Preconditioning Step Research Articles

Reduced order model enhanced source iteration with synthetic acceleration for parametric radiative transfer equation

Applications such as uncertainty quantification, shape optimization, and optical tomography, require solving the radiative transfer equation (RTE) many times for various parameters. Efficient solvers for RTE are highly desired.Source Iteration with Synthetic Acceleration (SISA) is a popular and successful iterative solver for RTE. Synthetic Acceleration (SA) acts as a preconditioning step to accelerate the convergence of Source Iteration (SI). After each source iteration, classical SA strategies introduce a correction to the macroscopic particle density by solving a low order approximation to a kinetic correction equation. For example, Diffusion Synthetic Acceleration (DSA) uses the diffusion limit. However, these strategies may become less effective when the underlying low order approximations are not accurate enough. Furthermore, they do not exploit low rank structures concerning the parameters of parametric problems.To address these issues, we propose enhancing SISA with data-driven ROMs for the parametric problem and the corresponding kinetic correction equation. First, the ROM for the parametric problem can be utilized to obtain an improved initial guess. Second, the ROM for the kinetic correction equation can be utilized to design a low rank approximation to it. Unlike the diffusion limit, this ROM-based approximation builds on the kinetic description of the correction equation and leverages low rank structures concerning the parameters. We further introduce a novel SA strategy called ROMSAD. ROMSAD initially adopts our ROM-based approximation to exploit its greater efficiency in the early stage of SISA, and then automatically switches to DSA to leverage its robustness in the later stage. Additionally, we propose an approach to construct the ROM for the kinetic correction equation without directly solving it.Through a series of numerical tests, we compare the proposed methods with SI-DSA and DSA preconditioned Krylov solver. Particularly, for a multiscale parametric pin-cell problem, ROMSAD achieves approximately 10 times the acceleration compared to SI-DSA and 4 times acceleration compared to DSA preconditioned GMRES.

Stemless InSb nanowire networks and nanoflakes grown on InP

Among the experimental realization of fault-tolerant topological circuits are interconnecting nanowires with minimal disorder. Out-of-plane indium antimonide (InSb) nanowire networks formed by merging are potential candidates. Yet, their growth requires a foreign material stem usually made of InP–InAs. This stem imposes limitations, which include restricting the size of the nanowire network, inducing disorder through grain boundaries and impurity incorporation. Here, we omit the stem allowing for the growth of stemless InSb nanowire networks on an InP substrate. To enable the growth without the stem, we show that a preconditioning step using arsine (AsH3) is required before InSb growth. High-yield of stemless nanowire growth is achieved by patterning the substrate with a selective-area mask with nanohole cavities, containing restricted gold droplets from which nanowires originate. Interestingly, these nanowires are bent, posing challenges for the synthesis of interconnecting nanowire networks due to merging failure. We attribute this bending to the non-homogeneous incorporation of arsenic impurities in the InSb nanowires and the interposed lattice-mismatch. By tuning the growth parameters, we can mitigate the bending, yielding large and single crystalline InSb nanowire networks and nanoflakes. The improved size and crystal quality of these nanostructures broaden the potential of this technique for fabricating advanced quantum devices.

Enabling fast debinding of ceramic vat photopolymerization prints with supercritical carbon dioxide as a solvent

Thermal debinding of 3D-printed ceramics that include a large fraction of polymeric binders, can take several days to perform, and often includes a long thermal pre-conditioning step. In addition, means to reduce the manufacturing time are limited. This applies for thermal debinding of dense ceramics, such as dental restorations, that have been printed using vat photopolymerization (VPP) based technologies. The thermal debinding of such ceramics can be made faster, more economical, and more ecological by extracting selected slurry components prior to thermal debinding. Thus, creating flow channels for pyrolysis gases originating from the remaining binder polymers to exit the material. Here, supercritical carbon dioxide (scCO2) extraction was used to create such gas flow channels and to study which slurry components can be dissolved from VPP printed polymeric and ceramic/polymeric parts without significant delamination, cracking, or part deformation. The scCO2 extraction resulted in the creation of significant 33 vol% of nanosized porosity in a pure polymeric as-built print, and 21 vol% of nanosized porosity in a ceramic/polymeric composite print, where the ceramic powder remains in the part. These gas flow channels may allow faster thermal debinding while avoiding the creation of other defects. In the process, 90 wt.-% of the dissolvable resin fraction was extracted from the ceramic/polymeric prints already in 2 h without significant delamination, while 70 wt.-% of the dissolvable resin fraction was extracted in 2 h from the pure polymeric prints as a reference. This presents a novel, successful demonstration of a chemistry that allows both VPP printing and scCO2 extraction.

Wind power curve modeling: A probabilistic Beta regression approach

Wind turbine power curves play a key role in various aspects during the life of a wind farm. Typical uses range from wind power forecasting to wind turbine condition monitoring. This paper addresses the identification of probabilistic models of wind power curves from observed data. The main challenge is the need to handle a statistical distribution of wind energy whose shape not only may be highly skewed, but can also change with wind speed. To address these issues, we resort to the framework of Generalized Linear Models (GLMs), proposing a Beta regression approach, with constant or variable dispersion and an appropriate preconditioning step. The proposed methodology was tested on three real SCADA measurements retrieved from public datasets, including a comparison with Quantile Regression Forests (QRFs), also in terms of robustness to outliers. The results suggest that Beta regression can be a valuable resource in the development of probabilistic models for wind energy, as it provides a high degree of flexibility while preserving an interpretable structure.

Constructive subsampling of finite frames with applications in optimal function recovery

In this paper we present new constructive methods, random and deterministic, for the efficient subsampling of finite frames in Cm. Based on a suitable random subsampling strategy, we are able to extract from any given frame with bounds 0<A≤B<∞ (and condition B/A) a similarly conditioned reweighted subframe consisting of merely O(mlog⁡m) elements. Further, utilizing a deterministic subsampling method based on principles developed by Batson, Spielman, and Srivastava to control the spectrum of sums of Hermitian rank-1 matrices, we are able to reduce the number of elements to O(m) (with a constant close to one). By controlling the weights via a preconditioning step, we can, in addition, preserve the lower frame bound in the unweighted case. This permits the derivation of new quasi-optimal unweighted (left) Marcinkiewicz-Zygmund inequalities for L2(D,ν) with constructible node sets of size O(m) for m-dimensional subspaces of bounded functions. Those can be applied e.g. for (plain) least-squares sampling reconstruction of functions, where we obtain new quasi-optimal results avoiding the Kadison-Singer theorem. Numerical experiments indicate the applicability of our results.

Accelerated nonlinear domain decomposition solver for multi-phase flow and transport in porous media

Development of robust and efficient nonlinear solution strategies for multi-phase flow and transport within natural porous media is challenging. Fully Implicit Method (FIM) is widely used in reservoir simulation for solving nonlinear algebraic systems. Previous studies showed that nonlinearities of flow and transport problems could exhibit large degrees of locality across timesteps and nonlinear iterations. Nonlinear domain decomposition (NDD) is an attractive class of methods for achieving localization and resolving spatially unbalanced nonlinearities. However, NDD is not robust as iterative solvers, because the outer iterations are essentially fixed-point type.We design a solution framework to accelerate the outer-loop convergence of NDD. We first apply a nonlinear acceleration (NA) technique to the fixed-point iterations, for correcting the boundary conditions of subdomains. Because flow problems are parabolic and exhibit global behaviors, long-range information is essential to capture main features of the flow field. We incorporate a preconditioning step based on CPR-AMG before each timestep, to provide better initial solutions. Additionally, smoother iterations can be taken to improve the boundary conditions during the NDD process.We evaluate the NDD methods using heterogeneous multi-phase models. The nonlinearities associated with the flow, transport and the coupling are locally resolved through the NDD iterations. Results demonstrate that the basic NDD solver is afflicted with severe nonlinear difficulties. By comparison, the accelerated NDD solver greatly improves the outer convergence performance. Moreover, the new solver can effectively exploit locality on the Newton level, for both the flow and transport problems.

Catalytic valorisation of the effluents generated during the defibrillation process of cellulose from almond hulls: A holistic zero-waste biorefinery approach

The acid-free hydrothermal microwave–assisted selective scissoring (Hymass concept) of cellulosic substrates is gaining keen interest in the field of materials science. Besides, implementing catalytic methodologies to upgrade side streams produced during this process could contribute to better, holistic utilisation of the starting feedstock. This work depicts a zero-waste biorefinery concept based on cellulose defibrillation from almond hulls using various hydrotreatment technologies (hydrogenation and hydrodeoxygenation) for downstream processing on a ruthenium catalyst. Therein, the hemicellulose separated from the biomass preconditioning step was converted into sugar alcohols and/or marketable polyols with a relatively high yield (47.4%) by hydrolytic hydrogenation (437 K, 3h, 5.0 MPa H2). Meanwhile, a family of bioactive compounds (3-hydroxypyridines) could be directly extracted from the hydrolysate stream derived from microwave digestion, along with an energy carrier that was chemically stabilised (503 K, 60 min, 4.0 MPa H2) to obtain fuel additives (diethyl succinic acid, DES) and/or fuel intermediates.

Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

Background and purposeLate radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria. Materials and methodsWe applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria. ResultsThe PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract. ConclusionThe risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.

Trap-assisted enhancement of the responsivity in asymmetric planar GaN-based nanodiodes at low temperature

The microwave detection capability of GaN-based asymmetric planar nanodiodes (so-called Self-Switching Diode, SSD, due to its non-linearity) has been characterized in a wide temperature range, from 70 K up to 300 K. At low temperature, microwave measurements reveal an enhancement of the responsivity at frequencies below 1 GHz, which, together with a pronounced hysteresis in the DC curves, indicate a significant influence of the surface states. This leads to a significant variability and non-repeatability which needs to be reduced since it degrades the accuracy of the detection. For this sake, the RF characterization was repeated after applying a positive/negative voltage able to fill/empty the surface states in order to have a well-established preconditioned state. As a consequence of the positive pre-soak bias, a significant enhancement of the measured responsivity, with a × 10 increase at low temperature. The RF detection measurements after such preconditioning contains a time dependence induced by the slow discharge mechanism of the traps, so that the improved responsivity remains even after 100s of seconds. On the other hand, a negative voltage pre-soak benefits the discharge process, thus suppressing the low frequency dispersion and the important variability of the detection without the pre-conditioning step. We also show that the relation between the voltage and current responsivities in each case allows to explain the impact of the surface charges in terms of the device impedance.

Abstract LB094: Arginine pre-conditioning improves T-cell potency and metabolic fitness measured by real-time impedance and seahorse assays

Abstract Background: Enhanced T cell performance and fitness are imperative for the success of adoptive T cell-based therapies. Beyond the types of genetic modifications to CAR/TCR T cells, there is a growing body of literature demonstrating that relatively simple preconditioning protocols can also be used to improve T cell fitness/function. We studied, the impact that preconditioning in elevated concentrations of leucine, glutamine, and arginine has on the killing efficacy and bioenergetics of MART-1-specific TCR T cells. Methods: Using the Agilent xCELLigence RTCA eSight and Seahorse we assessed the killing efficiency and bioenergetics of engineered T-cells after Arginine, Glutamine, and Leucine pre-conditioning using MART-1 specific TCR T cells. CD3+ T-cells (Hemacare, Seattle, WA) were transduced with retrovirus SAMEN-DMF5 with a CD34 marker gene, against MART-1. The T cells were pre-conditioned in a range of concentrations varying between 0-6mM for 7 days, followed by a killing assay using MART-1 expressing melanoma cell line as target cells (624.38) engineered to express a red-fluorescent nuclear protein. The comparison was made with transduced T-cells grown in RPMI (no added amino acid supplementation denoted as RPMI_TCR), RPMI supplemented with Arginine (Arg_TCR) and non-transduced T cells. The T cell killing was measured using impedance/imaging-based assays. CD34 assessment was performed using Novocyte and SRC (spare respiratory capacity) and oxygen consumption rate (OCR) were measured using seahorse assays. Results: Whereas supplementing the growth medium with 6 mM Arginine increased killing efficacy dramatically (up to ∼6-fold), elevated leucine and glutamine concentrations were found to have minimal impact on MART-1 TCR T cell killing of melanoma cells. Arginine (6 mM) supplementation increased basal respiration, ATP linked OCR, and maximal respiration compared to the RPMI control. SRC of Arg_TCR T cells was significantly higher than RPMI preconditioned T cells, a parameter previously correlated with T cell persistence. To check the effect of a shortened pre-conditioning period, 2, and 4 days of pre-conditioning were done along with the 7 days method. After a preconditioning step of only 2 days, Arginine preconditioned T cells acquired a killing efficacy that is &amp;gt;2x higher compared to their counterparts RPMI_TCR T cells. Extending the duration of preconditioning from 2 to 4 days has minimal impact on the RPMI control T cells but more than doubles the killing efficacy of the high Arg grown T cells. Conclusions: In conclusion, Arginine pre-conditioning significantly improved T cell potency and mitochondrial respiration through metabolic rewiring. Citation Format: Rashmi R. Pillai, Xiaoyu Zhang, Yama Abassi, Brandon Lamarche, Mark M. Garner. Arginine pre-conditioning improves T-cell potency and metabolic fitness measured by real-time impedance and seahorse assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB094.

Comparison of nonlinear field-split preconditioners for two-phase flow in heterogeneous porous media

This work focuses on the development of a two-step field-split nonlinear preconditioner to accelerate the convergence of two-phase flow and transport in heterogeneous porous media. We propose a field-split algorithm named Field-Split Multiplicative Schwarz Newton (FSMSN), consisting in two steps: first, we apply a preconditioning step to update pressure and saturations nonlinearly by solving approximately two subproblems in a sequential fashion; then, we apply a global step relying on a Newton update obtained by linearizing the system at the preconditioned state. Using challenging test cases, FSMSN is compared to an existing field-split preconditioner, Multiplicative Schwarz Preconditioned for Inexact Newton (MSPIN), and to standard solution strategies such as the Sequential Fully Implicit (SFI) method or the Fully Implicit Method (FIM). The comparison highlights the impact of the upwinding scheme in the algorithmic performance of the preconditioners and the importance of the dynamic adaptation of the subproblem tolerance in the preconditioning step. Our results demonstrate that the two-step nonlinear preconditioning approach—and in particular, FSMSN—results in a faster outer-loop convergence than with the SFI and FIM methods. The impact of the preconditioners on computational performance–i.e., measured by wall-clock time–will be studied in a subsequent publication.

Improving drum sieve performance in lightweight packaging waste recycling by automatic parameter adjustment through 3D laser triangulation-based process monitoring

Current lightweight packaging (LWP) sorting plants are underperforming, and one of the most important reasons is the fluctuation in LWP input materials. Drum sieving is an essential process in LWP waste processing and recycling, as it is one of the first preconditioning steps and could influence all subsequent processes. If the parameter settings of a drum sieve can be automatically adjusted, the sorting performance of the whole plant could be improved and more recyclates could be fed back into the material loop. Therefore, this study investigated the influence from different parameter settings (inclination angles, rotation speed and throughput/material feeding) on drum sieving efficiency using 3D laser triangulation (3DLT) sensors. The results confirmed the correlation between two parameters (inclination angles, rotation speed) and sieving efficiency. Besides, 3DLT sensors enable inline determination of filling levels, which can be used as a basis for an automatic parameter control concept.

A generalized bodyforce scheme for lattice Boltzmann simulations of incompressible flow in complex geometries

We present a general procedure for reducing compressibility effects in pressure driven lattice Boltzmann simulations involving complex geometries. We do this by introducing a preconditioning step for the flow system in order to reduce these often undesirable effects, rather than directly modifying the flow algorithm itself. The method consists of determining the geometry dependent contribution to a pressure field induced by a given set of pressure boundary conditions. We show that this may be done through solving a set of Laplace's equations that do not need re-evaluation during flow simulations. This preconditioning method is not limited to stationary flows but is directly applicable to time-varying flows, without any recalculation of the preconditioning step. In this paper, we apply the procedure to the lattice Boltzmann algorithm, but it may be utilized in any flow simulation algorithm based on artificial compressibility methods. The method is demonstrated in both stationary and non-stationary flow situations. This includes examples from flow in porous media and hemodynamics. All the presented examples are compared to conventional methods for implementing pressure driven flow in lattice Boltzmann. In all the examples, it is shown that the proposed method considerably reduces the undesirable features exhibited by the conventional methods.

Preconditioner influence on twin-screw extrusion cooking of starch-based feed pellets: The example of Fish Feed

This work compares the performance of pellets for adult Trout obtained in two different processing conditions. The first one consists of adopting the traditional Fish Feed process based on a preconditioning steam treatment step before twin-screw extrusion. The second one was a simplified process based only on a twin-extruder ensuring both the cooking and the extrusion of fish pellets in a single step. Six formulations were obtained while maintaining feed specifications (36 % of proteins and 30 % of lipids). The comparison between the two processing methodologies (i.e., with or without preconditioning) was then based on two fundamental aspects. Firstly, the gelatinization rate of starch contained inside pellets was determined. Secondly, the pellet’s characteristics (bulk density, expansion ratio, diameter and pellet’s porosity) and their usage properties (oil leakage rate, durability, hardness and floatability) were evaluated. The present work has evidenced that it is possible to produce pellets without preconditioning while ensuring a total starch gelatinization and maintaining their usage properties. The Fish Feed process has thus been partially simplified.

Preconditioning the Quad Dominant Mesh Generator for Ship Structural Analysis

This paper presents an algorithm for the fully automatic mesh generation for the finite element analysis of ships and offshore structures. The quality requirements on the mesh generator are imposed by the acceptance criteria of the classification societies as well as the need to avoid shear locking when using low degree shell elements. The meshing algorithm will be generating quadrilateral dominated meshes (consisting of quads and triangles) and the mesh quality requirements mandate that quadrilaterals with internal angles close to 90° are to be preferred. The geometry is described by a dictionary containing points, rods, surfaces, and openings. The first part of the proposed method consists of an algorithm to automatically clean the geometry. The corrected geometry is then meshed by the frontal Delaunay mesh generator as implemented in the gmsh package. We present a heuristic method to precondition the cross field of the fronatal quadrilateral mesher. In addition, the influence of the order in which the plates are meshed will be explored as a preconditioning step.

Dual applications of Chebyshev polynomials method: Efficiently finding thousands of central eigenvalues for many-spin systems

Computation of a large group of interior eigenvalues at the middle spectrum is an important problem for quantum many-body systems, where the level statistics provides characteristic signatures of quantum chaos. We propose an exact numerical method, dual applications of Chebyshev polynomials (DACP), to simultaneously find thousands of central eigenvalues, where the level space decreases exponentially with the system size. To disentangle the near-degenerate problem, we employ twice the Chebyshev polynomials, to construct an exponential semicircle filter as a preconditioning step and to generate a large set of proper basis states in the desired subspace. Numerical calculations on Ising spin chain and spin glass shards confirm the correctness and efficiency of DACP. As numerical results demonstrate, DACP is 30 times faster than the state-of-the-art shift-invert method for the Ising spin chain while 8 times faster for the spin glass shards. In contrast to the shift-invert method, the computation time of DACP is only weakly influenced by the required number of eigenvalues, which renders it a powerful tool for large scale eigenvalues computations. Moreover, the consumed memory also remains a small constant (5.6 GB) for spin-1/2 systems consisting of up to 20 spins, making it desirable for parallel computing.

Demineralization of high ash coal fines of Indian origin using Mahua oil

The cost of oil is perhaps the biggest prohibitive aspect of the oil agglomeration process. Furthermore, the high ash low grade coals of India are more difficult to demineralize owing to their unfavourable hydrophilic surface chemistry. The objective of our present study is to use an indigenous and underutilized oil derived from Mahua seeds as a potential bridging liquid for oil agglomeration of low grade coal from Kaniha (Odisha, India) with 35% ash. Based on experimental results, Mahua oil demonstrably proved efficient in demineralization by 45–50% at lower oil concentrations (10–15 wt%), devoid of surfactants. Wet grinding of the coal using a vibratory cup mill was an essential pre-treatment step. A detailed investigation of all the crucial process parameters was performed through variation of pH, oil dosages, solid to liquid ratio and agitation conditions. The results obtained were further validated by detailed characterization of both raw and clean coal using zeta potential, FTIR, SEM studies and petrography. FTIR and petrographic studies validated the inherent and surface hydrophilicity of Kaniha coal. Zeta potential studies clearly showed an increase in surface potential around neutral pΗ which was found to be optimum for oil agglomeration. SEM results conclusively supported the formation of porous large size agglomerates. To our knowledge, ours is the first thorough examination of agglomeration potential of Mahua oil, which is an industrially underutilized product, thus supporting the aim of waste utilization and cleaner fuel production. • Crude Mahua oil as bridging liquid for oil agglomeration of high ash low grade Indian coal (35% ash) has been investigated. • Lower dosage (15 wt%) of oil under neutral conditions gave satisfactory results. • Wet grinding in a vibratory cup mill has been implicated to be a crucial pre-treatment step. • Demineralization by 45–50% was observed. • No pre-conditioning step of surfactant was included.

Simultaneous detection of dual food adulterants using graphene oxide and gold nanoparticle based surface enhanced Raman scattering duplex DNA biosensor

Development of a facile, fast, sensitive and multiplex DNA detection for the quantitative verification of food adulterant is of high significance these days. RT-PCR, and nanoparticle based fluorescence and electrochemical detection techniques have been successfully used in clinical medicine. However, requirements of pre-conditioning steps and fluorescence dye labeling in RT-PCR, inherent photobleaching and overlapping spectra of fluorescent probe in fluorescent assay, are the few drawbacks that urge to find a suitable alternative. Surface-enhanced Raman scattering (SERS) provides molecule specific fingerprint spectra, could be a strategic to resolve the limitations. Here, we report a SERS based duplex DNA detection strategy for simultaneous and quantitative detection of a meat adulterant (pork) and an endangered species – Malayan Box Turtle (MBT). In the biosensing strategy, SERS active dual platforms – graphene oxide-gold nanoparticles (GO-AuNPs) and AuNPs, and uniquely designed Raman tag intercalated short-length signal probe (SP) sequences are used. The sensing principle relies on the covalent linking of SP sequences functionalized AuNPs and capture probe (CP) functionalized GO-AuNPs via hybridization with corresponding target DNA. Coupling of multi-component platforms contributes in huge SERS signal enhancement due to electromagnetic and charge transfer mechanism. The biosensor showed an improved sensitivity in simultaneous detection of both adulterants with limit of detection (LOD) is 1 × 10−14 M. Moreover, efficiency of SERS biosensor was validated by the DNA extracted from real samples with a LOD of 1 × 10-13 M. Furthermore, the biosensing approach showed excellent sequence specificity in discriminating DNA sequences of five non-target species and specificity towards single nucleotide differentiation.

Tensile Viscoelastic Properties of the Sclera after Glycosaminoglycan Depletion

ABSTRACT Purpose Fibrillar collagen network and glycosaminoglycans (GAGs) are the primary components of extracellular matrix (ECM) of the sclera. The main goal of this study was to investigate the possible structural roles of GAGs in the scleral tensile properties as a function of preconditioning and displacement rate. Methods Four-step uniaxial stress-relaxation tests were used for characterizing the viscoelastic tensile response of the posterior porcine sclera with and without enzymatic GAG removal. The scleral strips were divided into different groups based on the displacement rate and the presence or absence of a preconditioning step in the loading protocol. The groups were (1) displacement rate of 0.2 mm/min without preconditioning, (2) displacement rate of 1 mm/min without preconditioning, (3) displacement rate of 0.2 mm/min with preconditioning, and (4) displacement rate of 1 mm/min with preconditioning. The peak stress, equilibrium stress, and the equilibrium elastic modulus were calculated for all specimens and compared against each other. Results Increasing the displacement rate from 0.2 mm/min to 1.0 mm/min was found to cause an insignificant change in the equilibrium stress and equilibrium elastic modulus of porcine scleral strips. Removal of GAGs resulted in an overall stiffer tensile behavior independent of the displacement rate in samples that were not preconditioned (P < .05). The behavior of preconditioned samples with and without GAG removal was not significantly different from each other. Conclusions The experimental measurements of the present study showed that GAGs play an important role in the mechanical properties of the posterior porcine sclera. Furthermore, using a preconditioning step in the uniaxial testing protocol resulted in not being able to identify any significant difference in the tensile behavior of GAG depleted and normal scleral strips.

Development of LT-HSC-Reconstituted Non-Irradiated NBSGW Mice for the Study of Human Hematopoiesis In Vivo.

Humanized immune system (HIS) mouse models are useful tools for the in vivo investigation of human hematopoiesis. However, the majority of HIS models currently in use are biased towards lymphocyte development and fail to support long-term multilineage leucocytes and erythrocytes. Those that achieve successful multilineage reconstitution often require preconditioning steps which are expensive, cause animal morbidity, are technically demanding, and poorly reproducible. In this study, we address this challenge by using HSPC-NBSGW mice, in which NOD,B6.SCID IL-2rγ -/-KitW41/W41 (NBSGW) mice are engrafted with human CD133+ hematopoietic stem and progenitor cells (HSPCs) without the need for preconditioning by sublethal irradiation. These HSPCs are enriched in long-term hematopoietic stem cells (LT-HSCs), while NBSGW mice are permissive to human hematopoietic stem cell (HSC) engraftment, thus reducing the cell number required for successful HIS development. B cells reconstitute with the greatest efficiency, including mature B cells capable of class-switching following allogeneic stimulation and, within lymphoid organs and peripheral blood, T cells at a spectrum of stages of maturation. In the thymus, human thymocytes are identified at all major stages of development. Phenotypically distinct subsets of myeloid cells, including dendritic cells and mature monocytes, engraft to a variable degree in the bone marrow and spleen, and circulate in peripheral blood. Finally, we observe human erythrocytes which persist in the periphery at high levels following macrophage clearance. The HSPC-NBSGW model therefore provides a useful platform for the study of human hematological and immunological processes and pathologies.