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Monitoring snow depth is important for applications such as hydrology, energy planning, ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate snow depth for large regions can only do so in a spatial resolution of up to 1 km ground sampling distance (GSD). This limits their usage in high alpine areas, where this resolution fails to capture local snow distribution patterns caused by the pronounced topographical features. In this work we use a recurrent convolutional neural network to estimate snow depth at high spatial resolution (10 m GSD), weekly, and at large scale based on satellite data sources and elevation maps, without the need for measurement stations on the ground. The proposed method achieves unprecedented results for large-scale, high-resolution snow depth mapping. The resulting maps are evaluated over a period of three years against high-fidelity snow depth maps obtained with airborne photogrammetry. Finally, we also produce well-calibrated uncertainty estimates for every individual snow depth estimate via a probabilistic regression framework., ISPRS Journal of Photogrammetry and Remote Sensing, 197, ISSN:0924-2716

Imaging modalities capable of visualizing the human brain have led to major advances in neurology and brain research. Multi-spectral optoacoustic tomography (MSOT) has gained importance for studying cerebral function in rodent models due to its unique capability to map changes in multiple hemodynamic parameters and to directly visualize neural activity within the brain. The technique further provides molecular imaging capabilities that can facilitate early disease diagnosis and treatment monitoring. However, transcranial imaging of the human brain is hampered by acoustic attenuation and other distortions introduced by the skull. Here, we demonstrate non-invasive transcranial MSOT angiography of pial veins through the temporal bone of an adult healthy volunteer. Time-of-flight (TOF) magnetic resonance angiography (MRA) and T1-weighted structural magnetic resonance imaging (MRI) were further acquired to facilitate anatomical registration and interpretation. The superior middle cerebral vein in the temporal cortex was identified in the MSOT images, matching its location observed in the TOF-MRA images. These initial results pave the way toward the application of MSOT in clinical brain imaging.

Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts. However, classifying plant specimens based on image data alone is challenging: some species exhibit large variations in visual appearance, while at the same time different species are often visually similar; additionally, species observations follow a highly imbalanced, long-tailed distribution due to differences in abundance as well as observer biases. On the other hand, most species observations are accompanied by side information about the spatial, temporal and ecological context. Moreover, biological species are not an unordered list of classes but embedded in a hierarchical taxonomic structure. We propose a multimodal deep learning model that takes into account these additional cues in a unified framework. Our Digital Taxonomist is able to identify plant species in photographs better than a classifier trained on the image content alone, the performance gained is over 6 percent points in terms of accuracy., ISPRS Journal of Photogrammetry and Remote Sensing, 182, ISSN:0924-2716

Quantum yield of fluorescence (ϕ F ) is key to interpret remote measurements of sun-induced fluorescence (SIF), and whether the SIF signal is governed by photochemical quenching (PQ) or non-photochemical quenching (NPQ). Disentangling PQ from NPQ allows using SIF estimates in various applications in aquatic optics. However, obtaining ϕ F is challenging due to its high temporal and physiological variability, and the combined measurements needed to enclose all relevant optical paths. In inland waters, this type of data is scarce and information on diurnal and seasonal ϕ F dynamics are almost unknown. Using an autonomous hyperspectral Thetis profiler in Lake Geneva, we demonstrate how to estimate ϕ F using an ensemble of in-situ measurements acquired between 2018 to 2021. We use vertical and temporal changes in retrieved ϕ F to determine NPQ and PQ conditions. We observed NPQ in 36% of the total daytime profiles used in the ϕ F analysis. While downwelling irradiance is a significant contributor to ϕ F , its role cannot be easily interpreted. Other factors such as phytoplankton photoregulation and assemblages also likely play significant roles in quenching mechanisms. We conclude that an adapted approach exploiting in-situ data is suitable to determine diurnal and seasonal NPQ occurrence, and helps develop future remote sensing algorithms.

We report on the Falcon neuro event-based sensor (EBS) instrument that is designed to acquire data from lightning and sprite phenomena and is currently operating on the International Space Station. The instrument consists of two independent, identical EBS cameras pointing in two fixed directions, toward the nominal forward direction of flight and toward the nominal Nadir direction. The payload employs stock DAVIS 240C focal plane arrays along with custom-built control and readout electronics to remotely interface with the cameras. To predict the sensor’s ability to effectively record sprites and lightning, we explore temporal response characteristics of the DAVIS 240C and use lab measurements along with reported limitations to model the expected response to a characteristic sprite illumination time-series. These simulations indicate that with appropriate camera settings the instrument will be capable of capturing these transient luminous events when they occur. Finally, we include initial results from the instrument, representing the first reported EBS recordings successfully collected aboard a space-based platform and demonstrating proof of concept that a neuromorphic camera is capable of operating in the space environment., Optical Engineering, 61 (8), ISSN:0091-3286, ISSN:1560-2303

Human-centered applications using wearable sensors in combination with machine learning have received a great deal of attention in the last couple of years. At the same time, wearable sensors have also evolved and are now able to accurately measure physiological signals and are, therefore, suitable for detecting body reactions to stress. The field of machine learning, or more precisely, deep learning, has been able to produce outstanding results. However, in order to produce these good results, large amounts of labeled data are needed, which, in the context of physiological data related to stress detection, are a great challenge to collect, as they usually require costly experiments or expert knowledge. This usually results in an imbalanced and small dataset, which makes it difficult to train a deep learning algorithm. In recent studies, this problem is tackled with data augmentation via a Generative Adversarial Network (GAN). Conditional GANs (cGAN) are particularly suitable for this as they provide the opportunity to feed auxiliary information such as a class label into the training process to generate labeled data. However, it has been found that during the training process of GANs, different problems usually occur, such as mode collapse or vanishing gradients. To tackle the problems mentioned above, we propose a Long Short-Term Memory (LSTM) network, combined with a Fully Convolutional Network (FCN) cGAN architecture, with an additional diversity term to generate synthetic physiological data, which are used to augment the training dataset to improve the performance of a binary classifier for stress detection. We evaluated the methodology on our collected physiological measurement dataset, and we were able to show that using the method, the performance of an LSTM and an FCN classifier could be improved. Further, we showed that the generated data could not be distinguished from the real data any longer.

Abnormal cerebral accumulation of amyloid-beta peptide (Aβ) is a major hallmark of Alzheimer’s disease. Non-invasive monitoring of Aβ deposits enables assessing the disease burden in patients and animal models mimicking aspects of the human disease as well as evaluating the efficacy of Aβ-modulating therapies. Previous in vivo assessments of plaque load have been predominantly based on macroscopic fluorescence reflectance imaging (FRI) and confocal or two-photon microscopy using Aβ-specific imaging agents. However, the former method lacks depth resolution, whereas the latter is restricted by the limited field of view preventing a full coverage of the large brain region. Here, we utilized a fluorescence molecular tomography (FMT)-magnetic resonance imaging (MRI) pipeline with the curcumin derivative fluorescent probe CRANAD-2 to achieve full 3D brain coverage for detecting Aβ accumulation in the arcAβ mouse model of cerebral amyloidosis. A homebuilt FMT system was used for data acquisition, whereas a customized software platform enabled the integration of MRI-derived anatomical information as prior information for FMT image reconstruction. The results obtained from the FMT-MRI study were compared to those from conventional planar FRI recorded under similar physiological conditions, yielding comparable time courses of the fluorescence intensity following intravenous injection of CRANAD-2 in a region-of-interest comprising the brain. In conclusion, we have demonstrated the feasibility of visualizing Aβ deposition in 3D using a multimodal FMT-MRI strategy. This hybrid imaging method provides complementary anatomical, physiological and molecular information, thereby enabling the detailed characterization of the disease status in arcAβ mouse models, which can also facilitate monitoring the efficacy of putative treatments targeting Aβ.

openaire: EC/H2020/771049/EU//FREEDLES Physically-based methods in remote sensing provide benefits over statistical approaches in monitoring biophysical characteristics of vegetation. However, physically-based models still demand large computational resources and often require rather detailed informative priors on various aspects of vegetation and atmospheric status. Spectral invariants and photon recollision probability theories provide a solid theoretical framework for developing relatively simple models of forest canopy reflectance. Empirical validation of these theories is, however, scarce. Here we present results of a first empirical validation of a model based on photon recollision probability at the level of individual trees. Multiangular spectra of pine, spruce, and oak tree seedlings (height = 0.38–0.7 m) were measured using a goniometer, and tree hemispherical reflectance was derived from those measurements. We evaluated the agreement between modeled and measured tree reflectance. The model predicted the spectral signatures of the tree seedlings in the wavelength range between 400 and 2300 nm well, with wavelength-specific bias between −0.048 and 0.034 in reflectance units. In relative terms, the model errors were the smallest in the near-infrared (relative RMSE up to 4%, 7%, and 4% for pine, spruce, and oak seedlings, respectively) and the largest in the visible wavelength region (relative RMSE up to 34%, 20%, and 60%). The errors in the visible region could be partly attributed to wavelength-dependent directional scattering properties of the leaves. Including woody parts of tree seedlings in the model improved the results by reducing the relative RMSE by up to 10% depending on species and wavelength. Spectrally invariant model parameters, i.e. total and directional escape probabilities, depended on spherically averaged silhouette to total area ratio (STAR) of the tree seedlings. Overall, the modeled and measured tree reflectance mainly agreed within measurement uncertainties, but the results indicate that the assumption of isotropic scattering by the leaves can result in large errors in the visible wavelength region for some tree species. Our results help increasing the confidence when using photon recollision probability and spectral invariants -based models to interpret satellite images, but they also lead to an improved understanding of the assumptions and limitations of these theories.

High-contrast, high-resolution imaging of biomedical specimens is indispensable for studying organ function and pathologies. Conventional histology, the gold standard for soft-tissue visualization, is limited by its anisotropic spatial resolution, elaborate sample preparation, and lack of quantitative image information. X-ray absorption or phase tomography have been identified as promising alternatives enabling non-destructive, distortion-free three-dimensional (3D) imaging. However, reaching sufficient contrast and resolution with a simple experimental procedure remains a major challenge. Here, we present a solution based on x-ray phase tomography through speckle-based imaging (SBI). We demonstrate on a mouse kidney that SBI delivers comprehensive 3D maps of hydrated, unstained soft tissue, revealing its microstructure and delivering quantitative tissue-density values at a density resolution of better than 2 m g / c m 3 and spatial resolution of better than 8 µm. We expect that SBI virtual histology will find widespread application in biomedicine and will open up new possibilities for research and histopathology.

Optoacoustic mesoscopy combines rich optical absorption contrast with high spatial resolution at tissue depths beyond reach for microscopic techniques employing focused light excitation. The mesoscopic imaging performance is commonly hindered by the use of inaccurate delay-and-sum reconstruction approaches and idealized modeling assumptions. In principle, image reconstruction performance could be enhanced by simulating the optoacoustic signal generation, propagation, and detection path. However, for most realistic experimental scenarios, the underlying total impulse response (TIR) cannot be accurately modelled. Here we propose to capture the TIR by scanning of a sub-resolution sized absorber. Significant improvement of spatial resolution and depth uniformity is demonstrated over 3 mm range, outperforming delay-and-sum and model-based reconstruction implementations. Reconstruction performance is validated by imaging subcutaneous murine vasculature and human skin in vivo. The proposed experimental calibration and reconstruction paradigm facilitates quantitative inversions while averting complex physics-based simulations. It can readily be applied to other imaging modalities employing TIR-based reconstructions., Photoacoustics, 28, ISSN:2213-5979

Inertial Measurement Units (IMUs) have gained popularity in gait analysis and human motion tracking, and they provide certain advantages over stationary line-of-sight-dependent Optical Motion Capture (OMC) systems. IMUs appear as an appropriate alternative solution to reduce dependency on bulky, room-based hardware and facilitate the analysis of walking patterns in clinical settings and daily life activities. However, most inertial gait analysis methods are unpractical in clinical settings due to the necessity of precise sensor placement, the need for well-performed calibration movements and poses, and due to distorted magnetometer data in indoor environments as well as nearby ferromagnetic material and electronic devices. To address these limitations, recent literature has proposed methods for self-calibrating magnetometer-free inertial motion tracking, and acceptable performance has been achieved in mechanical joints and in individuals without neurological disorders. However, the performance of such methods has not been validated in clinical settings for individuals with neurological disorders, specifically individuals with incomplete Spinal Cord Injury (iSCI). In the present study, we used recently proposed inertial motion-tracking methods, which avoid magnetometer data and leverage kinematic constraints for anatomical calibration. We used these methods to determine the range of motion of the Flexion/Extension (F/E) hip and Abduction/Adduction (A/A) angles, the F/E knee angles, and the Dorsi/Plantar (D/P) flexion ankle joint angles during walking. Data (IMU and OMC) of five individuals with no neurological disorders (control group) and five participants with iSCI walking for two minutes on a treadmill in a self-paced mode were analyzed. For validation purposes, the OMC system was considered as a reference. The mean absolute difference (MAD) between calculated range of motion of joint angles was 5.00°, 5.02°, 5.26°, and 3.72° for hip F/E, hip A/A, knee F/E, and ankle D/P flexion angles, respectively. In addition, relative stance, swing, double support phases, and cadence were calculated and validated. The MAD for the relative gait phases (stance, swing, and double support) was 1.7%, and the average cadence error was 0.09 steps/min. The MAD values for RoM and relative gait phases can be considered as clinically acceptable. Therefore, we conclude that the proposed methodology is promising, enabling non-restrictive inertial gait analysis in clinical settings.

An ultrasound detection scheme based on a transparent polyvinylidene-fluoride indium-tin-oxide (PVDF-ITO) piezoelectric film is developed for ultrawideband sensitive detection of optoacoustic (OA) signals down to a noise equivalent pressure (NEP) of 8.4 Pa over an effective detection bandwidth extending beyond 30 MHz. The high signal-to-noise ratio and low noise performance are facilitated by employing a two-stage amplifier structure. The PVDF-ITO detector is directly mounted on a commercial high numerical aperture objective lens of a scanning optical resolution OA microscopy system to obtain submicron resolution images without signal averaging, as demonstrated both in phantoms and in vivo measurements in mice. The transparent detection scheme further allows for the OA imaging modality to be easily integrated with other imaging techniques for diverse multi-modal biomedical imaging investigations.

With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error in turn is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector machine. Prediction performance is strong for high levels of cognitive ability but grows weaker for low levels of cognitive ability. Classification into normal and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively-it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition.

Background: This study aimed to analyze the diagnostic reliability of radiographic assessment of cystic lesions using a pre-set, manufacturer-specific, low-dose mode compared to a standard-dose dental cone-beam computed tomography (CBCT) imaging protocol. Methods: Forty pig mandible models were prepared with cystic lesions and underwent both CBCT protocols on an Orthophos SL Unit (Dentsply-Sirona, Bensheim, Germany). Qualitative and quantitative analysis of CBCT data was performed by twelve investigators independently in SIDEXIS 4 (Dentsply-Sirona) using a trial-specific digital examination software tool. Thereby, the effect of the two dose types on overall detectability rate, the visibility on a scale of 1 (very low) to 10 (very high) and the difference between measured radiographic and actual lesion size was assessed. Results: Low-dose CBCT imaging showed no significant differences considering detectability (78.8% vs. 81.6%) and visibility (9.16 vs. 9.19) of cystic lesions compared to the standard protocol. Both imaging protocols performed very similarly in lesion size assessment, with an apparent underestimation of the actual size. Conclusion: Low-dose protocols providing confidential diagnostic evaluation with an improved benefit–risk ratio according to the ALADA principle could become a promising alternative as a primary diagnostic tool as well as for radiological follow-up in the treatment of cystic lesions.

Spinal cord injury (SCI) patients suffer from diverse gait deficits depending on the severity of their injury. Gait assessments can objectively track the progress during rehabilitation and support clinical decision making, but a comprehensive gait analysis requires far more complex setups and time-consuming protocols that are not feasible in the daily clinical routine. As using inertial sensors for mobile gait analysis has started to gain ground, this work aimed to develop a sensor-based gait analysis for the specific population of SCI patients that measures the spatio-temporal parameters of typical gait laboratories for day-to-day clinical applications. The proposed algorithm uses shank-mounted inertial sensors and personalized thresholds to detect steps and gait events according to the individual gait profiles. The method was validated in nine SCI patients and 17 healthy controls walking on an instrumented treadmill while wearing reflective markers for motion capture used as a gold standard. The sensor-based algorithm (i) performed similarly well for the two cohorts and (ii) is robust enough to cover the diverse gait deficits of SCI patients, from slow (0.3 m/s) to preferred walking speeds., Sensors, 21 (21), ISSN:1424-8220

The recent explosive growth in the number of smart technologies relying on data collected from sensors and processed with machine learning classifiers made the training data imbalance problem more visible than ever before. Class-imbalanced sets used to train models of various events of interest are among the main reasons for a smart technology to work incorrectly or even to completely fail. This paper presents an attempt to resolve the imbalance problem in sensor sequential (time-series) data through training data augmentation. An Unrolled Generative Adversarial Networks (Unrolled GAN)-powered framework is developed and successfully used to balance the training data of smartphone accelerometer and gyroscope sensors in different contexts of road surface monitoring. Experiments with other sensor data from an open data collection are also conducted. It is demonstrated that the proposed approach allows for improving the classification performance in the case of heavily imbalanced data (the F1 score increased from 0.69 to 0.72, p<0.01, in the presented case study). However, the effect is negligible in the case of slightly imbalanced or inadequate training sets. The latter determines the limitations of this study that would be resolved in future work aimed at incorporating mechanisms for assessing the training data quality into the proposed framework and improving its computational efficiency.

We present and validate a multi-wavelength time-domain near-infrared spectroscopy (TD-NIRS) system that avoids switching wavelengths and instead exploits the full capability of a supercontinuum light source by emitting and acquiring signals for the whole chosen range of wavelengths. The system was designed for muscle and brain oxygenation monitoring in a clinical environment. A pulsed supercontinuum laser emits broadband light and each of two detection modules acquires the distributions of times of flight of photons (DTOFs) for 16 spectral channels (used width 12.5 nm / channel), providing a total of 32 DTOFs at up to 3 Hz. Two emitting fibers and two detection fiber bundles allow simultaneous measurements at two positions on the tissue or at two source-detector separations. Three established protocols (BIP, MEDPHOT, and nEUROPt) were used to quantitatively assess the system’s performance, including linearity, coupling, accuracy, and depth sensitivity. Measurements were performed on 32 homogeneous phantoms and two inhomogeneous phantoms (solid and liquid). Furthermore, measurements on two blood-lipid phantoms with a varied amount of blood and Intralipid provide the strongest validation for accurate tissue oximetry. The retrieved hemoglobin concentrations and oxygen saturation match well with the reference values that were obtained using a commercially available NIRS system (OxiplexTS) and a blood gas analyzer (ABL90 FLEX), except a discrepancy occurs for the lowest amount of Intralipid. In-vivo measurements on the forearm of three healthy volunteers during arterial (250 mmHg) and venous (60 mmHg) cuff occlusions provide an example of tissue monitoring during the expected hemodynamic changes that follow previously well-described physiologies. All results, including quantitative parameters, can be compared to other systems that report similar tests. Overall, the presented TD-NIRS system has an exemplary performance evaluated with state-of-the-art performance assessment methods.

Countrywide winter and summer Sentinel-1 (S1) backscatter data, cloud-free summer Sentinel-2 (S2) images, an Airborne Laser Scanning (ALS)-based Digital Terrain Model (DTM) and a forest mask were used to model and subsequently map Dominant Leaf Type (DLT) with the thematic classes broadleaved and coniferous trees for the whole of Switzerland. A novel workflow was developed that is robust, cost-efficient and highly automated using reference data from aerial image interpretation. Two machine learning approaches based on Random Forest (RF) and deep learning (UNET) for the whole country with three sets of predictor variables were applied. 24 subareas based on aspect and slope categories were applied to explore effects of the complex mountainous topography on model performances. The reference data split into training, validation and test data sets was spatially stratified using a 25 km regular grid. Model accuracies of both RF and UNET were generally highest with Kappa (K) around 0.95 when predictors were included from both S1/S2 and the topographic variables aspect, elevation and slope from the DTM. While only slightly lower accuracies were obtained when using S2 and DTM data, lowest accuracies were obtained when only predictors from S1 and DTM were included, with RF performing worse than UNET. While on countrywide level RF and UNET performed overall similarly, substantial differences in model performances, i.e. higher variances and lower accuracies, were found in subareas with northwest to northeast orientations. The combined use of S1/S2 and DTM predictors mitigated these problems related to topography and shadows and was therefore superior to the single use of S1 and DTM or S2 and DTM data. The comparison with independent National Forest Inventory (NFI) plot data demonstrated precisions of K around 0.6 in the predictions of DLT and indicated a trend of increasing deviations in mixed forests. A comparison with the Copernicus High Resolution Layer (HRL) DLT 2018 revealed overall higher map accuracies with the exception of pure broadleaved forest. Although, spatial patterns of DTL were overall similar, UNET performed better than RF in areas with a distinct DLT on forest stand level, with the largest differences occurring when only S1 and DTM data was used. In contrast, predictions obtained from RF were more accurate in mixed stands. This study goes beyond the case study level and meets the requirements of countrywide data sets, in particular regarding repeatability, updating, costs and characteristics of training data sets. The 10 m countrywide DLT maps add complementary and spatially explicit information to the existing NFI estimates and are thus highly relevant for forestry practice and other related fields.

In a turbid medium such as biological tissue, near-infrared optical tomography (NIROT) can image the oxygenation, a highly relevant clinical parameter. To be an efficient diagnostic tool, NIROT has to have high spatial resolution and depth sensitivity, fast acquisition time, and be easy to use. Since many tissues cannot be penetrated by near-infrared light, such tissue needs to be measured in reflection mode, i.e., where light emission and detection components are placed on the same side. Thanks to the recent advance in single-photon avalanche diode (SPAD) array technology, we have developed a compact reflection-mode time-domain (TD) NIROT system with a large number of channels, which is expected to substantially increase the resolution and depth sensitivity of the oxygenation images. The aim was to test this experimentally for our SPAD camera-empowered TD NIROT system. Experiments with one and two inclusions, i.e., optically dense spheres of 5mm radius, immersed in turbid liquid were conducted. The inclusions were placed at depths from 10mm to 30mm and moved across the field-of-view. In the two-inclusion experiment, two identical spheres were placed at a lateral distance of 8mm. We also compared short exposure times of 1s, suitable for dynamic processes, with a long exposure of 100s. Additionally, we imaged complex geometries inside the turbid medium, which represented structural elements of a biological object. The quality of the reconstructed images was quantified by the root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and dice similarity. The two small spheres were successfully resolved up to a depth of 30mm. We demonstrated robust image reconstruction even at 1s exposure. Furthermore, the complex geometries were also successfully reconstructed. The results demonstrated a groundbreaking level of enhanced performance of the NIROT system based on a SPAD camera.

Significance: The highest absorption peaks of the main components of bone are in the mid-infrared region, making Er:YAG and CO2 lasers the most efficient lasers for cutting bone. Yet, studies of deep bone ablation in minimally invasive settings are very limited, as finding suitable materials for coupling high-power laser light with low attenuation beyond 2 μm is not trivial. Aim: The first aim of this study was to compare the performance of different optical fibers in terms of transmitting Er:YAG laser light with a 2.94-μm wavelength at high pulse energy close to 1 J. The second aim was to achieve deep bone ablation using the best-performing fiber, as determined by our experiments. Approach: In our study, various optical fibers with low attenuation (λ=2.94 μm) were used to couple the Er:YAG laser. The fibers were made of germanium oxide, sapphire, zirconium fluoride, and hollow-core silica, respectively. We compared the fibers in terms of transmission efficiency, resistance to high Er:YAG laser energy, and bending flexibility. The best-performing fiber was used to achieve deep bone ablation in a minimally invasive setting. To do this, we adapted the optimal settings for free-space deep bone ablation with an Er:YAG laser found in a previous study. Results: Three of the fibers endured energy per pulse as high as 820 mJ at a repetition rate of 10 Hz. The best-performing fiber, made of germanium oxide, provided higher transmission efficiency and greater bending flexibility than the other fibers. With an output energy of 370 mJ per pulse at 10 Hz repetition rate, we reached a cutting depth of 6.82±0.99 mm in sheep bone. Histology image analysis was performed on the bone tissue adjacent to the laser ablation crater; the images did not show any structural damage. Conclusions: The findings suggest that our prototype could be used in future generations of endoscopic devices for minimally invasive laserosteotomy.

We investigated actual daily life upper limb (UL) activity in relation to observed UL motor function and perceived UL activity in chronic stroke in order to better understand and improve UL activity in daily life. In 60 patients, we collected (1) observed UL motor function (Fugl-Meyer Assessment (FMA-UE)), (2) perceived UL activity (hand subscale of the Stroke Impact Scale (SIS-Hand)), and (3) daily life UL activity (bilateral wrist-worn accelerometers for 72 h) data. Data were compared between two groups of interest, namely (1) good observed (FMA-UE &gt, 50) function and good perceived (SIS-Hand &gt, 75) activity (good match, n = 16) and (2) good observed function but low perceived (SIS-Hand ≤75) activity (mismatch, n = 15) with Mann–Whitney U analysis. The mismatch group only differed from the good match group in perceived UL activity (median (Q1–Q3) = 50 (30–70) versus 93 (85–100), p &lt, 0.001). Despite similar observed UL motor function and other clinical characteristics, the affected UL in the mismatch group was less active in daily life compared to the good match group (p = 0.013), and the contribution of the affected UL compared to the unaffected UL for each second of activity (magnitude ratio) was lower (p = 0.022). We conclude that people with chronic stroke with low perceived UL activity indeed tend to use their affected UL less in daily life despite good observed UL motor function.

Extrinsically administered light-absorbing agents may greatly enhance the sensitivity and imaging performance of optoacoustic tomography (OAT). Beyond the use of targeted contrast agents in functional and molecular imaging applications, tracking of highly absorbing microparticles has recently been shown to facilitate super-resolution volumetric angiography and mapping of blood flow. However, in vivo characterization of new types of microparticulate absorbing agents is often hindered due to their potential toxicity, incompatible dimensions, or sub-optimal extinction spectrum shadowed by strong background absorption of hemoglobin. Herein, we used an intracardiac perfusion mouse model to individually track the perfusion of absorbing particles through the cerebral vasculature by acquiring a sequence of high-frame-rate 3D OAT images. The particles were injected in the left ventricle of the heart after substitution of blood by an artificial cerebrospinal fluid post mortem, which has further contributed to minimizing the background OAT signals induced by hemoglobin absorption. The presented approach can greatly aid the development of new microparticulate contrast agents with optimized performance for various OAT imaging applications.

Background: This study investigates the dart-throwing motion (DTM) by comparing an inertial measurement unit-based system previously validated for basic motion tasks with an optoelectronic motion capture system. The DTM is interesting as wrist movement during many activities of daily living occur in this movement plane, but the complex movement is difficult to assess clinically. Methods: Ten healthy subjects were recorded while performing the DTM with their right wrist using inertial sensors and skin markers. Maximum range of motion obtained by the different systems and the mean absolute difference were calculated. Results: In the flexion-extension plane, both systems calculated a range of motion of 100° with mean absolute differences of 8°, while in the radial-ulnar deviation plane, a mean absolute difference of 17° and range of motion values of 48° for the optoelectronic system and 59° for the inertial measurement units were found. Conclusions: This study shows the challenge of comparing results of different kinematic motion capture systems for complex movements while also highlighting inertial measurement units as promising for future clinical application in dynamic and coupled wrist movements. Possible sources of error and solutions are discussed., Sensors, 21 (16)

Minimizing the spread of viruses in the environment is the first defense line when fighting outbreaks and pandemics, but the current SARS-CoV-2 pandemic demonstrates how difficult this is on global scale, particularly in a sustainable and environmental-friendly way. Here we introduce and develop a sustainable and bio-degradable anti-viral filtration membrane composed of amyloid nanofibrils made from food-grade milk proteins and iron oxyhydroxide nanoparticles synthesized in-situ from iron salts by simple pH-tuning. Thus, all the membrane components are made of environmentally friendly, non-toxic and widely available materials. The membrane has outstanding efficacy against a broad range of viruses, including enveloped, non-enveloped, airborne and waterborne viruses, such as SARS-CoV-2, H1N1 (the influenza A virus strain responsible for the swine flu pandemic in 2009), and enterovirus 71 (EV71; a non-enveloped virus resistant to harsh conditions such as highly acidic pH), highlighting a possible role in fighting the current and future viral outbreaks and pandemics., Nature Nanotechnology, 16, ISSN:1748-3387, ISSN:1748-3395

The aim of this study is to test a series of methods relying on hyperspectral measurements to characterize phytoplankton in clear lake waters. The phytoplankton temporal evolutions were analyzed exploiting remote sensed indices and metrics linked to the amount of light reaching the target (EPAR), the chlorophyll-a concentration ([Chl-a]OC4) and the fluorescence emission proxy. The latter one evaluated by an adapted version of the Fluorescence Line Height algorithm (FFLH). A peculiar trend was observed around the solar noon during the clear sky days. It is characterized by a drop of the FFLH metric and the [Chl-a]OC4 index. In addition to remote sensed parameters, water samples were also collected and analyzed to characterize the water body and to evaluate the in-situ fluorescence (FF) and absorbed light (FA). The relations between the remote sensed quantities and the in-situ values were employed to develop and test several phytoplankton primary production (PP) models. Promising results were achieved replacing the FA by the EPAR or FFLH in the equation evaluating a PP proxy (R2 &gt, 0.65). This study represents a preliminary outcome supporting the PP monitoring in inland waters by means of remote sensing-based indices and fluorescence metrics.

We present a hardware agnostic error mitigation algorithm for near term quantum processors inspired by the classical Lanczos method. This technique can reduce the impact of different sources of noise at the sole cost of an increase in the number of measurements to be performed on the target quantum circuit, without additional experimental overhead. We demonstrate through numerical simulations and experiments on IBM Quantum hardware that the proposed scheme significantly increases the accuracy of cost functions evaluations within the framework of variational quantum algorithms, thus leading to improved ground state calculations for quantum chemistry and physics problems beyond state-of-the-art results., Quantum, 5, ISSN:2521-327X

Images rendered with common optoacoustic system implementations are often afflicted with distortions and poor visibility of structures, hindering reliable image interpretation and quantification of bio-chrome distribution. Among the practical limitations contributing to artifactual reconstructions are insufficient tomographic detection coverage and suboptimal illumination geometry, as well as inability to accurately account for acoustic reflections and speed of sound heterogeneities in the imaged tissues. Here we developed a convolutional neural network (CNN) approach for enhancement of optoacoustic image quality which combines training on both time-resolved signals and tomographic reconstructions. Reference human finger data for training the CNN were recorded using a full-ring array system that provides optimal tomographic coverage around the imaged object. The reconstructions were further refined with a dedicated algorithm that minimizes acoustic reflection artifacts induced by acoustically mismatch structures, such as bones. The combined methodology is shown to outperform other learning-based methods solely operating on image-domain data.

Water is a ubiquitous liquid with unique physicochemical properties, whose nature has shaped our planet and life as we know it. Water in restricted geometries has different properties than in bulk. Confinement can prevent low-temperature crystallization of the molecules into a hexagonal structure and thus create a state of amorphous water. To understand the survival of life at subzero temperatures, it is essential to elucidate this behaviour in the presence of nanoconfining lipidic membranes. Here we introduce a family of synthetic lipids with designed cyclopropyl modifications in the hydrophobic chains that exhibit unique liquid-crystalline behaviour at low temperature, which enables the maintenance of amorphous water down to ~10 K due to nanoconfinement. The combination of experiments and molecular dynamics simulations unveils a complex lipid–water phase diagram in which bicontinuous cubic and lamellar liquid crystalline phases that contain subzero liquid, glassy or ice water emerge as a competition between the two components, each pushing towards its thermodynamically favoured state. A specifically designed family of synthetic lipids exhibits unique liquid-crystalline behaviour at low temperature, enabling maintenance of amorphous water down to about 10 K due to nanoconfinement.

In optical coherence microscopy, optical aberrations commonly result in astigmatism-dominated wavefront errors in the peripheral regions of the optical objective, primarily elongating the microscope’s point-spread function along the radial direction in the vicinity of the focal plane. We report on enhanced-field-of-view optical coherence microscopy through computational aberration correction in the visible-light range. An isotropic spatial resolution of 2.5 µm was achieved over an enhanced lateral field of view spanning 1.3 mm × 1.6 mm, as experimentally verified in a micro-bead phantom and further demonstrated in ex vivo tissue samples. The extended field of view achieved by the digital aberration correction facilitates the use of low-cost systems by averting the need for high-quality objectives.

Graphical abstract The CR780RGD-NPs can easily penetrate the blood-brain-barrier and actively target the brain tumor, where the strong optoacoustic generation from CR780RGD-NPs can be efficiently monitored by multispectral optoacoustic tomography (MSOT)., Contrast enhancement in optoacoustic (photoacoustic) imaging can be achieved with agents that exhibit high absorption cross-sections, high photostability, low quantum yield, low toxicity, and preferential bio-distribution and clearance profiles. Based on advantageous photophysical properties of croconaine dyes, we explored croconaine-based nanoparticles (CR780RGD-NPs) as highly efficient contrast agents for targeted optoacoustic imaging of challenging preclinical tumor targets. Initial characterization of the CR780 dye was followed by modifications using polyethylene glycol and the cancer-targeting c(RGDyC) peptide, resulting in self-assembled ultrasmall particles with long circulation time and active tumor targeting. Preferential bio-distribution was demonstrated in orthotopic mouse brain tumor models by multispectral optoacoustic tomography (MSOT) imaging and histological analysis. Our findings showcase particle accumulation in brain tumors with sustainable strong optoacoustic signals and minimal toxic side effects. This work points to CR780RGD-NPs as a promising optoacoustic contrast agent for potential use in the diagnosis and image-guided resection of brain tumors.

Low back pain (LBP) is a commonly experienced symptom posing a tremendous healthcare burden to individuals and society at large. The LBP pathology is strongly linked to degeneration of the intervertebral disc (IVD), calling for development of early-stage diagnostic tools for visualizing biomolecular changes in IVD. Multimodal measurements of fluorescence molecular tomography (FMT) and magnetic resonance imaging (MRI) were performed on IVD whole organ culture model using an in-house built FMT system and a high-field MRI scanner. The resulted multimodal images were systematically validated through epifluorescence imaging of the IVD sections at a microscopic level. Multiple image contrasts were exploited, including fluorescence distribution, anatomical map associated with T1-weighted MRI contrast, and water content related with T2 relaxation time. The developed multimodality imaging approach may thus serve as a new assessment tool for early diagnosis of IVD degeneration and longitudinal monitoring of IVD organ culture status using fluorescence markers.

Single-layer hexagonal boron nitride is produced on 2 inch Pt(111)/sapphire wafers. The growth with borazine vapor deposition at process temperatures between 1000 and 1300 K is in situ investigated by photoelectron yield measurements. The growth kinetics is slower at higher temperatures and follows a tanh2 law which better fits for higher temperatures. The crystal-quality of hexagonal boron nitride (h-BN)/Pt(111) is inferred from scanning low energy electron diffraction (x-y LEED). The data indicate a strong dependence of the epitaxy on the growth temperature. The dominant structure is an aligned coincidence lattice with 10 h-BN on 9 Pt(1 × 1) unit cells and follows the substrate twinning at the millimeter scale.

Laser ablation (LA) is gaining acceptance for the treatment of tumors as a viable alternative to surgical resection. In parallel, optoacoustic tomography (OAT) has enabled defining new regimes for diagnosis and characterization of malignant neoplastic lesions with high sensitivity and specificity. Even though pulsed nanosecond lasers are commonly used for both imaging and therapeutic purposes, real-time thermal treatment monitoring with a single laser source has not been previously attempted. Herein, we demonstrate the feasibility of combined OAT and LA by percutaneous irradiation of subcutaneous tumors with a 100 mJ short-pulsed (~5 ns) laser operating at 1064 nm and 100 Hz pulse repetition frequency. The OAT images rendered with a spherical ultrasound transducer array enabled real-time monitoring of the LA lesion progression, which is essential for determining the optimal treatment end-point. Local changes in the optoacoustic signal intensity associated with the induced temperature changes as well as structural alterations in the tumor vasculature could clearly be observed. The optoacoustic volumetric projections further correlated with crosssections extracted from the excised tumors. This newly enabled capability anticipates new theranostic approaches in cancer research and treatment with potential applicability in a clinical setting.

Ultrasound-based shear wave elastography (SWE) provides the means to quantify tissue mechanical properties in vivo and has proven valuable in detecting degenerative processes in tendons. Its current mode of use is for two-dimensional rendering measurements, which are highly positiondependent. We therefore propose an approach to create a volumetric reconstruction of the mechanoacoustic properties of a structure of interest based on optically tracking the ultrasound probe during free-hand measurement sweeps. In the current work, we aimed (1) to assess the technical feasibility of the three-dimensional mapping of unidirectional shear wave velocity (SWV), (2) to evaluate the possible artefacts associated with hand-held image acquisition, (3) to investigate the reproducibility of the proposed technique, and (4) to study the potential of this method in detecting local adaptations in a longitudinal study setting. Operative and technical feasibility as well as potential artefacts associated with hand-held image acquisition were studied on a synthetic phantom containing discrete targets of known mechanical properties. Measurement reproducibility was assessed based on inter-day and inter-reader scans of the patellar, Achilles, and supraspinatus tendon of ten healthy volunteers and was compared to traditional two-dimensional image acquisition. The potential of this method in detecting local adaptations was studied by testing the effect of short-term voluntary isometric loading history on SWV along the tendon long axis. The suggested approach was technically feasible and reproducible, with a moderate to very good reliability and a standard error of measurement in the range of 0.300–0.591 m/s for the three assessed tendons at the two test-retest modalities. We found a consistent variation in SWV along the longitudinal axis of each tendon, and isometric loading resulted in regional increases in SWV in the patellar and Achilles tendons. The proposed method outperforms traditional two-dimensional measurement with regards to reproducibility and may prove valuable in the objective assessment of pathological tendon changes. © 2021 by the authors. Licensee MDPI, Basel, Switzerland., Sensors, 21 (5), ISSN:1424-8220

Background: Finger pulse oximeters are widely used to monitor physiological responses to high-altitude exposure, the progress of acclimatization, and/or the potential development of high-altitude related diseases. Although there is increasing evidence for its invaluable support at high altitude, some controversy remains, largely due to differences in individual preconditions, evaluation purposes, measurement methods, the use of different devices, and the lacking ability to interpret data correctly. Therefore, this review is aimed at providing information on the functioning of pulse oximeters, appropriate measurement methods and published time courses of pulse oximetry data (peripheral oxygen saturation, (SpO2) and heart rate (HR), recorded at rest and submaximal exercise during exposure to various altitudes. Results: The presented findings from the literature review confirm rather large variations of pulse oximetry measures (SpO2 and HR) during acute exposure and acclimatization to high altitude, related to the varying conditions between studies mentioned above. It turned out that particularly SpO2 levels decrease with acute altitude/hypoxia exposure and partly recover during acclimatization, with an opposite trend of HR. Moreover, the development of acute mountain sickness (AMS) was consistently associated with lower SpO2 values compared to individuals free from AMS. Conclusions: The use of finger pulse oximetry at high altitude is considered as a valuable tool in the evaluation of individual acclimatization to high altitude but also to monitor AMS progression and treatment efficacy.

The abnormal deposition of fibrillar beta-amyloid (Aβ) deposits in the brain is one of the major histopathological hallmarks of Alzheimer’s disease (AD). Here, we characterized curcumin-derivative CRANAD-2 for multi-spectral optoacoustic tomography and fluorescence imaging of brain Aβ deposits in the arcAβ mouse model of AD cerebral amyloidosis. CRANAD-2 showed a specific and quantitative detection of Aβ fibrils in vitro, even in complex mixtures, and it is capable of distinguishing between monomeric and fibrillar forms of Aβ. In vivo epi-fluorescence microscopy and optoacoustic tomography after intravenous CRANAD-2 administration demonstrated higher cortical retention in arcAβ compared to non-transgenic littermate mice. Immunohistochemistry showed co-localization of CRANAD-2 and Aβ deposits in arcAβ mouse brain sections, thus verifying the specificity of the probe. In conclusion, we demonstrate suitability of CRANAD-2 for optical detection of Aβ deposits in animal models of AD pathology, which facilitates mechanistic studies and the monitoring of putative treatments targeting Aβ deposits.

Multi-spectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) that offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of multi-modal images remains challenging, chiefly due to the entirely different image contrast rendered by these modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and accurate quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. Performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy., Comment: 15 pages, 5 figures

Several imaging techniques aim at identifying features of carotid plaque instability but come with limitations, such as the use of contrast agents, long examination times and poor portability. Multispectral optoacoustic tomography (MSOT) employs light and sound to resolve lipid and hemoglobin content, both features associated with plaque instability, in a label-free, fast and highly portable way. Herein, 5 patients with carotid atherosclerosis, 5 healthy volunteers and 2 excised plaques, were scanned with handheld MSOT. Spectral unmixing allowed visualization of lipid and hemoglobin content within three ROIs: whole arterial cross-section, plaque and arterial lumen. Calculation of the fat-blood-ratio (FBR) value within the ROIs enabled the differentiation between patients and healthy volunteers (P = 0.001) and between plaque and lumen in patients (P = 0.04). Our results introduce MSOT as a tool for molecular imaging of human carotid atherosclerosis and open new possibilities for research and clinical assessment of carotid plaques.

Applicability of optoacoustic imaging in biology and medicine is determined by several key performance characteristics. In particular, an inherent trade-off exists between the acquired field-of-view (FOV) and temporal resolution of the measurements, which may hinder studies looking at rapid biodynamics at the whole-body level. Here, we report on a single-sweep volumetric optoacoustic tomography (sSVOT) system that attains whole body three-dimensional mouse scans within 1.8 s with better than 200 μm spatial resolution. sSVOT employs a spherical matrix array transducer in combination with multibeam illumination, the latter playing a critical role in maximizing the effective FOV and imaging speed performance. The system further takes advantage of the spatial response of the individual ultrasound detection elements to mitigate common image artifacts related to limited-view tomographic geometry, thus enabling rapid acquisitions without compromising image quality and contrast. We compare performance metrics to the previously reported whole-body mouse imaging implementations and alternative image compounding and reconstruction strategies. It is anticipated that sSVOT will open new venues for studying large-scale biodynamics, such as accumulation and clearance of molecular agents and drugs across multiple organs, circulation of cells, and functional responses to stimuli.