Automated Processing of fNIRS Data-A Visual Guide to the Pitfalls and Consequences

With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site.

Ethanol-Based Post-processing of Generator-Derived ⁶⁸Ga Toward Kit-Type Preparation of ⁶⁸Ga-Radiopharmaceuticals

Post-processing by means of a cation-exchanger-based protocol is an efficient strategy for purification and concentration of generator-derived (68)Ga. It ensures the removal of (68)Ge before (68)Ga-radiopharmaceutical preparation and high labeling yields of (68)Ga-labeled radiopharmaceuticals for routine medical application.

METHODS

In an effort to overcome the problem associated with acetone in the currently applied method, we have investigated the feasibility of replacing it with ethanol. The purification of (68)Ga from coeluted metallic impurities ((68)Ge(4+), Fe(3+), Zn(2+), and Ti(4+)) on various cation-exchange columns has been investigated with a variety of post-processing solutions. As a proof of principle, the post-processed (68)Ga was used to radiolabel DOTATOC in combination with high-purity water and various buffer solutions.

RESULTS

An effective protocol for the processing of generator-produced (68)Ga on the basis of cation-exchange chromatography using EtOH/HCl medium has been developed. Up to 95% of the initially eluted (68)Ga activity can be collected in a 1-mL fraction of 90% EtOH/0.9N HCl after removal of (68)Ge-breakthrough in a washing step. The post-processed eluate has been used to radiolabel DOTATOC in yields of approximately 97% ± 0.25% at 80°C in 5 min.

CONCLUSION

The described novel protocol improves the radiolabeling efficiency and efficacy of DOTATOC, providing yields of greater than 99% (decay-corrected). As a result, further purification to separate the desired product from uncomplexed (68)Ga is not necessary. The developed post-processing and labeling protocols permit reliable and high-yield preparation of injectable (68)Ga-DOTATOC (or other (68)Ga-labeled radiopharmaceuticals) that are suitable for routine application. It is possible to incorporate this protocol into existing automated modules.

Dynamic imaging of the fetal heart using metric optimized gating

PURPOSE

Advances in fetal cardiovascular magnetic resonance imaging have been limited by the absence of a reliable cardiac gating signal. The purpose of this work was to develop and validate metric-optimized gating (MOG) for cine imaging of the fetal heart.

THEORY AND METHODS

Cine MR and electrocardiogram data were acquired in healthy adult volunteers for validation of the MOG method. Comparison of MOG and electrocardiogram reconstructions was performed based on the image quality for each method, and the difference between MOG and electrocardiogram trigger times. Fetal images were also acquired, their quality evaluated by experienced radiologists, and the theoretical error in the MOG trigger times were calculated.

RESULTS

Excellent agreement between electrocardiogram and MOG reconstructions was observed. The experimental errors in adult MOG trigger times for all five volunteers were ± (7, 25, 17, 8, and 13) ms. Fetal images captured normal and diseased cardiac dynamics.

CONCLUSION

MOG for cine imaging of the fetal myocardium was developed and validated in adults. Using MOG, the first gated MR images of the human fetal myocardium were obtained. Small moving structures were visualized during radial contraction, thus capturing normal fetal cardiac wall motion and permitting assessment of cardiac function.

Quantitative analysis of transmural gradients in myocardial perfusion magnetic resonance images

Conventional quantitative assessments of myocardial perfusion analyze the temporal relation between the arterial input function and the myocardial signal intensity curves, thereby neglecting the important spatial relation between the myocardial signal intensity curves. The new method presented in this article enables characterization of sub-endocardial to sub-epicardial gradients in myocardial perfusion based on a two dimensional, "gradientogram" representation, which displays the evolution of the transmural gradient in myocardial contrast uptake over time in all circumferential positions of the acquired images. Moreover, based on segmentation in these gradientograms, several new measurements that characterize transmural myocardial perfusion distribution over time are defined. In application to clinical image data, the new two-dimensional representations, as well as the newly defined measurements revealed a clear distinction between normal perfusion and inducible ischaemia. Thus, the new measurements may serve as diagnostic markers for the detection and characterization of epicardial coronary and microvascular disease.

Electrospun Fibers of Cyclodextrins and Poly(cyclodextrins)

Cyclodextrins (CDs) can endow electrospun fibers with outstanding performance characteristics that rely on their ability to form inclusion complexes. The inclusion complexes can be blended with electrospinnable polymers or used themselves as main components of electrospun nanofibers. In general, the presence of CDs promotes drug release in aqueous media, but they may also play other roles such as protection of the drug against adverse agents during and after electrospinning, and retention of volatile fragrances or therapeutic agents to be slowly released to the environment. Moreover, fibers prepared with empty CDs appear particularly suitable for affinity separation. The interest for CD-containing nanofibers is exponentially increasing as the scope of applications is widening. The aim of this review is to provide an overview of the state-of-the-art on CD-containing electrospun mats. The information has been classified into three main sections: (i) fibers of mixtures of CDs and polymers, including polypseudorotaxanes and post-functionalization; (ii) fibers of polymer-free CDs; and (iii) fibers of CD-based polymers (namely, polycyclodextrins). Processing conditions and applications are analyzed, including possibilities of development of stimuli-responsive fibers.

Post-Processing of FDM 3D-Printed Polylactic Acid Parts by Laser Beam Cutting

In this paper, the post-processing of 3D-printed poly lactic acid (PLA) parts is investigated. Workpieces are manufactured by fused deposition modeling (FDM) 3D printing, while they may have defects in some areas such as edges. A post-processing is introduced here for 3D-printed samples by low power CO₂ laser. The thickness of the FDM samples are 3.2 mm and printed by optimum conditions. Effects of process parameters such as focal plane position (-3.2-3.2 mm), laser power (20-40 W), and laser cutting speed (1-13 mm/s) are examined based on the design of experiments (DOE). Geometrical features of the kerf; top and bottom kerf; taper; ratio of top to the bottom kerf are considered as output responses. An analysis of the experimental results by statistical software is conducted to survey the effects of process parameters and to obtain regression equations. By optimizing of the laser cutting process; an appropriate kerf quality is obtained and also optimum input parameters are suggested. Experimental verification tests show a good agreement between empirical results and statistical predictions. The best optimum sample with 1.19 mm/s cutting speed, 36.49 W power and 0.53 mm focal plane position shows excellent physical features after the laser cutting process when 276.9 μm top and 261.5 μm bottom kerf width is cut by laser.

Multiscale cartilage biomechanics: technical challenges in realizing a high-throughput modelling and simulation workflow

Understanding the mechanical environment of articular cartilage and chondrocytes is of the utmost importance in evaluating tissue damage which is often related to failure of the fibre architecture and mechanical injury to the cells. This knowledge also has significant implications for understanding the mechanobiological response in healthy and diseased cartilage and can drive the development of intervention strategies, ranging from the design of tissue-engineered constructs to the establishment of rehabilitation protocols. Spanning multiple spatial scales, a wide range of biomechanical factors dictate this mechanical environment. Computational modelling and simulation provide descriptive and predictive tools to identify multiscale interactions, and can lead towards a greater comprehension of healthy and diseased cartilage function, possibly in an individualized manner. Cartilage and chondrocyte mechanics can be examined in silico, through post-processing or feed-forward approaches. First, joint-tissue level simulations, typically using the finite-element method, solve boundary value problems representing the joint articulation and underlying tissue, which can differentiate the role of compartmental joint loading in cartilage contact mechanics and macroscale cartilage field mechanics. Subsequently, tissue-cell scale simulations, driven by the macroscale cartilage mechanical field information, can predict chondrocyte deformation metrics along with the mechanics of the surrounding pericellular and extracellular matrices. A high-throughput modelling and simulation framework is necessary to develop models representative of regional and population-wide variations in cartilage and chondrocyte anatomy and mechanical properties, and to conduct large-scale analysis accommodating a multitude of loading scenarios. However, realization of such a framework is a daunting task, with technical difficulties hindering the processes of model development, scale coupling, simulation and interpretation of the results. This study aims to summarize various strategies to address the technical challenges of post-processing-based simulations of cartilage and chondrocyte mechanics with the ultimate goal of establishing the foundations of a high-throughput multiscale analysis framework. At the joint-tissue scale, rapid development of regional models of articular contact is possible by automating the process of generating parametric representations of cartilage boundaries and depth-dependent zonal delineation with associated constitutive relationships. At the tissue-cell scale, models descriptive of multicellular and fibrillar architecture of cartilage zones can also be generated in an automated fashion. Through post-processing, scripts can extract biphasic mechanical metrics at a desired point in the cartilage to assign loading and boundary conditions to models at the lower spatial scale of cells. Cell deformation metrics can be extracted from simulation results to provide a simplified description of individual chondrocyte responses. Simulations at the tissue-cell scale can be parallelized owing to the loosely coupled nature of the feed-forward approach. Verification studies illustrated the necessity of a second-order data passing scheme between scales and evaluated the role that the microscale representative volume size plays in appropriately predicting the mechanical response of the chondrocytes. The tools summarized in this study collectively provide a framework for high-throughput exploration of cartilage biomechanics, which includes minimally supervised model generation, and prediction of multiscale biomechanical metrics across a range of spatial scales, from joint regions and cartilage zones, down to that of the chondrocytes.

CENTER-ADJUSTED INFERENCE FOR A NONPARAMETRIC BAYESIAN RANDOM EFFECT DISTRIBUTION

Dirichlet process (DP) priors are a popular choice for semiparametric Bayesian random effect models. The fact that the DP prior implies a non-zero mean for the random effect distribution creates an identifiability problem that complicates the interpretation of, and inference for, the fixed effects that are paired with the random effects. Similarly, the interpretation of, and inference for, the variance components of the random effects also becomes a challenge. We propose an adjustment of conventional inference using a post-processing technique based on an analytic evaluation of the moments of the random moments of the DP. The adjustment for the moments of the DP can be conveniently incorporated into Markov chain Monte Carlo simulations at essentially no additional computational cost. We conduct simulation studies to evaluate the performance of the proposed inference procedure in both a linear mixed model and a logistic linear mixed effect model. We illustrate the method by applying it to a prostate specific antigen dataset. We provide an R function that allows one to implement the proposed adjustment in a post-processing step of posterior simulation output, without any change to the posterior simulation itself.

Mapping of prostate cancer by 1H MRSI

In many studies, it has been demonstrated that (1)H MRSI of the human prostate has great potential to aid prostate cancer management, e.g. in the detection and localisation of cancer foci in the prostate or in the assessment of its aggressiveness. It is particularly powerful in combination with T2 -weighted MRI. Nevertheless, the technique is currently mainly used in a research setting. This review provides an overview of the state-of-the-art of three-dimensional MRSI, including the specific hardware required, dedicated data acquisition sequences and information on the spectral content with background on the MR-visible metabolites. In clinical practice, it is important that relevant MRSI results become available rapidly, reliably and in an easy digestible way. However, this functionality is currently not fully available for prostate MRSI, which is a major obstacle for routine use by inexperienced clinicians. Routine use requires more automation in the processing of raw data than is currently available. Therefore, we pay specific attention in this review on the status and prospects of the automated handling of prostate MRSI data, including quality control. The clinical potential of three-dimensional MRSI of the prostate is illustrated with literature examples on prostate cancer detection, its localisation in the prostate, its role in the assessment of cancer aggressiveness and in the selection and monitoring of therapy.

Biocompatibility of Blank, Post-Processed and Coated 3D Printed Resin Structures with Electrogenic Cells

The widespread adaptation of 3D printing in the microfluidic, bioelectronic, and Bio-MEMS communities has been stifled by the lack of investigation into the biocompatibility of commercially available printer resins. By introducing an in-depth post-printing treatment of these resins, their biocompatibility can be dramatically improved up to that of a standard cell culture vessel (99.99%). Additionally, encapsulating resins that are less biocompatible with materials that are common constituents in biosensors further enhances the biocompatibility of the material. This investigation provides a clear pathway toward developing fully functional and biocompatible 3D printed biosensor devices, especially for interfacing with electrogenic cells, utilizing benchtop-based microfabrication, and post-processing techniques.

Reducing correlated noise in fMRI data

The sensitivity of functional MRI (fMRI) in detecting neuronal activation is dependent on the relative levels of signal and noise in the time-series data. The temporal noise level within a single voxel is generally substantially higher than the intrinsic NMR (thermal) noise, and the noise is often correlated between voxels. This work introduces and evaluates a method that allows fMRI sensitivity improvement by reduction of these correlated noise sources. The method allows model-free estimation of the correlated noise from brain regions not activated by the functional paradigm using a short (1-2 min) reference scan. A single regressor representing this noise-source estimate is added to the design matrix used in the data analysis. Results obtained from five volunteers show an average t-score improvement of 11.3% and a 24.2% increase in the size of the activated area.

Collagen immobilization on ultra-thin nanofiber membrane to promote in vitro endothelial monolayer formation

The endothelialization on the poly (ε-caprolactone) nanofiber has been limited due to its low hydrophilicity. The aim of this study was to immobilize collagen on an ultra-thin poly (ε-caprolactone) nanofiber membrane without altering the nanofiber structure and maintaining the endothelial cell homeostasis on it. We immobilized collagen on the poly (ε-caprolactone) nanofiber using hydrolysis by NaOH treatment and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide/sulfo-N-hydroxysulfosuccinimide reaction as a cost-effective and stable approach. NaOH was first applied to render the poly (ε-caprolactone) nanofiber hydrophilic. Subsequently, collagen was immobilized on the surface of the poly (ε-caprolactone) nanofibers using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide/sulfo-N-hydroxysulfosuccinimide. Scanning electron microscopy, Fourier transform infrared spectroscopy, transmission electron microscopy, and fluorescence microscopy were used to verify stable collagen immobilization on the surface of the poly (ε-caprolactone) nanofibers and the maintenance of the original structure of poly (ε-caprolactone) nanofibers. Furthermore, human endothelial cells were cultured on the collagen-immobilized poly (ε-caprolactone) nanofiber membrane and expressed tight junction proteins with the increase in transendothelial electrical resistance, which demonstrated the maintenance of the endothelial cell homeostasis on the collagen-immobilized-poly (ε-caprolactone) nanofiber membrane. Thus, we expected that this process would be promising for maintaining cell homeostasis on the ultra-thin poly (ε-caprolactone) nanofiber scaffolds.

Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT)

OBJECTIVE

To develop, and demonstrate the feasibility of, a novel image reconstruction method for absolute electrical impedance tomography (a-EIT) that pairs deep learning techniques with real-time robust D-bar methods and examine the influence of prior information on the reconstruction.

APPROACH

A D-bar method is paired with a trained convolutional neural network (CNN) as a post-processing step. Training data is simulated for the network using no knowledge of the boundary shape by using an associated nonphysical Beltrami equation rather than simulating the traditional current and voltage data specific to a given domain. This allows the training data to be boundary shape independent. The method is tested on experimental data from two EIT systems (ACT4 and KIT4) with separate training sets of varying prior information.

MAIN RESULTS

Post-processing the D-bar images with a CNN produces significant improvements in image quality measured by structural SIMilarity indices (SSIMs) as well as relative [Formula: see text] and [Formula: see text] image errors.

SIGNIFICANCE

This work demonstrates that more general networks can be trained without being specific about boundary shape, a key challenge in EIT image reconstruction. The work is promising for future studies involving databases of anatomical atlases.

Post-processing of structural MRI for individualized diagnostics

Currently, a relevant proportion of all histopathologically proven focal cortical dysplasia (FCD) escape visual detection; this shows the need for additional improvements in analyzing MRI data. A positive MRI is still the strongest prognostic factor for postoperative freedom of seizures. Among several post-processing methods voxel-based morphometry (VBM) of T1- and T2-weighted sequences and T2 relaxometry are routinely applied in pre-surgical diagnostics of cryptogenic epilepsy in epilepsy centers. VBM is superior to conventional visual analysis with 9-15% more identified epileptogenic foci, while T2 relaxometry has its main application in (mesial) temporal lobe epilepsy. Further methods such as surface-based morphometry (SBM) or diffusion tensor imaging are promising but there is a lack of current studies comparing their individual diagnostic value. Post-processing methods represent an important addition to conventional visual analysis but need to be interpreted with expertise and experience so that they should be apprehended as a complementary tool within the context of the multi-modal evaluation of epilepsy patients. This review will give an overview of existing post-processing methods of structural MRI and outline their clinical relevance in detection of epileptogenic structural changes.

In Vivo Biosynthesis of Inorganic Nanomaterials Using Eukaryotes-A Review

Bionanotechnology, the use of biological resources to produce novel, valuable nanomaterials, has witnessed tremendous developments over the past two decades. This eco-friendly and sustainable approach enables the synthesis of numerous, diverse types of useful nanomaterials for many medical, commercial, and scientific applications. Countless reviews describing the biosynthesis of nanomaterials have been published. However, to the best of our knowledge, no review has been exclusively focused on the in vivo biosynthesis of inorganic nanomaterials. Therefore, the present review is dedicated to filling this gap by describing the many different facets of the in vivo biosynthesis of nanoparticles (NPs) using living eukaryotic cells and organisms-more specifically, live plants and living biomass of several species of microalgae, yeast, fungus, mammalian cells, and animals. It also highlights the strengths and weaknesses of the synthesis methodologies and the NP characteristics, bio-applications, and proposed synthesis mechanisms. This comprehensive review also brings attention to enabling a better understanding between the living organisms themselves and the synthesis conditions that allow their exploitation as nanobiotechnological production platforms as these might serve as a robust resource to boost and expand the bio-production and use of desirable, functional inorganic nanomaterials.

A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection

Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people’s daily activities including transportation types and duration by taking advantage of individual’s smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm.

In situ and in-transit analysis of cosmological simulations

Modern cosmological simulations have reached the trillion-element scale, rendering data storage and subsequent analysis formidable tasks. To address this circumstance, we present a new MPI-parallel approach for analysis of simulation data while the simulation runs, as an alternative to the traditional workflow consisting of periodically saving large data sets to disk for subsequent 'offline' analysis. We demonstrate this approach in the compressible gasdynamics/N-body code Nyx, a hybrid MPI + OpenMP code based on the BoxLib framework, used for large-scale cosmological simulations. We have enabled on-the-fly workflows in two different ways: one is a straightforward approach consisting of all MPI processes periodically halting the main simulation and analyzing each component of data that they own ('in situ'). The other consists of partitioning processes into disjoint MPI groups, with one performing the simulation and periodically sending data to the other 'sidecar' group, which post-processes it while the simulation continues ('in-transit'). The two groups execute their tasks asynchronously, stopping only to synchronize when a new set of simulation data needs to be analyzed. For both the in situ and in-transit approaches, we experiment with two different analysis suites with distinct performance behavior: one which finds dark matter halos in the simulation using merge trees to calculate the mass contained within iso-density contours, and another which calculates probability distribution functions and power spectra of various fields in the simulation. Both are common analysis tasks for cosmology, and both result in summary statistics significantly smaller than the original data set. We study the behavior of each type of analysis in each workflow in order to determine the optimal configuration for the different data analysis algorithms.

Improvement on Selective Laser Sintering and Post-Processing of Polystyrene

Amorphous polymers are heavily utilized materials in selective laser sintering (SLS) due to their good dimensional accuracy. However, sintered parts of amorphous polymers cannot be used as functional parts owing to their poor forming performance, including their low relative densities and tensile strength. Therefore, post-processing methods are employed to enhance the mechanical properties of amorphous polymers SLS parts without damaging their relatively high dimensional accuracy. In this study, the forming process of selective laser sintering (SLS) and post-processing on polystyrene (PS) was investigated. The orthogonal experiment was designed to obtain the optimal combination of process parameters. The effect of a single process parameter and the laser volumetric energy density (LVED) on dimension accuracy and warpage of the sintered parts were also discussed. In addition, a three-dimensional (3D) thermal model was developed to analyze the temperature fields of single-layer SLS parts and PS powder sintering mechanism. Then, infiltrating with epoxy resin was employed to enhance the mechanical properties of the PS parts. Good resin-infiltrated formulation was obtained based on the mechanical property tests and fractured surface analysis. This research provides guidance for SLS process and post-processing technology in polymers.

Production of GMP-Compliant Clinical Amounts of Copper-61 Radiopharmaceuticals from Liquid Targets

PET imaging has gained significant momentum in the last few years, especially in the area of oncology, with an increasing focus on metal radioisotopes owing to their versatile chemistry and favourable physical properties. Copper-61 (t_1/2 = 3.33 h, 61% β⁺, E_max = 1.216 MeV) provides unique advantages versus the current clinical standard (i.e., gallium-68) even though, until now, no clinical amounts of ⁶¹Cu-based radiopharmaceuticals, other than thiosemicarbazone-based molecules, have been produced. This study aimed to establish a routine production, using a standard medical cyclotron, for a series of widely used somatostatin analogues, currently labelled with gallium-68, that could benefit from the improved characteristics of copper-61. We describe two possible routes to produce the radiopharmaceutical precursor, either from natural zinc or enriched zinc-64 liquid targets and further synthesis of [⁶¹Cu]Cu-DOTA-NOC, [⁶¹Cu]Cu-DOTA-TOC and [⁶¹Cu]Cu-DOTA-TATE with a fully automated GMP-compliant process. The production from enriched targets leads to twice the amount of activity (3.28 ± 0.41 GBq vs. 1.84 ± 0.24 GBq at EOB) and higher radionuclidic purity (99.97% vs. 98.49% at EOB). Our results demonstrate, for the first time, that clinical doses of ⁶¹Cu-based radiopharmaceuticals can easily be obtained in centres with a typical biomedical cyclotron optimised to produce ¹⁸F-based radiopharmaceuticals.

Post-CMOS fabrication of Working Electrodes for On-Chip Recordings of Transmitter Release

The release of neurotransmitters and hormones from secretory vesicles plays a fundamental role in the function of the nervous system including neuronal communication. High-throughput testing of drugs modulating transmitter release is becoming an increasingly important area in the fields of cell biology, neurobiology, and neurology. Carbon-fiber amperometry, provides high-resolution measurements of amount and time course of transmitter release from single vesicles, and their modulation by drugs and molecular manipulations. However, such methods do not allow the rapid collection of data from a large number of cells. To allow such testing, we have developed a CMOS potentiostat circuit that can be scaled to a large array. In this paper, we present two post-CMOS fabrication methods to incorporate the electrochemical electrode material. We demonstrate by proof of principle the feasibility of on-chip electrochemical measurements of dopamine, and catecholamine release from adrenal chromaffin cells. The measurement noise is consistent with the typical electrode noise in recordings with external amplifiers. The electronic noise of the potentiostat in recordings with 400 mus integration time is ~0.11 pA and is negligible compared to the inherent electrode noise.

Effect of Various Peening Methods on the Fatigue Properties of Titanium Alloy Ti6Al4V Manufactured by Direct Metal Laser Sintering and Electron Beam Melting

Titanium alloy Ti6Al4V manufactured by additive manufacturing (AM) is an attractive material, but the fatigue strength of AM Ti6Al4V is remarkably weak. Thus, post-processing is very important. Shot peening can improve the fatigue strength of metallic materials, and novel peening methods, such as cavitation peening and laser peening, have been developed. In the present paper, to demonstrate an improvement of the fatigue strength of AM Ti6Al4V, Ti6Al4V manufactured by direct metal laser sintering (DMLS) and electron beam melting (EBM) was treated by cavitation peening, laser peening, and shot peening, then tested by a plane bending fatigue test. To clarify the mechanism of the improvement of the fatigue strength of AM Ti6Al4V, the surface roughness, residual stress, and surface hardness were measured, and the surfaces with and without peening were also observed using a scanning electron microscope. It was revealed that the fatigue strength at N = 10⁷ of Ti6Al4V manufactured by DMLS was slightly better than that of Ti6Al4V manufactured by EBM, and the fatigue strength of both the DMLS and EBM specimens was improved by about two times through cavitation peening, compared with the as-built ones. An experimental formula to estimate fatigue strength from the mechanical properties of a surface was proposed.

Post-Processing and Surface Characterization of Additively Manufactured Stainless Steel 316L Lattice: Implications for BioMedical Use

Additive manufacturing of stainless steel is becoming increasingly accessible, allowing for the customisation of structure and surface characteristics; there is little guidance for the post-processing of these metals. We carried out this study to ascertain the effects of various combinations of post-processing methods on the surface of an additively manufactured stainless steel 316L lattice. We also characterized the nature of residual surface particles found after these processes via energy-dispersive X-ray spectroscopy. Finally, we measured the surface roughness of the post-processing lattices via digital microscopy. The native lattices had a predictably high surface roughness from partially molten particles. Sandblasting effectively removed this but damaged the surface, introducing a peel-off layer, as well as leaving surface residue from the glass beads used. The addition of either abrasive polishing or electropolishing removed the peel-off layer but introduced other surface deficiencies making it more susceptible to corrosion. Finally, when electropolishing was performed after the above processes, there was a significant reduction in residual surface particles. The constitution of the particulate debris as well as the lattice surface roughness following each post-processing method varied, with potential implications for clinical use. The work provides a good base for future development of post-processing methods for additively manufactured stainless steel.

Effects of Post-processing on the Surface Finish, Porosity, Residual Stresses, and Fatigue Performance of Additive Manufactured Metals: A Review

Additive manufacturing (AM) has attracted much attention due to its capability in building parts with complex geometries. Unfortunately, AM metals suffer from three major drawbacks, including high porosity, poor surface finish, and tensile residual stresses, all of which will significantly compromise the fatigue performance. These drawbacks present a major obstacle to the application of AM metals in industries that produce fatigue-sensitive components. Many post-processing methods, including heat treatment, hot isotropic pressing, laser shock peening, ultrasonic nanocrystal surface modification, advanced finishing and machining, and laser polishing, have been used to treat AM metals to decrease their porosity, improve the surface finish, and eliminate tensile residual stresses. As a result, significant improvement in fatigue performance has been observed. In this paper, the state of the art in utilizing post-processing techniques to treat AM metals and the effects of these treatments on the porosity, surface finish, and residual stresses of metal components and their resultant fatigue performance are reviewed.

Safety of Yam-Derived (Dioscorea rotundata) Foodstuffs-Chips, Flakes and Flour: Effect of Processing and Post-Processing Conditions

The production of yam-derived (Dioscorea rotundata) foodstuffs is mainly performed by small and medium scale processors that employ old traditional methods. This can lead to differences in quality from processor to processor, and from location to location, with consequent safety concerns. As such, the effects of processing and post-processing phases (i.e., storage, transport, etc.) on the safety of some yam-derived foodstuffs-namely chips, flakes, and flour-has been evaluated, with a focus on bacterial and fungal contamination, aflatoxins, pesticides, and heavy metals (Pb, Ni, Cd and Hg). Yams harvested and processed in Nigeria were screened, being that the country is the largest producer of the tuber, with 70⁻75% of the world production. Results highlighted no presence of pesticides, however, many samples showed high levels of bacterial and fungal contamination, together with heavy metal concentrations above the recommended safety levels. No trend was observed between the items considered; it was noticed, however, that samples purchased from the markets showed higher contamination levels than those freshly produced, especially regarding bacterial and aflatoxins presence. The processing stage was identified as the most critical, especially drying. Nonetheless, post-processing steps such as storage and handling at the point of sale also contributed for chemical contamination, such as aflatoxin and heavy metals. The results suggested that both the processing and post-processing phases have an impact on the safety of yam chips, flakes, and flour.

Effects of Post-Processing Parameters on 3D-Printed Dental Appliances: A Review

In recent years, additive manufacturing (AM) has been recognized as a transformative force in the dental industry, with the ability to address escalating demand, expedite production timelines, and reduce labor-intensive processes. Despite the proliferation of three-dimensional printing technologies in dentistry, the absence of well-established post-processing protocols has posed formidable challenges. This comprehensive review paper underscores the critical importance of precision in post-processing techniques for ensuring the acquisition of vital properties, encompassing mechanical strength, biocompatibility, dimensional accuracy, durability, stability, and aesthetic refinement in 3D-printed dental devices. Given that digital light processing (DLP) is the predominant 3D printing technology in dentistry, the main post-processing techniques and effects discussed in this review primarily apply to DLP printing. The four sequential stages of post-processing support removal, washing, secondary polymerization, and surface treatments are systematically navigated, with each phase requiring meticulous evaluation and parameter determination to attain optimal outcomes. From the careful selection of support removal tools to the consideration of solvent choice, washing methodology, and post-curing parameters, this review provides a comprehensive guide for practitioners and researchers. Additionally, the customization of post-processing approaches to suit the distinct characteristics of different resin materials is highlighted. A comprehensive understanding of post-processing techniques is offered, setting the stage for informed decision-making and guiding future research endeavors in the realm of dental additive manufacturing.