differentially expressed genes were determined based on the false discovery rate (FDR) threshold 0.01 and log2 fold change cutoff of 0.85 and clustered with Hierarchical Clustering algorithm.6 of 22RV1-LUC cells, which stably express luciferase and hygromycin-resistant genes, suspended in 100 μL Matrigel/PBS (50:50 mixture) were injected subcutaneously into the left flank of 6-week-old male athymic BALB/c nu/nu mice . Once the tumor sized reaches 50 to 100 mm3, tumor-bearing mice were randomly assigned to two groups  and treated intraperitoneally (once every three days) or orally (once every two days) with vehicle or BCT (1 mg/kg or 2 mg/kg body weight). For oral gavage administration, BCT was dissolved in 1% sodium carboxymethyl cellulose (Na-CMC) and 1% Tween 80 vehicle solution. Tumor volumes were measured twice a week in three dimensions using a caliper as described previously 6 of 22RV1-LUC cells were subcutaneously injected into nude mice. After 3 weeks, subcutaneous 22RV1 xenograft tumors were harvested, washed with PBS containing 2x penicillin/streptomycin solution (Gibco), minced with sterile scalpels, and further digested with Liberase TM research grade (Sigma-Aldrich) for 1 h. The pure 22RV1-LUC cell population (22RV1-LUC-T1) was obtained by subculturing 5 times in the presence of hygromycin (200 μg/mL) to remove host cell contamination. For metastasis experiments, 22RV1-LUC-T1 cells (5x105 cells) were injected into the left cardiac ventricle of 6-week-old male nude mice guided by ultrasound . The mice were randomly split into two groups  and treated orally once every two days with vehicle or BCT (2 mg/kg body weight). Bioluminescence imaging was performed weekly to monitor tumor metastasis. Animal experiments were conducted with the approval of the Institutional Animal Care and Use Committee of Laboratory Animal Research Center at Samsung Biomedical Research Institute.Mouse subcutaneous xenograft experiments were performed as described in our previous study 2O and then resuspended in coupling buffer . BCT (5 mg) was dissolved in coupling buffer and conjugated with Profinity epoxide resin at room temperature for 18 h. After washing with coupling buffer, remaining active epoxide groups were blocked with 1 M ethanolamine (pH 8.0) for 4 h. The BCT-conjugated beads were washed with coupling buffer, wash buffers , and PBS. The control unconjugated Profinity beads were prepared as described above without addition of BCT. The control or BCT-conjugated beads were incubated with cell lysates overnight and washed with FLAG lysis buffer. Bound proteins were detected with specific antibodies as indicated in figures.Profinity epoxide resin (Bio-Rad) was swollen and washed in ddHThe depletion of HSP90 was performed by transfection of 22RV1 cells with either non-specific (NS) siRNA or siRNA against HSP90α/β using Lipofectamine 2000 (ThermoFisher Scientific). The siRNA sequences used are listed in http://www.ccp4.ac.uk/MG/).Molecular modeling studies were performed using the Schrodinger Suite (Schrodinger Inc.). The X-ray crystal structure of bruceantin extracted from the Protein Data Bank (PDB code: 3G71.pdb) was processed for further docking studies using LigPrep module in OPLS_2005 force field with tautomer states. The protein structure of HSP90 (PDB code: 1BYQ.pdb) was optimized using the Protein Preparation Wizard module. The receptor grid box was generated using the Glide module followed by docking study with Standard Precision mode. The OPLS_2005 force field and all default parameters were applied in all procedures. Ribbon diagrams of BCT-bound HSP90 structure were generated using CCP4MG software (https://www.ncbi.nlm.nih.gov/geo) and Oncomine (http://www.oncomine.org) were analyzed for HSP90 and AR mRNA expression. Three PCa microarray datasets  from Oncomine were analyzed for HSP90 isoform (HSP90α/β) expression in normal prostate and PCa. Two PCa microarray datasets (GSE6919 and GSE35988) were analyzed using PRISM v5.0 to investigate mRNA levels of HSP90 isoforms and AR in normal prostate (or benign prostate hyperplasia), primary PCa, and metastatic PCa (or CRPC) tissues. The correlation between HSP90 isoforms and AR mRNA expression in GSE97284 dataset were analyzed using the R2 Genomics Analysis and Visualization Platform (http://r2.amc.nl). HSP90α/β mRNA expression in different Gleason score PCa was determined using Singh Prostate dataset from Oncomine. Box plot and volcano plot analyses were performed using R software package (https://www.r-project.org/). Statistical analyses were performed with PRISM v5.0. Data are statistically analyzed by unpaired or paired two-tailed Student's t-tests for two groups or one-way ANOVA for multiple comparisons followed by Tukey's post-hoc test.Publicly available datasets from Gene Expression Omnibus (GEO)   Figure A. In add) Figure B. BCT ale.g., E2F, mTORC1, Myc, and androgen response signalings) are downregulated by BCT  analyses using 22RV1 cells treated with or without BCT. RNA-seq analysis showed that 3085 genes were differentially expressed  by BCT treatment, with 1911 downregulated and 1174 upregulated genes Figure A. Gene Se.g., p53 pathway and apoptosis) and unfolded protein response. Further GSEA using the androgen target gene set revealed significant repression in the expression of androgen-upregulated genes and significant activation in androgen-downregulated gene expression  Figure A. IntrapGiven our results showing inhibitory effects of BCT on in vitro metastatic properties of CRPC cells Figure F, H-I, wTo obtain mechanistic insights, we first tested the effect of BCT on AR-FL and AR-V7 protein levels. BCT decreased protein levels of AR-FL and AR-V7 in 22RV1, C4-2B-MDVR, and VCaP cells in a dose and time-dependent manner Figure A. BCT alAR molecular chaperones, including HSP90, HSP70, and HSP40, play important roles in stabilizing both unliganded and hormone-bound AR and confer resistance to ENZ We previously reported that CHIP binds to AR-V7 and promotes ubiquitination-dependent degradation of AR-V7 by acting as an E3 ligase To identify BCT-binding proteins among the components of AR-chaperone complexes, we immobilized BCT onto epoxide-activated UNOsphere beads and performed pull-down assays using the BCT affinity resin and 22RV1 whole cell lysates. As shown in Figure HSP90 binds and hydrolyzes ATP using its NTD, and its ATPase activity is essential for client binding and its chaperone activity Inhibitor binding often induces conformational changes in HSP90 and facilitates dissociation of client proteins from HSP90, leading to their degradation through the ubiquitination-proteasome pathway We next evaluated the effects of BCT on the ATPase activity of HSP90 and found that BCT inhibited the ATPase activity of HSP90 in a dose-dependent manner Figure H. In addWe analyzed the expression levels of HSP90 in normal prostate and PCa tissues using Oncomine and GEO databases. HSP90 isoforms (HSP90AA1 and HSP90AB1) and AR-FL mRNA levels were increased in PCa tissues compared to normal prostate tissues in three independent Oncomine microarray datasets . In addiRecently, we reported that AR-FL and AR-V7 mRNA levels are significantly upregulated in CRPC compared to patient-matched primary PCa Current therapies for PCa have focused on blocking androgen-dependent activation of AR-FL and reducing de novo androgen production BCT is a natural tetracyclic triterpene quassinoid isolated from Brucea species and has been reported to have anticancer activity against cancer cells including leukemia, lymphoma, and myeloma cells Drug repositioning has gained considerable attention as an effective strategy to find new targets and indications for existing drugs and compounds We previously reported that CHIP, a co-chaperone and E3 ubiquitin ligase, interacts with and downregulates AR-V7 levels by ubiquitinating and targeting AR-V7 for proteasomal degradation HSP90 is a molecular chaperone that facilitates the conformational maturation of more than 200 proteins including cell survival and oncogenic signaling proteins, such as MYC, AKT, and nuclear receptors including AR and glucocorticoid receptor (GR) e.g., HSP70, HSP40, and HSP27) via the heat shock response induced by previous generations of HSP90 inhibitors (HSP90 has been extensively investigated as a promising anticancer target hibitors G-I, indiIn conclusion, we identified and characterized BCT as a promising therapeutic candidate against CRPC. Our preclinical data showed that BCT is efficacious in inhibiting growth and metastasis of CRPC cells through targeting HSP90. Our findings provide a proof-of-concept that targeting HSP90 may be a promising strategy to treat CRPC that has become resistant to conventional hormone therapies.Supplementary materials and methods, figures, tables.Click here for additional data file.