An airplane is statistically struck by lightning every year. The need for lightweight aircraft to reduce the production of carbon dioxide has significantly reduced the presence of metals in favour of composites, resulting in lower lightning strike protection efficiency. In this perspective, we critically review the state of technologies in lightning strike protection solutions based on carbon materials, graphene, and MXenes. Furthermore, we comment on possible future research directions in the field. The strike of global climate crisis is driving research on novel solutions to reduce the impact of human industries on the planet. For instance, the CORSIA  program sets a scheme towards a sustainable aeronautical industry increasing the demand for novel efficient composite structures  with theIn the last 50 years, the percentage of composite materials in structural parts of modern passenger aircraft increased from 5 to 50 % with a substantial optimization of the aircraft weight see a 3,4]. ,4. 3,4].Thus, in recent decades efforts have been made to clarify, on the one hand, how lightning strikes and the damage evolves, and on the other hand, how to optimize coatings able to sustain such a short transfer of electrical current along the aircraft surface. To test the LSP capability of aeronautical structures, SAE ARP 5412 defines typical lightning flash current waveforms see a. ComponStill, this task is challenging because the coating needs to express multifunctionalities such as light weight and reduced thickness, resistance to erosion, adhesion to the composite structure, great mechanical stability to avoid crack propagation, and stability in a large range of temperature that would allow an aircraft to lift off from an airport with 40 °C, operate at temperatures down to −40 °C in-flight, and land in one with temperatures of 0 °C.Herein, we report the current solutions proposed in terms of LSP. Then, we critically discuss the possible future trends in this field and materials that could make a breakthrough in this research field.2) of the surface without reasonable depth penetration. Commercial LSP systems use copper meshes with a thickness of about 0.10–0.20 mm [2) to the structure, dampening the positive weight-saving effect of composite usage. Moreover, metallic meshes  are prone to corrosion deteriorating their performance within the composite system. Therefore, careful design avoiding galvanic corrosion  or corrosion-resistive coating of the mesh is necessary. Further, delamination problems can be tackled with adhesion-enhancing coatings on the copper meshes [Preferential solutions for effective LSP are metallic meshes  integrated in the outer layers of the composite structure via impregnation with adhesives or sandwiching between two adhesive films. Providing reasonable conductivity, the copper meshes channel electrical currents and reduce thermal stress on the composite. By tuning the mesh thickness and open hole area, performance of the metallic mesh can be adjusted to the LSP requirements. If correctly applied, metallic meshes can limit the damage to a small area few 100 mm0 mm2 of –0.20 mm ,12. Clear meshes .Alternatively to metallic meshes, the integration of metallic fibres into the fibre ply or the application of metallic coatings onto the fibres can provide a similar protection mechanism, although corrosion and fibre/matrix adhesion challenges remain to be solved . MetalliIn recent decades, conductive carbon materials emerged as an alternative to metallic systems offering good conductivity and easy processing for lightweight composites. The use of carbon nanofillers offers the possibility to introduce lightning strike protection into the matrix structure and is therefore favourable in saving process steps during production . Carbon ntration . Using cntration . Similarntration . To imprntration . With CN3, single-layer graphene sheets appear an ideal solution for LSP. Although these properties are promising, technical realisation of graphene-based LSP systems appears challenging.The need for higher conductivity in composites drives the application of two-dimensional materials (2DM) in aeronautical coating research. Since its discovery in 2004 . Due to 106 S/m , a therm103 W/mK , and a dTo form a conductive network within the composite, implementation of the graphene filler is the most important step. A simple form of implementation is mixing it into a polymer matrix by high-shear mixing ,29, caleTo use the full potential of graphene sheets, confined systems are proposed for LSP concentrating graphene in thin layers within the composite.A simple approach to produce concentrated graphene films represents vacuum filtration of previously dispersed graphene sheets. Following filtration, a compression step increases film stability and uniformity producing standalone films with thicknesses of about 25 µm to 150 µm. Electrical conductivity of these compacted graphene sheets are in the range of 1.32 ××105 S/m . The lat2. Further increase in rGO concentration is not possible due to flakes blocking the resin flow through the filter paper at higher amounts. Surpassing the percolation threshold at 2 g/m2, the layers sheet resistance is less than 0.55 Alternatively, production of concentrated graphene layers during vacuum bagging is achieved via resin film infusion (RFI) . By mixi2 hindering performance improvements with higher concentrations. Deteriorating flow dynamics within fibre impregnation, potentially leading to irregularities in matrix distribution, could also be a problematic factor during industrial processing. Nonetheless, vacuum-assisted techniques appear superior to mechanical mixing in terms of LSP application.Compared to ex-situ vacuum-filtration-based film production, in-situ RFI produces thinner concentrated layers with superior conductivity and avoids delamination problems by ensuring impregnation of the rGO layer during composite production. On the downside, the technique is limited to loadings up to 9 g/mBeside vacuum-assisted film production, production in combination with additives can be used for producing conductive graphene films. Commonly, polymers, such as polyvinyl alcohol (PVA) , polyani for LSP . Similar for LSP . GraphenNonetheless, addition of polymers to graphene films deteriorates the electrical conductivity, being about two magnitudes lower than the conductivity of pure graphene films. Conductive enhancement of polymer/graphene films can be achieved via incorporation of metallised polymer veils . The siltion see a was emption see b. The reA summary of the mentioned conductive graphene-based composites is shown in To achieve conductivity comparable to currently used copper mesh, implementation techniques producing confined layers of graphene are necessary. Vacuum-assisted infusion  or film For instance, large scale films are often fragile due to a lack of stabilizers. Adding stabilizing polymers is not an ideal solution as they will hamper the conductivity of the film . Additio2 for an aeronautical LSP copper mesh [2 [Another element that goes against the substitution of copper mesh with graphene-based materials is the production cost of high-quality graphene. Prices are about 94 USD/mper mesh , whereasper mesh  costs ab mesh [2 . Still, The mentioned challenges of performance enhancement, delamination, and improvement of integration and material costs need to be solved to achieve industrialisation of graphene-based LSP systems. This goal can be reached by acting directly on the graphene preparation or by combining it with metals such as silver nanoparticles or other 2DM.The aspect ratio is a crucial factor on the conductive performance of graphene nanofillers in composites. Higher aspect ratios improve the performance, significantly lowering the percolation threshold and reducing contact resistance between particles ,41. PossIn principle, single-layer graphene sheets would be the most suitable option concerning LSP, but the production of defect-free single-layer graphene via CVD is costly, wherefore exfoliation of graphite is a preferred route for industrial application. Graphite is intercalated in aqueous solution by various agents , leading to exfoliation. This often produces defects within the aromatic structures, such as oxidic groups or crystal defects deteriorating the conductivity of the flakes.+-ions prior to the reduction process, graphene oxide and Ag+-ions are simultaneously reduced to rGO and metallic silver by L-Ascorbic acid  [3 [An emerging field of 2DM is MXenes  with their currently most prominent material Ti3C2Tx . Recent 3C2Tx  were abl×106 S/m ) and exh8 V/cm2) . In addi/cm2) [3 ) compareures See a show a Additionally, the combination of rGO and MXenes offers the possibility of layer self-assembly to generate an oriented network, i.e., heterostructures . It has Direct application of MXenes in lightning strike tests comes as follow. MXene-doped thermosetting coatings with a conductivity of about 100 S/m showed reduction in lightning-induced surface damage of a CFRP panel by 67% see c, while The demand for lightweight aircraft to reduce the production of carbon dioxide reveals the need for advanced lightning strike protection (LSP) that does not rely on metal materials. Two-dimensional-material-based LSP systems show promising results to substitute currently used copper mesh in LSP Zone 2. Specifically, the utilization of graphene, MXenes, or a combination of both, has provided inspiring results to address LSP. Still, there are many aspects to keep in consideration while researching 2DM-LSP.First, the fusion of 2DM films and composites is not optimized to the prototype or higher technological level of readiness yet. This implies fundamental investigation on the functionalisation of 2DM to improve the contact points between 2DM flakes dispersed in a composite. Surface analysis will be crucial in this effort, comparing techniques to verify active functional groups and quantify elements at the first atomic layer . Moreover, further improvement in 2DM research will need to pay attention to the environmental impact of the chemistry in use. In this sense, green chemicals are essential for industries to comply with the goals set in the European Green Deal, as discussed in our previous work .Second, instead of pursuing a no-metal strategy, it could be beneficial to approach the challenge as a minimization problem. For instance, the presence of copper in a mesh could be reduced to particle distribution in 2DM composites. As shown in Third, MXenes are undoubtedly one of the most promising 2DMs in terms of conductivity and versatility . The mosFourth, higher conductivity might not necessarily mean a better LSP performance. As discussed in the work of Zhao et al. , despiteIn conclusion, although graphene-based LSP systems could reduce weight up to two order of magnitude to the currently used copper meshes, further investigation is essential to obtain a light composite material able to sustain a lightning strike. Thus, 2DMs are probably the key area that as scientists we must exploit in order to unlock this technology and fly on safe and environmentally friendly aeroplanes.


Sensor arrays are currently attracting the interest of researchers due to their potential of overcoming the limitations of single sensors regarding selectivity, required by specific applications. Among the materials used to develop sensor arrays, graphene has not been so far extensively exploited, despite its remarkable sensing capability. Here we present the development of a graphene-based sensor array prepared by dropcasting nanostructure and nanocomposite graphene solution on interdigitated substrates, with the aim to investigate the capability of the array to discriminate several gases related to specific applications, including environmental monitoring, food quality tracking, and breathomics. This goal is achieved in two steps: at first the sensing properties of the array have been assessed through ammonia exposures, drawing the calibration curves, estimating the limit of detection, which has been found in the ppb range for all sensors, and investigating stability and sensitivity; then, after performing exposures to acetone, ethanol, 2-propanol, sodium hypochlorite, and water vapour, chemometric tools have been exploited to investigate the discrimination capability of the array, including principal component analysis (PCA), linear discriminant analysis (LDA), and Mahalanobis distance. PCA shows that the array was able to discriminate all the tested gases with an explained variance around 95%, while with an LDA approach the array can be trained to accurately recognize unknown gas contribution, with an accuracy higher than 94%. In the last few decades, being set at front-end in IoT (Internet of Things), gas sensors have become quite important in everyday life and therefore, the interest of researchers in their development has noticeably increased. Market demands have pushed scientists to put their efforts in the development of extremely sensitive, low cost, reliable, stable, highly selective, and with rapid response and recovery, single sensors ,2, exploFor several applications, including breathomics, food quality tracking, or environmental monitoring, a single sensor’s high selectivity is particularly important, since the analyte under investigation should be recognized and detected among many interfering gases ,18. AddiFor these application fields, the development of a single chemiresistor sensor may not be the ultimate solution, on one hand due to the difficulties to maintain a high selectivity in presence of many interfering gases ,18,20, oAs mentioned, among the materials exploited to develop gas sensors and sensor arrays, carbon-based materials are of particular interest due to their outstanding electrical properties ,25. CarbFurthermore, such studies often focus mainly on the sensing performances and disclosure of the sensing mechanism, neglecting a more in dept chemometric analysis on the data provided by the array.The main scope of this work is to develop a chemiresistor graphene-based sensor array to investigate as a proof of concepts the possibility to exploit not only PCA for data discrimination, but to explore quantitative chemometric tools such as linear discriminant analysis (LDA) and LDA coupled with a Mahalanobis distance algorithm.3O4/graphene nanocomposite (Gr_Fe3O4), CoPt/graphene nanocomposite (Gr_CoPt), and TiO2/graphene nanocomposite (Gr_TiO2). These materials have been selected with the aim of exploring and comparing their sensing properties when used in a chemiresistor configuration, and to check their capability to work in an array. All sensing layers are related to a common platform material, i.e., graphene, which allowed to diversify the single layers by morphological properties, in the case of graphene dispersion and graphene nanoplatelets, or through the nanoparticles used for graphene decoration .In this regard, an array composed of 5 graphene-based sensors on alumina/silver interdigitated substrates has been developed exploiting a very simple, easy, and low-cost technique, i.e., dropcasting from solution. In particular, solutions of graphene nanostructures and graphene nanocomposites have been considered: graphene dispersion (hereafter Gr dispersion), graphene nanoplatelets (hereafter Gr_nanoplatelets), FeAfter characterization through Raman spectroscopy, the prepared samples have been put on a properly designed platform, which is able to simultaneously monitor the response from all sensors and allows for a direct comparison of the sensor performances under the same working conditions. Since ammonia is often considered as a benchmarking molecule for novel nanocarbon-based sensors ,43, expoThe discrimination capability of the array against other gases has then been tested through exposures to acetone, ethanol, 2-propanol, water, and sodium hypochlorite, producing a dataset for chemometrics analysis. PCA performed on the dataset shows that the developed e-nose is able to discriminate all the tested gases. Furthermore, the contribution of each sensor in the discrimination is investigated and discussed, supporting the claims with experimental evidence. Finally, we demonstrated that with an LDA approach the array can be trained to accurately recognize each gas contribution, despite the limited dataset. Additionally, the Mahalanobis distance has been evaluated to offer an alternative approach for the identification of unknown data.Incidentally, it is worth mentioning that all the tested gases are relevant for several applications, including breathomics, environmental monitoring, safety, and food quality tracking. Indeed, ammonia is recognized as one of the precursors of fine particulate matter, whose inhalation can provoke severe damage to human and animal respiratory systems . Spoiled3O4/graphene nanocomposite , CoPt/graphene nanocomposite , and TiO2/graphene nanocomposite  have been purchased from Sigma Aldrich and used as received.Graphene dispersion (1 mg/mL in DMF), graphene nanoplatelets , FeThe size of silver interdigitates electrodes on alumina substrate was 7 mm × 13.4 mm. The interdigitates contacts consisted of 7 + 7 branches and each silver line has a 210 μm width. 3O4, Gr_CoPt, and Gr_TiO2.In order to prepare the sensors, 15 droplets of each graphene-based solution have been drop-casted on 5 interdigitated alumina substrates and let dry upon solvent evaporation at room temperature. The prepared samples will be labelled as Gr dispersion, Gr_nanoplatelets, Gr_FeRaman spectra have been collected on the CGS device with a Renishaw-Invia system, equipped with a 532 nm laser source. The laser light has always been focused onto the sample with a 100× objective. An 1800 lines/mm grating and a laser power of 1 mW have been used for the measurements.The five prepared samples have been mounted on a properly designed platform and they can work simultaneously (scheme in L) in series with the sensor and by applying a constant voltage (V = 5 V) and monitoring the output voltage across the sample (VOUT), it is possible to track the resistance R of the sensor. The response of the sensor is then defined as ΔR/R0 = (R − R0)/R0, where R0 is the baseline sensor resistance before the gas exposure, and ΔR= R − R0 is the resistance variation due to the interaction with the gas molecules.The developed graphene-based array works in a chemiresistor configuration b: the gaGas exposures have been performed at room temperature (T = (25 ± 4) °C) and with a relative humidity (RH) value around 50 ± 5%, in order to investigate the sensing behaviour of the array in a working condition close to the application environment. Indeed, both the temperature and humidity tested range could be considered suitable for application in environmental monitoring, safety, and food quality tracking, where the array would work in STP conditions.0 versus the gas concentration.The recovery is always achieved in the same condition, fluxing humid air on the array. A scheme of the set up for the gas exposure is reported in A commercial sensor  has been exploited to monitor and evaluate ammonia concentration. The reliability of this sensor calibration curve has been cross-checked by exposing the sensor in a testing chamber with calibrated mass flow controllers (MFCs) and certified ammonia and air cylinders.To assess the discrimination capability of the array, the data collected from exposures to six different gases have been analyzed at first with principal component analysis (PCA) and then with linear discriminant analysis (LDA) and Mahalanobis distance, all implemented into the R software.PCA is a chemometric method, which is unsupervised and can reduce the problem dimensionality while maximizing the variance of the data; it reorganizes the data into a set of principal orthogonal components (PCs), where PC1 is the component that has the greatest variance, PC2 represents the component with the second greatest variance, and so on. The aim of this statistical tool is to obtain a 2-dimensional or 3-dimensional space where a clear discrimination of the tested gases could be identified ,59.LDA is a supervised chemometric tool, therefore it takes into account the class properties of each sample; it assumes that each class has an identical covariance, and unlike the PCA, it deals only with the covariance of the data matrix. The optimal clustering among different classes  is obtained by maximizing the distance between classes and minimizing the scattering of the data inside each class, resulting in a reduction of the problem dimensionality, and obtaining a new space generated by new coordinates, called discriminant factors . LDA givThe Mahalanobis distance has been selected to evaluate the distance between a gas contribution and each cluster in an LDA space, with the aim to present an alternative approach for the identification of unknown data. Indeed, the Mahalanobis distance is the lowest for the class which a datum belongs to. The algorithm has been written in R as in ref. .The data collected from the gas exposures have been pre-treated only by column mean-centering to feed the PCA, LDA, and Mahalanobis distance algorithm and a set of 32 exposures to six different gases has been considered.−1 and related to the C-C stretching of the sp2 atoms in the honeycomb lattice of graphene pow) has been selected to interpolate the data and the fitting parameters, reported in 0 = y + A [LOD]pow pow ,67, wherThe calibration curves present a sublinear behaviour, which is quite common in the field of gas sensor based on carbon nanomaterials ,68,69.3O4 (response range: 0–0.025), and finally, the lowest response is detected by Gr_TiO2 (response range: 0–0.008).Looking at the calibration curve, it is clear that the highest responses are obtained for the Gr_CoPt sensor (response range: 0–0.09), followed by Gr dispersion (response range: 0–0.05), Gr_nanoplatelets (response range: 0–0.04), Gr_FeThe stability and reproducibility of the sensor response have been proven for ammonia detection and the results clearly demonstrated a good stability and reproducibility up to 3 months after the samples preparation see .Moreover, the stability of the sensors’ response to ammonia exposures in a working temperature range suitable for application in environmental monitoring, breathomics, and also food quality tracking  has been assessed see .As for the sensing mechanisms, the resistance increase upon ammonia exposure indicates that the all layers exhibit an overall p-type nature, consistently with the fact that the electron injection from ammonia reduces the density of holes in these layers.Regarding Gr dispersion and Gr_nanoplatelets layers, the sensing mechanism is well known in literature: an electron transfer from ammonia to graphene occurs when the N atom of ammonia faces the graphene lattice, as shown in 3O4 and Gr_TiO2 layers, although both Fe3O4 and TiO2 nanoparticles act as catalytic active centers, while graphene provides the conductive pathway, in addition to being itself a site for the interaction, the sensing mechanism is slightly different. In the case of Fe3O4, as reported in . Of note, only papers based on graphene and clearly reporting gas concentration and sensor response/sensitivity, operating at room temperature and in a chemiresistor configuration have been taken into account for this benchmarking.Finally, a benchmarking with literature data has been performed considering the sensitivity parameter, defined as S = 100 × , in order to become predictive on the probability of a point to belong to a specific cluster .On the contrary, LDA is a chemometric analysis that can be considered predictive and quantitative. In general, a predictive algorithm should be trained with an initial dataset , which should provide the optimal linear combination of sensor results and the best separation between different clusters. In this context, LDA can be used to better separate the clusters related to the different gases, since it works maximizing the distance between classes and minimizing the scattering of the data inside each class.After the training, a test dataset can be projected on the newly built model; in this way, unknown data can be recognized.Performing LDA on the 32 exposures dataset, we also perform internal cross validation (CV) of the model with CV segment equal to 6; this mean that to build the model, the LDA algorithm randomly removes 6 datapoints and uses these data to test the newly built model, providing an accuracy of the model itself. After using the whole dataset to investigate the capability of the array to identify unknown data through LDA, we randomly split the dataset into two subgroups: a training and a test dataset, both containing data from the all the 6 tested gases, and we perform LDA on the training set only. The test set is then projected on the LDA direction to assess the capability of the array to correctly identify the nature of an unknown.As an example, After the LDA model has been drawn, the test dataset composed of 6 unidentified data (in this particular case one point from each gas class has been randomly selected to be part of the test dataset) has been used to validate the model. The model has been able to recognize and correctly assign each point to the correct class, as reported in Moreover, after external validation it is possible to investigate the distance of each point in the test dataset from the different gas clusters, generated by the LDA model, and recognize the belonging class as function of the distance: the smallest distance identifies the class which the point belongs to. To carry out this task, Mahalanobis distance algorithm, being the most exploited for classification, has been considered and the results are reported in In this example, it is clear that point 1 belongs to the ammonia cluster, point 2 to acetone class, point 3 to the 2-propanol class, and point 6 to water class. Regarding points 4 and 5, the smallest distances are for ethanol and sodium hypochlorite, respectively, but those points are quite close also to sodium hypochlorite and ethanol groups, respectively. This result is in agreement also with the LDA model presented in Of note is also that the highest distance values are obtained considering point 1 and water and point 6 and ammonia; also in this case, this result is quite expected since point 1 belongs to ammonia cluster and point 6 to water group and ammonia and water contributions are quite distant in the LDA model b.Finally, The present results then support the application potential for this sensor array, even with a relatively small dataset, such as the one reported in this study. While PCA performs remarkably well in the discrimination of each gas contribution, for a practical application where prediction is required with specific accuracy, LDA should be preferred.2 are required to discriminate acetone, and Gr_nanoplatelets is essential for sodium hypochlorite discrimination. The capability of the array to recognize unknown data with a high accuracy has been demonstrated by LDA, also investigating different training and test dataset dimensions; specifically, the prediction accuracy is always above 94%, when also halving the training dataset dimension.The goal of the present work was to develop a graphene-based sensor array in a chemiresistor configuration and to explore the discrimination capability of this array in the identification of different gases, related to specific applications, such as breathomics, environmental monitoring, and food quality tracking, exploiting few chemometric tools. The array has been prepared by dropcasting graphene nanostructures and graphene nanocomposites solutions on interdigitate alumina/silver substrates and after characterization, five different samples have been put on a properly designed platform. The goal has then been achieved in two steps: (i) ammonia exposures have been performed, the calibration curve of each sensor has been drawn, the limit of detection has been evaluated and it is in the low ppb range for all the sensors, a remarkable stability up to 3 months of all sensors response has been found and finally, the obtained results have been compared to literature, disclosing a good sensitivity for the present chemiresistors, which is in line with what has been so far published; (ii) exposures to acetone, 2-propanol, ethanol, sodium hypochlorite, and water have been carried out with the aim to prepare a dataset for chemometric analyses. These gases have been selected because of their interfering nature in many applications, including the ones for which the presented array is aimed to. In detail, at first it has been proven that the array can clearly discriminate the tested gases in a PCA space, considering both PC1-PC2 and PC1-PC-3 2D subspaces. Moreover, the role of each sensor in the discrimination is investigated and discussed, supporting the claims with experimental evidence. In particular, thanks to the loading plots, it has been found that all sensors are responsible for the discrimination of ammonia and water vapour, while Gr dispersion and Gr_TiOFinally, an approach to identify unknown data has been suggested and tested by exploiting the Mahalanobis distance algorithm.0 response could be expected from the optimization of each layer thickness, as observed for instance in CNTs chemiresistors [Further improvements in the ΔR/Resistors . This isThe low-cost and simple preparation technique, as well as the easiness of use and simplicity of the sensor array itself, coupled with its remarkable discrimination and predictive capability, make the presented array suitable and promising for environmental monitoring, food quality tracking, and breathomics applications.


Integrating fleets of mobile service robots into the operating room wing (OR wing) has the potential to help overcome staff shortages and reduce the amount of dull or unhealthy tasks for humans. However, the OR wing has been little studied in this regard and the requirements for realizing this vision have not yet been fully identified. This includes fundamental aspects such as fleet size and composition, which we have now studied comprehensively for the first time.Using simulation, 150 different scenarios with varying fleet compositions, robot speeds and workloads were studied for a setup based on a real-life OR wing. The simulation included battery recharging cycles and queueing due to shared resources.For all simulated scenarios we report results regarding total duration of execution, average task response times and fleet utilization. The relationship between these performance measures and global scenario parameters—such as fleet size, fleet composition, robot velocity and the number of operating rooms to be served—is visualized.Our simulation-based studies have proven to be a valuable tool for individualized dimensioning of mobile robotic fleets, based on realistic workflows and environmental models. Thereby, important implications for future developments of mobile robots have been identified and a basis of decision-making regarding fleet size, fleet composition, robot capabilities and robot velocities can be provided. Due to costs, space limitations and safety requirements, these aspects must be carefully considered to successfully integrate mobile robotic technology into real-world OR wing environments. Shortage of personnel is among the most pressing problems of modern healthcare systems around the world . This isTechnologic means for overcoming the shortage of healthcare staff are widely explored in academia and industry and mainly draw from the fields of artificial intelligence, digitalization and robotics. This includes the application of mobile robotic assistance systems, which are very promising due to their high level of autonomy and the ability to traverse the environment. While mobile robotics has been explored for various use cases within the hospital –9, appliAutonomous Self-Navigating Robotic OR Assistance (AURORA) aims at designing and implementing a robotic circulating nurse for executing tasks within non-sterile areas of the OR wing. While a single such system operating independently can already be useful for supporting surgical interventions, the possibilities regarding workforce and intelligent workload distribution are limited. To use mobile robotic technology to its fullest potential, the formation of mobile robotic fleets for the OR wing is envisioned by the authors. This would allow for handling higher workloads and for implementing a context-dependent management of robotic resources, offering support where it is currently needed most.Motivated by this vision, the current research project Clearly, a successful clinical translation of this vision to real OR wing environments is not straight-forward and requires careful considerations regarding safety, hygiene and available space. One fundamental question is how large a robotic fleet should be to robustly handle the imposed workload, while limiting the required space and cost of operation to an acceptable level. We argue that this aspect is an important corner stone for demonstrating the general applicability of mobile robotics to OR environments and also sparks important requirements regarding the design of individual robots forming a fleet . This includes the necessity of reaching performance levels comparable to human-only operation, or even surpassing it.Similar investigations have been made by Jeon et al. regarding other hospital environments . The autTo the best of our knowledge, the unique characteristics and requirements of the OR wing  have not yet been studied comprehensively in the context of mobile robotics. To bridge this knowledge gap and provide valuable insights for the AURORA project, we herein present the results of a simulation-based study and draw conclusions regarding optimal fleet sizes and compositions for a variety of different scenarios.The results presented herein have been collected by means of simulation. Since commercially available or open-source tools failed to meet our specific requirements regarding flexibility and extensibility, we chose to implement a dedicated simulation software to be able to control every step of the process. Our ROSThe environment has been modeled closely to the real-life OR wing of a German university hospital. The layout is schematically shown in Fig. Prior to our simulation-based study, we have recorded the tasks of the circulating nurse during 20 laparoscopic cholecystectomies. We have selected this type of procedure since it is highly standardized  and thuTask Scheduling). After task execution is finished, the workflow module is notified and continues to process the recorded surgery workflow.Using the workflow module of our simulation tool, an intervention schedule can be assembled for each operating room of our modeled environment based on recorded interventions. According to this schedule, the workflow module then simulates the workflow of the OR circulator by replaying the recorded data. This is achieved by spawning task requests according to the recorded request time and waiting until task execution has finished. The task requests are sent to the scheduler and then dispatched to the simulated robotic fleet for execution . All different task types considered in our simulated scenarios are summarized in Table For pre- and postoperative phases, the entire collections of tasks are requested at once and the workflow module monitors, whether all tasks are completed within The fleet module of our simulation tool implements the behavior of different types of mobile robots and provides means for assembling a mobile robotic fleet based on these models. Table driving speedEach simulated robotic fleet member provides an interface for receiving task requests dispatched by the scheduler. The execution of a task request is simulated by running a succession of the following three types of activities: driving, manipulating and waiting. For example, to simulate task type T1 (transportation of sterile goods), the robot first needs to navigate to the nearest storage containing the desired article (driving), then retrieve the article from the storage (manipulating), navigate to the operating table (driving) and hand over the article to the scrub nurse (manipulating). In case that one or multiple robots are currently blocking the storage or the hand-over position at the sterile table, the robot is enqueued and needs to wait for its turn (waiting). Driving is simulated by awaiting the travel duration, which is calculated based on the robot’s Since the battery capacity is an important factor limiting the operation of robotic resources, a battery simulation has been implemented as part of the robot models. While—depending on the power demand and battery capacity—overnight charging may suffice for some robotic systems, it may be a considerable limitation for others, especially when aiming at a lightweight robot design, which is preferable in the OR wing context. Therefore, we chose to include this aspect in our investigations.During task execution, the robot’s battery is drained as follows, with To better reflect the individual power consumption associated with different parts of task execution, specific drain rates can be defined for driving (Auto-Navigation Task Scheduling for the Operating Room (ANTS-OR) [In previous work, we have proposed the score-based context-adaptive scheduling algorithm ANTS-OR) . This scThe scheduling process is split into two parts: prioritization and dispatching. Prioritization is done by ranking all currently requested tasks with respect to their time of reception. Starting with the highest ranking task, the algorithm then dispatches the tasks to currently available fleet members. During this “match-making” process, the individual capabilities of the robots as well as their current state  are taken into account. Robots with a shorter traveling path during task execution are preferred over such with longer paths. Robots that are currently not needed for task executions or those with a battery level below In order to be able to make a statement regarding optimal fleet size, 150 different scenarios were simulated, which are summarized by Table The parameters of the battery simulation were set to constant values for all simulated scenarios and were identical for each robot class. Based on performance characteristics that we have identified as being favorable in the context of our AURORA robot, the lower battery level threshold was set to The aim of fleet size dimensioning is to keep operational costs as low as possible, while guaranteeing that the number of robots is high enough to sufficiently handle the workload imposed on the fleet. As soon as the fleet is no longer able to keep up with the demand, processing durations increase, which in turn leads to delays that are potentially affecting the entire OR wing operation.total duration of surgical interventions, which ideally should not increase when compared to the human-only status quo. The elongation of surgical interventions results in increased costs  and less time for treating subsequent patients, while the revenue per intervention remains constant.One measure for quantifying this aspect is the task response time is another relevant measure, which refers to the timespan between task request From the perspectives of the surgical team and the patients currently undergoing surgery, Lastly, from an economic perspective, resources are usually meant to be used to their full capacity with as few down time as possible. This also translates to robotic fleet operation, where it is desired to avoid robots that are unoccupied (or in a fault state) for long time spans, while not creating any value. In the following, we quantify this aspect by defining the robot utilization using the active time The total durations of all simulated scenarios are shown in Fig. Average task response times are shown in Fig. Fleet efficiencies are shown in Fig. Figure The results presented in the previous provide a valuable foundation for considerations regarding the dimensioning of mobile robotic fleets for the operating room wing. Based thereon, multiple conclusions can be drawn.Our results demonstrate that robot velocity has a tremendous influence on all considered evaluation parameters. For example, task group G6 . While Our second conclusion is that enhancing the fleet size does not necessarily result in a better performance. While there is an obvious “natural” limit to fleet size due to the space required for driving and parking, other effects take over at an even earlier stage. After an increase of fleet size beyond a certain point, performance cannot be further improved. For scenarios with two operating rooms , we have even observed an inversion of total scenario durations. This effect can be explained with increased driving times, since the two ORs in the corresponding scenarios are located at one side of the OR wing  will still be considered acceptable. Regarding scenarios with all-rounder robots, this results in an optimal fleet size of 1 robot per OR for Workflows in Methods). In this regard, our simulations are quite conservative.When considering the human performance as a reference  human performance levels for more scenarios than currently observed would be the implementation of a task prediction algorithm , which Throughout the simulation results, a superior performance of all-rounder robots compared to specialized robots can be observed. For example, scenario group G6 (all-rounders, 6 ORs, A limitation of our simulation-based study is that, as of yet, only one architectural layout has been considered. However, to ensure a high level of realism, the layout was closely modelled after the floorplan of an existing OR wing. In future work, we plan to investigate further existing and speculative layouts, which could be the foundation for formulating recommendations regarding optimal layouts for the integration of mobile robots. Furthermore, acceleration phases and collision avoidance between robots and obstacles were not considered by the simulation and would lead to elongated driving times in practice. It must also be mentioned that the recorded interventions were limited to cholecystectomies. Other types of interventions could potentially require the execution of more tasks per timespan and thus lead to a higher overall workload per OR that must be handled by the robotic fleet. This will also be further explored in future work.Lastly, there is potential for increasing fleet performance by adopting approaches from related application domains, such as industrial intra-logistics. One example is smart vehicle routing, where robots execute multiple tasks along a planned route . TherebyIn this paper, we have presented a comprehensive simulation-based study regarding the dimensioning of mobile robotic fleets within the OR wing. We conclude that robot velocity is a crucial parameter when it comes to fleet performance and could be a potential hurdle regarding the integration of mobile robotic fleets into the OR wing. We further conclude that increasing the fleet size beyond a certain point does not necessarily lead to better results, especially when aiming for a good balance between fleet size and fleet performance. According recommendations regarding fleet size were given for the studied scenarios. Furthermore, we were able to confirm that a fleet consisting of all-rounder robots is advantageous regarding performance and utilization, as compared to a fleet of specialized robots. Therefore, it is desirable to design mobile robotic assistance systems that are capable of executing more than just a single specialized task—an important insight for future development efforts.While some challenges have been identified, we consider our results to support the general feasibility of mobile robotic fleets for the OR wing. Depending on the studied parameters, scenarios have been identified that show an acceptable level of performance while requiring moderately sized robotic fleets.


T+ Itpr3tf/J (BTBR) mice are used as a model of autism spectrum disorder (ASD), displaying similar behavioral and physiological deficits observed in patients with ASD. Our recent study found that implementation of an enriched environment (EE) in BTBR mice improved metabolic and behavioral outcomes. Brain-derived neurotrophic factor (Bdnf) and its receptor tropomyosin kinase receptor B (Ntrk2) were upregulated in the hypothalamus, hippocampus, and amygdala by implementing EE in BTBR mice, suggesting that BDNF-TrkB signaling plays a role in the EE-BTBR phenotype. Here, we used an adeno-associated virus (AAV) vector to overexpress the TrkB full-length (TrkB.FL) BDNF receptor in the BTBR mouse hypothalamus in order to assess whether hypothalamic BDNF-TrkB signaling is responsible for the improved metabolic and behavioral phenotypes associated with EE. Normal chow diet (NCD)-fed and high fat diet (HFD)-fed BTBR mice were randomized to receive either bilateral injections of AAV-TrkB.FL or AAV-YFP as control, and were subjected to metabolic and behavioral assessments up to 24 weeks post-injection. Both NCD and HFD TrkB.FL overexpressing mice displayed improved metabolic outcomes, characterized as reduced percent weight gain and increased energy expenditure. NCD TrkB.FL mice showed improved glycemic control, reduced adiposity, and increased lean mass. In NCD mice, TrkB.FL overexpression altered the ratio of TrkB.FL/TrkB.T1 protein expression and increased phosphorylation of PLCγ in the hypothalamus. TrkB.FL overexpression also upregulated expression of hypothalamic genes involved in energy regulation and altered expression of genes involved in thermogenesis, lipolysis, and energy expenditure in white adipose tissue and brown adipose tissue. In HFD mice, TrkB.FL overexpression increased phosphorylation of PLCγ. TrkB.FL overexpression in the hypothalamus did not improve behavioral deficits in either NCD or HFD mice. Together, these results suggest that enhancing hypothalamic TrkB.FL signaling improves metabolic health in BTBR mice.BTBR  Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by deficits in social communication and social interaction and by repetitive and restricted patterns of behaviors, interests, and activities . As of 2Past studies in our lab have extensively examined the effects of an enriched environment (EE) in a variety of mouse models of disease, including cancer and obesity. We have found that placing mice in an EE providing physical, social, and cognitive stimuli induces an anti-obesity, anti-cancer, and anxiolytic phenotype –10. We hT+ Itpr3tf/J (BTBR) murine model of ASD. We found that placing BTBR mice in an EE mitigated both metabolic and behavioral deficits. Enriched male BTBR mice displayed lower adiposity, increased lean mass, lower levels of circulating leptin, and improved glucose tolerance. Behavioral tests suggested that an EE decreased anxiety-like behavior and improved social affiliation. Gene expression of Bdnf was significantly upregulated, which was consistent with HSA axis activation. Gene expression of Ntrk2, encoding the BDNF receptor tropomyosin kinase receptor B (TrkB), was upregulated 3 to 6 folds in the hypothalamus, hippocampus, and amygdala of EE mice, an extent larger than Bdnf [Based on the beneficial effects we saw in models of obesity and cancer after EE intervention, we recently investigated the effects of EE in the BTBR han Bdnf .The BTBR mouse was originally bred for studies on insulin-resistance, diabetes-induced nephropathy and phenylketonuria . BTBR miOver a decade ago, researchers discovered that BTBR mice could be used as a model of ASD, as it displayed relevant key diagnostic symptoms of ASD—selectively reduced social approach, low reciprocal social interactions, impaired juvenile play, and repetitive behaviors . Since tBdnf and Ntrk2 were upregulated following EE. The actions of BDNF are mediated by two major TrkB isoforms—full length TrkB (TrkB.FL) and truncated TrkB (TrkB.T1) [Our previous cancer and obesity studies identified BDNF as the key brain mediator for improved metabolic and immunity outcomes following EE, and our EE-BTBR study found that TrkB.T1) . TherefoT+ Itpr3tf/J (Jackson Laboratory #002282) mice were used to investigate the effects of adeno-associated virus (AAV) mediated hypothalamic TrkB.FL overexpression. Five cohorts of male mice were used—a long-term (24 wks), NCD  fed cohort (n = 16); a long-term (23 wks), HFD  fed cohort (n = 11); a short-term (4 wks) NCD fed cohort (n = 10); a short-term (4 wks) HFD fed cohort (n = 10), and a short-term C57BL/6 NCD fed cohort (n = 10). Ages of the mice were 15–17 weeks old (long-term NCD), 4–5 weeks old (long-term HFD), 4 weeks old (short-term NCD), 8–16 weeks old (short-term HFD), and 7–9 weeks old (short-term C57BL/6 NCD). For the HFD cohorts, mice were fed HFD for five weeks prior to AAV injections and maintained on the HFD for the duration of the studies. Weekly food consumption and body weights were recorded. One mouse from the short-term NCD cohort and one mouse from the short-term HFD cohort died during the study and were excluded from subsequent analysis. Experimental timelines for the long-term NCD and long-term HFD groups can be seen in ad libitum access to food and water. All mice were group housed (3–5 mice) in standard laboratory environment cages and housed in temperature (22–23°C) and humidity (30–70%) controlled rooms under a 12:12 light:dark cycle. All animal experiments were approved by The Ohio State University Institutional Animal Care and Use Committee.Male BTBR GAATTCATGTCGCCCTGGCTGA-3’5’-AATTAA and reverse, GAATTCTTA CAGATCCTCTTCTGAGA-3’5’-AATATA. PCR product of TrkB-FL was then cloned into EcoRI site of the rAAV plasmid. The insert sequence was confirmed by sequencing at the OSU core facility. Amplified TrkB-FL retains a Myc tag at C-terminal before stop codon. rAAV plasmids containing TrkB-FL or yellow fluorescence protein (YFP) were packaged into serotype AAV1 vectors. The details of generation of rAAV were described previously [The rAAV plasmid contains an expression cassette consisting of the CMV enhancer and chicken β-actin (CBA) promoter, woodchuck post-transcriptional regulatory element (WPRE) and bovine growth hormone poly-A flanked by AAV2 inverted terminal repeats. cDNA of TrkB full length  was amplified by using a mouse clone of Ntrk2  as a template through polymerase chain reaction (PCR), with EcoRI flanking at each side. Primers for TrkB.FL amplification are as follows: forward, −1; i.p.) and secured via ear bars and incisor bar on a Kopf stereotaxic frame. A mid-line incision was made through the scalp to reveal the skull and two small holes were drilled into the skull with a dental drill above the injection sites . rAAV vectors (2.5 × 109 genomic particles per site) were injected bilaterally into the hypothalamus at a rate of 0.1 μl minute−1 using a 10 μl Hamilton syringe attached to Micro4 Micro Syringe Pump Controller . At the end of infusion, the syringe was slowly raised from the brain and the scalp was sutured. Animals were placed back into a clean cage and carefully monitored until recovery from anesthesia.Mice were randomized to receive either bilateral injections of AAV-YFP or AAV-TrkB.FL to the hypothalamus. Mice were anaesthetized with a single dose of ketamine/xylazine . Body composition analysis was performed with an echoMRI 3-in-1 Analyzer at the Small Animal Imaging Core of The Dorothy M. Davis Heart & Lung Research Institute, The Ohio State University.ad libitum access to food and water. Mice were acclimatized in the metabolic chambers for 18 hours, then behavior and physiological parameters  were recorded for 24 hours at room temperature.At 17 wpi (long-term NCD) and 12 wpi (long-term HFD), mice underwent indirect calorimetry using the Oxymax Comprehensive Lab Animal Monitoring System (CLAMS) . Mice were singly housed with A glucose tolerance test (GTT) was conducted at 22 wpi (long-term NCD) and 11 wpi (long-term HFD). Mice were fasted for 16 hours overnight, then injected with glucose solution intraperitoneally (1.0 mg glucose/kg body weight). Blood was collected from the tail at baseline, 15, 30, 60, 90, and 120 minutes post glucose injection. Blood glucose concentrations were measured with a portable glucose meter (Bayer Contour Next).Methods for the behavioral assays can be viewed in Mice were sacrificed at 24 wpi (long-term NCD), 23 wpi (long-term HFD), and 4 wpi (short-term BTBR and C57BL/6 NCD and HFD BTBR). Mice were anesthetized by isoflurane and decapitated. Brown adipose tissue (BAT), gonadal WAT (gWAT), inguinal WAT (iWAT), and retroperitoneal WAT (rWAT), and liver were collected and weighed from both long-term groups. Gastrocnemius and pancreas were also dissected and weighed from the long-term NCD group. Hypothalamus was dissected from all groups. Tissues were flash-frozen on dry ice and stored at -80° C until further analysis.Hprt1 for hypothalamus and Actinb for adipose tissues and the relative gene expression was quantified using the 2 -ΔΔCT method [Total RNA was isolated from the hypothalamus, iWAT, gWAT, and BAT using the RNeasy Mini Kit plus RNase-free DNase treatment (Qiagen #74804). First-strand cDNA was generated using TaqMan Reverse Transcription Reagent (Applied Biosystems #N8080234). Quantitative real-time PCR was performed using Power SYBR Green PCR Master Mix (Applied Biosystems #A25742) on a StepOnePlus Real-Time PCR System (Applied Biosystems). Primer sequences can be viewed in T method .Hypothalamus, iWAT, gWAT, and BAT were homogenized in ice-cold Pierce RIPA buffer containing 1× Roche PhosSTOP and Calbiochem protease inhibitor cocktail III, then spun at 13,000 rpm for 15 min. Tissue lysates were separated by gradient gel  and transferred to a nitrocellulose membrane (Bio-Rad). Blots were incubated overnight at 4°C with primary antibodies listed in P ≤0.05 was considered statistically significant. Data were tested for normality using the Shapiro-Wilk test. If data violated assumptions of normality, either log2 transforms or Mann-Whitney tests were performed, and analysis was repeated. Welch’s correction was performed for data that violated assumptions of homogeneity of variance. Power analyses were conducted post-hoc. An ANOVA mixed effects model was used to analyze all longitudinal data, and Bonferroni’s test was used for corrections post-hoc.Data are expressed as mean ± SEM. GraphPad Prism 7 software  was used to analyze our data, using Student’s t-tests. We firstly assessed effects of hypothalamic TrkB.FL overexpression in BTBR mice fed on NCD . All outin vivo echoMRI was performed at 8 wpi to assess body composition. AAV-TrkB.FL mice displayed significantly decreased percent fat mass (P = 0.0086) and significantly increased percent lean mass as compared to AAV-YFP mice (P = 0.028)  .At sacrifice, tissue weights of iWAT, gWAT and BAT were significantly reduced  in the long-term NCD AAV-TrkB.FL mice as compared to AAV-YFP mice . When noWe next examined whether hypothalamic gene transfer of TrkB.FL alters obesity and associated metabolic dysfunction in obese BTBR mice. AAV-TrkB.FL and AAV-YFP mice had a significant weight difference at the start of HFD feeding and did not display significant differences in total weight gain over the course of the experiment . Due to At termination of the experiment 23 wpi, relative liver weight was significantly reduced (P = 0.0067)  while reIn our previous study investigating the effects of enriched environment on BTBR metabolism and behavior, we found significant differences between groups for the open field test and three chambered sociability, so we repeated those tests in this study. To measure anxiety-like behavior and locomotion, an open field test was performed at 12 wpi (long-term NCD) and 22 wpi (long-term HFD). Total distance traveled, distance traveled in the periphery/distance traveled total ratio, and distance traveled in the center/distance traveled total ratio were measured. There was no significant difference between AAV-TrkB.FL and AAV-YFP mice for any measures, in both the long-term NCD  and the Another test to measure anxiety-like behavior is the novelty suppressed feeding test, which was performed at 18 wpi for the long-term HFD group. Mice were fasted overnight and then placed in a new cage with a piece of chow. Latency to feed and food consumed were measured. No significant differences were found between AAV-TrkB.FL and AAV-YFP mice for either measure .In addition, long-term HFD mice were subjected to the third anxiety-like behavioral test, cold induced defecation test, at 18 wpi. Cold temperatures can induce stress in mice and stress increases defecation , 28. We For the long-term HFD experiment, the three-chambered sociability (3CS) test was conducted at 18 wpi. For the first phase, the time spent in the mouse-filled chamber, time spent in the center, and time spent in the empty chamber was recorded. The social affiliation index was calculated by taking the ratio of the time spent in the mouse chamber over the time spent in the empty chamber. No significant differences were found between AAV-TrkB.FL mice and AAV-YFP mice for any of these measures. For the second phase, the time spent in each chamber was recorded. The social novelty index was calculated by taking the ratio of time spent in the novel mouse chamber over the familiar mouse chamber. Again, there were no significant differences between AAV-TrkB.FL and AAV-YFP mice for any measures . In ASD Bdnf, Mc4r (encoding melanocortin-4 receptor), Vgf (encoding nerve growth factor inducible), Insr (encoding insulin receptor), Obrb (encoding long form leptin receptor), Crh (encoding corticotropin-releasing hormone), Npy (encoding neuropeptide Y), and Pomc (encoding proopiomelanocortin). Interestingly, both anorexigenic Pomc and orexigenic Npy were significantly upregulated in AAV-TrkB.FL injected mice   .Il1b (encoding interleukin-1β) (P = 0.037) while upregulated Apoe (encoding apolipoprotein) (P = 0.039). It is reported that BTBR mice show an increased proportion of MHC class II (encoded by H2ab1)-expressing microglia compared to sociable strain C57BL/6 [H2ab1 and Tnfa (encoding tumor factor-α) expression showed a trend of downregulation in the AAV-TrkB.FL mice although not reaching significance . No changes were observed among Il33 (encoding interleukin-33), Ccl2 (encoding C-C motif chemokine ligand 2), or Cx3cr1 (encoding C-X3-C motif chemokine receptor 1) (Neuroinflammation is implicated in the aberrant behaviors of BTBR mice . Accordi C57BL/6 . Hypothaeptor 1) .We profiled hypothalamic gene expression in the long-term HFD experiment including BDNF-TrkB relevant genes and the genes whose expression was altered by TrkB.FL overexpression in the long-term NCD experiment . TrkB.FLAdipoq , Hsl , Vegfa  and downregulated Lep  (Adrb3 (encoding adrenoceptor β3) expression in all adipose depots examined, gWAT (P = 0.0218), iWAT (P = 0.0226), and BAT (P = 0.0359). Ppargc1α  was upregulated in iWAT of TrkB.FL mice (P = 0.0095) , Elovl3 , Prdm16 , and Ucp1  were significantly upregulated in TrkB.FL injected mice  . Hypotha 0.0095) . In BAT,ted mice .The TrkB family includes at least three well-characterized TrkB isoforms through alternative RNA splicing. TrkB.FL is a receptor tyrosine kinase containing an extracellular ligand-binding domain, a single transmembrane domain, and a typical intracellular domain for tyrosine kinases. Two other isoforms (TrkB.T1 and TrkB.T2) are truncated for their intracellular kinase binding domain . TrkB.FLThree canonical pathways are activated following TrkB receptor activation—MAPK/ERK, PI3K/AKT, and PLCγ . BecauseTo determine which pathways were activated following TrkB.FL overexpression in obese BTBR mice, we carried out a short-term HFD experiment. Hypothalamic samples were collected at 4 weeks post AAV injection. As in the short-term NCD experiment, no changes in body weight were observed for this experiment . We perfTo examine hypothalamic signaling changes that occur following TrkB.FL overexpression in C57BL/6 mice of normal weight, we performed a short-term experiment mirroring the BTBR short-term experiments. Hypothalamic samples were collected 4 weeks post injection. There were no changes in body weight over the course of this experiment . Myc expIn this study, we found that hypothalamic TrkB.FL gene transfer in BTBR mice improved metabolic outcomes, but not behavioral deficits. Both long-term NCD and long-term HFD AAV-TrkB.FL groups displayed lower percent body weight gain in the absence of decrease of food intake. Long-term NCD AAV-TrkB.FL animals showed increased leanness and decreased adiposity as well as improved glycemic control. The effects of TrkB.FL overexpression on metabolic outcomes were not as drastic in HFD-fed animals as in NCD-fed animals, and TrkB.FL NCD-fed mice displayed lower relative weight of iWAT while HFD-fed did not show a difference between AAV-YFP and AAV-TrkB.FL. BTBR animals show metabolic impairments at baseline . It is pIl1b expression and alteration of the expression of some microglial-related genes in the hypothalamus of AAV-TrkB.FL injected mice. These molecular changes were observed in the long-term NCD study a few months after systemic metabolic effects occurred. We did not see these inflammatory changes in the long-term HFD study. Mice fed a high-fat diet display chronic hypothalamic inflammation [Pomc and Npy in TrkB.FL NCD mice, but not in TrkB.FL HFD mice. Pomc and Npy expressing neurons act antagonistically to control energy homeostasis, and a disruption in this system is associated with obesity. Particularly, a HFD downregulates Pomc and Npy [Pomc and Npy involved in HFD feeding. New studies are required to investigate whether the potential modulation of neuroinflammation and/or microglial functions is a direct effect of hypothalamic neuronal BDNF-TrkB activation, or alternatively, a feedback of the systemic metabolic outcomes induced by hypothalamic TrkB.FL.One notable finding of the study is the downregulation of proinflammatory ammation , 46. BDNammation , 48. It  and Npy , 50. AgaResearch points toward impaired neural circuit development as a potential cause of ASD. Due to the importance of neurotrophins in synaptic plasticity and neural growth and development, alterations in neurotrophin levels and their associated signaling pathways are one area of research into the pathophysiology of ASD in human subjects and in animal models of ASD . SeveralBdnf and its receptor Ntrk2 were both up-regulated, suggesting BDNF-TrkB signaling plays a role in mediating the EE-BTBR phenotype. Therefore, hypothalamic TrkB.FL overexpression in BTBR mice would also promote its downstream pathway signaling, thereby leading to the improved beneficial outcomes we saw in the EE-BTBR study.BDNF-TrkB activation initiates three main signaling pathways—MAPK, PI3K, and PLCγ. Activation of TrkB at its Tyr490 and Tyr515 sites results in recruitment of GTPase Ras, which activates the MAPK/ERK pathway. MAPK/ERK pathway activation ultimately drives BDNF expression to regulate neuronal survival, differentiation, and synaptic plasticity . RecruitIn the NCD group, strong Myc bands in the AAV-TrkB.FL group confirmed on-target transduction of the AAV vector . There win vivo in a Bdnf heterozygous knockout mouse partially rescued the obesity phenotype [In this study, we assessed the ratio of TrkB.FL to TrkB.T1. TrkB.T1 is a truncated isoform of TrkB that does not contain an intracellular kinase domain, and therefore it does not activate the classical signaling pathways associated with TrkB.FL. TrkB.T1 can bind BDNF to prevent activation of the MAPK/ERK, PI3K/AKT, and PLCγ pathways by forming a heterodimer with TrkB.FL, acting as a dominant negative inhibitor of TrkB.FL. Increased expression of TrkB.T1 can thus prevent BDNF mediated cell survival and proliferation . Our prehenotype . Here, TpPLCγ was significantly increased in the AAV-TrkB.FL group, leading us to believe that the pPLCγ pathway is important for the improved metabolic outcomes we saw in the NCD group. As previously mentioned, activation of the PLCγ pathway is important for synaptic plasticity. Over the course of the NCD experiment, AAV-TrkB.FL animals ate significantly more relative food , but gaiTo investigate a mechanism downstream of hypothalamic TrkB.FL signaling that may explain the improved metabolic outcomes, we profiled a number of metabolism related genes in iWAT, gWAT, and BAT. WAT and BAT make up an organism’s adipose organ. WAT stores excess energy and acts as an endocrine organ, while BAT is metabolically active and dissipates energy as heat for thermoregulation. BAT dissipates energy as heat through thermogenesis, whereby uncoupling protein-1 (UCP1) mediates the process of uncoupling fatty acid oxidation from ATP production . BAT is Adipoq was upregulated and Lep was downregulated and iWAT Adipoq was upregulated. This is consistent with the reduction in body weight we observed, as well as our previous studies showing that activation of the HSA axis increases adiponectin and decreases leptin in WAT adipocytes. β-adrenergic signaling is important for the activation of BAT in response to cold and for the regulation of adiposity and it also plays an important role in inducing beiging of WAT [Adrb3, suggesting increased sympathetic tone to the adipose tissues and possible induction of beiging in the WAT. Hsl was upregulated in gWAT of TrkB.FL overexpressing mice, suggesting increased lipolysis in WAT [Vegfa as a key component of the HSA axis underlying the beiging effect of WAT, likely directly caused by the SNS stimulation to the WAT [Vegfa expression in gWAT, suggesting beiging may be occurring in WAT. Ppargc1a encodes for PGC-1α, which has been shown to be an important modulator of UCP1 expression and thermogenesis in BAT and also plays an important role in the brown adipocyte differentiation process [Ppargc1a was upregulated in the iWAT of TrkB.FL overexpressing animals, which suggests beiging of WAT. Prdm16 controls a cell fate switch determining formation and function of brown adipocytes [Cidea, Elovl3, and Ucp1, which are all BAT selective markers and are positively regulated by Prdm16 [Based on these previous studies we hypothesized that overexpression of TrkB.FL, similar to upregulation of BDNF, would activate the HSA axis leading to adipose remodeling and metabolic improvement. Adiponectin and leptin levels are both associated with adiposity, with adiponectin expression being reduced as adiposity is increased, and leptin being increased as adiposity is increased . We foung of WAT , 71. We s in WAT . Our pre the WAT . We foun process . We founipocytes  and was y Prdm16 . OverallTrkB.FL overexpression resulted in signaling changes in the HFD group similar to those in the NCD group. Just as in the NCD group, TrkB.FL overexpression in the HFD group significantly increased phospho-PLCy, while there were no significant differences for phospho-AKT, phospho-ERK, PTEN, or Ras. Thus, it seems that the PLCγ pathway is important for the improved metabolic outcomes in both NCD BTBR mice and HFD BTBR mice. In the AAV-TrkB.FL animals, increased TrkB.FL levels and the increased TrkB.FL/TrkB.T1 ratio trended toward significance, but did not reach the highly increased levels found in the NCD study. In C57BL/6 mice, a HFD has been found to decrease hippocampal TrkB activation and expression and reduce activation of hippocampal BDNF-TrkB signaling pathways , 76. It’To investigate whether the effects of TrkB.FL overexpression can be generalized to non-BTBR mice, we injected C57BL/6 mice with either TrkB.FL or YFP and examined protein expression of signaling mediators downstream of TrkB.FL. Similar to BTBR NCD mice, we found that TrkB.FL overexpression altered the ratio of TrkB.FL/TrkB.T1. In contrast to BTBR mice we found that TrkB.FL overexpression did not upregulate pPLCγ, suggesting strain-dependent response to TrkB.FL overexpression in mice of normal weight. We did not perform behavioral testing for C57Bl/6J mice in this study. The purpose of including C57Bl/6J mice was to compare the changes in TrkB.FL signaling pathways following TrkB.FL overexpression between C57Bl/6J mice and BTBR mice. Because C57Bl/6J mice do not have baseline deficits in behavior and sociability like BTBR mice we did not expect to see any significant changes in these groups following TrkB.FL overexpression. We have not overexpressed neuronal TrkB.FL in C57Bl/6 mice before, but we have overexpressed neuronal BDNF in the same area of the brain. We found that in C57Bl/6 mice fed a NCD, overexpressing BDNF led to weight loss; reduced adiposity; a decrease in serum leptin, insulin, cholesterol, triglycerides, and IGF-1 and an increase in adiponectin; and altered expression of metabolism related genes in the hypothalamus and adipose tissue , 39. In Ntrk2 was significantly upregulated by 3 to 6 folds in the hypothalamus, hippocampus, and amygdala of male mice upon EE exposure [In our previous EE-BTBR study, we found EE improved metabolic outcomes and several behavioral measures. Because of our additional prior studies showing that hypothalamic BDNF mediates the improved metabolism and cancer outcomes in previous obesity and cancer EE studies, we hypothesized that TrkB signaling was also responsible for the outcomes we saw in the BTBR EE study. In the current study, we found that hypothalamic TrkB.FL overexpression specifically in the hypothalamus of BTBR mice improved the metabolic outcomes, but not behavioral deficits. This suggests that there is an additional mechanism responsible for the EE-induced behavioral improvements in BTBR mice. Much of the research surrounding BTBR behavior has focused on characterizing the behavioral phenotypes , 78 or aexposure . It is pexposure . Anotherexposure . These sexposure  while agexposure . Future Ntrk2 induced by EE was only observed in male mice [Another path to examine is sex differences. Our previous EE-BTBR study found significant sex-dependent outcomes. EE induced more robust improvements in metabolic outcomes in male mice, with only modest improvements in females. It is worthy of noting that upregulation of ale mice , which pale mice , so inveale mice , 8.One drawback to this study are the age differences and the differences in time points of metabolic and behavioral measurements between the long-term NCD group and the long-term HFD group. As such, we were unable to directly compare these two groups. Age-matched experiments would have allowed us to examine exact differences in response to hypothalamic TrkB.FL overexpression between BTBR mice of a normal weight and obese BTBR mice and controlled for possible age effects. Unfortunately, COVID-19 restrictions placed limitations on the timings of the experiments and breeding availability. Due to these age differences we must note that direct comparisons or concrete conclusions cannot be made regarding the differences between the long-term NCD and long-term HFD groups.To date, this is the first study investigating hypothalamic BDNF-TrkB signaling pathways and their impact on metabolism and behavior in the BTBR mouse model of ASD or in a diet-induced obesity BTBR model. Dysregulated BDNF-TrkB signaling has been implicated in both ASD and in obesity, with dysfunction of the hypothalamus being critical in obesity, but the changes in signaling mediators downstream of hypothalamic TrkB in BTBR mice have not been studied sufficiently. Here we provide evidence that hypothalamic TrkB.FL overexpression changes downstream signaling mediators, leading to improved metabolic outcomes in the BTBR mouse model of ASD.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.S3 Data(XLSX)Click here for additional data file.S1 Raw images(PDF)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 FigRepresentative image was taken at 10x magnification. ARC, arcuate nucleus; VMH, ventromedial hypothalamus.(TIF)Click here for additional data file.S2 Fig(A) Open field test total distance. (B) Open field test center/total distance ratio. (C) Open field test periphery/total distance ratio. Data are means ±SEM. AAV-YFP: n = 8, AAV-TrkB.FL: n = 8.(TIF)Click here for additional data file.S3 Fig(A) Open field test total distance. (B) Open field test center/total distance ratio. (C) Open field test periphery/total distance ratio. (D) Novelty suppressed feeding food consumed. (E) Novelty suppressed feeding latency to feed. (F) Cold induced defecation fecal boli. Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 6.(TIF)Click here for additional data file.S4 Fig(A) Three chamber sociability social affiliation. (B) Three chamber test sociability social affiliation index. (C) Three chamber sociability test social novelty. (D) Three chamber sociability test social novelty index. Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 6.(TIF)Click here for additional data file.S5 Fig(A) Body weight. Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 4.(TIF)Click here for additional data file.S6 Fig(A) Body weight. Data are means ±SEM. AAV-YFP: n = 4, AAV-TrkB.FL: n = 5.(TIF)Click here for additional data file.S7 Fig(A) Body weight. Data are means ±SEM. AAV-YFP: n = 5, AAV-TrkB.FL: n = 5.(TIF)Click here for additional data file.


Older adults are at risk for severe disease following respiratory syncytial virus (RSV) infection. We assessed knowledge and attitudes regarding RSV infection and prevention among residents of long-term care facilities (LTCFs).Between October and December of 2021, surveys were administered to residents of LTCFs before and after 1.25-hour educational sessions led by health care professionals (HCPs) at 5 centers.P < .001) and less likely to utilize unreliable sources.Surveys were completed by 167 residents . Residents reported low levels of knowledge of RSV-related topics, which increased significantly after the sessions . Before the sessions, 43% used family/friends and social media to obtain information about RSV and other infectious diseases, while only one-fourth consulted an HCP to learn about these topics. After the sessions, residents were more likely to confer with their HCP (P = .016). After attending the sessions, more than 80% reported that they plan to get an RSV vaccine when one is available and be more diligent in taking actions to prevent the spread of infectious diseases.Although most residents indicated that they would get tested as soon as possible if they came down with symptoms suggestive of RSV or other infections, only 64% reported high confidence in preventing spread. Although most residents believed that it was important to get vaccinated against respiratory viruses and that FDA-approved vaccines are safe, only 48% would be very willing to get an RSV vaccine if available. This proportion increased to 74% after the educational sessions . Less than one-half were very comfortable sharing questions and concerns about vaccines with HCPs, which increased to 62% after the sessions (While residents of LTCFs recognize the importance of RSV testing and prevention, they exhibit low levels of knowledge about RSV-related topics, lack of confidence in reducing the spread of disease, discomfort in raising issues regarding vaccination with their HCP, and unwillingness to get the RSV vaccine once available. Educational sessions can help to narrow knowledge gaps, increasing the potential for changes in behavior related to RSV prevention.Stefan Gravenstein, MD, MPH, Genentech: Advisor/Consultant|Genentech: Grant/Research Support|Janssen: Advisor/Consultant|Janssen: Honoraria|Longeveron: Honoraria|Merck: Honoraria|Moderna: Advisor/Consultant|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Sanofi: Grant/Research Support|Sanofi: Honoraria|Seqirus: Grant/Research Support|Seqirus: Honoraria.


We numerically investigated the use of graphene nanoribbons placed on top of silicon-on-insulator (SOI) strip waveguides for light polarization control in silicon photonic-integrated waveguides. We found that two factors mainly affected the polarization control: the graphene chemical potential and the geometrical parameters of the waveguide, such as the waveguide and nanoribbon widths and distance. We show that the graphene chemical potential influences both TE and TM polarizations almost in the same way, while the waveguide width tapering enables both TE-pass and TM-pass polarizing functionalities. Overall, by increasing the oxide spacer thickness between the silicon waveguide and the top graphene layer, the device insertion losses can be reduced, while preserving a high polarization extinction ratio. Quantum Key Distribution (QKD) enables the provably secure distribution of private keys, relying on quantum mechanics fundamental principles . Silicon2). For the implementation of integrated QKD systems, on-chip devices are needed to accurately manipulate the polarization state of the light injected in photonic-integrated circuits (PICs). Polarizers, polarization beam splitters (PBSs) and polarization rotators (PRs) are employed to this aim. Polarization manipulation in PICs usually relies on an asymmetric waveguide design to allow the optical power transfer between orthogonal polarization modes [Graphene, 2D nanomaterials and semiconductor nanowires can be used to build nanodevices that are able to coherently and dynamically manipulate the photon polarization . In 2D mon modes . Birefrion modes . 5 cm2 V−1 s−1 at a ~1011 cm−2 electron concentration [2 buffer layer. A fabrication goal is to ensure a flat SiO2 surface for easy graphene transfer and patterning. The graphene transfer is usually efficiently performed using polymer-based pick-up techniques [At low doping, graphene operates as a TE polarizer, its conductivity being dominated by inter-band chiral transitions . When thntration . In thischniques . In prevchniques . Recent chniques . In partIn this work, we now propose a polarization control technique using graphene nanoribbons. In graphene–silicon heterostructures, optical absorption is allowed only for the fraction of the electric field that is parallel to the graphene plane, and the birefringent behavior thus takes place because this fraction is generally different for the TE and TM modes in silicon nanophotonic waveguides ,24,25. G2 deposited on a bulk Si wafer. All the standard optical components  are usually realized on the same PIC on a standard 220-nm-thick SOI platform. A critical task to be accomplished is the coupling of the light from (to) an optical fiber to (from) the PIC. The most common platform for PIC is silicon-on-insulator (SOI), constituted by a thin layer of Si lying on a thick  layer of SiOIn this work, we numerically analyze graphene’s capability of controlling the TE–TM polarization of light beams traveling down a coupled graphene-silicon waveguide. For this purpose, 2D simulations of the fundamental modes propagating in the waveguide under study have been carried out to characterize the different coupling of the modal electric field components in parallel and orthogonal directions with respect to the graphene sheet.wg, wgr and td), i.e., the silicon waveguide width, the graphene nanoribbon width and the oxide spacer thickness between silicon and graphene. A standard silicon-on-insulator (SOI) photonic waveguide is optically coupled to a graphene nanoribbon placed on top of the silicon layer. The upper oxide cladding provides electrical insulation between the graphene and silicon layers, with the possibility to locally create flat surfaces for easier transfer of graphene nanoribbons [wg of 450 nm and narrow 300 nm value, changing the graphene width wgr from 50 nm up to 3 μm and the top oxide thickness td from 10 nm up to 700 nm. In oribbons . In this15 rad·s−1  waveguides suffer from large polarization birefringence, because of the high refractive index contrast between silicon and silicon dioxide SiOτ = 20 fs. We can see that when increasing the chemical potential (cµ), the normalized effective refractive index of the TM mode grows more rapidly compared to that of the TE one, and that both have a maximum close to 0.4 eV; a further increase in the chemical potential leads to an interesting crossing, with the TE mode index exceeding that of the TM mode. Regarding the absorption losses, we see that at small cµ, the TE mode suffers much smaller losses compared to the TM one, while, with increasing cµ, the losses for the two modes progressively reduce and approach one another. The numerical analysis reveals that the cµ tuning affects the TE and TM polarizations in a similar way, while it has a different effect on the optical loss of the two modes (cµ > 0.5 eV.The presence of the graphene sheet modifies the propagation properties of these two modes, because of their overlap with the 2D material. The mode properties have been found to be strongly dependent on Graphene’s chemical potential. wo modes . We founcµ = 1.6 eV is shown in The spatial distribution of the TE and TM modes at chemical potential In In In cµ = 0.6 eV and the graphene electronic relaxation time to 20 fs, keeping it constant in all simulations, while varying the other geometrical parameters. As a general rule, an increase in graphene chemical potential helps in reducing optical insertion losses, at the expense of weaker interaction strength with the optical mode. Our first results (In our calculations, we study the behavior of the proposed device by setting the silicon waveguide width, the graphene nanoribbon width and the oxide spacer thickness as design parameters. As already reported in , graphen results , obtaine results .cµ tuning similarly affects both TE and TM polarizations, despite inducing different optical losses. We demonstrate that to reduce the optical losses, reasonable working points are the ones with the graphene chemical potential cµ > 0.5 eV. Another interesting and applicable result is that, when increasing the thickness of the oxide spacer between the silicon waveguide and of the top graphene layer, the device insertion losses are reduced, maintaining a reasonable polarization extinction ratio. The tapering of a standard waveguide can then be harnessed to create devices with TE-pass or TM-pass polarizing functionalities. These results are applicable to silicon photonics polarization-encoded QKD devices working in the telecom 1.55 μm C-band. In conclusion, we have presented the idea of a new architecture for TE–TM polarization control and tuning in SOI waveguides by means of graphene nanoribbons. The graphene material has been modeled as a two dimensional 2D boundary conditions using the Kubo formula for surface conductivity. By performing numerical analysis, we found that the