Inflammation of the bile ducts and surrounding tissues can impede bile flow from the liver into the intestines. If this occurs, a plastic or self-expanding metal (SEM) stent is placed to restore bile drainage. United States (US) Food and Drug Administration (FDA)-approved plastic biliary stents are less expensive than SEMs but have limited patency and can occlude bile flow if placed spanning a duct juncture. Recently, we investigated the effects of variations to post-processing and autoclaving on a commercially available stereolithography (SLA) resin in an effort to produce a suitable material for use in a biliary stent, an FDA Class II medical device. We tested six variations from the manufacturer\'s recommended post-processing and found that tripling the isopropanol (IPA) wash time to 60 min and reducing the time and temperature of the UV cure to 10 min at 40 °C, followed by a 30 min gravity autoclave cycle, yielded a polymer that was flexible and non-cytotoxic. In turn, we designed and fabricated customizable, SLA 3D-printed polymeric biliary stents that permit bile flow at a duct juncture and can be deployed via catheter. Next, we generated an in silico stent 3-point bend test to predict displacements and peak stresses in the stent designs. We confirmed our simulation accuracy with experimental data from 3-point bend tests on SLA 3D-printed stents. Unfortunately, our 3-point bend test simulation indicates that, when bent to the degree needed for placement via catheter (~30°), the peak stress the stents are predicted to experience would exceed the yield stress of the polymer. Thus, the risk of permanent deformation or damage during placement via catheter to a stent printed and post-processed as we have described would be significant. Moving forward, we will test alternative resins and post-processing parameters that have increased elasticity but would still be compatible with use in a Class II medical device.

The MR transverse relaxation rate, R2*, has been widely used to detect iron and myelin content in tissue. However, it is also sensitive to macroscopic B0 inhomogeneities. One approach to correct for the B0 effect is to fit gradient-echo signals with the three-parameter model, a sinc function-weighted monoexponential decay. However, such three-parameter models are subject to increased noise sensitivity. To address this issue, this study presents a two-stage fitting procedure based on the three-parameter model to mitigate the B0 effect and reduce the noise sensitivity of R2* measurement in the mouse brain at 7T. MRI scans were performed on eight healthy mice. The gradient-echo signals were fitted with the two-stage fitting procedure to generate R2corr_t*. The signals were also fitted with the monoexponential and three-parameter models to generate R2nocorr* and R2corr*, respectively. Regions of interest (ROIs), including the corpus callosum, internal capsule, somatosensory cortex, caudo-putamen, thalamus, and lateral ventricle, were selected to evaluate the within-ROI mean and standard deviation (SD) of the R2* measurements. The results showed that the Akaike information criterion of the monoexponential model was significantly reduced by using the three-parameter model in the selected ROIs (p = 0.0039-0.0078). However, the within-ROI SD of R2corr* using the three-parameter model was significantly higher than that of the R2nocorr* in the internal capsule, caudo-putamen, and thalamus regions (p = 0.0039), a consequence partially due to the increased noise sensitivity of the three-parameter model. With the two-stage fitting procedure, the within-ROI SD of R2corr* was significantly reduced by 7.7-30.2% in all ROIs, except for the somatosensory cortex region with a fast in-plane variation of the B0 gradient field (p = 0.0039-0.0078). These results support the utilization of the two-stage fitting procedure to mitigate the B0 effect and reduce noise sensitivity for R2* measurement in the mouse brain.

The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a 3D rib reconstruction method based on point cloud adaptive smoothing and denoising. We converted voxel data from CT images to multi-attribute point cloud data. Then, we applied point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques were employed to achieve high-accuracy and comprehensive 3D rib reconstruction results. Experimental calculations demonstrated that compared to voxel-based 3D rib reconstruction methods, the 3D rib models generated by the proposed method achieved a 40% improvement in reconstruction accuracy and were twice as efficient as the former.

Path planning is an crucial research area in robotics. Compared to other path planning algorithms, the Rapidly-exploring Random Tree (RRT) algorithm possesses both search and random sampling properties, and thus has more potential to generate high-quality paths that can balance the global optimum and local optimum. This paper reviews the research on RRT-based improved algorithms from 2021 to 2023, including theoretical improvements and application implementations. At the theoretical level, branching strategy improvement, sampling strategy improvement, post-processing improvement, and model-driven RRT are highlighted, at the application level, application scenarios of RRT under welding robots, assembly robots, search and rescue robots, surgical robots, free-floating space robots, and inspection robots are detailed, and finally, many challenges faced by RRT at both the theoretical and application levels are summarized. This review suggests that although RRT-based improved algorithms has advantages in large-scale scenarios, real-time performance, and uncertain environments, and some strategies that are difficult to be quantitatively described can be designed based on model-driven RRT, RRT-based improved algorithms still suffer from the problems of difficult to design the hyper-parameters and weak generalization, and in the practical application level, the reliability and accuracy of the hardware such as controllers, actuators, sensors, communication, power supply and data acquisition efficiency all pose challenges to the long-term stability of RRT in large-scale unstructured scenarios. As a part of autonomous robots, the upper limit of RRT path planning performance also depends on the robot localization and scene modeling performance, and there are still architectural and strategic choices in multi-robot collaboration, in addition to the ethics and morality that has to be faced. To address the above issues, I believe that multi-type robot collaboration, human-robot collaboration, real-time path planning, self-tuning of hyper-parameters, task- or application-scene oriented algorithms and hardware design, and path planning in highly dynamic environments are future trends.

Climate change and anthropogenic activities have influenced the frequency and magnitude of forest fires both globally and regionally. While skilful short- to extended-range prediction of forest fires is essential for effective mitigation in local communities, it is also important to identify the implications of forest fires on different sectors, including water resources and sustainable development. Limited studies have investigated the association between forest fires and hydrometeorological variables at the regional scale in developing countries due to the lack of necessary datasets, which can now be leveraged using the newly hosted global reanalysis of fire danger indices (referred to as fire indices). The current study presents a comprehensive analysis of the spatio-temporal variations of eight fire indices across India, as well as their association with hydro-meteorological variables, such as precipitation, temperature, and the streamflow of a major river basin (Mahanadi) in India. The accuracy of these indices in capturing real fire events and the potential benefit of incorporating fire indices into long-term hydrologic simulations are also explored. The results show that fire indices can accurately yield fire seasons (i.e., post-monsoon and summer) in India. Furthermore, forest fires are found to be strongly associated with hydro-meteorological variables, typically resulting in low streamflow regimes. Fire indices can also capture actual fire events, maintaining high scalar accuracy. Finally, an improvement in uncalibrated hydrologic model simulations is observed when simulated streamflow is post-processed using the fire indices as predictors. Overall, the current study has valuable implications for fire indices forecasting and hydrologic simulations in ungauged basins.

OBJECTIVE: To investigate the impact of a centrifugation method on the surface characteristics, flexural properties, and cytotoxicity of an additively manufactured denture base polymer.
METHODS: The tested specimens were prepared by digital light processing (DLP). A centrifugation method (CENT) was used to remove the residual uncured resin. In addition, the specimens were post-processed with different post-rinsing solutions: isopropanol (IPA), ethanol (EtOH), and tripropylene glycol monomethyl ether (TPM), respectively. A commercial heat-polymerized polymethyl methacrylate was used as a reference (REF). First, the values of surface topography, arithmetical mean height (Sa), and root mean square height (Sq) were measured. Next, flexural strength (FS) and modulus were evaluated. Finally, cytotoxicity was assessed using an extract test. The data were statistically analyzed using a one-way analysis of variance, followed by Tukey\'s multiple comparison test for post-hoc analysis.
RESULTS: The Sa value in the CENT group was lower than in the IPA, EtOH, TPM, and REF groups (p < 0.001). Moreover, the CENT group had lower Sq values than other groups (p < 0.001). The centrifugation method showed a higher FS value (80.92 ± 8.65 MPa) than the EtOH (61.71 ± 12.25 MPa, p < 0.001) and TPM (67.01 ± 9.751 MPa, p = 0.027), while affecting IPA (72.26 ± 8.80 MPa, p = 0.268) and REF (71.39 ± 10.44 MPa, p = 0.231). Also, the centrifugation method showed no evident cytotoxic effects.
CONCLUSIONS: The surfaces treated with a centrifugation method were relatively smooth. Simultaneously, the flexural strength of denture base polymers was enhanced through centrifugation. Finally, no evident cytotoxic effects could be observed from different post-processing procedures.
CONCLUSIONS: The centrifugation method could optimize surface quality and flexural strength of DLP-printed denture base polymers without compromising cytocompatibility, offering an alternative to conventional rinsing post-processing.

This study aims to assess the efficacy of post-curing guidance supplied by 3D printing resin manufacturers. Current guidance applies generically to all geometries with the caveat that post-curing should be extended for \'large\' or \'complex\' geometries but specific guidance is not provided. Two vat-polymerisation 3D printers (Form3B, Figure 4 Standalone) were used to print test models in 6 biocompatible resins (Pro Black, Med White, Med Amber, Biomed Black, Biomed White, Biomed Amber). The test model is of a complex geometry whilst also housing ISO 527 test specimens in concentric layers. Two separate intervals of curing were applied (100%, 500% stated guidance) creating different curing treatments of the specimens throughout the model. Post processed test models were disassembled and pull testing performed on each of the specimens to assess the mechanical properties. The analysis showed that extending the curing duration had significant effects on the mechanical properties of some materials but not all. The layers of the model had a significant effect except for elongation at break for the Med Amber material. This research demonstrates that generic post-curing guidance regarding UV exposures is not sufficient to achieve homogenous material strength properties for complex geometries. Large variations in mechanical properties throughout the models suggest some material was not fully-cured. This raises a query if such materials as originally marketed as biocompatible are fully cured and therefore safe to use for medical applications involving complex geometries.

Objective.Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works within vivodata and (5) to test whether NBC is stable across model and perturbation geometries.Approach.EIT was performedin silicoin a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the error in the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested forin vivoEIT data of lung ventilation in a human thorax and cortical activity in a rat brain.Main results.On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally andin silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. Forin vivodata, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation.Significance.In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.

UNASSIGNED: Post-processing (PP) is performed to improve the surface, which can favor microbial adhesion and consequent pathological manifestations that impair the indication of polylactic acid (PLA) obtained by fused filament fabrication (FFF) for biomedical applications. This aims to evaluate the influence of chemical, thermal, and mechanical PP on the adhesion of Streptococcus mutants and Candida albicans, roughness, and wettability of the PLA obtained by FFF with and without thermal aging.
UNASSIGNED: The specimens were designed in the 3D modeling program and printed. The chemical PP was performed by immersion in chloroform, the thermal by the annealing method, and the mechanical by polishing. Thermal aging was performed by alternating the temperature from 5 °C to 55 °C with 5000 cycles. Colony-forming unit (CFU/mL) counting was performed on dual-species biofilm of C. albicans and S. mutans. Roughness was analyzed by rugosimeter and wettability by the sessile drop technique. Data were verified for normality using the Shapiro-Wilk test, two-way ANOVA (α = 0.05) applied for CFU and wettability, and Kruskal-Wallis (α = 0.05) for roughness.
UNASSIGNED: Chemical, thermal, and mechanical PP methods showed no influence on CFU/mL of C. albicans (p = 0.296) and S. mutans (p = 0.055). Thermal aging did not influence microbial adhesion. Chemical PP had lower roughness, which had increased after aging. Wettability of the mechanical PP was lower.
UNASSIGNED: Post-processing techniques, do not present an influence on the adhesion of S. mutans and C. albicans in PLA obtained by FFF, chemical PP reduced roughness, and mechanical reduced wettability. Thermal aging did not alter the microbial adhesion and altered the roughness and wettability.

Objective.This study developed an unsupervised motion artifact reduction method for magnetic resonance imaging (MRI) images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images.Approach.The proposed framework included two generators, two discriminators, and two feature extractor networks. A 3-fold cross-validation was used to train and fine-tune the hyperparameters of the proposed model using 230 brain MRI images with tumors, which were then tested on 148 patients\'in-vivodatasets. An ablation was performed to evaluate the model\'s compartments. Our model was compared with Pix2pix and CycleGAN. Six evaluation metrics were reported, including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale-SSIM (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). Artifact reduction and consistency of tumor regions, image contrast, and sharpness were evaluated by three evaluators using Likert scales and compared with ANOVA and Tukey\'s HSD tests.Main results.On average, our method outperforms comparative models to remove heavy motion artifacts with the lowest NMSE (18.34±5.07%) and MS-GMSD (0.07 ± 0.03) for heavy motion artifact level. Additionally, our method creates motion-free images with the highest SSIM (0.93 ± 0.04), PSNR (30.63 ± 4.96), and VIF (0.45 ± 0.05) values, along with comparable MS-SSIM (0.96 ± 0.31). Similarly, our method outperformed comparative models in removingin-vivomotion artifacts for different distortion levels except for MS- SSIM and VIF, which have comparable performance with CycleGAN. Moreover, our method had a consistent performance for different artifact levels. For the heavy level of motion artifacts, our method got the highest Likert scores of 2.82 ± 0.52, 1.88 ± 0.71, and 1.02 ± 0.14 (p-values≪0.0001) for our method, CycleGAN, and Pix2pix respectively. Similar trends were also found for other motion artifact levels.Significance.Our proposed unsupervised method was demonstrated to reduce motion artifacts from the ceT1W brain images under a multi-parametric framework.