Spatial datasets of 30-year (1991-2020) average monthly total precipitation and minimum/maximum temperature for Canada and the United States.

Thin plate smoothing spline models, covering Canada and the continental United States, were developed using ANUSPLIN for 30-year (1991-2020) monthly mean maximum and minimum temperature and precipitation. These models employed monthly weather station values from the North American dataset published by National Oceanic and Atmospheric Administration's (NOAA's) National Centers for Environmental Information (NCEI). Maximum temperature mean absolute errors (MAEs) ranged between 0.54 °C and 0.64 °C (approaching measurement error), while minimum temperature MAEs were slightly higher, varying from 0.87 °C to 1.0 °C. On average, thirty-year precipitation estimates were accurate to within approximately 10 % of total precipitation levels, ranging from 9.0 % in the summer to 12.2 % in the winter. Error rates were higher in Canada compared to estimates in the United States, consistent with a less dense station network in Canada relative to the United States. Precipitation estimates in Canada exhibited MAEs representing 14.7 % of mean total precipitation compared to 9.7 % in the United States. The datasets exhibited minimal bias overall; 0.004 °C for maximum temperature, 0.01 °C for minimum temperature, and 0.5 % for precipitation. Winter months showed a greater dry bias (0.8 % of total winter precipitation) compared to other seasons (-0.4 % of precipitation). These 30-year gridded datasets are available at ∼2 km resolution.

Nondestructive quantification of internal raster path for additively manufactured components via ultrasonic testing.

This work investigates the viability of discerning the raster pattern of additively manufactured components using high frequency ultrasonic nondestructive testing. Test coupons were fabricated from poly cyclohexylenedimethylene terephthalate glycol using the fused filament fabrication process, in which layers were deposited at various predetermined raster angles. Each printed part was scanned using spherically focused, high-resolution, ultrasonic transducers of various peak frequencies between 7.5 and 15 MHz. From the captured waveform data, images are extracted to observe the raster pattern in a layer-by-layer manner, with the results from the 10 MHz element yielding the best performance. An in-house MATLAB script was developed to analyze the transducer signal to investigate C-scan images at various depths throughout the component. From the resulting C-scan images, one can consistently identify the proper raster orientation within 2°-4° in each of the first 10 deposited layers, with the accuracy decreasing as a function of depth into the component. Due to signal attenuation, there is insufficient data at depths beyond the 11th and 12th layer, to properly analyze the present data sets accurately. Validation was performed using X-ray computed tomography scans to demonstrate the accuracy of the ultrasonic inspection method.

Intelligent Vehicle Path Planning Based on Optimized A* Algorithm.

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.

Immediate Effect of Simulated High Heels on Pelvic and Spinal Posture in Healthy Young Subjects: A Cross-Sectional Study.

Background Investigations regarding the role of high-heeled shoes in the alteration of the spinopelvic profile attempted to identify a correlation with pain in the lower back. Conclusions from these studies, however, are controversial. In authors knowledge no studies were carried out to investigate the effect of heels on male population, which has been overlooked due to gender-related customs. Research question What is the immediate effect of the height of heels on the sagittal back profile (trunk inclination (TI), pelvic inclination, lordotic lumbar angle (ITL-ILS), kyphotic dorsal angle, lumbar arrow, and cervical arrow) in females and males, not used to wearing high-heeled shoes? Methods One hundred healthy young adult subjects were enrolled. Three were excluded. The remaining 97 subjects (48 female and 49 male) underwent a three-dimensional analysis of the posterior surface of the trunk, using rasterstereography. The spinopelvic profile in the barefoot condition, and with the heel raised by 3 and 7 cm, was recorded. To evaluate the reproducibility of the measure, the neutral evaluation was repeated twice in 23 subjects (13 males, 10 females). Results The change of heel height did not show statistically significant differences for any of the variables used; instead, significant differences were found stratifying the results according to the sex of the subjects tested. Test-retest evaluation in the neutral condition showed no significant differences using the Student's t-test (p > 0.05). Repeatability was excellent and significant for all data used (minimum TI r = 0.85, maximum ITL-ILS r = 0.97). Significance Studying the effect of heels on the spino-pelvic profile also in the male population is crucial for promoting gender-inclusive healthcare, enhancing occupational health practices and developing possible preventive measures. Nevertheless, in the sample of females and males evaluated in this study, the different heights of heel lift did not immediately induce significant changes in pelvis and spine posture. If there is therefore a correlation between low-back pain and the use of heels, it should not reasonably be sought in the immediate change of the spino-pelvic profile caused by raising the heels. However, the variables analyzed differed according to sex.

Differences in upper body posture between patients with lumbar spine syndrome and healthy individuals under the consideration of sex, age and BMI.

BACKGROUND: Work-related forced postures, such as prolonged standing work, can lead to complaints in the lower back. Current research suggests that there is increased evidence of associations between patients with low back pain (LBP) and reduced lordosis in the lumbar spine and generally less spinal tilt in the sagittal plane. The aim of this study is to extend the influence of LBP to other parameters of upper body posture in standing, taking into account the rotational and frontal planes. METHODS: The study included a no-LBP group (418 males, 412 females, aged 21-65 years) and an LBP group (138 subjects: 80 females, 58 males, aged 18-86 years) with medically diagnosed lumbar spine syndrome (LSS). The "ABW BodyMapper" back scanner from ABW GmbH in Germany was used for posture assessment using video raster stereography. Statistical analyses employed two-sample t-tests or Wilcoxon-Mann-Whitney-U tests to assess the relationship between the LBP/no-LBP groups and back posture parameters. Linear and logarithmic regressions were used with independent variables including group, sex, height, weight and body mass index (BMI). Significance level: α = 0.05 (95% confidence). RESULTS: The regression analysis showed that sagittal parameters of the spine (sagittal trunk decline, thoracic and lumbar bending angle, kyphosis and lordosis angles) depend primarily on sex, age, BMI, height and/or weight but not on group membership (LBP/no-LBP). In the shoulder region, a significant dependency between group membership and scapular rotation was found. In the pelvic region, there were only significant dependencies in the transverse plane, particularly between pelvic torsion and BMI, weight, height and between pelvic rotation and group membership, age and sex. CONCLUSION: No difference between the patients and healthy controls were found. In addition, sex appears to be the main influencing factor for upper body posture. Other influencing factors such as BMI, height or weight also seem to have a significant influence on upper body posture more frequently than group affiliation.

Beam delivery characteristics of the Hitachi carbon ion scanning system at Osaka Heavy Ion Medical Accelerator in Kansai (HIMAK).

BACKGROUND: Using the pencil beam raster scanning method employed at most carbon beam treatment facilities, spots can be moved without interrupting the beam, allowing for the delivery of a dose between spots (move dose). This technique is also known as Dose-Driven-Continuous-Scanning (DDCS). To minimize its impact on HIMAK patient dosimetry, there's an upper limit to the move dose. Spots within a layer are grouped into sets, or "break points," allowing continuous irradiation. The beam is turned off when transitioning between sets or at the end of a treatment layer or spill. The control system beam-off is accomplished by turning off the RF Knockout (RFKO) extraction and after a brief delay the High Speed Steering Magnet (HSST) redirects the beam transport away from isocenter to a beam dump. PURPOSE: The influence of the move dose and beam on/off control on the dose distribution and irradiation time was evaluated by measurements never before reported and modelled for Hitachi Carbon DDCS. METHOD: We conducted fixed-point and scanning irradiation experiments at three different energies, both with and without breakpoints. For fixed-point irradiation, we utilized a 2D array detector and an oscilloscope to measure beam intensity over time. The oscilloscope data enabled us to confirm beam-off and beam-on timing due to breakpoints, as well as the relative timing of the RFKO signal, HSST signal, and dose monitor (DM) signals. From these measurements, we analyzed and modelled the temporal characteristics of the beam intensity. We also developed a model for the spot shape and amplitude at isocenter occurring after the beam-off signal which we called flap dose and its dependence on beam intensity. In the case of scanning irradiation, we measured move doses using the 2D array detector and compared these measurements with our model. RESULT: We observed that the most dominant time variation of the beam intensity was at 1 kHz and its harmonic frequencies. Our findings revealed that the derived beam intensity cannot reach the preset beam intensity when each spot belongs to different breakpoints. The beam-off time due to breakpoints was approximately 100 ms, while the beam rise time and fall time (tdecay ) were remarkably fast, about 10 ms and 0.2 ms, respectively. Moreover, we measured the time lag (tdelay ) of approximately 0.2 ms between the RFKO and HSST signals. Since tdelay ≈ tdecay at HIMAK then the HSST is activated after the residual beam intensity, resulting in essentially zero flap dose at isocenter from the HSST. Our measurements of the move dose demonstrated excellent agreement with the modelled move dose. CONCLUSION: We conducted the first move dose measurement for a Hitachi Carbon synchrotron, and our findings, considering beam on/off control details, indicate that Hitachi's carbon synchrotron provides a stable beam at HIMAK. Our work suggests that measuring both move dose and flap dose should be part of the commissioning process and possibly using our model in the Treatment Planning System (TPS) for new facilities with treatment delivery control systems with higher beam intensities and faster beam-off control.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery.

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.

Is the existing methods sustainable? A hybrid approach to flood risk mapping.

The hydraulic and integrated modeling approaches appear to stand out in the sequence of flood risk models that have been presented because of their predictive accuracy. The former has a high probability of under predicting and the latter has a high tendency to over-predict. This study proposed a methodological approach that combines the hydraulic and integrated models using analytical hierarchical raster fusion techniques to strengthen the weaknesses of the individual models. This study seeks to undertake a flood inundation model, a runoff model, and raster fusion models using GIS and HEC-RAS rain-on-grid methods to map flood risk in the Ona river basin of Ibadan city. •A hydraulic model was used to identify flood depth and inundation areas along a major stream channel, which was then extracted, rasterized, resampled, and reclassified to a spatial resolution of 5 m.•Several raster datasets (indicators) were created from land use, elevation, soil, and geological data layers using advanced GIS techniques.•AHP assisted raster data fusion model was used to combine all of the raster indicators into a single consolidated hybrid flood raster layer that revealed flood risk areas by magnitude.

Heating degree day spatial datasets for Canada.

Heating degree days (HDD) represent a concise measure of heating energy requirements used to inform decision making about the impact of climate change on heating energy demand. This data paper presents spatial datasets of heating degree days (HDD) for Canada for two thirty-year periods, 1951-1980 and 1981-2010, using daily temperature gauge observations over these time periods. Stations with fewer than nine missing days in a year and greater than nine years of data over each thirty-year period were included, resulting in 1339 and 1679 stations for the 1951-1980 and 1981-2010 periods respectively. Mean absolute error (MAE) of the spatial models ranged from 124.2 Celsius degree days (C-days) for the 1951-1980 model (2.4% of the surface mean) to 137.6 C-days for the 1981-2010 model (2.7%). This note presents maps illustrating cross validation errors at a set of representative stations. The grids are available at ∼2 km resolutions.

VeerNet: Using Deep Neural Networks for Curve Classification and Digitization of Raster Well-Log Images.

Raster logs are scanned representations of the analog data recorded in subsurface drilling. Geologists rely on these images to interpret well-log curves and deduce the physical properties of geological formations. Scanned images contain various artifacts, including hand-written texts, brightness variability, scan defects, etc. The manual effort involved in reading the data is substantial. To mitigate this, unsupervised computer vision techniques are employed to extract and interpret the curves digitally. Existing algorithms predominantly require manual intervention, resulting in slow processing times, and are erroneous. This research aims to address these challenges by proposing VeerNet, a deep neural network architecture designed to semantically segment the raster images from the background grid to classify and digitize (i.e., extracting the analytic formulation of the written curve) the well-log data. The proposed approach is based on a modified UNet-inspired architecture leveraging an attention-augmented read-process-write strategy to balance retaining key signals while dealing with the different input-output sizes. The reported results show that the proposed architecture efficiently classifies and digitizes the curves with an overall F1 score of 35% and Intersection over Union of 30%, achieving 97% recall and 0.11 Mean Absolute Error when compared with real data on binary segmentation of multiple curves. Finally, we analyzed VeerNet's ability in predicting Gamma-ray values, achieving a Pearson coefficient score of 0.62 when compared to measured data.

Predicting Rice Lodging Risk from the Distribution of Available Nitrogen in Soil Using UAS Images in a Paddy Field.

Rice lodging causes a loss of yield and leads to lower-quality rice. In Japan, Koshihikari is the most popular rice variety, and it has been widely cultivated for many years despite its susceptibility to lodging. Reducing basal fertilizer is recommended when the available nitrogen in soil (SAN) exceeds the optimum level (80-200 mg N kg-1). However, many commercial farmers prefer to simultaneously apply one-shot basal fertilizer at transplant time. This study investigated the relationship between the rice lodging and SAN content by assessing their spatial distributions from unmanned aircraft system (UAS) images in a Koshihikari paddy field where one-shot basal fertilizer was applied. We analyzed the severity of lodging using the canopy height model and spatially clarified a heavily lodged area and a non-lodged area. For the SAN assessment, we selected green and red band pixel digital numbers from multispectral images and developed a SAN estimating equation by regression analysis. The estimated SAN values were rasterized and compiled into a 1 m mesh to create a soil fertility map. The heavily lodged area roughly coincided with the higher SAN area. A negative correlation was observed between the rice inclination angle and the estimated SAN, and rice lodging occurred even within the optimum SAN level. These results show that the amount of one-shot basal fertilizer applied to Koshihikari should be reduced when absorbable nitrogen (SAN + fertilizer nitrogen) exceeds 200 mg N kg-1.

Performance evaluation of mesoscopic photoacoustic imaging.

Photoacoustic mesoscopy visualises vascular architecture at high-resolution up to ~3 mm depth. Despite promise in preclinical and clinical imaging studies, with applications in oncology and dermatology, the accuracy and precision of photoacoustic mesoscopy is not well established. Here, we evaluate a commercial photoacoustic mesoscopy system for imaging vascular structures. Typical artefact types are first highlighted and limitations due to non-isotropic illumination and detection are evaluated with respect to rotation, angularity, and depth of the target. Then, using tailored phantoms and mouse models, we investigate system precision, showing coefficients of variation (COV) between repeated scans [short term (1 h): COV= 1.2%; long term (25 days): COV= 9.6%], from target repositioning (without: COV=1.2%, with: COV=4.1%), or from varying in vivo user experience (experienced: COV=15.9%, unexperienced: COV=20.2%). Our findings show robustness of the technique, but also underscore general challenges of limited-view photoacoustic systems in accurately imaging vessel-like structures, thereby guiding users when interpreting biologically-relevant information.

Optimization of Fluence Map for CyberKnife Raster Scanning Intensity Modulated Radiotherapy.

BACKGROUND/AIM: Stereotactic body radiotherapy for prostate cancer using CyberKnife with circular cone requires a long treatment time. Raster scanning intensity modulated radiotherapy (RS-IMRT) has a potential of improving treatment efficacy, introducing shorter treatment time, better target dose uniformity, and lower organ at risk (OAR) dose. The purpose of the study was to develop a fluence optimization system for RS-IMRT. PATIENTS AND METHODS: RS-IMRT plans were created for five prostate cancer patients treated with the Novalis system and parameters were compared to the Novalis treatment plans. From 80 nodes available for the CyberKnife, twelve nodes were arbitrarily selected. On the beam's eye view of each beam, a 100×100 matrix of optimization points was created at the target center plane. The beam fluence map was optimized using the attraction-repulsion model (ARM). The beam fluence maps were converted to the scanning sequence using the ARM and a final dose calculation was performed. RESULTS: For planning target volume (PTV), RS-IMRT plans showed higher dose covering 2% of the volume (D2%) and lower D98% compared to the Novalis plans. However, the homogeneity was within our Institutional clinical protocol. The RS-IMRT plans showed significantly lower OAR dose parameters including bladder volume receiving 100% of prescribed dose (V100%) and dose delivered to 5 cm3 of rectum (D5 cc). CONCLUSION: We developed a fluence optimization system for RS-IMRT that performs the entire RS-IMRT treatment planning process, including scanning sequence optimization and final dose calculation. The RS-IMRT was capable of generating clinically acceptable plans.

The Effect of Active Stretching Training in Patients with Chronic Venous Insufficiency Monitored by Raster-Stereography.

(1) Background: Musculoskeletal disorders can be associated with advanced clinical stages of chronic venous insufficiency (CVI). The aim of the study is to investigate the effect of active stretching (AS) training on lower limb venous function and quality of life in patients affected by CVI. (2) Methods: A prospective two-armed pilot randomized controlled was conducted. Twenty (20) CVI patients were randomly assigned to an AS training or to a control group (C) who did not receive any exercise indication. At baseline and after three months all the participants were tested for leg volumetry (LV), air plethysmography (APG), and quality of life (QoL) measured by a disease specific validated questionnaire (VVSymQ), ankle range of motion (ROM), and postural deformities using an optoelectronic body posture machine. (3) Results: At the end of the training in the AS group a significant leg volume reduction was detected (from 2340 ± 239 mL to 2239 ± 237 mL (4.3%); p < 0.0001), whereas in the C group no significant volume changes were found. The ejection fraction rate (EF%) increased significantly from 49.3 ± 9.3 to 61.1 ± 14.5, p < 0.005. A moderate-strong linear correlation with EF% and ankle ROM variation was found (R2 = 0.6790; p < 0.0034). Several postural outcomes such as pelvic tilt, pelvic torsion, and lordotic angle significantly improved in the AS group (p < 0.01, p < 0.04, p < 0.01 respectively). (4) Conclusion: The AS training impacts on the APG parameters related to the musculoskeletal pump efficiency, opening a further possibility in the management of CVI patients by means of an appropriate adapted physical exercise program.

Evaluation of medical services from the perspective of COVID-19 vaccine demand satisfaction in Hangzhou, China.

The outbreak of COVID-19 has had a huge global impact, and it continues to test the resilience of medical services to emergencies worldwide. In the current post-epidemic era, vaccination has become a highly effective strategy to prevent the spread of COVID-19. However, using conventional mathematical models to evaluate the spatial distribution of medical resources, including vaccination, ignore people's behaviors and choices and make simplifications to the real world. In this study, we use an enhanced model based on the Theory of People Behavior (TPB) to perform a macro analysis of the satisfaction ability of medical resources for vaccination in Hangzhou, China, and attribute the city to a three-level structure. According to the allocation, the supply capacity of vaccination sites is calculated and divided into four categories (good, normal, not bad, and bad). Meanwhile, we raise an assumption based on the result and the general development law of the city and analyze the reasons for the impact of personal behavior on the spatial distribution of medical resources, as well as the relationship between the demand distribution and spatial distribution of medical resources and future development strategies. It is considered that the overall medical resources, especially vaccination in Hangzhou, feature the situation of central supply overflow, and are found to hardly meet the needs of population points in surrounding areas, requiring a more flexible strategy to allocate facilities in these areas.

A Multiscale Molecular Dynamic Analysis Reveals the Effect of Sialylation on EGFR Clustering in a CRISPR/Cas9-Derived Model.

Bacterial and viral pathogens can modulate the glycosylation of key host proteins to facilitate pathogenesis by using various glycosidases, particularly sialidases. Epidermal growth factor receptor (EGFR) signaling is activated by ligand-induced receptor dimerization and oligomerization. Ligand binding induces conformational changes in EGFR, leading to clusters and aggregation. However, information on the relevance of EGFR clustering in the pattern of glycosylation during bacterial and viral invasion remains unclear. In this study, (1) we established CRISPR/Cas9-mediated GFP knock-in (EGFP-KI) HeLa cells expressing fluorescently tagged EGFR at close to endogenous levels to study EGF-induced EGFR clustering and molecular dynamics; (2) We studied the effect of sialylation on EGF-induced EGFR clustering and localization in live cells using a high content analysis platform and raster image correlation spectroscopy (RICS) coupled with a number and brightness (N&B) analysis; (3) Our data reveal that the removal of cell surface sialic acids by sialidase treatment significantly decreases EGF receptor clustering with reduced fluorescence intensity, number, and area of EGFR-GFP clusters per cell upon EGF stimulation. Sialylation appears to mediate EGF-induced EGFR clustering as demonstrated by the change of EGFR-GFP clusters in the diffusion coefficient and molecular brightness, providing new insights into the role of sialylation in EGF-induced EGFR activation; and (4) We envision that the combination of CRISPR/Cas9-mediated fluorescent tagging of endogenous proteins and fluorescence imaging techniques can be the method of choice for studying the molecular dynamics and interactions of proteins in live cells.

Ecogeographical variable dataset for species distribution modelling, describing forest landscape in Latvia, 2017.

In this article, we provide access to, and information on the production of 42 ecogeographical variables (EGVs) used to describe the landscape of Latvia at three scales (local and two landscape). Layers are focused on the description of forest heterogeneity, but account for other ecosystems and land cover types as well. The more temporarily changing land use and land cover (LULC) types as forest and agricultural lands are described from 2017 databases. With most of the other LULC information was gathered from the topographic map (2016) at the scale of 1:10 000. All the raster layers provided here are in the Latvian projected coordinate reference system (epsg:3059) with a grid cell size of 25 ha. Each layer provides quantitative information on the area, shape, and edge of habitat classes and additionally, age, time since the last forestry disturbance, relative soil humidity, relative soil richness etc. for forests. The three scales represent information from within 25 ha grid cell, and two radii - 1250 and 2500 m - around the centre of the grid cell.

Exploring a multisource-data framework for assessing ecological environment conditions in the Yellow River Basin, China.

Ecological environment conditions (EEC) assessment plays an important role in watershed management. However, due to insufficient field data, EEC assessment in large-scale watersheds faces challenges. Our study was conducted to develop an effective EEC assessment method framework that was capable of reducing the use of field data. Three indicators were developed from multisource data, including landscape ecological risk index (LERI), road network density (RND), and industry density (ID). The knowledge-based raster mapping approach integrated the three indicators into an overall score of the EEC. Then model validation was conducted with principal components of water quality from field sampling data by Pearson correlation analysis methods. Finally, we applied and demonstrated the constructed method framework in the EEC assessment of the YRB.The results showed that bad EEC (0.5326 < Overall score ≤ 0.7679) areas were mainly distributed in the northern part of the YRB, showing a circular distribution pattern. The areas with bad EEC were 15.84 million km2, accounting for 19.87 % of the YRB. The area of the highest LERI (0.157 < LERI≤0.246), the highest RND (4.4435 < RND ≤ 8.5574), and the highest ID (0.1403 < ID≤0.2597) finally converted to bad EEC was 7.22 million km2, 0.78 million km2, and 0.91 million km2, respectively. The results indicated that the ecological risk factors were the primary challenges for improving EEC, followed by industrial agglomeration and road network factors. The primary factors affecting EEC varied between the provinces in the YRB, suggesting that provinces take the management strategies and measures should be adaptive. The correlation coefficients between EEC and the principal components of water quality characteristics were between 0.022 and 0.241, P < 0.05. These findings validated that our method framework could distinguish the spatial variation of EEC in detail and further provide effective support for watershed management.

An Investigation on the Hardness of Polylactic Acid Parts Fabricated via Fused Deposition Modeling.

This paper investigated the hardness property of the fused deposition modeling (FDM)-printed PLA samples via different process parameters of printing and raster angles. The hardness data were sampled from the flat and edge surfaces of the samples. In addition, the effect of hardness characters after the ultraviolet (UV) curing process was analyzed. Furthermore, this research found that the printing and raster angles significantly affected the hardness value of the PLA part, which slightly increased after the UV irradiation. Moreover, the results of this study will provide a reference for the field of FDM application.

High-resolution label-free mapping of murine kidney vasculature by raster-scanning optoacoustic mesoscopy: an ex vivo study.

BACKGROUND: Chronic kidney disease (CKD) is a global burden affecting both children and adults. Novel imaging modalities hold great promise to visualize and quantify structural, functional, and molecular organ damage. The aim of the study was to visualize and quantify murine renal vasculature using label-free raster scanning optoacoustic mesoscopy (RSOM) in explanted organs from mice with renal injury. MATERIAL AND METHODS: For the experiments, freshly bisected kidneys of alpha 8 integrin knock-out (KO) and wildtype mice (WT) were used. A total of n=7 female (n=4 KO, n=3 WT) and n=6 male animals (n=2 KO, n=4 WT) aged 6 weeks were examined with RSOM optoacoustic imaging systems (RSOM Explorer P50 at SWL 532nm and/or ms-P50 imaging system at 532 nm, 555 nm, 579 nm, and 606 nm). Images were reconstructed using a dedicated software, analyzed for size and vascular area and compared to standard histologic sections. RESULTS: RSOM enabled mapping of murine kidney size and vascular area, revealing differences between kidney sizes of male (m) and female (f) mice (merged frequencies (MF) f vs. m: 52.42±6.24 mm2 vs. 69.18±15.96 mm2, p=0.0156) and absolute vascular area (MF f vs. m: 35.67±4.22 mm2 vs. 49.07±13.48 mm2, p=0.0036). Without respect to sex, the absolute kidney area was found to be smaller in knock-out (KO) than in wildtype (WT) mice (WT vs. KO: MF: p=0.0255) and showed a similar trend for the relative vessel area (WT vs. KO: MF p=0.0031). Also the absolute vessel areas of KO compared to WT were found significantly different (MF p=0.0089). A significant decrease in absolute vessel area was found in KO compared to WT male mice (MF WT vs. KO: 54.37±9.35 mm2 vs. 34.93±13.82 mm2, p=0.0232). In addition, multispectral RSOM allowed visualization of oxygenated and deoxygenated parenchymal regions by spectral unmixing. CONCLUSION: This study demonstrates the capability of RSOM for label-free visualization of differences in vascular morphology in ex vivo murine renal tissue at high resolution. Due to its scalability optoacoustic imaging provides an emerging modality with potential for further preclinical and clinical imaging applications.