Intermediate Processing Steps Research Articles

Boosting Microfluidic Enzymatic Cascade Reactions with pH-Responsive Polymersomes by Spatio-Chemical Activity Control.

Microfluidic flow reactors permit the implementation of sensitive biocatalysts in polymeric environments (e.g., hydrogel dots), mimicking nature through the use of diverse microstructures within defined confinements. However, establishing complex hybrid structures to mimic biological processes and functions under continuous flow with optimal utilization of all components involved in the reaction process represents a significant scientific challenge. To achieve spatial, chemical, and temporal control for any microfluidic application, compartmentalization is required, as well as the unification of different sensitive compartments in the reaction chamber for the microfluidic flow design. This study presents a self-regulating microfluidic system fabricated by a sequential photostructuring process with an intermediate chemical process step to realize pH-sensitive hybrid structures for the fabrication of a microfluidic double chamber reactor for controlled enzymatic cascade reaction (ECR). The key point is the adaptation and retention of the function of pH-responsive horseradish peroxidase-loaded polymersomes in a microfluidic chip under continuous flow. ECR is successfully triggered and controlled by an interplay between glucose oxidase-converted glucose, the membrane state of pH-responsive polymersomes, and other parameters (e.g., flow rate and fluid composition). This study establishes a promising noninvasive regulatory platform for extended spatio-chemical control of current and future ECR and other cascade reaction systems.

The End-to-End Fetal Head Circumference Detection and Estimation in Ultrasound Images.

In prenatal examinations, the fetal head circumference (HC) measurement is essential for assessing fetal weight and health conditions. The sonographers obtain the fetal HC manually by fitting peripheral skull ellipse in clinical practice, which is highly subjective, time-consuming, and experience-dependent. Recently, many fetal HC automatic measurement algorithms have been proposed to improve workflow efficiency in prenatal examination. But most automatic measurement algorithms focus on using fetal head segmentation as an intermediate processing step, and HC estimation relies heavily on segmentation results, which causes the accumulation of errors in the above two stages. Independent of the segmentation method, we design a regression network to generate the oriented bounding box to detect the head contour, and directly obtain the fetal head parameters with a pixel-based ellipse regression (PER) loss. Moreover, an effective 3D attention mechanism is integrated into the network to estimate HC more precisely without adding parameters in complex ultrasound images. The extensive experimental results on the public HC18 and our clinical dataset show that the proposed network provides a feasible scheme for end-to-end estimating fetal HC, and avoids the mistake brought by the intermediary processes.

Characterization and impact of oxygenates in post-consumer plastic waste-derived pyrolysis oils on steam cracking process efficiency

Mechanical recycling of mixed packaging waste has limitations due to challenging separation into pure polyolefin streams which often leads to low quality recyclates. A solution to improve overall recycling rates of polyolefins is chemical recycling. One promising approach is conversion into a liquid product via pyrolysis and subsequent steam cracking of the pyrolysis oil towards light olefins. In this study, distilled fractions of pyrolysis oils from PE and PP-rich waste were characterized via GC × GC including normal and reversed-phase column configurations with a special focus on oxygenated components. Distilled pyrolysis oils were subsequently steam cracked in 20/80 weight‐based blends with conventional naphtha. All pyrolysis oil samples contained substantial amounts of oxygenates (1–10 wt%) which poses a huge challenge for steam cracker feedstocks and it was found that ketones are the most prevailing oxygenate group. Steam cracking experiments showed slightly increased ethylene yields of the blends compared to pure fossil naphtha, with the PE-derived samples outperforming the PP-derived samples. Furthermore, diesel-range distillation cuts led to higher formation of heavy products compared to the naphtha-range cuts. Increased formation of CO was observed as a consequence of the substantial oxygen contamination which can be a deal-breaker in industrial steam crackers. This work shows that distilled pyrolysis oils with conventional naphtha are promising steam cracking feedstocks if intermediate processing steps are performed to remove the oxygenates and other impurities using, for instance, hydrotreatment. With the findings of this work, such upgrading processes can be designed in a more effective way.

Correlations reveal the hierarchical organization of biological networks with latent variables

Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons.

Imparting high elastocaloric cooling potential to NiTi alloy by two-step enhancements

The large adiabatic temperature change during the stress-induced phase transformation in the superelastic NiTi alloy makes it a leading candidate material for solid-state elastocaloric cooling applications. However, NiTi with a uniform grain structure generally offers either large cooling capacity or low energy dissipation, but not both. In an earlier study, we demonstrated that the alloy with graded grain sizes, obtained using differential annealing of heavily cold-worked sheets, can overcome this intrinsic limitation. However, non-transformable components (residual martensite and amorphous phases), which are introduced into the microstructure during cold rolling, impair the full exploitation of the cooling potential of the graded NiTi. Here, we introduce an intermediate processing step, namely post-deformation annealing, which eliminates the undesirable phases while maintaining nanocrystallinity, before imparting a microstructural gradient. Results show that the synergy between various microstructural elements results in a significant enhancement in the cooling capacity while keeping the energy dissipation low. Moreover, the functional fatigue performance of the graded alloy is also preferable to that of the conventional coarse-grained NiTi. The micromechanical reasons behind these improvements are elucidated by recourse to detailed experiments.

Large-scale crystallization as an intermediate processing step in insulin downstream process: explored advantages and identified tool for process intensification.

The rising global prevalence of diabetes and increasing demand for insulin, calls for an increase in accessibility and affordability of insulin drugs through efficient and cost-effective manufacturing processes. Often downstream operations become manufacturing bottlenecks while processing a high volume of product. Thus, process integration and intensification play an important role in reducing process steps and time, volume reduction, and lower equipment footprints, which brings additional process efficiencies and lowers the production cost. Manufacturers thrive to optimize existing unit operation to maximize its benefit replacing with simple but different efficient technologies. In this manuscript, the typical property of insulin in forming the pH-dependent zinc-insulin complex is explored. The benefit of zinc chloride precipitation/crystallization has been shown to increase the in-process product purity by reducing the product and process-related impurities. Incorporation of such unit operation in the insulin process has also a clear potential for replacing the high cost involved capture chromatography step. Same time, the reduction in volume of operation, buffer consumption, equipment footprint, and capabilities of product long time storage brings manufacturing flexibility and efficiencies. The data and capabilities of simple operation captured here would be significantly helpful for insulins and other biosimilar manufacturer to make progresses on cost-effective productions.

Economic competitiveness of pultruded fiber composites for wind turbine applications

Pultrusion manufacturing of fiber reinforced polymers has been shown to yield some of the highest mechanical properties for unidirectional composites, having a high degree of fiber alignment with consistent performance. Pultrusions offer a low-cost manufacturing approach for producing unidirectional composites with a constant cross-section and are used in many applications, including spar caps of wind turbine blades. However, as an intermediate processing step for wind blades, the additional cost of manufacturing pultrusions must be accompanied by sufficient increases in mechanical performance and system benefits. Wind turbine blades are manufactured using vacuum-assisted resin transfer molding with infused unidirectional fiberglass or carbon pultrusions for the spar cap. Infused fiberglass composites are among the most cost-effective structural materials available and replacing this material in the cost-driven wind industry has proven challenging, where infused fiberglass spar caps are still the predominant material system in use. To evaluate alternative material systems in a pultruded composite form, it is necessary to understand the costs for this additional manufacturing step which are shown to add 33%–55% on top of the material costs. A pultrusion cost model has been developed and used to quantify cost sensitivities to various processing parameters. The mechanical performance for pultruded composites is improved versus resin-infusion manufacturing with a 17% increase in design strength at a constant fiber volume fraction, but also enables higher achievable fiber volume fractions. The cost-specific mechanical performance is compared as a function of processing parameters for pultruded composites to identify the opportunities for alternative material and manufacturing approaches for wind turbine spar caps. Four materials are compared in a representative wind turbine blade model to assess the performance of pultruded carbon fiber systems and pultruded fiberglass relative to infused fiberglass, where the pultruded systems produce lower weight blades with various cost distinctions.

Fully automated AI-based cardiac motion parameter extraction – application to mitral and tricuspid valves on long-axis cine MR images

PurposeIn cardiac MRI, valve motion parameters can be useful for the diagnosis of cardiac dysfunction. In this study, a fully automated AI-based valve tracking system was developed and evaluated on 2- or 4-chamber view cine series on a large cardiac MR dataset. Automatically derived motion parameters include atrioventricular plane displacement (AVPD), velocities (AVPV), mitral or tricuspid annular plane systolic excursion (MAPSE, TAPSE), or longitudinal shortening (LS). MethodTwo sequential neural networks with an intermediate processing step are applied to localize the target and track the landmarks throughout the cardiac cycle. Initially, a localisation network is used to perform heatmap regression of the target landmarks, such as mitral, tricuspid valve annulus as well as apex points. Then, a registration network is applied to track these landmarks using deformation fields. Based on these outputs, motion parameters were derived. ResultsThe accuracy of the system resulted in deviations of 1.44 ± 1.32 mm, 1.51 ± 1.46 cm/s, 2.21 ± 1.81 mm, 2.40 ± 1.97 mm, 2.50 ± 2.06 mm for AVPD, AVPV, MAPSE, TAPSE and LS, respectively. Application on a large patient database (N = 5289) revealed a mean MAPSE and LS of 9.5 ± 3.0 mm and 15.9 ± 3.9 % on 2-chamber and 4-chamber views, respectively. A mean TAPSE and LS of 13.4 ± 4.7 mm and 21.4 ± 6.9 % was measured. ConclusionThe results demonstrate the versatility of the proposed system for automatic extraction of various valve-related motion parameters.

The Additive-Subtractive Process Chain - a Review

In recent years, metal additive manufacturing developed intensively and became a relevant technology in industrial production of highly complex and function integrated parts. However, almost all additively manufactured parts must be post-processed in order to fulfil geometric tolerances, surface quality demands and the desired functional properties. Thus, additive manufacturing actually means the implementation of additive-subtractive process chains. Starting with the most relevant additive processes (powder-based PBF-LB, LMD-p and wire-based WAAM and LMD-w/WLAM), considering intermediate process steps (heat treatment and shot peening) and ending up with post-processing material removal processes (with defined and undefined cutting edges), this paper gives an overview of recent research findings with respect to a comprehensive scientific investigation of influences and interactions within the additive-subtractive process chain. This includes both the macroscopic geometric scale and the microscopic scale of the material structure. Finally, conclusions and future perspectives are derived and discussed.

Combined wind lidar and cloud radar for high-resolution wind profiling

Abstract. This paper introduces an experimental setup for retrieving horizontal wind speed and direction profiles with a high temporal and vertical resolution for process studies and validation of convection-permitting model simulations. The CMTRACE (tracing convective momentum transport in complex cloudy atmospheres) campaign used collocated wind lidar and cloud radar measurements to retrieve seamless wind profiles from near the surface up to cloud tops. It took place in Cabauw, the Netherlands, between 13 September and 3 October 2021. The intermediate processing steps for generating the level 1 and level 2 data, such as second trip echoes filtering, offset correction, wind retrieval, re-gridding, and flagging, are described. In level 1 (https://doi.org/10.5281/zenodo.6926483, Dias Neto, 2022a), the data from lidar and radars are kept in the original spatial and temporal resolution, while in level 2 (https://doi.org/10.5281/zenodo.6926605, Dias Neto, 2022b), they are regridded to a common spatial and temporal resolution. Statistical analyses of the lidar's and radar's wind speed and direction profiles indicate a correlation higher than 0.95 for both variables. The bias of wind direction and speed calculated between radar's and lidar's observations are 0.24∘ and −0.16 m s−1, respectively. The foreseen initial application of the datasets includes the study of convective momentum transport and its validation in regional weather forecasts and large-eddy simulation hindcasts.

Structural Insights into the Advances and Mechanistic Understanding of Human Dicer.

The RNase III endoribonuclease Dicer was discovered to be associated with cleavage of double-stranded RNA in 2001. Since then, many advances in our understanding of Dicer function have revealed that the enzyme plays a major role not only in microRNA biology but also in multiple RNA interference-related pathways. Yet, there is still much to be learned regarding Dicer structure-function in relation to how Dicer and Dicer-like enzymes initiate their cleavage reaction and release the desired RNA product. This Perspective describes the latest advances in Dicer structural studies, expands on what we have learned from this data, and outlines key gaps in knowledge that remain to be addressed. More specifically, we focus on human Dicer and highlight the intermediate processing steps where there is a lack of structural data to understand how the enzyme traverses from pre-cleavage to cleavage-competent states. Understanding these details is necessary to model Dicer's function as well as develop more specific microRNA-targeted therapeutics for the treatment of human diseases.

Diverted from Landfill: Reuse of Single-Use Plastic Packaging Waste

Low-density polyethylene (LDPE) based packaging films mostly end up in landfill after single-use as they are not commonly recycled due to their flexible nature, low strength and low cost. Additionally, the necessity to separate and sort different plastic waste streams is the most costly step in plastics recycling, and is a major barrier to increasing recycling rates. This cost can be reduced through using waste mixed plastics (wMP) as a raw material. This research investigates the properties of PE-based wMP coming from film packaging wastes that constitutes different grades of PE with traces of polypropylene (PP). Their properties are compared with segregated individual recycled polyolefins and virgin LDPE. The plastic plaques are produced directly from the wMP shreds as well as after extruding the wMP shreds into a more uniform material. The effect of different material forms and processing conditions on the mechanical properties are investigated. The results of the investigation show that measured properties of the wMP fall well within the range of properties of various grades of virgin polyethylene, indicating the maximum possible variations between different batches. Addition of an intermediate processing step of extrusion before compression moulding is found to have no effect on the tensile properties but results in a noticeably different failure behaviour. The wMP does not show any thermal degradation during processing that was confirmed by thermogravimetric analysis. The results give a scientific insight into the adoption of wMP in real world products that can divert them from landfill creating a more circular economy.

Spectrogram Data Set for Deep-Learning-Based RF Frame Detection

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks such as difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared with manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labeling a high variety of frames in different environments is too challenging, an artificial data generation pipeline was developed. The data set consists of 20,000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enable the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples.

Anthocyanin Content of Crackers and Bread Made with Purple and Blue Wheat Varieties.

Purple and blue wheats contain anthocyanins in the outer layers of the wheat kernel, and therefore purple and blue wholemeals can be a source of anthocyanins when developing processed cereal products. However, cereal processing is anticipated to cause significant anthocyanin losses. In this study, the anthocyanin content of crackers and bread made from one purple and three blue wholemeals was measured during processing and after baking. LC-MS/MS was used to confirm the presence of anthocyanins, and to tentatively identify them. Mixing and baking steps significantly decreased the anthocyanin content, whereas resting and fermentation steps did not. Purple and blue wholemeal samples reacted differently, indicating that the starting anthocyanin content, localization and composition may have some impact on anthocyanin retention. Additionally, dough systems with decreased pH were more protective of anthocyanins during intermediate processing steps, as were high-temperature, short-time baking procedures. This research provides insights into the processing steps that cause significant anthocyanin losses, and proposes some modifications to formulation and processing conditions which can further reduce losses.

Specification-driven acceptance criteria for validation of biopharmaceutical processes.

Intermediate acceptance criteria are the foundation for developing control strategies in process validation stage 1 in the pharmaceutical industry. At drug substance or product level such intermediate acceptance criteria for quality are available and referred to as specification limits. However, it often remains a challenge to define acceptance criteria for intermediate process steps. Available guidelines underpin the importance of intermediate acceptance criteria, because they are an integral part for setting up a control strategy for the manufacturing process. The guidelines recommend to base the definition of acceptance criteria on the entirety of process knowledge. Nevertheless, the guidelines remain unclear on how to derive such limits. Within this contribution we aim to present a sound data science methodology for the definition of intermediate acceptance criteria by putting the guidelines recommendations into practice (ICH Q6B, 1999). By using an integrated process model approach, we leverage manufacturing data and experimental data from small scale to derive intermediate acceptance criteria. The novelty of this approach is that the acceptance criteria are based on pre-defined out-of-specification probabilities, while also considering manufacturing variability in process parameters. In a case study we compare this methodology to a conventional +/- 3 standard deviations (3SD) approach and demonstrate that the presented methodology is superior to conventional approaches and provides a solid line of reasoning for justifying them in audits and regulatory submission.

X-ray phase contrast imaging model: application on tomography with a single 2D phase grating

In this paper, we propose a model dedicated to X-ray phase contrast imaging, which is well adapted to the characterization or inspection of low attenuating samples. We introduce a hybrid approach that combines a ray-tracing step with a wave propagation computation. The mathematical basis of our model is described and we present a comparison of the model to experimental results, for the case of an optical fiber sample, in the framework of a free-propagation phase technique. The extension to the 3D imaging is proposed on simulated data using a grating based technique or more precisely, a multilateral shearing interferometry. This technique uses a single 2D phase grating, which has the advantage of a simpler experimental setup and can be coupled with a standard micro-focus X-ray tube and a high-resolution detector. Our phase model was implemented on the CIVA CT simulation platform and used to generate easily different sets of projection data for any type of sample. While the method for 3D reconstruction has the same basis as the classical CT, we focus mainly on the intermediate processing steps, which are required for the phase retrieval and present the results for a phantom composed of spherical objects in different materials.

Identification of Potentially Hazardous Microorganisms and Assessment of Physicochemical Deterioration of Thermally Processed King Coconut (Cocos nucifera var. aurantiaca) Water under Different Processing Conditions in Sri Lanka

King coconut water (KCW) is a sweet relish product that is more prone to rapid quality deterioration, and several safety concerns are emerging due to its inappropriate thermal processing. Therefore, the objective of this study was to identify the potential spoilage/pathogenic microorganisms associated with the processing of KCW, with the assessment of possible physicochemical changes as providing preliminary information required for the thermal process validation of bottled KCW. Samples (n = 6, 150 ml/sample) were collected from three different KCW processing facilities at five critical processing stepsP1−P5. A facility survey, physicochemical analyses, and microbial enumeration and isolation, along with their molecular identifications, were conducted. It was found that all tested physicochemical properties were significantly changedp<0.05among sampling points at each processing facility. The colour of thermally processed KCW samples has significantly changedp<0.05compared to the fresh KCW, which causes a distinct effect on the appealing quality of the final product. A pattern of initial lower counts with gradually increased microbial counts at intermediate processing steps (1.0 × 103–5.3 × 106 CFU/ml) and significantly loweredp<0.05counts after thermal treatment was observed. Among the bacterial and fungal isolates identified, several potential pathogenic bacterial species, such as Pantoea dispersa, Bacillus siamensis, Pseudomonas stutzeri, and Acinetobacter lactucae; a few thermal resistant yeasts, Pichia kudriavzevii, Debaryomyces nepalensis, and Candida carpophila; and moulds, Penicillium citrinum, Microdochium fisheri, and Trichosporon asahii, have survived in the thermally processed KCW. Based on the results of the study, it is suggested that the thermal process validation of KCW should be targeted according to the revealed knowledge on the identified hazardous microorganisms, while adhering to Good Manufacturing and Hygienic Practices with minimized handling time to avoid rapid quality deterioration.

Design of an EEG analytical methodology for the analysis and interpretation of cerebral connectivity signals

The objective of this study is to design an Electroencephalographic (EEG) analytic methodology that allows to develop a variety of analysis and interpretations of brain signals. The initial phase considers the acquisition and filtering of EEG signals, the division into bands in data ranges, and the storage of EEG signals in a cloud data base. Then, an analytical phase considering descriptive, predictive and prescriptive analysis is accomplished. A sequence of analytic intermediate processing steps is done in order to render a graphic visualization of significant correlations between pairs of EEG channels. Pearson correlation is utilized to detect synchronic connectivity through the brain areas. Time series in nearly instantaneous time lapses are treated by using Hilbert Huang Transform. An experimental design by submitting a set of students to an abbreviated version Raven visual test is made providing results in correlation maps of cerebral connectivity.

Functionally Assessing the Age-Related Decline in the Detection Rate of Photons by Cone Photoreceptors.

Age-related decline in visual perception is usually attributed to optical factors of the eye and neural factors. However, the detection of light by cones converting light into neural signals is a crucial intermediate processing step of vision. Interestingly, a novel functional approach can evaluate many aspects of the visual system including the detection of photons by cones. This approach was used to investigate the underlying cause of age-related visual decline and found that the detection rate of cones was considerably affected with healthy aging. This functional test enabling to evaluate the detection of photons by cones could be particularly useful to screen for retinal pathologies affecting cones such as age-related macular degeneration. However, the paradigm used to functionally measure the detection of photons was complex as it was evaluating many other properties of the visual system. The aim of the current mini review is to clarify the underlying rationale of functionally evaluating the detection of photons by cones, describe a simpler approach to evaluate it, and review the impact of aging on the detection rate of cones.

Data-Driven Retrospective Correction of B1 Field Inhomogeneity in Fast Macromolecular Proton Fraction and R1 Mapping.

Correction of B1 field non-uniformity is critical for many quantitative MRI methods including variable flip angle (VFA) T1 mapping and single-point macromolecular proton fraction (MPF) mapping. The latter method showed promising results as a fast and robust quantitative myelin imaging approach and involves VFA-based R1=1/T1 map reconstruction as an intermediate processing step. The need for B1 correction restricts applications of the above methods, since B1 mapping sequences increase the examination time and are not commonly available in clinics. A new algorithm was developed to enable retrospective data-driven simultaneous B1 correction in VFA R1 and single-point MPF mapping. The principle of the algorithm is based on different mathematical dependences of B1 -related errors in R1 and MPF allowing extraction of a surrogate B1 field map from uncorrected R1 and MPF maps. To validate the method, whole-brain R1 and MPF maps with isotropic 1.25 mm3 resolution were obtained on a 3 T MRI scanner from 11 volunteers. Mean parameter values in segmented brain tissues were compared between three reconstruction options including the absence of correction, actual B1 correction, and surrogate B1 correction. Surrogate B1 maps closely reproduced actual patterns of B1 inhomogeneity. Without correction, B1 non-uniformity caused highly significant biases in R1 and MPF ( ). Surrogate B1 field correction reduced the biases in both R1 and MPF to a non-significant level ( 0.1 ≤ P ≤ 0.8 ). The described algorithm obviates the use of dedicated B1 mapping sequences in fast single-point MPF mapping and provides an alternative solution for correction of B1 non-uniformities in VFA R1 mapping.