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Background Cyclic motor patterns (CMP) are the predominant motor pattern in the distal colon, and are important in both health and disease. Their origin, mechanism and relation to bioelectrical slow-waves remain incompletely understood. During abdominal surgery, an increase in the CMP occurs in the distal colon. This study aimed to evaluate the feasibility of detecting propagating slow waves and spike waves in the distal human colon through intraoperative, high-resolution (HR), serosal electrical mapping. Methods HR electrical recordings were obtained from the distal colon using validated flexible PCB arrays (6 × 16 electrodes; 4 mm inter-electrode spacing; 2.4 cm2, 0.3 mm diameter) for up to 15 min. Passive unipolar signals were obtained and analysed. Results Eleven patients (33–71 years; 6 females) undergoing colorectal surgery under general anaesthesia (4 with epidurals) were recruited. After artefact removal and comprehensive manual and automated analytics, events consistent with regular propagating activity between 2 and 6 cpm were not identified in any patient. Intermittent clusters of spike-like activities lasting 10–180 s with frequencies of each cluster ranging between 24 and 42 cpm, and an average amplitude of 0.54 ± 0.37 mV were recorded. Conclusions Intraoperative colonic serosal mapping in humans is feasible, but unlike in the stomach and small bowel, revealed no regular propagating electrical activity. Although sporadic, synchronous spike-wave events were identifiable. Alternative techniques are required to characterise the mechanisms underlying the hyperactive CMP observed in the intra- and post-operative period. New findings The aim of this study was to assess the feasibility of detecting propagating electrical activity that may correlate to the cyclic motor pattern in the distal human colon through intraoperative, high-resolution, serosal electrical mapping. High-resolution electrical mapping of the human colon revealed no regular propagating activity, but does reveal sporadic spike-wave events. These findings indicate that further research into appropriate techniques is required to identify the mechanism of hyperactive cyclic motor pattern observed in the intra- and post-operative period in humans.

Deep learning is increasingly popular in remote sensing communities and already successful in land cover classification and semantic segmentation. However, most studies are limited to the utilization of optical datasets. Despite few attempts applied to synthetic aperture radar (SAR) using deep learning, the huge potential, especially for the very high resolution (VHR) SAR, are still underexploited. Taking building segmentation as an example, the VHR SAR datasets are still missing to the best of our knowledge. A comparable baseline for SAR building segmentation does not exist, and which segmentation method is more suitable for SAR image is poorly understood. This article first provides a benchmark high-resolution (1 m) GaoFen-3 SAR datasets, which cover nine cities from seven countries, review the state-of-the-art semantic segmentation methods applied to SAR, and then summarize the potential operations to improve the performance. With these comprehensive assessments, we hope to provide the recommendation and roadmap for future SAR semantic segmentation.

Background High-resolution ultrasound (HRUS) and magnetic resonance neurography (MRN) are considered complementary to clinical and neurophysiological assessment for neuropathies. Aims The aim of our study was to compare the accuracy of HRUS and MRN for detecting various peripheral nerve pathologies, to choose the correct investigation to facilitate prompt patient management. Materials and Methods This prospective study was done using HRUS with 14 MHz linear-transducer and 3 or 1.5T MR in cases referred for the assessment of peripheral nerve pathologies. Image interpretation was done using a scoring system (score 0–3 confidence level) to assess for nerve continuity/discontinuity, increased nerve signal/edema, fascicular change, caliber change, and neuroma/mass lesion. We determined the accuracy, sensitivity, and specificity of these modalities compared with the diagnostic standard determined by surgical and/or histopathological, if not performed then clinical and/or electrodiagnostic evaluation. Results The overall accuracy of MRN was 89.3% (specificity: 66.6%, sensitivity: 92.6%, negative predictive value [NPV]: 57.1%, positive predictive value [PPV]: 95%) and that of HRUS was 82.9% (specificity: 100%, sensitivity: 80.4%, NPV: 42.8, PPV: 100). The confidence level for detecting nerve discontinuity and change in nerve caliber was found to be higher on ultrasonography than magnetic resonance imaging (MRI) (100 vs. 70% and 100 vs. 50%, respectively). Pathology of submillimeter caliber nerves was accurately detected by HRUS and these could not be well-visualized on MRI. Conclusion HRUS is a powerful tool that may be used as the first-line imaging modality for the evaluation of peripheral nerve pathologies, and a better means of evaluation of peripheral nerves with submillimeter caliber.

Background Hand tendon injuries are recognized clinical entities that are frequently seen. Clinical examinations usually warrant radiological correlative studies for confirmation and as a postoperative screening test. Here is a prospective observational cohort study enrolling 30 patients who were diagnosed clinically to have hand tendon injuries either pre- or postoperative; their ages were ranging from 5 to 64 years with a mean ± SD of 31.43 ± 12.19 years; 23 male patients (76.7%) and 7 female patients (23.3%) were evaluated by high-resolution ultrasound examination and a correlative evaluation was done by either intra-operative assessment or MRI study as gold standards. Results High-resolution ultrasound (HRUS) findings were binned into seventeen cases (56%) that had tendon tears, of which 10 cases (33.3%) had a complete tear and 7 cases (23.3%) had a partial tear. Postoperative tendon integrity was present in 13 cases (43.3%), a tendon callus was found in 2 cases (6.66%), and a postoperative abnormal motion on the dynamic study was present in 15 cases (50%). Intra-tendinous foreign bodies were detected in two cases (6.66%), a gap between the torn ends was found in 10 cases (33.3%), and re-tear (rupture) of the repaired tendons was present in four cases (13.3%). Coexistent nerve injuries were seen in two cases (6.66%); for the forementioned findings, HRUS had gained high accuracy measures as correlated to the gold standards (100% sensitivity and 100% specificity). Conclusion High-resolution ultrasound serves as a highly accurate potential diagnostic modality for preoperative evaluation of hand tendon injuries and the postoperative follow-up.

Combining liquid crystals (LCs) with well-designed anchoring patterns at the substrates offers tremendous potential for the development of functional electro-optic devices or stimuli-responsive actuators. Photo-alignment techniques nowadays allow an almost arbitrary control over the surface anchoring and this flexibility is used to design highly efficient flat optical LC components with different functionalities. Part of this research, dealing with nematic and chiral nematic LC between substrates with patterned azimuthal anchoring, is reviewed here. The focus is on understanding the self-assembly of complex structures, steered by an interplay between surface anchoring and LC elasticity. Additional insight into the LC bulk behaviour is obtained by comparing experimental results with numerical simulations of the director configuration. Periodic anchoring patterns with azimuthal rotation at the top and bottom substrate are studied, as well as ring-shaped alignment patterns with a 180 degrees or 360 degrees azimuthal rotation in a confined region in space. Different combinations of anchoring patterns at the top and bottom substrates are investigated and in addition to nematic liquid crystal (NLC), also short and long-pitch chiral nematic liquid crystal (CLC) is considered.

Background: Chest high-resolution computed tomography [HRCT] is considered the “gold” standard for radiological diagnosis of interstitial lung disease [ILD]. However, it is not available in all health care facilities and its high cost is a limitation for its wide use. Ultrasound could provide a suitable alternative. Aim of the work: To detect the role of transthoracic lung ultrasonography [LUS] in diagnosis and assessment of ILD and to correlate it with HRCT as a gold-standard diagnostic modality. Methods:This included fifty patients who were admitted with clinical features suggestive of ILD. All were clinically evaluated [by full history and physical examination], submitted to pulmonary functions, arterial blood gases, lung ultrasound and HRCT. Results: The total sum of B-lines was 72.64 ± 39.45 and the total positive chest areas [> 3 B-lines] 6.47 ± 2.35. The most common ultrasound finding was thick pleural line. Finally, there was positive [proportional], moderate, significant correlation between Warrick’s score [HRCT] and B-lines distance [LUS]. However, Warrick score correlated negatively with each of six-minute walk test, partial arterial oxygen tension and forced vital capacity. Conclusions: Lung ultrasound as a diagnostic modality for ILD reveled that, it is a useful tool, as there was good correlation between LUS and HRCT. LUS is cheap, accessible and radiation-free diagnostic tool. It could play a stand alone or a complementary role in the diagnosis and monitoring of ILD.

Páramos, a neotropical alpine grassland‐peatland biome of the northern Andes and Central America, play an essential role in regional and global cycles of water, carbon, and nutrients. They act as water towers, delivering water and ecosystem services from the high mountains down to the Pacific, Caribbean, and Amazon regions. Páramos are also widely recognized as a biodiversity and climate change hot spots, yet they are threatened by anthropogenic activities and environmental changes. Despite their importance for water security and carbon storage, and their vulnerability to human activities, only three decades ago, páramos were severely understudied. Increasing awareness of the need for hydrological evidence to guide sustainable management of páramos prompted action for generating data and for filling long‐standing knowledge gaps. This has led to a remarkably successful increase in scientific knowledge, induced by a strong interaction between the scientific, policy, and (local) management communities. A combination of well‐established and innovative approaches has been applied to data collection, processing, and analysis. In this review, we provide a short overview of the historical development of research and state of knowledge of the hydrometeorology, flux dynamics, anthropogenic impacts, and the influence of extreme events in páramos. We then present emerging technologies for hydrology and water resources research and management applied to páramos. We discuss how converging science and policy efforts have leveraged traditional and new observational techniques to generate an evidence base that can support the sustainable management of páramos. We conclude that this co‐evolution of science and policy was able to successfully cover different spatial and temporal scales. Lastly, we outline future research directions to showcase how sustainable long‐term data collection can foster the responsible conservation of páramos water towers.

1.AbstractAt ultra-high field, fMRI voxels can span the sub-millimeter range, allowing the recording of blood oxygenation level dependent (BOLD) responses at the level of fundamental units of neural computation, such as cortical columns and layers. This sub-millimeter resolution, however, is only nominal in nature as a number of factors limit the spatial acuity of functional voxels. Multivoxel Pattern Analysis (MVPA) may provide a means to detect information at finer spatial scales that may otherwise not be visible at the single voxel level due to limitations in sensitivity and specificity. Here, we evaluate the spatial scale of stimuli specific BOLD responses in multivoxel patterns exploited by linear Support Vector Machine, Linear Discriminant Analysis and Naïve Bayesian classifiers across cortical depths in V1. To this end, we artificially misaligned the testing relative to the training portion of the data in increasing spatial steps, then investigated the breakdown of the classifiers’ performances. A one voxel shift led to a significant decrease in decoding accuracy (p

Semantic segmentation is performed to understand an image at the pixel level; it is widely used in the field of autonomous driving. In recent years, deep neural networks achieve good accuracy performance; however, there exist few models that have a good trade-off between high accuracy and low inference time. In this paper, we propose a fast spatial feature network (FSFNet), an optimized lightweight semantic segmentation model using an accelerator, offering high performance as well as faster inference speed than current methods. FSFNet employs the FSF and MRA modules. The FSF module has three different types of subset modules to extract spatial features efficiently. They are designed in consideration of the size of the spatial domain. The multi-resolution aggregation module combines features that are extracted at different resolutions to reconstruct the segmentation image accurately. Our approach is able to run at over 203 FPS at full resolution ( $1024\times 2048$ ) in a single NVIDIA 1080Ti GPU, and obtains a result of 69.13% mIoU on the Cityscapes test dataset. Compared with existing models in real-time semantic segmentation, our proposed model retains remarkable accuracy while having high FPS that is over 30% faster than the state-of-the-art model. The experimental results proved that our model is an ideal approach for the Cityscapes dataset. The code is publicly available at: https://github.com/computervision8/FSFNet .

Micro-displays based on micro-LEDs are becoming more and more attractive in AR/MR (Augmented/Mixed Reality) applications. A display size of 0.5 to 0.7-inch is preferred, with 5,000 PPI (Pixel Per Inch) or higher. Due to this pixel density and size, a CMOS (Complementary Metal-Oxide-Silicon) backplane is an ideal solution to drive these pixelized micro-LEDs. As the required pixel size gets smaller, the design of the appropriate pixel circuit becomes more challenging. The simplest 2T1C (2 transistors & 1 capacitor) pixel circuit has potential problems, due to the leakage current of the switch transistor and the voltage drop on the matrix array layout. In this paper, a pixel circuit is proposed as a solution to overcome these two issues. Our simulation results show that the variation of the driving current to the LED is improved by 95 %, and the IR drop error rate is around 2.2 % compared to the 2T1C circuit. The test results also show that the error rate of IPIXEL for the whole region of display is under 2.5 %. This work is verified using a test chip implementation with 180 nm CMOS process technology.

Automatic extracting road intersection points is essential for applications such as data registration between vector data and remote sensing images, aircraft-assisted navigation. However, at a large scale, it is difficult to quickly and accurately extract road intersection points due to the problems caused by complex structures, geometric texture noise interference. In this context, taking OpenStreetMap (OSM) data as priori knowledge, we propose a method for automatic extraction of complex road intersection points based on fuzzy inference. First, OSM data are analyzed to obtain structural information of intersection points. Local search areas are built around the intersection points. Second, within the local search area, the candidate intersection point set are generated. Meanwhile the input image is segmented using multiresolution segmentation; then we establish a fuzzy rule to infer the road area from the segmentation result. The fuzzy indexes and rules are established for the candidate intersection point set to deduce the road intersection area. Finally, based on the results of the previous step, the road intersection points are extracted based on the line segment constraint, structure matching, and linkage equation. Three sets of high-resolution remote sensing images were used to verify the feasibility of the method. We demonstrate that the correctness and positioning accuracy of this method are superior to those of other methods through contrastive analysis.

Metal halide perovskites have emerged as promising photovoltaic materials, but, despite ultralow thermal conductivity, progress on developing them for thermoelectrics has been limited. Here, we report the thermoelectric properties of all-inorganic tin based perovskites with enhanced air stability. Fine tuning the thermoelectric properties of the films is achieved by self-doping through the oxidation of tin (ΙΙ) to tin (ΙV) in a thin surface-layer that transfers charge to the bulk. This separates the doping defects from the transport region, enabling enhanced electrical conductivity. We show that this arises due to a chlorine-rich surface layer that acts simultaneously as the source of free charges and a sacrificial layer protecting the bulk from oxidation. Moreover, we achieve a figure-of-merit (ZT) of 0.14 ± 0.01 when chlorine-doping and degree of the oxidation are optimised in tandem., Metal halide perovskites show excellent optoelectronic properties but the understanding on their thermoelectric properties has been limited. Here Liu et al. develop a strategy for controlled doping and achieve a high figure of merit ZT value of 0.14 via careful composition engineering.

National mapping agencies are responsible for creating and maintaining country wide geospatial datasets that are highly accurate and homogenous. The Netherlands’ Cadastre, Land Registry and Mapping Agency, in short, the Kadaster, has created a database of information related to solar installations, using GeoAI. Deep Learning techniques were employed to detect small and medium-scale solar installations on buildings from very high-resolution aerial images for the whole of the Netherlands. The impact of data pre-processing and post-processing are addressed and evaluated. The process was automatized to deal with enormous data and the method was scaled-up nation-wide with the help of cloud solutions. In order to make this information visible, consistent and usable, we built-upon the existing TernausNet; a convolution neural network (CNN) architecture. Model metrics were evaluated after post-processing. The algorithm when used in combination with automated or custom post-processing improves the results. The precision and recall rates of the model for 3 different regions were evaluated and are on average about 0.93 and 0.92 respectively after implementation of post-processing. Use of custom post-processing improves the results by removing the false positives by at least 50%. The final results were compared with the existing national PV register. Overall, the results are not only useful for policy makers to assist them to take the necessary steps in achieving the energy transition goals but also serves as a register for infrastructure planning.

The increased availability of ultra-high field scanners provides an opportunity to perform fMRI at sub-millimeter spatial scales and enables in vivo probing of laminar function in the human brain. In most previous studies, the definition of cortical layers, or depths, is based on an anatomical reference image that is collected by a different acquisition sequence and exhibits different geometric distortion compared to the functional images. Here, we propose to generate the anatomical image with the fMRI acquisition technique by incorporating magnetization transfer (MT) weighted imaging. Small flip angle binomial pulse trains are used as MT preparation, with a flexible duration (several to tens of milliseconds), which can be applied before each EPI segment without constraining the acquisition length (segment or slice number). The method's feasibility was demonstrated at 7T for coverage of either a small slab or the near-whole brain at 0.8 mm isotropic resolution. Tissue contrast was found to be similar to that obtained with a state-of-art anatomical reference based on MP2RAGE. This MT-weighted EPI image allows an automatic reconstruction of the cortical surface to support laminar analysis in native fMRI space, obviating the need for distortion correction and registration.

Coastal waters are highly productive and diverse ecosystems, often dominated by marine submerged aquatic vegetation (SAV) and strongly affected by a range of human pressures. Due to their important ecosystem functions, for decades, both researchers and managers have investigated changes in SAV abundance and growth dynamics to understand linkages to human perturbations. In European coastal waters, monitoring of marine SAV communities traditionally combines diver observations and/or video recordings to determine, for example, spatial coverage and species composition. While these techniques provide very useful data, they are rather time consuming, labor-intensive, and limited in their spatial coverage. In this study, we compare traditional and emerging remote sensing technologies used to monitor marine SAV, which include satellite and occupied aircraft operations, aerial drones, and acoustics. We introduce these techniques and identify their main strengths and limitations. Finally, we provide recommendations for researchers and managers to choose the appropriate techniques for future surveys and monitoring programs. Integr Environ Assess Manag 2022;18:892-908. © 2021 SETAC.

Optical coherence tomography (OCT) has become a novel approach to noninvasive imaging in the past three decades, bringing a significant potential to biological research and medical biopsy in situ, particularly in three-dimensional (3D) in vivo conditions. Specifically, OCT systems using broad bandwidth sources, mainly centered at near-infrared-II, allow significantly higher imaging depth, as well as maintain a high-resolution and better signal-to-noise ratio than the traditional microscope, which avoids the scattering blur and thus obtains more details from delicate biological structures not just limited to the surface. Furthermore, OCT systems combined the spectrometer with novel light sources, such as multiplexed superluminescent diodes or ultra-broadband supercontinuum laser sources, to obtain sub-micron resolution imaging with high-speed achieve widespread clinical applications. Besides improving OCT performance, the functional extensions of OCT with other designs and instrumentations, taking polarization state or birefringence into account, have further improved OCT properties and functions. We summarized the conventional principle of OCT systems, including time-domain OCT, Fourier-domain OCT, and several typical OCT extensions, compared their different components and properties, and analyzed factors that affect OCT performance. We also reviewed current applications of OCT in the biomedical field, especially in hearing science, discussed existing limitations and challenges, and looked forward to future development, which may provide a guideline for those with 3D in vivo imaging desires.

Current guidelines for primary and secondary prevention of stroke in patients with carotid atherosclerosis are based on the quantification of the degree of stenosis and symptom status. Recent publications have demonstrated that plaque morphology and composition, independent of the degree of stenosis, are important in the risk stratification of carotid atherosclerotic disease. This finding raises the question as to whether current guidelines are adequate or if they should be updated with new evidence, including imaging for plaque phenotyping, risk stratification, and clinical decision-making in addition to the degree of stenosis. To further this discussion, this roadmap consensus article defines the limits of luminal imaging and highlights the current evidence supporting the role of plaque imaging. Furthermore, we identify gaps in current knowledge and suggest steps to generate high-quality evidence, to add relevant information to guidelines currently based on the quantification of stenosis.

Gay, bisexual, and other men who have sex with men (MSM) experience disproportionately high burdens of Human Papilloma Virus (HPV)-associated anal cancers. Recent focus has shifted to anorectal cancer prevention through high-resolution anoscopy (HRA); however, little is known about sexual minority men’s perceptions, attitudes, or beliefs regarding HRA. We conducted 4 qualitative Focus Group Discussions (FGDs) (n = 15) with sexual minority men, focusing on their beliefs, attitudes, and perceptions of undergoing HRA. Participants discussed their experiences of HPV/HRA as influenced by both their gender and sexuality, including unawareness of HPV disease as a male health issue, challenges relating to female-oriented HPV/HRA language, conception of HPV/HRA as related to prostate health, and connecting their sexual behavior identification as “bottoms” to their need for HRA. As efforts to improve HRA knowledge, access, and uptake among sexual and gender minority communities increase, special attention should be paid to language and messaging choices around HRA.

Background Movement decisions made in space and time define how wildlife meet competing extrinsic and intrinsic demands to maximize fitness. Differential selection of resource patches provides one example of how to measure how animals balance conflicting demands. We hypothesized that individual spatial selection of patch types between dynamic seasons would signify flexible strategies used to minimize risk and optimize foraging efforts. Methods We used data collected from GPS loggers on golden-mantled ground squirrels (Callospermophilus lateralis) to model selection or avoidance of resources in two seasons of seed availability and one season in which no seeds were available. Movement decisions were measured in short-term discrete time intervals using high resolution location data. Selection or avoidance of specific resource features that entail fitness consequences were then assessed using resource selection functions. Results Seasonality of food availability, food type, and spatial distribution of food largely influenced how individuals selected resources within their home ranges. Overall, when seeds were available, individuals mediated risks of predation and loss of food by using patches closer to refuge and selected intermediate distances to the burrow. When food was not available, individuals minimized exposure to heightened risk by staying close to the burrow and avoiding riskier patch types. Conclusions Results indicate that individuals used flexible, dynamic strategies to select habitat patches which may allow them to balance conflicting seasonal demands. Advances in GPS technology for research of small mammals provide greater insight into how prey species in high risk environments differentially use resources to minimize risk and maintain fitness.

SUMMARYMicroseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-to-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2*wavelength is used to divide the data into segments. Later on, 191 features have been extracted and used as the input data to train the support vector machine (SVM) model. These features include 63 1-D time/spectral-domain features, and 128 2-D texture features, which indicate the continuity, smoothness, and irregularity of the events/noise. The proposed feature extraction maximally exploits the limited information of each segment. Afterward, we use a combination of univariate feature selection and random-forest-based recursive feature elimination for feature selection to avoid overfitting. This feature selection strategy not only finds the best features, but also decides the optimal number of features that are needed for the best accuracy. Regarding the training process, SVM with a Gaussian kernel is used. In addition, a cross-validation (CV) process is implemented for automatic parameter setting. In the end, a group of synthetic and field microseismic data with different levels of complexity show that the proposed workflow is much more robust than the state-of-the-art short-term-average over long-term-average ratio (STA/LTA) method and also performs better than the convolutional-neural-networks (CNN), for this case where the amount of training data sets is limited. A demo for the synthetic example is available: https://github.com/shanqu91/ML_event_detection_microseismic.

The intermingling of regenerated nerve fibers inside nerve grafts is the main reason for mismatched nerve fibers. This is one of the key factors affecting limb function recovery after nerve injury. Previous research has shown that the accuracy of axon regeneration can be improved by a bionic structural implant. To this aim, iodine and freeze-drying high-resolution micro-computed tomography was performed to visualize the 3D topography of the New Zealand rabbit sciatic nerve (25 mm). A series of 1-, 2-, 3-, and 4-custom anatomy-based nerve conduits (CANCs) were fabricated based on the anatomical structure of the nerve fascicle. The match index, luminal surface, and mechanical properties of CANCs were evaluated before implanting in a 10-mm gap of the sciatic nerve. Recovery was evaluated by histomorphometric analyses, electrophysiological study, gastrocnemius muscle weight recovery ratio, and behavioral assessments at 12 and 24 weeks postoperatively. The accuracy of nerve regeneration was determined by changes in fluorescence-labeled profile number during simultaneous retrograde tracing. Our results showed that the optimal preprocessing condition for high-resolution micro-computed tomography visualization was treatment of the sciatic nerve with 40% Lugol's solution for 3 days followed by lyophilization for 2 days. In vitro experiments demonstrated that the match index was highest in the 3-CANC group, followed by the 2-, 1-, and 4-CANC groups. The luminal surface was lowest in the 1-CANC group. Mechanical properties (transverse compressive and bending properties) were higher in the 3- and 4-CANC groups than in the 1-CANC group. In vivo experiments demonstrated that the recovery (morphology of regenerated fibers, compound muscle action potential, gastrocnemius muscle weight recovery ratio, pain-related autotomy behaviors, and range of motion) in the 3-CANC group was superior to the other CANC groups, and achieved the same therapeutic effect as the autograft. The simultaneous retrograde tracing results showed that the percentages of double-labeled profiles of the 2-, 3-, and 4-CANC groups were comparatively lower than that of the 1-CANC group, which indicates that regenerated nerve fascicles were less intermingled in the 2-, 3-, and 4-CANC groups. These findings demonstrate that the visualization of the rabbit sciatic nerve can be achieved by iodine and freeze-drying high-resolution micro-computed tomography, and that this method can be used to design CANCs with different channels that are based on the anatomical structure of the nerve. Compared with the 1-CANC, 3-CANC had a higher match index and luminal surface, and improved the accuracy of nerve regeneration by limiting the intermingling of the regenerated fascicles. All procedures were approved by the Animal Care and Use Committee, Xinjiang Medical University, China on April 4, 2017 (ethics approval No. IACUC20170315-02).

Earth-like planets, dark energy and variability of fundamental physical constants can be discovered by observing wavelength shifts in the optical spectra of astronomical objects1–5. These wavelength shifts are so tiny that exquisitely accurate and precise wavelength calibration of astronomical spectrometers is required. Laser frequency combs, broadband spectra of laser lines with absolutely known optical frequencies, are uniquely suited for this purpose6–13, provided their lines are resolved by the spectrometer. Generating such astronomical laser frequency combs (‘astrocombs’) remains challenging. Here, a microphotonic astrocomb is demonstrated via temporal dissipative Kerr solitons14–16 in photonic-chip-based silicon nitride microresonators17, directly providing a spurious-free spectrum of resolvable calibration lines. Sub-harmonically driven by temporally structured light18, the astrocomb is stabilized to a frequency standard, resulting in absolute calibration with a precision of 25 cm s–1 (radial velocity equivalent), relevant for Earth-like planet detection and cosmological research. The microphotonic technology can be extended in spectral span17,19–24, further boosting the calibration precision. A microphotonic astrocomb is demonstrated via temporal dissipative Kerr solitons in photonic-chip-based silicon nitride microresonators with a precision of 25 cm s–1 (radial velocity equivalent), useful for Earth-like planet detection and cosmological research.

Background and Aims: Legalization of cannabis (CB) for both medicinal and, in some states, recreational use, has given rise to increasing usage rates across the country. Of particular concern are indications that frequent CB use may be selectively harmful to the developing adolescent brain compared with adult-onset usage. However, the long-term effects of heavy, adolescent CB use on brain structure and cognitive performance in late-life remain unknown. A critical brain region is the hippocampus (HC), where there is a striking intersection between high concentrations of cannabinoid 1 (CB1) receptors and age-related pathology. Design: We investigated whether older adults (average age=66.6+7.2 years old) with a history of early life CB use show morphological differences in hippocampal subregions compared with older, nonusers. Methods: We performed high-resolution magnetic resonance imaging combined with computational techniques to assess cortical thickness of the medial temporal lobe, neuropsychological testing, and extensive drug use histories on 50 subjects (24 formerly heavy cannabis users [CB+ group] abstinent for an average of 28.7 years, 26 nonusers [CB− group]). We investigated group differences in hippocampal subregions, controlling for age, sex, and intelligence (as measured by the Wechsler Test of Adult Reading), years of education, and cigarette use. Results: The CB+ subjects exhibited thinner cortices in subfields cornu ammonis 1 [CA1; F(1,42)=9.96, p=0.0003], and CA2, 3, and the dentate gyrus [CA23DG; F(1,42)=23.17, p

International audience; PURPOSE: High-resolution free-breathing late gadolinium enhancement (HR-LGE) was shown valuable for the diagnosis of acute coronary syndromes with non-obstructed coronary arteries. The method may be useful to detect COVID-related myocardial injuries but is hampered by prolonged acquisition times. We aimed to introduce an accelerated HR-LGE technique for the diagnosis of COVID-related myocardial injuries. METHOD: An undersampled navigator-gated HR-LGE (acquired resolution of 1.25 mm(3)) sequence combined with advanced patch-based low-rank reconstruction was developed and validated in a phantom and in 23 patients with structural heart disease (test cohort; 15 men; 55 ± 16 years). Twenty patients with laboratory-confirmed COVID-19 infection associated with troponin rise (COVID cohort; 15 men; 46 ± 24 years) prospectively underwent cardiovascular magnetic resonance (CMR) with the proposed sequence in our center. Image sharpness, quality, signal intensity differences and diagnostic value of free-breathing HR-LGE were compared against conventional breath-held low-resolution LGE (LR-LGE, voxel size 1.8x1.4x6mm). RESULTS: Structures sharpness in the phantom showed no differences with the fully sampled image up to an undersampling factor of x3.8 (P > 0.5). In patients (N = 43), this acceleration allowed for acquisition times of 7min21s ± 1min12s at 1.25 mm(3) resolution. Compared with LR-LGE, HR-LGE showed higher image quality (P = 0.03) and comparable signal intensity differences (P > 0.5). In patients with structural heart disease, all LGE-positive segments on LR-LGE were also detected on HR-LGE (80/391) with 21 additional enhanced segments visible only on HR-LGE (101/391, P

Building integrated photovoltaic (BIPV) systems provide an opportunity for renewable energy generation in the built environment. In order to quantify the BIPV potential, numerical models of varying levels of complexity have been developed. This paper investigates how the complexity of BIPV models affects their predictions. The study starts with a detailed multi-physics BIPV model that combines a high-resolution one-diode model with physics-based thermal and airflow models. Next, simplifications are introduced into the model. The model predictions are compared to experimental data from a BIPV curtain wall installed in a test building in Leuven, Belgium. The results show that the detailed BIPV model is capable of estimating the BIPV daily energy yield with an average difference of 6.2 % (2.0 % for clear sky days) and the back-of-module temperature with an average difference of 1.74 °C. The use of a linear power model instead of a high-resolution one-diode model affects the average differences, but not significantly: 8.7 % for daily energy yield predictions (4.5 % for clear sky days) and 1.71 °C for temperature predictions. The use of two different empirical temperature correlations instead of a physics-based approach increases the average temperature difference to 3.5 and 4.4 °C. The average difference in daily energy yield increases to 10.2 and 10.4 %, respectively (5.9 and 5.5 % for clear sky days). These findings indicate that the detailed version of multi-physics BIPV model provides the best agreement with experimental data, but it is still possible to reduce the model complexity with acceptable accuracy.

The scanning electron microscope (SEM) is widely used in the analysis and research of materials, including fracture analysis, microstructure morphology, and nanomaterial analysis. With the rapid development of materials science and computer vision technology, the level of detection technology is constantly improving. In this paper, the deep learning method is used to intelligently identify microcracks in the microscopic morphology of SEM image. A deep learning model based on image level is selected to reduce the interference of other complex microscopic topography, and a detection method with dense continuous bounding boxes suitable for SEM images is proposed. The dense and continuous bounding boxes were used to obtain the local features of the cracks and rotating the bounding boxes to reduce the feature differences between the bounding boxes. Finally, the bounding boxes with filled regression were used to highlight the microcrack detection effect. The results show that the detection accuracy of our approach reached 71.12%, and the highest mIOU reached 64.13%. Also, microcracks in different magnifications and in different backgrounds were detected successfully. open access

The lensless on-chip microscope is an emerging technology in the recent decade that can realize the imaging and analysis of biological samples with a wide field-of-view without huge optical devices and any lenses. Because of its small size, low cost, and being easy to hold and operate, it can be used as an alternative tool for large microscopes in resource-poor or remote areas, which is of great significance for the diagnosis, treatment, and prevention of diseases. To improve the low-resolution characteristics of the existing lensless shadow imaging systems and to meet the high-resolution needs of point-of-care testing, here, we propose a high-precision on-chip microscope based on in-line holographic technology. We demonstrated the ability of the iterative phase recovery algorithm to recover sample information and evaluated it with image quality evaluation algorithms with or without reference. The results showed that the resolution of the holographic image after iterative phase recovery is 1.41 times that of traditional shadow imaging. Moreover, we used machine learning tools to identify and count the mixed samples of mouse ascites tumor cells and micro-particles that were iterative phase recovered. The results showed that the on-chip cell counter had high-precision counting characteristics as compared with manual counting of the microscope reference image. Therefore, the proposed high-precision lensless microscope on a chip based on in-line holographic imaging provides one promising solution for future point-of-care testing (POCT).

Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0.016, high whole-brain averaged peak signal-to-noise ratio of ~33.5 dB and structural similarity index of ~0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter–white matter surface placement, gray matter–cerebrospinal fluid surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan variability of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction at sub-millimeter resolution.

Mass Spectrometry Imaging (MSI) is an established and powerful MS technique that enables molecular mapping of tissues and cells finding widespread applications in academic, medical, and pharmaceutical industries. As both the applications and MSI technology have undergone rapid growth and improvement, the challenges associated both with analyzing large datasets and identifying the many detected molecular species have become apparent. The lack of readily available and comprehensive software covering all necessary data analysis steps has further compounded this challenge. To address this issue we developed LipostarMSI, comprehensive and vendor-neutral software for targeted and untargeted MSI data analysis. Through user-friendly implementation of image visualization and co-registration, univariate and multivariate image and spectral analysis, and for the first time, advanced lipid, metabolite, and drug metabolite (MetID) automated identification, LipostarMSI effectively streamlines biochemical interpretation of the data. Here, we introduce LipostarMSI and case studies demonstrating the versatility and many capabilities of the software.

In this paper, a terahertz hyperspectral imaging architecture based on an electro-optic terahertz dual-comb source is presented and demonstrated. In contrast to single frequency sources, this multiheterodyne system allows for the characterization of the whole spectral response of the sample in parallel for all the frequency points along the spectral range of the system. This hence provides rapid, highly consistent results and minimizes measurement artifacts. The terahertz illumination signal can be tailored (in spectral coverage and resolution) with high fexibility to meet the requirements of any particular application or experimental scenario while maximizing the signal-to-noise ratio of the measurement. Besides this, the system provides absolute frequency accuracy and a very high coherence that allows for direct signal detection without inter-comb synchronization mechanisms, adaptive acquisition, or post-processing. Using a feld-efect transistor-based terahertz resonant 300GHz detector and the raster-scanning method we demonstrate the two-dimensional hyperspectral imaging of samples of diferent kinds to illustrate the remarkable capabilities of this innovative architecture. A proof-of-concept demonstration has been performed in which tree leaves and a complex plastic fragment have been analyzed in the 300 GHz range with a frequency resolution of 10 GHz. This project has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222 and from the Spanish Ministry of Economy and Competitiveness under Project TEC2017-86271-R. The work has also been partially funded by the EU H2020 Celta project under Grant Agreement 675683 and the Foundation for Polish Science under grant IRA CENTERA. Viktor Krozer is thankful for partial financial support in the frame of the Chairs of Excellence program of the Universidad Carlos III, Madrid, Spain.

INTRODUCTION: Retinal imaging is a key investigation in ophthalmology. New devices continue to be created to keep up with the demand for better imaging modalities in this field. This review looks to highlight current trends and the future of retinal imaging. AREAS COVERED: This review looks at the advances in topographical imaging, photoacoustic microscopy, optical coherence tomography and molecular imaging. There is future scoping on further advances in retinal imaging. EXPERT OPINION: Retinal imaging continues to develop at a rapid pace to improve diagnosis and management of patients. We will see the development of big data to gain powerful insights and new technologies such as teleophthalmology mature in the future.

To control the spread of COVID-19, China has imposed national lockdown policies to restrict the movement of its population since the Chinese New Year of January 2020. In this study, we quantitatively analyzed the changes of pollution sources in Shanghai during the COVID-19 lockdown; a high-resolution emission inventory of typical pollution sources including stationary source, mobile source, and oil and gas storage and transportation source was established based on pollution source data from January to February 2020. The results show that the total emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter (PM), and volatile organic compounds (VOCs) were 9520.2, 37,978.6, 2796.7, and 7236.9 tons, respectively, during the study period. Affected by the COVID-19 lockdown, the mobile source experienced the largest decline. The car mileage and oil sales decreased by about 80% during the COVID-19 lockdown (P3) when compared with those during the pre-Spring Festival (P1). The number of aircraft activity decreased by approximately 50%. The impact of the COVID-19 epidemic on industries such as iron and steel and petrochemicals was less significant, while the greater impact was on coatings, chemicals, rubber, and plastic. The emissions of SO2, NOx, PM2.5, and VOCs decreased by 11%, 39%, 37%, and 47%, respectively, during P3 when compared with those during P1. The results show that the measures to control the spread of the COVID-19 epidemic made a significant contribution to emission reductions. This study may provide a reference for other countries to assess the impact of the COVID-19 epidemic on emissions and help establish regulatory actions to improve air quality. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-020-11858-x.

The following article introduces technologies that build three dimensional (3D) objects by adding layer-upon-layer of material, also called additive manufacturing technologies. Furthermore, most important features supporting the conscious choice of 3D printing methods for applications in micro and nanomanufacturing are covered. The micromanufacturing method covers photopolymerization-based methods such as stereolithography (SLA), digital light processing (DLP), the liquid crystal display–DLP coupled method, two-photon polymerization (TPP), and inkjet-based methods. Functional photocurable materials, with magnetic, conductive, or specific optical applications in the 3D printing processes are also reviewed.

In recent decades, high-resolution (HR) remote sensing images have shown considerable potential for providing detailed information for change detection. The traditional change detection methods based on HR remote sensing images mostly only detect a single land type or only the change range, and cannot simultaneously detect the change of all object types and pixel-level range changes in the area. To overcome this difficulty, we propose a new coarse-to-fine deep learning-based land-use change detection method. We independently created a new scene classification dataset called NS-55, and innovatively considered the adaptation relationship between the convolutional neural network (CNN) and the scene complexity by selecting the CNN that best fit the scene complexity. The CNN trained by NS-55 was used to detect the category of the scene, define the final category of the scene according to the majority voting method, and obtain the changed scene by comparison to obtain the so-called coarse change result. Then, we created a multi-scale threshold (MST) method, which is a new method for obtaining high-quality training samples. We used the high-quality samples selected by MST to train the deep belief network to obtain the pixel-level range change detection results. By mapping coarse scene changes to range changes, we could obtain fine multi-type land-use change detection results. Experiments were conducted on the Multi-temporal Scene Wuhan dataset and aerial images of a particular area of Dapeng New District, Shenzhen, where promising results were achieved by the proposed method. This demonstrates that the proposed method is practical, easy-to-implement, and the NS-55 dataset is physically justified. The proposed method has the potential to be applied in the large scale land use fine change detection problem and qualitative and quantitative research on land use/cover change based on HR remote sensing data.

It is generally difficult to characterize inert gases through chemical reactions due to their inert chemical properties. The phase interference-sensing system based on high-resolution surface plasmon resonance (SPR) has an excellent refractive index detection limit. Based on this, this paper presents a simple and workable method for the characterization and detection of inert gases. The phase of light for the present SPR sensor is more sensitive to the change in the external dielectric environment than an amplitude SPR sensor. The limit of detection (LOD) is usually in the order of 10&minus, 6 to 10&minus, 7 RIU, which is superior to LSPR (Localized Surface Plasmon Resonance) sensors and traditional SPR sensors. The sensor parameters are simulated and optimized. Our simulation shows that a 36 nm-thick gold film is more suitable for the SPR sensing of inert gases. By periodically switching between the two inert gases, helium and argon, the resolution of the system is tested. The SPR sensing system can achieve distinguishable difference signals, enabling a clear distinction and characterization of helium and argon. The doping of argon in helium has a detection limit of 1098 ppm.

Image fusion is an effective complementary method to obtain information from multi-source data. In particular, the fusion of synthetic aperture radar (SAR) and panchromatic images contributes to the better visual perception of objects and compensates for spatial information. However, conventional fusion methods fail to address the differences in imaging mechanism and, therefore, they cannot fully consider all information. Thus, this paper proposes a novel fusion method that both considers the differences in imaging mechanisms and sufficiently provides spatial information. The proposed method is learning-based, it first selects data to be used for learning. Then, to reduce the complexity, classification is performed on the stacked image, and the learning is performed independently for each class. Subsequently, to consider sufficient information, various features are extracted from the SAR image. Learning is performed based on the model&rsquo, s ability to establish non-linear relationships, minimizing the differences in imaging mechanisms. It uses a representative non-linear regression model, random forest regression. Finally, the performance of the proposed method is evaluated by comparison with conventional methods. The experimental results show that the proposed method is superior in terms of visual and quantitative aspects, thus verifying its applicability.

SPECT systems using pinhole apertures permit radiolabelled molecular spatial resolution, good energy resolution, and high sensitivity are required. We designed what we consider the “optimal” radionuclide detector system for this task. It should allow studying both detection of unstable atherosclerotic plaques and monitoring the effect of therapies. Using mice is particularly challenging in situations that require several intravenous injections of radiotracers, possibly for weeks or even months, in chronically ill animals. Thus, alternative routes of delivering the radiotracer in tail vein should be investigated. In this study, we have performed preliminary measurements of detection of atherosclerotic plaques in genetically modified mice with high-resolution prototype detector. We have also evaluated the feasibility of assessing left ventricular perfusion by intraperitoneal distributions to be imaged in vivo in small animals. Nevertheless, studying cardiovascular diseases in small animal models is very challenging, and in particular, submillimeter delivering of MIBI-Tc in healthy mice.

The design and capabilities of the momentum-resolved high-energy-resolution inelastic X-ray spectrometer (HERIX) at beamline 30-ID of the Advanced Photon Source are reported., Inelastic X-ray scattering is a powerful and versatile technique for studying lattice dynamics in materials of scientific and technological importance. In this article, the design and capabilities of the momentum-resolved high-energy-resolution inelastic X-ray spectrometer (HERIX) at beamline 30-ID of the Advanced Photon Source are reported. The instrument operates at 23.724 keV and has an energy resolution of 1.3–1.7 meV. It can accommodate momentum transfers of up to 72 nm−1, at a typical X-ray flux of 4.5 × 109 photons s−1 meV−1 at the sample. A suite of in situ sample environments are provided, including high pressure, static magnetic fields and uniaxial strains, all at high or cryogenic temperatures.

Rupture of a vulnerable carotid plaque is one of the leading causes of stroke. Carotid magnetic resonance imaging (MRI) is able to visualize all the main hallmarks of plaque vulnerability. Various MRI sequences have been developed in the last two decades to quantify carotid plaque burden and composition. Often, a combination of multiple sequences is used. These MRI techniques have been extensively validated with histological analysis of carotid endarterectomy specimens. High agreement between the MRI and histological measures of plaque burden, intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC) status, inflammation and neovascularization has been demonstrated. Novel MRI sequences allow to generate three-dimensional isotropic images with a large longitudinal coverage. Other new sequences can acquire multiple contrasts using a single sequence leading to a tremendous reduction in scan time. IPH can be easily identified as a hyperintense signal in the bulk of the plaque on strongly T(1)-weighted images, such as magnetization-prepared rapid acquisition gradient echo images, acquired within a few minutes with a standard neurovascular coil. Carotid MRI can also be used to evaluate treatment effects. Several meta-analyses have demonstrated a strong predictive value of IPH, LRNC, thinning or rupture of the FC for ischemic cerebrovascular events. Recently, in a large meta-analysis based on individual patient data of asymptomatic and symptomatic individuals with carotid artery stenosis, it was shown that IPH on MRI is an independent risk predictor for stroke, stronger than any known clinical risk parameter. Expert recommendations on carotid plaque MRI protocols have recently been described in a white paper. The present review provides an overview of the current status and applications of carotid plaque MR imaging and its future potential in daily clinical practice.

The detection of vehicles is an important and challenging topic that is relevant for many applications. In this work, we present a workflow that utilizes optical and elevation data to detect vehicles in remotely sensed urban data. This workflow consists of three consecutive stages: candidate identification, classification, and single vehicle extraction. Unlike in most previous approaches, fusion of both data sources is strongly pursued at all stages. While the first stage utilizes the fact that most man-made objects are rectangular in shape, the second and third stages employ machine learning techniques combined with specific features. The stages are designed to handle multiple sensor input, which results in a significant improvement. A detailed evaluation shows the benefits of our workflow, which includes hand-tailored features; even in comparison with classification approaches based on Convolutional Neural Networks, which are state of the art in computer vision, we could obtain a comparable or superior performance (F1 score of 0.96–0.94).

Techniques for testis immobilization can facilitate high-resolution MR imaging applications for testicular diseases by assuring good positioning of the testis on small radiofrequency coils and reducing motion artifacts. We tested negative pressure suction to immobilize the testis of rats during MR image acquisitions. Suction pressure between -5 and -10 kPa assured good positioning, suppressed motion artifacts, and allowed the observation of blood vessels and seminiferous tubules.

A new capacitive coupling method to enhance the driving performance of hydrogenated amorphous silicon thin-film transistors (a-Si:H TFTs) in high-resolution applications is presented. The gate voltage can be enlarged by the entirely transmitted high voltage of a global direct current power source line ( $V_{\mathrm{ DD}}$ ). Established models that are based on the measured electrical characteristics of fabricated a-Si:H TFTs with different aspect ratios are used to evaluate the feasibility of this proposed method in the gate driver. The maximum voltage of the gate voltage can be increased from 37.3 V to 47.6 V when $V_{\mathrm{ DD}}$ is set to 20 V, improving the driving capability of the gate driver by more than 17%, based on the specifications of a 5.99 inch HD + ( $720 \times 1440$ ) panel at a frame rate of 120 Hz.

Recently, deep learning has enabled a huge leap forward in image inpainting. However, due to the memory and computational limitation, most existing methods are able to handle only low-resolution inputs, typically less than 1 K. With the improvement of Internet transmission capacity and mobile device cameras, the resolution of image and video sources available to users via the cloud or locally is increasing. For high-resolution images, the common inpainting methods simply upsample the inpainted result of the shrinked image to yield a blurry result. In recent years, there is an urgent need to reconstruct the missing high-frequency information in high-resolution images and generate sharp texture details. Hence, we propose a general deep learning framework for high-resolution image inpainting, which first hallucinates a semantically continuous blurred result using low-resolution inpainting and suppresses computational overhead. Then the sharp high-frequency details with original resolution are reconstructed using super-resolution refinement. Experimentally, our method achieves inspiring inpainting quality on 2K and 4K resolution images, ahead of the state-of-the-art high-resolution inpainting technique. This framework is expected to be popularized for high-resolution image editing tasks on personal computers and mobile devices in the future.

The system spatial resolution of whole-body positron emission tomography (PET) is limited to around 2 mm due to positron physics and the large diameter of the bore. To stay below this 'physics'-limit a scintillation detector with an intrinsic spatial resolution of around 1.3 mm is needed. Currently used detector technology consists of arrays of 2.6-5 mm segmented scintillator pixels which are the dominant factor contributing to the system resolution. Pixelated detectors using smaller pixels exist but face major drawbacks in sensitivity, timing, energy resolution and cost. Monolithic continuous detectors, where the spatial resolution is determined by the shape of the light distribution on the photodetector array, are a promising alternative. Without having the drawbacks of pixelated detectors, monolithic ones can also provide depth-of-interaction (DOI) information. In this work we present a monolithic detector design aiming to serve high-resolution clinical PET systems while maintaining high sensitivity. A 50 x 50 x 16 mm(3) Lutetium-Yttrium oxyorthosilicate scintillation crystal with silicon photomultiplier (SiPM) backside readout is calibrated in singles mode by a collimated beam obtaining a reference dataset for the event positioning. A mean nearest neighbour (MNN) algorithm and an artificial neural network for positioning are compared. The targeted intrinsic detector resolution of 1.3 mm needed to reach a 2 mm resolution on system level was accomplished with both algorithms. The neural network achieved a mean spatial resolution of 1.14 mm FWHM for the whole detector and 1.02 mm in the centre (30 x 30 mm(2)). The MNN algorithm performed slightly worse with 1.17 mm for the whole detector and 1.13 mm in the centre. The intrinsic DOI information will also result in uniform system spatial resolution over the full field of view.

HighlY constrained back-PRojection (HYPR) is a post-processing de-noising technique originally developed for time-resolved magnetic resonance imaging. It has been recently applied to dynamic imaging for positron emission tomography and shown promising results. In this work, we have developed an iterative reconstruction algorithm (HYPR-OSEM) which improves the signal-to-noise ratio (SNR) in static imaging (i.e. single frame reconstruction) by incorporating HYPR de-noising directly within the ordered subsets expectation maximization (OSEM) algorithm. The proposed HYPR operator in this work operates on the target image(s) from each subset of OSEM and uses the sum of the preceding subset images as the composite which is updated every iteration. Three strategies were used to apply the HYPR operator in OSEM: (i) within the image space modeling component of the system matrix in forward-projection only, (ii) within the image space modeling component in both forward-projection and back-projection, and (iii) on the image estimate after the OSEM update for each subset thus generating three forms: (i) HYPR-F-OSEM, (ii) HYPR-FB-OSEM, and (iii) HYPR-AU-OSEM. Resolution and contrast phantom simulations with various sizes of hot and cold regions as well as experimental phantom and patient data were used to evaluate the performance of the three forms of HYPR-OSEM, and the results were compared to OSEM with and without a post reconstruction filter. It was observed that the convergence in contrast recovery coefficients (CRC) obtained from all forms of HYPR-OSEM was slower than that obtained from OSEM. Nevertheless, HYPR-OSEM improved SNR without degrading accuracy in terms of resolution and contrast. It achieved better accuracy in CRC at equivalent noise level and better precision than OSEM and better accuracy than filtered OSEM in general. In addition, HYPR-AU-OSEM has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision based on the studies conducted in this work.

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

Diffuse phosphorus (P) losses from arable land need to be reduced in a cost-efficient way, taking into account their temporal and spatial variability. This study, based on 16 farms across southern Sweden, examined possibilities for identifying critical source areas for P losses based on the combined results of high-resolution erosion modelling, independent risk assessments by farmers, soil survey and SWOT analysis performed by farmers. Statistically significant differences in dissolved P release were found between soil P test classes in the studied area, whereas soil textural classes and not P content governed potential mobilisation of soil particles and unreactive P. Spatial comparison of problem areas identified by farmers and modelled features showed that the modelled erosion pathways intersected 109 in a total of 128 (85%) observed problem areas. The study demonstrates the value in involving farmers in the identification of critical source areas in order to select and support implementation of effective countermeasures.

Assessment of microvasculature and tissue perfusion can provide diagnostic information on local or systemic diseases. Photoacoustic (PA) imaging has strong clinical potential because of its sensitivity to hemoglobin. We used a hand-held PA probe with integrated diode lasers and examined its feasibility and validity in the detection of increasing blood volume and (sub) dermal vascularization. Blood volume detection was tested in custom-made perfusion phantoms. Results showed that an increase of blood volume in a physiological range of 1.3% to 5.4% could be detected. The results were validated with power Doppler sonography. Using a motorized scanning setup, areas of the skin were imaged at relatively short scanning times (

BACKGROUND: Colon cancer is currently staged with CT. However, MRI is superior in the detection of colorectal liver metastasis, and MRI is standard in local staging of rectal cancer. Optimal (local) staging of colon cancer could become crucial in selecting patients for neoadjuvant treatment in the near future (Fluoropyrimidine Oxaliplatin and Targeted Receptor Preoperative Therapy trial).OBJECTIVE: The purpose of this study was to evaluate the diagnostic performance of MRI for local staging of colon cancer.DESIGN: This was a retrospective study.SETTINGS: The study was conducted at the Maastricht University Medical Centre.PATIENTS: In total, 55 patients with biopsy-proven colon carcinoma were included.MAIN OUTCOME MEASURES: All of the patients underwent an MRI (1.5-tesla; T2 and diffusion-weighted imaging) of the abdomen and were retrospectively analyzed by 2 blinded, independent readers. Histopathology after resection was the reference standard. Both readers evaluated tumor characteristics, including invasion through bowel wall (T3/T4 tumors), invasion beyond bowel wall of >= 5 mm and/or invasion of surrounding organs (T3cd/T4), serosal involvement, extramural vascular invasion, and malignant lymph nodes (N+). Interobserver agreement was compared using. statistics.RESULTS: MRI had a high sensitivity (72%-91%) and specificity (84%-89%) in detecting T3/T4 tumors (35/55) and a low sensitivity (43%-67%) and high specificity (75%88%) in detecting T3cd/T4 tumors (15/55). For detecting serosal involvement and extramural vascular invasion, MRI had a high sensitivity and moderate specificity, as well as a moderate sensitivity and specificity in the detection of nodal involvement. Interobserver agreements were predominantly good; the more experienced reader achieved better results in the majority of these categories.LIMITATIONS: The study was limited by its retrospective nature and moderate number of inclusions.CONCLUSIONS: MRI has a good sensitivity for tumor invasion through the bowel wall, extramural vascular invasion, and serosal involvement. In addition, together with its superior liver imaging, MRI might become the optimal staging modality for colon cancer. However, more research is needed to confirm this. See Video Abstract at http://links. lww. com/DCR/A309.

Key Clinical Message Catheter ablation is a recommended therapy option for ventricular tachycardia (VT). The antegrade transseptal approach for targeting VT with left ventricular origin is feasible with the high‐resolution basket catheter. High‐resolution mapping offers the potential to quickly acquire detailed voltage and activation maps. This may help to identify the crucial VT‐substrate even in patients with huge scar areas and hemodynamically unstable VT.