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OBJECTIVES The objective of the study was to establish the repeatability of baseline diagnostic images of the dorsum of the hands acquired using a high resolution Laser Doppler imager in patients with Raynaud's phenomenon secondary to Systemic Sclerosis (SSc). METHODS The dorsal side of the hands of 22 patients (8 male 14 female) , age range 29-73, median 62, with SSc and secondary Raynaud's phenomenon were imaged over two consecutive days at approximately the same time using a Moor Instruments High Resolution Laser Doppler imaging unit. The images were analysed by taking regions of interest at discrete locations in the images to calculate dimensionless values of flux(PU). Repeatability of the diagnostic investigation was assessed by using methods described by Bland and Altman and by also plotting the results from visit 1 against visit 2 and calculating the line of best fit. RESULTS AND CONCLUSIONS Based on the criteria that 95% of all measurement differences should be within a factor of 1.96 of the standard deviations of the mean values, then high resolution Laser Doppler Imaging technique is probably repeatable when acquiring and analysing baseline images of patients with Raynaud's phenomenon secondary to SSc. However a larger study with more patients is required to prove this conclusively - as only data from 19 patients was analysed [3 patients were not included due to technical issues] - and was therefore susceptible to marked clinical variations in patients presenting on different days for the investigations.

Quantitative trait locus (QTL) mapping is a useful method for revealing the mechanism of complex genetic traits and identifying new genomic information to accelerate crop improvement. In the present study, 154 F2:3 strains and their parents were used for restriction site-associated DNA sequencing, single-nucleotide polymorphism (SNP) identification, and genetic map construction. After filtering based on stringent filtering standards, 297.5 Gb of clean data were obtained. Further, 5,191 polymorphic SNP markers were identified from each sample, of which 1,997 polymorphic SNP markers were successfully mapped onto 18 different linkage groups. Six QTLs (QPH, QFBW, QDBW, QFW, QFT, and QFC) were identified based on the genetic map using the multiple QTL mapping (MQM) method, which were then assigned to three linkage groups, LG16, LG8, and LG3. QPH, QFBW, QDBW, and QFW were related to fiber yield, while QFT and QFC were related to fiber quality. This is the first study of its kind to map QTL of fiber yield and fiber quality, which will facilitate further understanding of the molecular genetic basis of these traits. However, there are limitations regarding the utilization of this map because several large gaps remain in some linkage groups. Therefore, additional markers need to be developed to further narrow these regions.

Remote Sensing satellites acquire information about an area by analyzing the transmitted and reflected radio waves. The Earth Imaging satellites capture high resolution images which occupy more storage space on-board and consume a lot of bandwidth for downlink transmission. Multispectral sensors represent information of the images in multiple bands which are typically less than 15. The multispectral image compression algorithms aim to reduce the size of the images while preserving their quality. This paper involves a study of various algorithms used for compression of multispectral imagery.

Diabetic Retinopathy (DR) is the condition caused due to uncontrolled diabetes that can lead to vision impairment. It greatly affects the retinal blood vessels and diminishes the fundus light-sensitive inner coating. Early diagnosis and regular screening of this disease are essential for prompt processing through artificial intelligence techniques. This paper targets assessing the latest techniques for screening and diagnosing DR, including 94 articles based on the Detection and grading of DR. For every analyzed approach, tables are summarized detailing imaging procedure used, datasets, performance metrics used. The research gaps are also highlighted in this paper. Despite the consistent progression and methods actualized in this field, a couple of issues actually should be centered on. The noise and contrast of the image in Image enhancement are still in the infancy stage for high resolution. This study covers a review of existing image techniques, the gold standard and private datasets available, performance measures used for detection and grading of DR. Now the future research focuses on the amalgamation of the dataset as well as techniques to make the generalized technique for detecting the lesion in DR through an automated system. Moreover, various research gaps have also been taken into account for further research. This review is beneficial to the researchers working in the field of medical imaging to screen and diagnose diseases.

Recently, 3D bioprinting has been explored as a promising technology for biomedical applications with the potential to create complex structures with precise features. Cell encapsulated hydrogels composed of materials such as gelatin, collagen, hyaluronic acid, alginate and polyethylene glycol have been widely used as bioinks for 3D bioprinting. However, since most hydrogel-based bioinks may not allow rapid stabilization immediately after 3D bioprinting, achieving high resolution and fidelity to the intended architecture is a common challenge in 3D bioprinting of hydrogels. In this study, we have utilized shear-thinning and self-healing ionically crosslinked oxidized and methacrylated alginates (OMAs) as a bioink, which can be rapidly gelled by its self-healing property after bioprinting and further stabilized via secondary crosslinking. It was successfully demonstrated that stem cell-laden calcium-crosslinked OMA hydrogels can be bioprinted into complicated 3D tissue structures with both high resolution and fidelity. Additional photocrosslinking enables long-term culture of 3D bioprinted constructs for formation of functional tissue by differentiation of encapsulated human mesenchymal stem cells.

Roadside concentrations of harmful pollutants such as NOx are highly variable in both space and time. This is rarely considered when assessing pedestrian and cyclist exposures. We aim to fully describe the spatio-temporal variability of exposures of pedestrians and cyclists travelling along a road at high resolution. We evaluate the value added of high spatio-temporal resolution compared to high spatial resolution only. We also compare high resolution vehicle emissions modelling to using a constant volume source. We highlight conditions of peak exposures, and discuss implications for health impact assessments. Using the large eddy simulation code Fluidity we simulate NOx concentrations at a resolution of 2 m and 1 s along a 350 m road segment in a complex real-world street geometry including an intersection and bus stops. We then simulate pedestrian and cyclist journeys for different routes and departure times. For the high spatio-temporal method, the standard deviation in 1 s concentration experienced by pedestrians (50.9 μg.m-3) is nearly three times greater than that predicted by the high-spatial only (17.5 μg.m-3) or constant volume source (17.6 μg.m-3) methods. This exposure is characterised by low concentrations punctuated by short duration, peak exposures which elevate the mean exposure and are not captured by the other two methods. We also find that the mean exposure of cyclists on the road (31.8 μg.m-3) is significantly greater than that of cyclists on a roadside path (25.6 μg.m-3) and that of pedestrians on a sidewalk (17.6 μg.m-3). We conclude that ignoring high resolution temporal air pollution variability experienced at the breathing time scale can lead to a mischaracterization of pedestrian and cyclist exposures, and therefore also potentially the harm caused. High resolution methods reveal that peaks, and hence mean exposures, can be meaningfully reduced by avoiding hyper-local hotspots such as bus stops and junctions.

Plants have a long history of use for their medicinal properties. The complexity of botanical extracts presents unique challenges and necessitates the application of innovative approaches to correctly identify and quantify bioactive compounds. For this study, we used untargeted metabolomics to explore the antimicrobial activity of Rumex crispus (yellow dock), a member of the Polygonaceae family used as an herbal remedy for bacterial infections. Ultra-performance liquid chromatography coupled with high resolution mass-spectrometry (UPLC-MS) was used to identify and quantify the known antimicrobial compound emodin. In addition, we used biochemometric approaches to integrate data measuring antimicrobial activity from R. crispus root starting material and fractions against methicillin-resistant Staphylococcus aureus (MRSA) with UPLC-MS data. Our results support the hypothesis that multiple constituents, including the anthraquinone emodin, contribute to the antimicrobial activity of R. crispus against MRSA.

This dataset includesthe yearly estimates of urban heat extremesandurban populationexposureto extreme heat in 13,126 global urban settlements during 1979–2016, based on high spatial resolution(1 km×1 km) population and daily minimum (Tmin), maximum (Tmax),and mean (Tavg)near-surface air temperatures.Extreme heat events are identified when daily temperature (Tmin, Tmax, or Tave) is greater than its corresponding 90thpercentile of the calendar day centered on a 15-day window in 1981–2010. Heat extremes with different durations (i.e., ≥1 day, ≥3 days, ≥6 days, and 3–5 days) are identified, and heat metrics including frequency, duration, intensity, and timing (i.e., onset and termination dates) are calculated. Urban population exposure iscalculated as the yearly number of extreme heat days multiplied by the count of people exposed to extreme heat. Daily temperatures are derived from the CHELSA-W5E5 v1.0 dataset (https://data.isimip.org/10.48364/ISIMIP.836809.2), and population counts in global urban settlements are derived from the GHS-UCDB R2019A v1.2 dataset (https://ghsl.jrc.ec.europa.eu/ghs_stat_ucdb2015mt_r2019a.php). If you have any questions or comments, please feel free to contact MissHui Zhang viazhangh573@mail2.sysu.edu.cn, Dr. Ming Luo vialuom38@mail.sysu.edu.cn, or Dr. Tao Pei viapeit@lreis.ac.cn. Reference: Zhang, H., Luo, M., Pei, T., et al., 2023. Unequal urban heat burdens impede climate justice and equity goals.The Innovation(Pre-accepted for publication).

Comparisons of recent global forest products at higher resolutions that are only available annually have shown major disagreements among forested areas in highly fragmented landscapes. A holistic reductionist framework and colourimetry were applied to create a chorologic typology of environmental indicators to map forest extent with an emphasis on large-scale performance, interpretability/communication, and spatial–temporal scalability. Interpretation keys were created to identify forest and non-forest features, and a set of candidate tree cover indices were developed and compared with a decision matrix of prescribed criteria. The candidate indices were intentionally limited to those applying only the visible and NIR bands to obtain the highest possible resolution and be compatible with commonly available multispectral satellites and higher resolution sensors, including aerial and potentially UAV/drone sensors. A new High-Resolution Tree Cover Index (HRTCI) in combination with the Green band was selected as the best index based on scores from the decision matrix. To further improve the performance of the indices, the chorologic typology included two insolation indices, a water index and a NIR surface saturation index, to exclude any remaining spectrally similar but unrelated land cover features such as agriculture, water, and built-up features using a process of elimination. The approach was applied to the four seasons across a wide range of ecosystems in south-eastern Australia, with and without regionalisation, to identify which season produces the most accurate results for each ecoregion and to assess the potential for mitigating the spatial–temporal scaling effects of the Modifiable Spatio-Temporal Unit Problem. Autumn was found to be the most effective season, yielding overall accuracies of 94.19% for the full extent, 95.79% for the temperate zone, and 95.71% for the arid zone. It produced the greatest spatial agreement between two recognised global products, the GEDI forest heights extent and the ESA WorldCover Tree cover class. The performance, transparency, and scalability of the approach should provide the basis for a framework for globally relatable forest monitoring.

The pixel size of the Infrared Atmospheric Sounding Interferometer (IASI) remote sensor is much smaller than the horizontal grid size of current Chemical Transport Models (CTMs). In order to assimilate the maximum of information from the IASI retrievals, we have increased the horizontal resolution of our model MOCAGE to be consistent with the IASI pixel size. Experiments are carried out with the Valentina data assimilation system using the standard and the high resolution versions of the model. Two resolutions of the horizontal Gaussian grid have been used for the model: with a T42 and a T170 triangular truncations. Our study is based on the combination of data from the IASI instrument and from the Microwave Limb Sounder (MLS), since this latter dataset allows the information to be spread through the whole atmospheric columns at a low computational cost. Two datasets of ozone super-observations have been constructed by averaging the IASI data on the two model grids. Direct model simulations without data assimilation first show that the increase of the horizontal resolution modifies the ozone smallest scale structures as well as the ozone meridional distribution. This modification results from a better representation of the vertical velocity with the T170 configuration. When the ozone assimilation is performed there is less influence of the horizontal resolution of the model. Nevertheless, in a general way, comparisons with independent data show large reductions of the ozone standard deviations when the resolution is increased. When the ozone assimilation is performed with the high resolution dataset, the high resolution model does not improve the ozone analysis compared to the one obtained with the same model resolution but with the low resolution IASI dataset. This result is due to the difficulty to combine IASI data and MLS data. For assimilating IASI data at high resolution the horizontal correlation length-scale has to be decreased to catch the small scale structures present in the dataset. By doing so the influence of the coarser resolution MLS data is decreased and part of the information brought on the vertical shape of the ozone profile is lost. It is concluded that it is essential to add information on the vertical distribution of ozone column when the IASI data is assimilated at a resolution close to the pixel size. Using IASI averaging kernels would likely improve the simulations, but the computational cost would be much higher. Alternatively, better results might be obtained by a careful tuning of the horizontal correlation length-scale.

The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The data were acquired by energy meters, both consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO2 level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station. The data were collected either during normal operation of the building, with observation periods between 2 weeks and 2 months, or during experiments designed to excite the thermal mass of the building, with observation periods of approximately one week. The data have a time resolution varying between 1 min and 15 min; in some case the highest resolution data are also averaged at larger intervals, up to 30 min.

The unveiling of MOF growth mechanism is hampered by the lack of fundamental knowledge about the very early stage of nucleation, especially the form and ratio of molecular species in the solution for crystal growth. Herein, we report the detection of growth species for a series of MOFs with mono-linker, Cu-MOF-2-BDC and Cu-MOF-2-NDC, and two linkers, MTV-MOF-2-(C4H4)x, by high resolution ESI-MS, where a large variety of Cu-containing species are identified unambiguously. The solvent molecules such as H2O, methanol and DMF participate in the formation of these species, other than ethanol. Furthermore, in the growth solution of MTV-MOF-2-(C4H4)x, growth species containing two different organic linkers are observed. The feeding ratio is not the only factor controlling the distribution of growth species for MTV-MOFs, but also the solvent involves in coordination, an aspect usually overlooked previously.

Better predictions of global warming can be enabled by tuning legacy and current computer simulations to Earth radiation budget (ERB) measurements. Since the 1970s, such orbital results exist, and the next-generation instruments such as one called “Libera” are in production. Climate communities have requested that new ERB observing system missions like these have calibration accuracy obtaining significantly improved calibration SI traceability and stability. This is to prevent untracked instrument calibration drifts that could lead to false conclusions on climate change. Based on experience from previous ERB missions, the alternative concept presented here utilizes directly viewing solar calibration, for cloud-size Earth measurement resolution at

Synaptic transmission via neurochemical release is the fundamental process that integrates and relays encoded information in the brain to regulate physiological function, cognition, and emotion. To unravel the biochemical, biophysical, and computational mechanisms of signal processing, one needs to precisely measure the neurochemical release dynamics with molecular and cell-type specificity and high resolution. Here we reviewed the development of analytical, electrochemical, and fluorescence imaging approaches to detect neurotransmitter and neuromodulator release. We discussed the advantages and practicality in implementation of each technology for ease-of-use, flexibility for multimodal studies, and challenges for future optimization. We hope this review will provide a versatile guide for tool engineering and applications for recording neurochemical release.

Resumen Objetivos Valorar la calidad de imagen y la dosis de radiacion en tomografia computarizada (TC) de penascos adquiridos con una TC multidetector (TCMD) con filtro de estano, detectores de alta resolucion y reconstruccion iterativa, comparandola con otro equipo sin filtro de estano y con reconstruccion por retroproyeccion filtrada. Material y metodos Se incluyeron retrospectivamente 32 pacientes con TC de penascos realizadas con dosis ultrabaja en una 32-TCMD (130 kV con filtro de estano y reconstruccion iterativa). Se compararon con 36 estudios realizados en una 16-TCMD (120 kV y retroproyeccion filtrada). Se cuantificaron la densidad muscular, osea y el ruido de fondo, y se calculo la relacion senal/ruido. Para valorar la calidad de imagen, dos radiologos evaluaron de forma subjetiva e independiente la visualizacion de las diferentes estructuras del oido (0=no se visualiza; 3=se identifica y delimita perfectamente). Se calculo el coeficiente de concordancia interobservador kappa. Utilizando un software comercial, se cuantifico a diferentes niveles anatomicos la dosis efectiva con el producto dosis-longitud. Resultados En el analisis cuantitativo de las imagenes no se observaron diferencias significativas en el ruido de fondo. En el analisis cualitativo se obtuvo una puntuacion subjetiva similar o ligeramente menor en la delimitacion de las diferentes estructuras de la cadena osicular y coclea en la 32-TCMD, con diferencias estadisticamente significativas. La dosis media efectiva fue de 0,16±0,04 mSv para la 32-TCMD frente a 1,25±0,30 mSv para la 16-TCMD. Conclusiones La utilizacion de equipos con filtro de estano, detectores de alta resolucion y reconstruccion iterativa permite obtener TC con dosis de radiacion ultrabaja (0,16±0,04 mSv) con una calidad de imagen adecuada para valorar las estructuras de los penascos.

Since projection-based 3D bioprinting (PBP) could provide high resolution, it is well suited for printing delicate structures for tissue regeneration. However, the low crosslinking density and low photo-crosslinking rate of photocurable bioink make it difficult to print fine structures. Currently, an in-depth understanding of the is lacking. Here, a research framework is established for the analysis of printability during PBP. The gelatin methacryloyl (GelMA)-based bioink is used as an example, and the printability is systematically investigated. We analyze the photo-crosslinking reactions during the PBP process and summarize the specific requirements of bioinks for PBP. Two standard quantized models are established to evaluate 2D and 3D printing errors. Finally, the better strategies for bioprinting five typical structures, including solid organs, vascular structures, nerve conduits, thin-wall scaffolds, and micro needles, are presented., Graphical abstract Image 1, Highlights • The photo-crosslinking mechanism and operation specifications is systematically explored during PBP. • The mechanism of printing errors are analyzed, and a guide is provided to decrease the printing errors theoretically. • Two standard models are proposed for evaluating the printing errors. • Five typical applications are summarized and the suggestion printing strategies are formulated to improve the print quality.

BACKGROUND Additional benefit of cryoballoon left atrial roof line ablation (CB-RA) beyond cryoballoon pulmonary vein isolation (CB-PVI) is suggested in patients with persistent atrial fibrillation (PsAF). We sought to investigate the feasibility of CB-RA for PsAF and to determine the ablation area. METHODS AND RESULTS Fifty-three PsAF patients (67[58.5-75.5] years, 36 men, 11 longstanding PsAF) underwent CB-PVI. Subsequently, 44(83.0%) out of 53 patients underwent additional CB-RA. Voltage maps were created in all patients with a high-resolution mapping system. The total number and duration of CB-RAs were 3.9±0.7 and 468±84 seconds. LA roof areas were complete low voltage areas (LVAs) /scar in 37/44(84.1%) patients ("complete roof modification"). The normal LA posterior wall (LAPW) voltage area was 6.1(4.1-8.4)cm2, and the %LAPW isolation area was 61.0(47.2-71.7)%. The %LAPW isolation area was significantly greater in CB-RA patients than those without (64.0[54.2-73.2] vs. 45.0[39.5-50.5]%, p = 0.041) despite significantly larger LAs in the former group. The %LAPW isolation area was significantly greater in patients with transverse LA diameters

Small-bowel atresias are among the most common causes of intestinal obstruction in newborns, and they often require urgent surgical treatment. Imaging techniques play a very important role in their diagnosis, which is often suspected on prenatal obstetric ultrasound and confirmed on postnatal plain-film X-rays. Abdominal ultrasound's lack of ionizing radiation, wide availability, low cost, and high resolution is making this technique increasingly important in confirming atresias and in detecting possible complications in newborns. This review analyzes a series of cases seen at our center. It summarizes the different types of small-bowel atresias, focusing on the clinical presentation, imaging findings on different modalities, presence of associated disease, management, clinical course, and outcomes.

A novel mismatch error shaping (MES) method is proposed in noise-shaping (NS) SAR ADCs to break the SNDR limitation caused by DAC mismatch induced non-linearity. Through sampling the signal twice for one conversion, the input range of the ADC is increased to 2V {ref}. After the first sampling, only the MSB is resolved and the results feed back to the opposite side of the DAC. After the second sampling, the MSB result is reversed and a +text{V}_{ref} /2 reference is generated at the side of the DAC which has low input while a - text{V}_{ref} /2 reference is generated at the other side. Through this method, the dynamic range deduction caused by the MES technique is solved. The proposed SAR ADC is implemented in TSMC 65nm CMOS technology. The simulation results show that the new MES method improves the SFDR from 54 dB to 104.5 dB. The SNDR in 20kHz bandwidth is 98.6dB while power consumption is 513.2~ {mu }text{W} under a 1 V power supply at 20MS/s sampling rate.

In vivo studies on association between wall shear stress (WSS) and intracranial plaque are deficient. Based on the three-dimensional T1-weighted high-resolution magnetic resonance imaging (3DT1 HR-MRI) data of patients with low-grade stenotic (

Rapid and accurate canopy attributes estimation is highly critical in fruit crops production management as this information can be used for canopy and crop load management as well as to develop nutrient/chemical prescription application maps. However, the existing ground based canopy sensing and attribute estimation methods are laborious and often involve complexity with field data collection and analysis. Manual methods can be subjective as well. Therefore, this study explores aerial photogrammetry based method of tree–row–volume (TRV), leaf–wall–area (LWA), canopy volume (CV) and canopy cover (CC) estimation for grapevine and apple canopies. Remote sensing data was collected using a consumer–grade small unmanned aerial system (UAS) with an RGB imaging sensor flying at different flight altitudes i.e., 15 m (Ground sampling distance, GSD = 0.45 cm pixel−1 at 65° sensor inclination), 30 m (0.90 and 0.85 cm pixel−1 at 65°and 75°, respectively), 45 m (1.35 and 1.27 cm pixel−1 at 65°and 75°, respectively) and 60 m (1.81 and 1.69 cm pixel−1 at 65°and 75°, respectively). Crop surface model (CSM) was derived from such data to estimate canopy height, width and foliage vigor, which are further used to estimate TRV, LWA, CV and CC. The ground measured and aerial imagery estimated TRV had a strong relationship with the data collected at the lowest GSD within grapevine canopies (R2 = 0.77 at 0.45 cm pixel−1) as well as for apple canopies (R2 = 0.82 at 0.90 cm pixel−1). Similar trends were observed for the LWA (R2 = 0.77 and 0.86), CV (R2 = 0.43 and 0.64) and CC (R2 = 0.61 and 0.68) estimates for grapevine and apple canopies, respectively. Increasing GSD (≥0.45 cm pixel−1 in grapevine and ≥ 0.90 cm pixel−1 in apple) resulted in a weak relationship between ground measurements and aerial imagery data-based estimates for grapevines (R2 ≤ 0.36) and apple canopies (R2 = 0.39–0.78). Overall, the aerial flights with lower GSD and double grid missions with RGB imaging sensor in 65° orientation aided in the development of site–specific high–quality canopy vigor maps that can be used in precision crop inputs management related decision making.

Scintillation crystals Gd3Al2Ga3O12 (GAGG) are an excellent candidate for application in ionizing-radiation detectors because of their high radiation resistance, density and light yield. These crystals can be used in combination with lead tungstate (PbWO4 or PWO) crystals for the development of a new generation of electromagnetic calorimeter with advanced spatial and energy resolutions in a broad energy range. PWO crystals enable the accurate detection of high-energy photons, while GAGG crystals provide the possibility of precisely measuring photon energies, down to a few MeV. Different options for a composite electromagnetic calorimeter based on PWO and GAGG crystals are considered to optimize spatial and energy resolutions in a broad energy range (from 1 MeV to 100 GeV). In particular, different lengths of the GAGG section of the calorimeter are considered, from 0.5 to 10 cm. The separation of signals from photons and hadrons is also taken into consideration through the study of shower shape in the calorimeter. The optimization is based on Geant4 simulations, considering light collection as well as the use of different photodetectors and electronic noise. Simulations are verified with light yield measurements of GAGG samples obtained using radioactive sources and test beam measurements of the prototype of the PWO-based Photon Spectrometer of the ALICE experiment at CERN.

The study aims to implement a high-resolution Extended Elastic Impedance (EEI) inversion to estimate the petrophysical properties (e.g., porosity, saturation and volume of shale) from seismic and well log data. The inversion resolves the pitfall of basic EEI inversion in inverting below-tuning seismic data. The resolution, dimensionality and absolute value of basic EEI inversion are improved by employing stochastic perturbation constrained by integrated energy spectra attribute in a Bayesian Markov Chain Monte Carlo framework. A general regression neural network (GRNN) is trained to learn and memorize the relationship between the stochastically perturbed EEI and the associated well petrophysical log data. The trained GRNN is then used to predict the petrophysical properties of any given stochastic processed EEI. The proposed inversion was successfully conducted to invert the volume of shale, porosity and water saturation of a 4.0 m thick gas sand reservoir in Sarawak Basin, Malaysia. The three petrophysical geobodies were successfully built using the discovery wells cut-off values, showing that the inverted petrophysical properties satisfactorily reconstruct the well petrophysical logs with sufficient resolution and an accurate absolute value at the well site and are laterally conformable with seismic data. Inversion provides reliable petrophysical properties prediction that potentially helps further reservoir development for the study field.

This paper describes the transformation of a traditional in silico classification network into an optical fully convolutional neural network with high-resolution feature maps and kernels. When using the free-space 4f system to accelerate the inference speed of neural networks, higher resolutions of feature maps and kernels can be used without the loss in frame rate. We present FatNet for the classification of images, which is more compatible with free-space acceleration than standard convolutional classifiers. It neglects the standard combination of convolutional feature extraction and classifier dense layers by performing both in one fully convolutional network. This approach takes full advantage of the parallelism in the 4f free-space system and performs fewer conversions between electronics and optics by reducing the number of channels and increasing the resolution, making this network faster in optics than off-the-shelf networks. To demonstrate the capabilities of FatNet, it was trained with the CIFAR100 dataset on GPU and the simulator of the 4f system. A comparison of the results against ResNet-18 shows 8.2 times fewer convolution operations at the cost of only 6% lower accuracy. This demonstrates that the optical implementation of FatNet results in significantly faster inference than the optical implementation of the original ResNet-18. These are promising results for the approach of training deep learning with high-resolution kernels in the direction toward the upcoming optics era.

Soybean is one of the world’s most consumed crops. As the human population continuously increases, new phenotyping technology is needed to develop new soybean varieties with high-yield, stress-tolerant, and disease-tolerant traits. Hyperspectral imaging (HSI) is one of the most used technologies for phenotyping. The current HSI techniques with indoor imaging towers and unmanned aerial vehicles (UAVs) suffer from multiple major noise sources, such as changes in ambient lighting conditions, leaf slopes, and environmental conditions. To reduce the noise, a portable single-leaf high-resolution HSI imager named LeafSpec was developed. However, the original design does not work efficiently for the size and shape of dicot leaves, such as soybean leaves. In addition, there is a potential to make the dicot leaf scanning much faster and easier by automating the manual scan effort in the original design. Therefore, a renovated design of a LeafSpec with increased efficiency and imaging quality for dicot leaves is presented in this paper. The new design collects an image of a dicot leaf within 20 s. The data quality of this new device is validated by detecting the effect of nitrogen treatment on soybean plants. The improved spatial resolution allows users to utilize the Normalized Difference Vegetative Index (NDVI) spatial distribution heatmap of the entire leaf to predict the nitrogen content of a soybean plant. This preliminary NDVI distribution analysis result shows a strong correlation (R2 = 0.871) between the image collected by the device and the nitrogen content measured by a commercial laboratory. Therefore, it is concluded that the new LeafSpec-Dicot device can provide high-quality hyperspectral leaf images with high spatial resolution, high spectral resolution, and increased throughput for more accurate phenotyping. This enables phenotyping researchers to develop novel HSI image processing algorithms to utilize both spatial and spectral information to reveal more signals in soybean leaf images.

Advanced towing configurations which combine distributed multi-sources with high-density multi-sensor streamer spreads have been frequently used for hydrocarbon exploration in recent years. These marine seismic acquisition solutions enable accurate imaging from very shallow targets and geohazards to deep geological structures in a cost-effective manner. The improved near offset coverage and the dense spatial sampling provided by the wide-tow multi-source (e.g., 6.25m x 6.25m nominal acquisition bin sizes) enables subsurface imaging with frequencies above 200Hz and thus temporal and spatial resolution in the meter-range. Dense spreads with short streamers can be complemented with longer tails if refracted waves are analysed as part of the geophysical studies, and if velocity building or quantitative interpretation (QI) requires access to longer offsets. The same survey design principles can be applied to near-surface high-resolution or ultra-high-resolution studies such as Carbon Capture and Storage (CCS) site characterization and CCS 4D monitoring, deep-sea mineral exploration, or offshore wind farm 3D site surveying. Typical site survey and near-surface seismic technologies are based on hydrophone-only streamers that are towed very shallow, i.e., only a few metres below the sea surface. While the shallow tow mitigates the receiver ghost problem at high frequencies, the operations are exposed to weather related downtime. With multi-sensor streamers, the receiver ghost problem is solved at all frequencies by combining pressure and particle motion recordings. This means the streamers can be towed deep (e.g., at 25 m) and rough sea surface effects are avoided. In our presentation, we will revisit modern streamer seismic configurations and show how the same concepts have recently been used to design and acquire the first larger CCS site characterisation and baseline surveys in Europe in 2022., Open-Access Online Publication: May 31, 2023

Shallow convective clouds are important players in Earth’s energy budget and hydrological cycle, and are abundant in the tropical and subtropical belts. They greatly contribute to the uncertainty in climate predictions due to their unresolved, complex processes that include coupling between the dynamics and microphysics. Analysis of cloud structure can be simplified by considering cloud motions as a combination of moist adiabatic motions like adiabatic updrafts and turbulent motions leading to deviation from adiabaticity. In this work, we study the sizes and occurrence of adiabatic regions in shallow cumulus clouds during their growth and mature stages, and use the adiabatic fraction (AF) as a continuous metric to describe cloud processes and properties from the core to the edge. To do so, we simulate isolated trade wind cumulus clouds of different sizes using the System of Atmospheric Modeling (SAM) model in high resolution (10 m) with the Hebrew University spectral bin microphysics (SBM). The fine features in the clouds’ dynamics and microphysics, including small near-adiabatic volumes and a thin transition zone at the edge of the cloud (∼20–40 m in width), are captured. The AF is shown to be an efficient measure for analyzing cloud properties and key processes determining the droplet-size distribution formation and shape during the cloud evolution. Physical processes governing the properties of droplet size distributions at different cloud regions (e.g., core, edge) are analyzed in relation to AF. Significance Statement 1) This study investigates the evolution of cumulus clouds (Cu) using a 10-m-resolution LES model with spectral bin microphysics. 2) The study improves the understanding of the mutual effects of adiabatic updrafts and lateral entrainment and mixing. 3) The study demonstrates the existence of an adiabatic core in nonprecipitating Cu. 4) Shapes of the droplet size distributions are closely related to the adiabatic fraction values. 5) Utilization of high resolution reveals the existence of physically significant small features in the cloud structure, such as a narrow cloud interface zone and small adiabatic volumes.

Forecasts from numerical weather prediction (NWP) models play a critical role in many sectors of the U.S. economy. Improvements to operational NWP model forecasts are generally assumed to provide significant economic savings through better decision-making. But is this true? Since 2014, several new versions of the High-Resolution Rapid Refresh (HRRR) model were released into operation within the National Weather Service. Practically, forecasts have an economic impact only if they lead to a different action than what would be taken under an alternative information set. And in many sectors, these decisions only need to be considered during certain weather conditions. We estimate the economic impacts of improvements made to the HRRR, using 12-h wind, precipitation, and temperature forecasts in several cases where they can have “economically meaningful” behavioral consequences. We examine three different components of the U.S. economy where such information matters: 1) better integration of wind energy resources into the electric grid, 2) increased worker output due to better precipitation forecasts that allow workers to arrive to their jobs on time, and 3) better decisions by agricultural producers in preparing for freezing conditions. These applications demonstrate some of the challenges in ascertaining the economic impacts of improved weather forecasts, including highlighting key assumptions that must be made to make the problem tractable. For these sectors, we demonstrate that there was a marked economic gain for the United States between HRRR versions 1 and 2 and a smaller, but still appreciable economic gain between versions 2 and 3.

У статті показані особливості стану слизової оболонки стравоходу, шлунка та дванадцятипалої кишки у хворих на гастроезофагеальну рефлюксну хворобу. Показано, що застосування вузькоспектрального дослідження та збільшення дозволяє виявляти метаплазований циліндричний епітелій у стравоході, атрофію та кишкову метаплазію слизової оболонки шлунка, тобто використання ендоскопії зі збільшенням, хромоскопії та вузькоспектральної ендоскопії значно покращує якість огляду, скорочує час дослідження й число виконуваних біопсій.

Although cryo-electron microscopy has revolutionized structural biology, its applicability to high-resolution structural analysis of comparatively small enzymes so far remains largely unexplored. Here, two cryo-EM structures of plant borneol dehydrogenases of ∼120 kDa at or below 2 Å resolution are reported., Enzyme catalysis has emerged as a key technology for developing efficient, sustainable processes in the chemical, biotechnological and pharmaceutical industries. Plants provide large and diverse pools of biosynthetic enzymes that facilitate complex reactions, such as the formation of intricate terpene carbon skeletons, with exquisite specificity. High-resolution structural analysis of these enzymes is crucial in order to understand their mechanisms and modulate their properties by targeted engineering. Although cryo-electron microscopy (cryoEM) has revolutionized structural biology, its applicability to high-resolution structural analysis of comparatively small enzymes has so far been largely unexplored. Here, it is shown that cryoEM can reveal the structures of plant borneol dehydrogenases of ∼120 kDa at or below 2 Å resolution, paving the way for the rapid development of new biocatalysts that can provide access to bioactive terpenes and terpenoids.

International audience; A surface soil moisture (SM) condition at high spatiotemportal resolutions is required by regional Earth system applications. Here, we mapped daily 1-km SM in the Babao River Basin in the northwest of China during the summers from 2013 to 2015 using a random forest (RF) method by merging SM information retrieved from in situ measurements, optical/thermal remote sensing, and topographical indices. Relative importance analysis was used to determine the optimal predictors for estimating high-resolution SM. A specific RF model (RFVI+sup) was constructed using the optimal predictors including remote sensing albedo, apparent thermal inertia (ATI), normalized difference vegetation index, normalized difference infrared index 5, soil adjusted vegetation index, and topographical indices (aspect and elevation). The RFVI+sup also accounted for missing observations of the thermal index (e.g., ATI) over the mountainous regions. In the comparison between the SM estimates using the new RFVI+sup model and other RF models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0.75, the unbiased root-mean-squared difference values decreased to 0.032 m(3)/m(3). Thus, the proposed RF method provided accurate SM estimates with high spatiotemporal resolution over the mountainous regions, by merging multiresource datasets from in situ measurements, remotely-sensed, and topographical indices.

Convolutional neural networks (CNNs) have achieved remarkable results in semantic segmentation of high-resolution remote sensing images (HRRSIs). However, the scales and textures of HRRSIs are diverse, which makes it difficult for a fixed-layer CNN to obtain rich features. In this regard, we propose an end-to-end ensemble fully convolutional network (EFCNet), which mainly includes two modules: the adaptive fusion module (AFM) and the separable convolutional module (SCM). The AFM can fuse features of different scales based on ensemble learning, whereas the SCM can reduce the complexity of the model under multifeature fusion. In the experiment, we use UNet and PSPNet to verify the framework on the ISPRS Vaihingen and Potsdam datasets. The experimental results show that the EFCNet can effectively improve the final segmentation performance and reduce the complexity of the ensemble model.

Objectives Carbamylation is a non-enzymatic post-translational reaction of a primary amino group of a protein with isocyanate. The albumin carbamylation is a negative prognostic factor in chronic kidney disease (CKD) patients and induce charge difference implying an observed shift in electrophoretic mobility that can be measured through a symmetry factor (SF). Methods The Helena V8 and the Sebia Capillarys 2 systems were used for all experiments. The effect of in vitro carbamylation on the SF by spiking increasing concentrations of potassium isocyanate (KCNO) in serum of three healthy volunteers was investigated. Theoretical plate numbers (N) as a surrogate of separation efficiency were also calculated and correlations between SF and renal function biomarkers were performed on 284 patients. Results A dose-dependent impact of KCNO on the SF was observed for both methods with the Helena V8 being more sensitive. The mean N was significantly higher on the Helena V8 as compared to the Sebia Capillarys 2 (2,972 vs. 444.1, p Conclusions Thanks to a higher mean N, the Helena V8 might offer new possibilities, including detection of carbamylated albumin through SF calculation. Further studies are still needed to confirm the interest of using this type of assays in clinical routine.

In this paper, we propose a portrait relighting method based on deep reinforcement learning (called PR-RL). Our PR-RL model could conduct portrait relighting by sequentially predicting local light editing strokes, and use strokes to conduct dodge and burn operations on the image lightness, simulating image editing by artists using brush strokes. Reinforcement learning with Deep Deterministic Policy Gradient is introduced to design our PR-RL model, defining the action (stroke parameters) in a continuous space, through which a reward can be designed to guide the agent to learn and relight a portrait image like an artist. To optimize the relighting effect, we further enable the reward to be location relevant and hence a coarse-to-fine strategy can be applied to select corresponding actions and maximize the performance of the proposed method. In comparison with the existing efforts, our proposed PR-RL method is locally effective, scale-invariant and interpretable. We apply the proposed method to tasks of portrait relighting based on both SH-lighting and reference images. The experiments show that our PR-RL method outperforms state-of-the-art methods in generating locally effective and interpretable high resolution relighting results for wild portrait images.

We present a microfluidic device compatible with high resolution light sheet and super-resolution microscopy. The device is a 150 μm thick chamber with a transparent fluorinated ethylene propylene (FEP) cover that has a similar refractive index (1.34) to water (1.33), making it compatible with top-down imaging used in light sheet microscopy. We provide a detailed fabrication protocol and characterize the optical performance of the device. We demonstrate that the device supports long-term imaging of cell growth and differentiation as well as the rapid addition and removal of reagents while simultaneously maintaining sterile culture conditions by physically isolating the sample from the dipping lenses used for imaging. Finally, we demonstrate that the device can be used for super-resolution imaging using lattice light sheet structured illumination microscopy (LLS-SIM) and DNA PAINT. We anticipate that FEP-based microfluidics, as shown here, will be broadly useful to researchers using light sheet microscopy due to the ability to switch reagents, image weakly adherent cells, maintain sterility, and physically isolate the specimen from the optics of the instruments.

Aim: In the theme of quantitative LC–MS bioanalysis of oligonucleotides free of ion-pairing, a 22-mer RNA oligonucleotide took center stage. The focus was on a unique polar-based retention scheme to produce a high-recovery extraction presenting a high-performance alternative extraction means, also there was the opportunity to involve hydrophilic-interaction liquid chromatography and contemporary high-resolution MS as the end point. Results: Original LC–MS methodology was developed for the oligonucleotide and the performance was robust for both nominal and accurate mass detection, the latter affording 10× improvement in sensitivity and 4000-fold linear dynamic range, 500 pM to 2000 nM. Conclusion: A novel means of solid-phase extraction is exhibited within a robust pair-free methodology, reaching pM sensitivity with the demonstrably beneficial accurate mass platform.

High content screening (HCS) plays an important role in biological applications, and drug development. Existing techniques fail to simultaneously meet multiple needs of throughput, efficiency in sample and chemical consumption, real-time imaging of a large view at high resolution. Leveraging advances in microfluidics and imaging technology, we setup a new paradigm of large-scale, high-content drug screening solutions for rapid biological processes, like cardiotoxicity. The designed microfluidic chips enable 10 types of drugs each with 5 concentrations to be assayed simultaneously. The imaging system RUSH has 30 Hz video rate for a centimeter filed-of-view at 0.8 m resolution. Using the HCS system, we assayed 12 small molecules through their effects on the Ca2+ ion signal of cardiomyocytes. Experimental results demonstrated the unparallelized capability of the system in revealing the spatiotemporal patterns of Ca2+ imaging of cardiomyocytes, and validated the cardiotoxicity of certain molecules. We envision this new HCS paradigm and cutting-edge platform as the most advanced alternative to well-plate based methods.