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Abstract Loss-of-function mutations in the homotrimeric serine protease HTRA1 cause cerebral vasculopathy. Here, we establish independent approaches to achieve the functional correction of trimer assembly defects. Focusing on the prototypical R274Q mutation, we identify an HTRA1 variant that promotes trimer formation thus restoring enzymatic activity in vitro. Genetic experiments in Htra1 R274Q mice further demonstrate that expression of this protein-based corrector in trans is sufficient to stabilize HtrA1-R274Q and restore the proteomic signature of the brain vasculature. An alternative approach employs supramolecular chemical ligands that shift the monomer-trimer equilibrium towards proteolytically active trimers. Moreover, we identify a peptidic ligand that activates HTRA1 monomers. Our findings open perspectives for tailored protein repair strategies.

Abstract Colonoscopy and endoscopic ultrasound play pivotal roles in the assessment of rectal diseases, especially rectal cancer and inflammatory bowel diseases. Optical coherence tomography (OCT) offers a superior depth resolution, which is a critical factor for individualizing the therapeutic concept and evaluating the therapy response. We developed two distinct rectoscope prototypes, which were integrated into a 1300 nm MHz-OCT system constructed at our facility. The rapid rotation of the distal scanning probe at 40,000 revolutions per minute facilitates a 667 Hz OCT frame rate, enabling real-time endoscopic imaging of large areas. The performance of these OCT-rectoscopes was assessed in an ex vivo porcine colon and a post mortem human in-situ colon. The OCT-rectoscope consistently distinguished various layers of the intestinal wall, identified gut-associated lymphatic tissue, and visualized a rectal polyp during the imaging procedure with 3D-reconstruction in real time. Subsequent histological examination confirmed these findings. The body donor was preserved using an ethanol-glycerol-lysoformin-based technique for true-to-life tissue consistency. We could demonstrate that the novel MHZ-OCT-rectoscope effectively discriminates rectal wall layers and crucial tissue characteristics in a post mortem human colon in-situ. This real-time-3D-OCT holds promise as a valuable future diagnostic tool for assessing disease state and therapy response on-site in rectal diseases.

In this paper, the feasibility of employing a fast intensity modulator and a chirped fibre Bragg grating to build a dispersion-tuned mode-locked swept laser (DTML-SS) for OCT at 850 nm is evaluated. Stable mode-locking is achieved by applying 50 ps pulses at 1 GHz to the modulator, obtaining 35 nm tuning range at 10 KHz sweep rate and 1 mm axial range.

Abstract The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.

Abstract X-ray detectors are of pivotal importance for the scientific and technological progress in a wide range of medical, industrial, and scientific applications. Here, we take advantage of the printability of perovskite-based semiconductors and achieve a high X-ray sensitivity combined with the potential of an exceptional high spatial resolution by our origami-inspired folded perovskite X-ray detector. The high performance of our device is reached solely by the folded detector architecture and does not require any photolithography. The design and fabrication of a foldable perovskite sensor array is presented and the detector is characterized as a planar and as a folded device. Exposed to 50 kVp−150 kVp X-ray radiation, the planar detector reaches X-ray sensitivities of 25−35 μC/(Gyaircm2), whereas the folded detector achieves remarkably increased X-ray sensitivities of several hundred μC/(Gyaircm2) and a record value of 1409 μC/(Gyaircm2) at 150 kVp without photoconductive gain. Finally, the potential of an exceptional high spatial resolution of the folded detector of more than 20 lp/mm under 150 kVp X-ray radiation is demonstrated.

Learning from the experiences of other countries can support efforts to improve agricultural policies. Switzerland provides an interesting case because its policy is exceptionally targeted towards the establishment of sustainable production systems. We describe the history and the current state of Swiss agricultural policy, review evaluations of policy reforms, summarise their impact and outline the lessons learned for policy developments in other countries. We discuss four implications: i) some goals have been met, albeit at a high cost, and so, increasing efficiency of policies is key; ii) there is a need for more coherence and coordination regarding the different policy programmes (i.e. in the sense of a ‘food system policy’); iii) cross-compliance measures (i.e. minimum standards for receiving support) have an important leverage effect; and iv) policy differentiation (e.g. by spatial targeting) and increasing farmers’ discretion over how to achieve goals (e.g. by implementing results-based payments) are key for future policies.

Facial expressions play a crucial role in human interactions. Typically, a positive (negative) expression evokes a congruent positive (negative) reaction within the observer. This congruent behavior is inverted, however, when the same positive (negative) expression is displayed by an outgroup member. Two approaches provide an explanation for this phenomenon. The social intentions account proposes underlying social messages within the facial display, whereas the processing conflict account assumes an affective conflict triggered by incongruent combinations of emotion and the affective connotation of group membership. In three experiments, we aimed at further substantiating the processing conflict account by separating the affective conflict from potential social intentions. For this, we created a new paradigm, in which the participant was an outside observer of a social interaction scene between two faces. Participants were required to respond to the emotional target person that could represent an ingroup or outgroup member. In all three experiments, irrespective of any social intention, responses were consistently affected by the group relation between participant and emotional target, i.e., the affective (in)congruency of the target seen by participants. These results further support the processing conflict account. The implications for the two theoretical accounts are discussed.

Abstract Fully-printed temperature sensor arrays—based on a flexible substrate and featuring a high spatial-temperature resolution—are immensely advantageous across a host of disciplines. These range from healthcare, quality and environmental monitoring to emerging technologies, such as artificial skins in soft robotics. Other noteworthy applications extend to the fields of power electronics and microelectronics, particularly thermal management for multi-core processor chips. However, the scope of temperature sensors is currently hindered by costly and complex manufacturing processes. Meanwhile, printed versions are rife with challenges pertaining to array size and sensor density. In this paper, we present a passive matrix sensor design consisting of two separate silver electrodes that sandwich one layer of sensing material, composed of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). This results in appreciably high sensor densities of 100 sensor pixels per cm $$^2$$ 2 for spatial-temperature readings, while a small array size is maintained. Thus, a major impediment to the expansive application of these sensors is efficiently resolved. To realize fast and accurate interpretation of the sensor data, a neural network (NN) is trained and employed for temperature predictions. This successfully accounts for potential crosstalk between adjacent sensors. The spatial-temperature resolution is investigated with a specially-printed silver micro-heater structure. Ultimately, a fairly high spatial temperature prediction accuracy of 1.22 °C is attained.

Fourier domain mode-locked lasers provide coherent, wavelength swept light useful for imaging applications, but the phase relation between frequencies is not understood. Here, experimental and numerical data is presented that suggests a fixed phase relation and comb-like structure of the sweep.

Abstract Grasslands cover a major share of the world’s agricultural land and their management influences ecosystem services. Spatially targeted policy instruments can increase the provision of ecosystem services by exploiting how they respond to spatial differences in environmental characteristics such as altitude, slope, or soil quality. However, most policy instruments focus on individual farms, where spatial differences are small. Here we assess the economic value of three grassland ecosystem services (i.e., forage provision, carbon sequestration, and habitat maintenance) and its variability in a Swiss region of 791 km2 that consists of 19,000 farmland parcels when managed at the regional and farm scale, respectively. Our spatially explicit bio-economic simulation approach combines biophysical information on grassland ecosystem services and their economic values. We find that in our case study region, spatial targeting on a regional scale management increases the economic value of ecosystem services by 45% compared to targeting at farm scale. We also find that the heterogeneity of economic values coming from prices and willingness to pay estimates is higher than the economic gains from spatial targeting that make use of the spatial difference in environmental characteristics. This implies that heterogeneity in prices and/or societal demand of these three ecosystem services is more important for grassland management than spatial heterogeneity in our case study region. The here applied framework allows for an ex-ante assessment of economic gains from spatial targeting and thus provides basic information for the implementation of incentive mechanisms addressing the nexus of food production and ecosystem service provision in grasslands.

PurposeIn brain tumor surgery, it is crucial to achieve complete tumor resection while conserving adjacent noncancerous brain tissue. Several groups have demonstrated that optical coherence tomography (OCT) has the potential of identifying tumorous brain tissue. However, there is little evidence on human in vivo application of this technology, especially regarding applicability and accuracy of residual tumor detection (RTD). In this study, we execute a systematic analysis of a microscope integrated OCT-system for this purpose.Experimental designMultiple 3-dimensional in vivo OCT-scans were taken at protocol-defined sites at the resection edge in 21 brain tumor patients. The system was evaluated for its intraoperative applicability. Tissue biopsies were obtained at these locations, labeled by a neuropathologist and used as ground truth for further analysis. OCT-scans were visually assessed with a qualitative classifier, optical OCT-properties were obtained and two artificial intelligence (AI)-assisted methods were used for automated scan classification. All approaches were investigated for accuracy of RTD and compared to common techniques.ResultsVisual OCT-scan classification correlated well with histopathological findings. Classification with measured OCT image-properties achieved a balanced accuracy of 85%. A neuronal network approach for scan feature recognition achieved 82% and an auto-encoder approach 85% balanced accuracy. Overall applicability showed need for improvement.ConclusionContactless in vivo OCT scanning has shown to achieve high values of accuracy for RTD, supporting what has well been described for ex vivo OCT brain tumor scanning, complementing current intraoperative techniques and even exceeding them in accuracy, while not yet in applicability.

Abstract With further digitalization and automation, new applications fields require new solutions and sensors. Healthcare, soft robotics, and battery research are only three examples striving for sensor solutions capable of measuring the spatial and temporal temperature distribution of surfaces. This requires the deposition of multiple sensor pixels with a high spatial resolution on potentially flexible substrates. Printing technologies are well suited to fulfill the associated requirements of quality, speed, feature size, and costs. Combining screen printing and aerosol jet printing allows for fast large‐scale processing and when needed high‐precision fabrication for small features. The presented high‐density temperature sensor array provides 625 sensor pixels with a size of 60×60 µm2, evenly distributed over an area of 12×12 mm2. The sensor is operated without encapsulation between 5 and 90 °C showing an average deviation of less than 0.5 °C. The sensor stack consists of a bottom and top electrode sandwiching a carbon black based thermistor layer. The material shows a strong negative‐temperature‐coefficient (NTC) effect with a sensitivity of 3.6 % °C−1 at 5 °C. As an application example, the temperature profile induced by a laser beam is mapped with the temperature sensor array.

The goal of this study is to provide a universally applicable procedure for a systematic evaluation of in situ measured data from single sensors regarding quantifying the uncertainty of the measurement results. As determining uncertainty for an environmental parameter also depends on the parameter itself, the focus here will be set on the variable water temperature in the first place. A separate analysis for salinity and other data will follow in later publications. With this first of a series of planned manuscripts on different parameters, we aim at providing a common understanding of how measurement uncertainty on single sensor measurements can be derived. Using an experimental in situ set-up with 6 different standard CTD sensors of two different brands, we created a four month-long, high-quality data set to be used to develop a reliable method for quantifying measurement uncertainties. Although the CTDs were deployed in a mooring in a coastal environment the described method can be extended to other deployment configurations as well. The described procedures have evolved as a stepwise process that takes the different perspectives of the involved authors into account, as well as the special conditions for environmental measurements, which are collected while the observed volume/area is undergoing a constant change. By sharing the ideas with other stakeholders, the basic concept can be extended to other observing programs and to other essential ocean variables.

Biologic therapies are increasingly used to treat chronic inflammatory skin diseases such as psoriasis and atopic dermatitis. In clinical practice, scores based on evaluation of objective and subjective symptoms are used to assess disease severity, leading to evaluation of treatment goals with clinical decisions on treatment initiation, switch to another treatment modality or to discontinue current treatment. However, this visual-based scoring is relatively subjective and inaccurate due to inter- and intraobserver reliability. Optical coherence tomography (OCT) is a fast, high-resolution, in vivo imaging modality that enables the visualization of skin structure and vasculature. We evaluated the use of OCT for quantification and monitoring of skin inflammation to improve objective assessment of disease activity in patients with psoriasis and atopic dermatitis. We assessed the following imaging parameters including epidermal thickness, vascular density, plexus depth, vessel diameter, and vessel count. A total of four patients with psoriasis or atopic dermatitis were treated with biologic agents according to current treatment guidelines. OCT was used to monitor their individual treatment response in a target lesion representing disease activity for 52 weeks. Psoriatic and eczema lesions exhibited higher epidermal thickness, increased vascular density, and higher vessel count compared to uninvolved skin. An upward shift of the superficial vascular plexus accompanied by smaller vessel diameters was seen in psoriasis in contrast to atopic dermatitis, where larger vessels were observed. A response to biologic therapy was characterized by normalization of the imaging parameters in the target lesions in comparison to uninvolved skin during the observation period of 52 weeks. Optical coherence tomography potentially serves as an instrument to monitor biologic therapy in inflammatory skin diseases. Imaging parameters may enable objective quantification of inflammation in psoriasis or atopic dermatitis in selected representative skin areas. OCT may reveal persistent subclinical inflammation in atopic dermatitis beyond clinical remission.

Quantifying urease activity is an important task for Microbial Induced Calcite Precipitation research. A new urease activity microplate assay using a fluorescent pH indicator is presented. The method is also suitable for automated measurements during microbioreactor experiments. The assay reagent consists of the green fluorescent pH-indicator fluorescein, urea and a phosphate buffer. After sample addition, the microbial urease hydrolyses urea, which results in a pH and hence fluorescence increase. The fluorescence signal can be measured with a microplate reader or with the microbioreactor system BioLector, allowing for automated urease activity measurements during cultivation experiments. In both measurement systems, the fluorescence signal slope highly correlates with the urease activity measured offline with standard methods. Automated measurement is possible, as no sample preparation such as centrifugation or adjusting of the optical density is required. The assay was developed so that the culture samples turbidity, salinity or buffer concentration does not have a negative impact on the fluorescence signal. The assay allows for straightforward, non-hazardous, parallelized, cheap and reliable measurements, making research on ureolytic bacteria for Microbial Induced Calcite Precipitation more efficient. The assay could be adapted to other enzymes, which have a strong impact on the pH value.

The discrimination of tumor-infiltrated tissue from non-tumorous brain tissue during neurosurgical tumor excision is a major challenge in neurosurgery. It is critical to achieve full tumor removal since it directly correlates with the survival rate of the patient. Optical coherence tomography (OCT) might be an additional imaging method in the field of neurosurgery that enables the classification of different levels of tumor infiltration and non-tumorous tissue. This work investigated two OCT systems with different imaging wavelengths (930 nm/1310 nm) and different resolutions (axial (air): 4.9 μm/16 μm, lateral: 5.2 μm/22 μm) in their ability to identify different levels of tumor infiltration based on freshly excised ex vivo brain samples. A convolutional neural network was used for the classification. For both systems, the neural network could achieve classification accuracies above 91% for discriminating between healthy white matter and highly tumor infiltrated white matter (tumor infiltration >60%) .This work shows that both OCT systems with different optical properties achieve similar results regarding the identification of different stages of brain tumor infiltration.

Abstract Microbial induced calcite precipitation (MICP) based on ureolysis has a high potential for many applications, e.g. restoration of construction materials. The gram-positive bacterium Sporosarcina pasteurii is the most commonly used microorganism for MICP due to its high ureolytic activity. However, Sporosarcina pasteurii is so far cultivated almost exclusively in complex media, which only results in moderate biomass concentrations at the best. Cultivation of Sporosarcina pasteurii must be strongly improved in order to make technological application of MICP economically feasible. The growth of Sporosarcina pasteurii DSM 33 was boosted by detecting auxotrophic deficiencies (L-methionine, L-cysteine, thiamine, nicotinic acid), nutritional requirements (phosphate, trace elements) and useful carbon sources (glucose, maltose, lactose, fructose, sucrose, acetate, L-proline, L-alanine). These were determined by microplate cultivations with online monitoring of biomass in a chemically defined medium and systematically omitting or substituting medium components. Persisting growth limitations were also detected, allowing further improvement of the chemically defined medium by the addition of glutamate group amino acids. Common complex media based on peptone and yeast extract were supplemented based on these findings. Optical density at the end of each cultivation of the improved peptone and yeast extract media roughly increased fivefold respectively. A maximum OD600 of 26.6 ± 0.7 (CDW: 17.1 ± 0.5 g/L) was reached with the improved yeast extract medium. Finally, culture performance and media improvement was analysed by measuring the oxygen transfer rate as well as the backscatter during shake flask cultivation.

We present data on the stated preference for the adoption of variable rate technologies from 418 crop farmers in Switzerland. The online survey was conducted online in spring 2021. It consisted of two parts: 1) a choice experiment and 2) questions about farmers' characteristics, expectations and beliefs, as well as their risk preferences. In the choice experiment, farmers were presented with eight consecutive choice tasks. Each task consisted of three alternatives, two hypothetical scenarios for variable rate technologies adoption and the status quo option. We used a split-sample approach and varied the additional profit margin gained through higher yields, label premiums or subsidies for one subsample (focussing on the willigness to accept) and additional cost (acquisition, maintenance and other costs) for the other subsample (focussing on the willigness to pay). Non-monetary attributes include 1) ownership of the technology; 2) potential to increase nitrogen use efficiency and thus reduce nitrogen losses to the environment; 3) uncertainties about the actual impact of the technology on yields and profits (reliability); 4) support in case of technical difficulties. We also collected data on farmers' experiences, attitudes and goals, as well as their risk preferences. Additionally, the survey data were matched with data from the cantonal farm census, which contains information on farm characteristics.

The oceans are a fundamental source for climate balance, sustainability of resources and life on Earth, therefore society has a strong and pressing interest in maintaining and, where possible, restoring the health of the marine ecosystems. Effective, integrated ocean observation is key to suggesting actions to reduce anthropogenic impact from coastal to deep-sea environments and address the main challenges of the 21st century, which are summarized in the UN Sustainable Development Goals and Blue Growth strategies. The European Multidisciplinary Seafloor and water column Observatory (EMSO), is a European Research Infrastructure Consortium (ERIC), with the aim of providing long-term observations via fixed-point ocean observatories in key environmental locations across European seas from the Arctic to the Black Sea. These may be supported by ship-based observations and autonomous systems such as gliders. In this paper, we present the EMSO Generic Instrument Module (EGIM), a deployment ready multi-sensor instrumentation module, designed to measure physical, biogeochemical, biological and ecosystem variables consistently, in a range of marine environments, over long periods of time. Here, we describe the system, features, configuration, operation and data management. We demonstrate, through a series of coastal and oceanic pilot experiments that the EGIM is a valuable standard ocean observation module, which can significantly improve the capacity of existing ocean observatories and provides the basis for new observatories. The diverse examples of use included the monitoring of fish activity response upon oceanographic variability, hydrothermal vent fluids and particle dispersion, passive acoustic monitoring of marine mammals and time series of environmental variation in the water column. With the EGIM available to all the EMSO Regional Facilities, EMSO will be reaching a milestone in standardization and interoperability, marking a key capability advancement in addressing issues of sustainability in resource and habitat management of the oceans.

Maladaptive operant conditioning contributes to development of neuropsychiatric disorders. Candidate genes have been identified that contribute to this maladaptive plasticity, but the neural basis of operant conditioning in genetic model organisms remains poorly understood. The fruit fly Drosophila melanogaster is a versatile genetic model organism that readily forms operant associations with punishment stimuli. However, operant conditioning with a food reward has not been demonstrated in flies, limiting the types of neural circuits that can be studied. Here we present the first sucrose-reinforced operant conditioning paradigm for flies. In the paradigm, flies walk along a Y-shaped track with reward locations at the terminus of each hallway. When flies turn in the reinforced direction at the center of the track, they receive a sucrose reward at the end of the hallway. Only flies that rest early in training learn the reward contingency normally. Flies rewarded independently of their behavior do not form a learned association but have the same amount of rest as trained flies, showing that rest is not driven by learning. Optogenetically-induced sleep does not promote learning, indicating that sleep itself is not sufficient for learning the operant task. We validated the sensitivity of this assay to detect the effect of genetic manipulations by testing the classic learning mutant dunce. Dunce flies are learning-impaired in the Y-Track task, indicating a likely role for cAMP in the operant coincidence detector. This novel training paradigm will provide valuable insight into the molecular mechanisms of disease and the link between sleep and learning.

Imaging techniques have evolved impressively lately, allowing whole new concepts like multimodal imaging, personal medicine, theranostic therapies, and molecular imaging to increase general awareness of possiblities of imaging to medicine field. Here, we have collected the selected (3D) imaging modalities and evaluated the recent findings on preclinical and clinical inflammation imaging. The focus has been on the feasibility of imaging to aid in inflammation precision medicine, and the key challenges and opportunities of the imaging modalities are presented. Some examples of the current usage in clinics/close to clinics have been brought out as an example. This review evaluates the future prospects of the imaging technologies for clinical applications in precision medicine from the pre-clinical development point of view.

We present social network data of Swiss farmers, focusing on exchange and advice relations regarding agricultural climate change mitigation. The data were generated via face-to-face interviews in 2019 using the survey software Network Canvas (https://networkcanvas.com). We interviewed 50 farmers, with 25 of these participating in a regional climate protection initiative in Switzerland as well as 25 farmers located in the same region who did not participate in the initiative. Farmers were asked to indicate the persons with whom they regularly exchanged on topics related to climate change and mitigation in agriculture. The farmers assessed the type and strength of their relationships and were asked to rate the knowledge of their contacts regarding climate change mitigation. We also collected data on the perceived influence of farmers and other persons on farming decisions. Information on farmers’ adoption of climate change mitigation measures and behavioural characteristics was collected in a previous online survey. Farm characteristics were obtained from census data.

Funders and policy makers have strongly recommended the uptake of the FAIR principles in scientific data management. Several initiatives are working on the implementation of the principles and standardized applications to systematically evaluate data FAIRness. This paper presents practical solutions, namely metrics and tools, developed by the FAIRsFAIR project to pilot the FAIR assessment of research data objects in trustworthy data repositories. The metrics are mainly built on the indicators developed by the RDA FAIR Data Maturity Model Working Group. The tools’ design and evaluation followed an iterative process. We present two applications of the metrics: an awareness-raising self-assessment tool and an automated FAIR data assessment tool. Initial results of testing the tools with researchers and data repositories are discussed, and future improvements suggested including the next steps to enable FAIR data assessment in the broader research data ecosystem.

Recent methodological advances in studying large scale animal movements have let researchers gather rich datasets from behaving animals. Often collected in small sample sizes due to logistical constraints, these datasets are however, ideal for multivariate explorations into behavioral complexity. In behavioral studies of domestic dogs, although automated data loggers have recently seen increasing use, a comprehensive framework to identify complex behavioral axes is lacking. Dog behavioral studies frequently rely on subjective ratings, despite demonstrable evidence that these are insufficient for identifying behavioral variables. Taking advantage of dogs’ innate running abilities and readily available GPS data loggers, we extracted latitude-longitude coordinates from running dogs in a large field setup. By extracting multiple variables from each logged coordinate, we generated a complex dataset from limited numbers of dog runs. Individual variables were successful in classifying aerobic competence, social awareness, and different exploratory patterns of dogs. Multivariate analyses identified latent features in movement patterns of dogs which were primarily comprised of two behavioral axes: spatial acuity and social awareness. Individual dogs were then behaviorally classified into independent clusters through unsupervised learning. Interestingly, even though field dogs clustered primarily with each other in varying degrees of energetic exploration and handler focus, some house pets displayed moderately high exploration abilities as well. We expect our proof of principle quantitative pipeline to provide a robust framework for behavioral classification, generating case-control clusters based solely on complex behavioral axes, and greatly benefiting genetic association studies of dog behavior.

We present survey data on the adoption of agricultural climate change mitigation measures collected among 105 farmers in a region in Switzerland in 2019. We surveyed measures farmers use to reduce greenhouse gas emissions on the farm level. The list comprised 13 measures related to energy production and use, herd and manure management as well as crop production. Additionally, data was collected with regard to farmers’ individual concerns and perceptions of climate change, attitudes and goals, self-efficacy and locus of control, income satisfaction and social influences. Moreover, risk preferences as well as loss aversion and probability weighting were elicited using a multiple price list. The survey data was matched with cantonal farm census data, containing information on farm size, farm type and age of the farmers.

Ecosystem Service (ES) mapping has become a key tool in scientific assessments of human-nature interactions and is being increasingly used in environmental planning and policy-making. However, the associated epistemic uncertainty underlying these maps often is not systematically considered. This paper proposes a basic procedure to present areas with lower statistical reliability in a map of an ES indicator, the vegetation carbon stock, when extrapolating field data to larger case study regions. To illustrate our approach, we use regression analyses to model the spatial distribution of vegetation carbon stock in the Brazilian Amazon forest in the State of Pará. In our analysis, we used field data measurements for the carbon stock in three study sites as the response variable and various land characteristics derived from remote sensing as explanatory variables for the ES indicator. We performed regression methods to map the carbon stocks and calculated three indicators of reliability: RMSE-Root-mean-square-error, R2-coefficient of determination - from an out-of-sample validation and prediction intervals. We obtained a map of carbon stocks and made explicit its associated uncertainty using a general indicator of reliability and a map presenting the areas where our prediction is the most uncertain. Finally, we highlighted the role of environmental factors on the range of uncertainty. The results have two implications. (1) Mapping prediction interval indicates areas where the map's reliability is the highest. This information increases the usefulness of ES maps in environmental planning and governance. (2) In the case of the studied indicator, the reliability of our prediction is very dependent on land cover type, on the site location and its biophysical, socioeconomic and political characteristics. A better understanding of the relationship between carbon stock and land-use classes would increase the reliability of the maps. Results of our analysis help to direct future research and fieldwork and to prevent decision-making based on unreliable maps.

Optical coherence tomography (OCT) has been recently suggested as a promising method to obtain in vivo and real-time high-resolution images of tissue structure in brain tumor surgery. This review focuses on the basics of OCT imaging, types of OCT images and currently suggested OCT scanner devices and the results of their application in neurosurgery. OCT can assist in achieving intraoperative precision identification of tumor infiltration within surrounding brain parenchyma by using qualitative or quantitative OCT image analysis of scanned tissue. OCT is able to identify tumorous tissue and blood vessels detection during stereotactic biopsy procedures. The combination of OCT with traditional imaging such as MRI, ultrasound and 5-ALA fluorescence has the potential to increase the safety and accuracy of the resection. OCT can improve the extent of resection by offering the direct visualization of tumor with cellular resolution when using microscopic OCT contact probes. The theranostic implementation of OCT as a part of intelligent optical diagnosis and automated lesion localization and ablation could achieve high precision, automation and intelligence in brain tumor surgery. We present this review for the increase of knowledge and formation of critical opinion in the field of OCT implementation in brain tumor surgery.

A manufacturing process for sheet-based stacked scaffolds (SSCs) based on laser-cutting (LC) was developed. The sheets consist of Polycaprolactone/Hydroxyapatite (PCL/HA) composite material. Single sheets were cut from a PCL/HA foil and stacked to scaffolds with interconnecting pores of defined sizes. HA quantities up to 50% were processable with high reproducibility, while the accuracy was dependent on the applied laser power. The smallest achievable pore sizes were about 40 µm, while the smallest stable solid structures were about 125 µm. The human mesenchymal stem cell line SCP-1 was cultured on the manufactured PCL/HA scaffolds. The cells developed a natural morphology and were able to differentiate to functional osteoblasts. The generation of PCL/HA SSCs via LC offers new possibilities for tissue engineering (TE) approaches. It is reliable and fast, with high resolution. The SSC approach allows for facile cell seeding and analysis of cell fate within the three-dimensional cell culture, thus allowing for the generation of functional tissue constructs.