Volume Size Research Articles

Reconstructing Kaolinite Compounds for Remarkably Enhanced Adsorption of Congo Red

Organic dyes are widely used in many important areas, but they also bring many issues for water pollution. To address the above issues, a reconstructed kaolinite hybrid compound (γ-AlOOH@A-Kaol) was obtained from raw kaolinite (Kaol) in this work. The product was then characterized by X-ray diffraction (XRD), Fourier-transform infrared (ATR-FTIR), Brunauer-Emmett-Teller (BET), and scanning electron microscopy (SEM), and the absorption properties of γ-AlOOH@A-Kaol for congo red were further studied. The results demonstrated that flower-like γ-AlOOH with nanolamellae were uniformly loaded on the surface of acid-treated Kaol with a porous structure (A-Kaol). In addition, the surface area (36.5 m2/g), pore volume (0.146 cm3/g), and pore size (13.0 nm) of γ-AlOOH@A-Kaol were different from those of A-Kaol (127.4 m2/g, 0.127 cm3/g, and 4.28 nm, respectively) and γ-AlOOH (34.1 m2/g, 0.315 cm3/g, and 21.5 nm, respectively). The unique structure could significantly enhance the sorption capacity for congo red, which could exceed 1000 mg/g. The reasons may be ascribed to the abundant groups of -OH, large specific surface area, and porous structure of γ-AlOOH@A-Kaol. This work provides an efficient route for comprehensive utilization and production of Kaol-based compound materials that could be used in the field of environmental conservation.

Abstract PO1-25-12: Metformin enhances response to chemotherapy combined with immunotherapy in a triple negative breast cancer in vivo model

Abstract Background Triple-negative breast cancer (TNBC) is a heterogeneous group comprising several well-defined molecular subtypes. Classical chemotherapy remains the standard of care for advanced TNBC, although blockade of the PD-1/PD-L1 axis has emerged as a promising therapeutic option to enhance antitumor immunity. Atezolizumab is an immune checkpoint inhibitor targeting PD-L1 that has been approved for use in combination with paclitaxel for the treatment of advanced TNBC. Metformin (MTF) is a prescribed drug for type 2 diabetes with well-tolerated side-effects and has been reported to have anticancer effects. Xenograft mouse models studies of breast, lung and prostate cancer, have reveal a synergistic effect of MTF when combined with paclitaxel, carboplatin or doxorubicin and has been reported to enhance the efficacy of immunotherapy. In this work we want to explore the effect of including MTF in a chemo-immunotherapy scheme using an in vivo model. Methods 1x106 EMT6 TNBC cell line (ATCC® CRL-2755™) were implanted subcutaneously in the back of immunocompetent BALB/c mice. Treatment began when tumours reached a volume size of 30-100 mm3. At that moment, mice received the drug, or drugs combinations, for 21 days. All drugs were administered intraperitoneally, except for MTF, which was given orally in drinking water at a concentration of 1g/L. The anti PD-L1 drug anti-hPD-L1-mIgG1 (mATZ, 300 mg/week) was administered twice per week and doxorubicin (DOXO) every three days (0.3 mg/Kg or 6 mg/kg). The primary outcome will be tumour response. Tumour size was measured using a digital caliper every day until mice were euthanized. For tumour volume determination (mm3), two measures were taken (length and width). Volume was calculated as (length x wide2)/2. We tested three different arms: DOXO(0,3mg/Kg)+m-ATZ, DOXO(0,3mg/Kg)+m-ATZ+MTF, and DOXO(6mg/Kg)+m-ATZ. Each arm included 10 mice. Differences in tumour volume and mice weight were assessed by Kruskall-Wallis analysis. Results EMT6 cells produced a tumour mass in twenty seven out of thirty mice. Follow-up in mice in the DOXO(0.3mg/Kg)+m-ATZ and DOXO(6mg/Kg)+m-ATZ arms was interrupted in two weeks due to extended tumour growth and quality of live criteria, respectively. Mice in the DOXO(0.3mg/Kg)+m-ATZ+MTF arm were followed for three weeks. Increase of tumoral volume was assessed at 14 days. Mean increase was 205, 1096 and 365 mm3in the DOXO(6mg/Kg)+m-ATZ, DOXO(0,3mg/Kg)+m-ATZ, and DOXO(0,3mg/Kg)+m-ATZ+MTF arms respectively, being the volume increase between DOXO(0,3mg/Kg)+m-ATZ treated mice significantly higher. Regarding mice, mean weight at the beginning of the treatments was 19.8 gr for the complete mice population, whereas mean weight after 14 days of treatment was 15.4, 20.9 and 21.7 gr in the DOXO(6mg/Kg)+m-ATZ, DOXO(0,3mg/Kg)+m-ATZ, and DOXO(0,3mg/Kg)+m-ATZ+MTF arms respectively, showing a significant decrease of body weight in mice receiving a high dose of DOX, while no differences associated to the inclusion of MTF in the treatment scheme was found. Conclusions MTF addition to a combination of chemotherapy and immunotherapy in the treatment of TNBC increased therapy efficacy, with no added side effects. These result paves the way to explore treatment schemes with lower chemotherapy doses, decreasing toxicity side effects. Citation Format: Pilar Zamora, Angelo Gamez Pozo, Luz Vega-Clemente, Elena Lopez-Camacho, Lucia Trilla-Fuertes, David Alonso-Martin, Juan Angel Fresno Vara, Mariano Garcia-Arranz, Enrique Espinosa. Metformin enhances response to chemotherapy combined with immunotherapy in a triple negative breast cancer in vivo model [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-25-12.

Mesenchymal Stem Cell Therapy for Bone Repair of Human Hip Osteonecrosis with Bilateral Match-Control Evaluation: Impact of Tissue Source, Cell Count, Disease Stage, and Volume Size on 908 Hips

We investigated the impact of mesenchymal stem cell (MSC) therapy on treating bilateral human hip osteonecrosis, analyzing 908 cases. This study assesses factors such as tissue source and cell count, comparing core decompression with various cell therapies. This research emphasizes bone repair according to pre-treatment conditions and the specificities of cell therapy in osteonecrosis repair, indicating a potential for improved bone repair strategies in hips without femoral head collapse. This study utilized a single-center retrospective analysis to investigate the efficacy of cellular approaches in the bone repair of osteonecrosis. It examined the impact on bone repair of tissue source (autologous bone marrow concentrate, allogeneic expanded, autologous expanded), cell quantity (from none in core decompression alone to millions in cell therapy), and osteonecrosis stage and volume. Excluding hips with femoral head collapse, it focused on patients who had bilateral hip osteonecrosis, both pre-operative and post-operative MRIs, and a follow-up of over five years. The analysis divided these patients into seven groups based on match control treatment variations in bilateral hip osteonecrosis, primarily investigating the outcomes between core decompression, washing effect, and different tissue sources of MSCs. Younger patients (<30 years) demonstrated significantly better repair volumes, particularly in stage II lesions, than older counterparts. Additionally, bone repair volume increased with the number of implanted MSCs up to 1,000,000, beyond which no additional benefits were observed. No significant difference was observed in repair outcomes between different sources of MSCs (BMAC, allogenic, or expanded cells). The study also highlighted that a ‘washing effect’ was beneficial, particularly for larger-volume osteonecrosis when combined with core decompression. Partial bone repair was the more frequent event observed, while total bone repair of osteonecrosis was rare. The volume and stage of osteonecrosis, alongside the number of injected cells, significantly affected treatment outcomes. In summary, this study provides comprehensive insights into the effectiveness and variables influencing the use of mesenchymal stem cells in treating human hip osteonecrosis. It emphasizes the potential of cell therapy while acknowledging the complexity and variability of results based on factors such as age, cell count, and disease stage.

Axial compressive behavior of corroded H-shaped steel columns

The unpredictability of corrosion in steel structures, especially in H-section columns, is a topic that has received limited research attention. Using Monte Carlo simulation (MCS) and finite element analysis (FEA), this study investigates the effect of random pitting corrosion on H-section steel columns' ultimate bearing capacity under axial compression. In this study, MCS was employed to address the random distribution of pitting corrosion. The reliability of MCS and FEA was validated through existing literature on compression tests of H-shaped steel columns. To comprehensively assess the detrimental effects of pitting corrosion, a total of 3650 models of H-shaped columns, each exhibiting different pitting corrosion characteristics, were subjected to analysis. These models incorporated three corrosion parameters (volume loss rate, size, and depth) and two geometric parameters (slenderness and section size). A nonlinear buckling analysis was carried out to investigate the ultimate bearing capacity of the columns. The findings indicate that a volume loss rate of 20% can cause a decrease in the column's ultimate bearing capacity by approximately 50%. Furthermore, the study reveals that at equal volume loss rates, corrosion depth and slenderness significantly influence the reduction factor, while corrosion size and section size exert a minimal impact. As a result, this paper introduces a probabilistic assessment method for estimating the reduction factor in ultimate bearing capacity for H-section steel columns. Notably, with every 10% increase in the volume loss rate, the mean reduction factor in the ultimate bearing capacity decreases by approximately 0.12.

Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment

Objective. Monte Carlo (MC) simulations are the benchmark for accurate radiotherapy dose calculations, notably in patient-specific high dose rate brachytherapy (HDR BT), in cases where considering tissue heterogeneities is critical. However, the lengthy computational time limits the practical application of MC simulations. Prior research used deep learning (DL) for dose prediction as an alternative to MC simulations. While accurate dose predictions akin to MC were attained, graphics processing unit limitations constrained these predictions to large voxels of 3 mm × 3 mm × 3 mm. This study aimed to enable dose predictions as accurate as MC simulations in 1 mm × 1 mm × 1 mm voxels within a clinically acceptable timeframe. Approach. Computed tomography scans of 98 breast cancer patients treated with Iridium-192-based HDR BT were used: 70 for training, 14 for validation, and 14 for testing. A new cropping strategy based on the distance to the seed was devised to reduce the volume size, enabling efficient training of 3D DL models using 1 mm × 1 mm × 1 mm dose grids. Additionally, novel DL architecture with layer-level fusion were proposed to predict MC simulated dose to medium-in-medium (D m,m ). These architectures fuse information from TG-43 dose to water-in-water (D w,w ) with patient tissue composition at the layer-level. Different inputs describing patient body composition were investigated. Main results. The proposed approach demonstrated state-of-the-art performance, on par with the MC D m,m maps, but 300 times faster. The mean absolute percent error for dosimetric indices between the MC and DL-predicted complete treatment plans was 0.17% ± 0.15% for the planning target volume V 100, 0.30% ± 0.32% for the skin D 2cc , 0.82% ± 0.79% for the lung D 2cc , 0.34% ± 0.29% for the chest wall D 2cc and 1.08% ± 0.98% for the heart D 2cc . Significance. Unlike the time-consuming MC simulations, the proposed novel strategy efficiently converts TG-43 D w,w maps into precise D m,m maps at high resolution, enabling clinical integration.

A study of polarity effect for various ionization chambers in kilovoltage x-ray beams.

Ionization chambers play an essential role in dosimetry measurements for kilovoltage (kV) x-ray beams. Despite their widespread use, there is limited data on the absolute values for the polarity correction factors across a range of commonly employed ionization chambers. This study aimed to investigate the polarity effects for five different ionization chambers in kV x-ray beams. Two plane-parallel chambers being the Advanced Markus and Roos and three cylindrical chambers; 3D PinPoint, Semiflex and Farmer chamber (PTW, Freiburg, Germany), were employed to measure the polarity correction factors. The kV x-ray beams were produced from an Xstrahl 300 unit (Xstrahl Ltd., UK). All measurements were acquired at 2cm depth in a PTW-MP1 water tank for beams between 60 kVp (HVL 1.29mm Al) and 300 kVp (HVL 3.08mm Cu), and field sizes of 2-10cm diameter for 30cm focus-source distance (FSD) and 4 × 4 cm2 - 20 × 20 cm2 for 50cm FSD. The ionization chambers were connected to a PTW-UNIDOS electrometer, and the polarity effect was determined using the AAPM TG-61 code of practice methodology. The study revealed significant polarity effects in ionization chambers, especially in those with smaller volumes. For the plane-parallel chambers, the Advanced Markus chamber exhibited a maximum polarity effect of 2.5%, whereas the Roos chamber showed 0.3% at 150 KVp with the 10cm circular diameter open-ended applicator. Among the cylindrical chambers at the same beam energy and applicator, the Pinpoint chamber exhibited a 3% polarity effect, followed by Semiflex with 1.7%, and Farmer with 0.4%. However, as the beam energy increased to 300 kVp, the polarity effect significantly increased reaching 8.5% for the Advanced Markus chamber and 13.5% for the PinPoint chamber at a 20 × 20 cm2 field size. Notably, the magnitude of the polarity effect increased with both the field size and beam energy, and was significantly influenced by the size of the chamber's sensitive volume. The findings demonstrate that ionization chambers can exhibit substantial polarity effects in kV x-ray beams, particularly for those chambers with smaller volumes. Therefore, it is important to account for polarity corrections when conducting relative dose measurements in kV x-ray beams to enhance the dosimetry accuracy and improve patient dose calculations.

Development of a scoring system to quantify errors from semantic characteristics in incident reports.

Incident reporting systems are widely used to identify risks and enable organisational learning. Free-text descriptions contain important information about factors associated with incidents. This study aimed to develop error scores by extracting information about the presence of error factors in incidents using an original decision-making model that partly relies on natural language processing techniques. We retrospectively analysed free-text data from reports of incidents between January 2012 and December 2022 from Nagoya University Hospital, Japan. The sample data were randomly allocated to equal-sized training and validation datasets. We conducted morphological analysis on free text to segment terms from sentences in the training dataset. We calculated error scores for terms, individual reports and reports from staff groups according to report volume size and compared these with conventional classifications by patient safety experts. We also calculated accuracy, recall, precision and F-score values from the proposed 'report error score'. Overall, 114 013 reports were included. We calculated 36 131 'term error scores' from the 57 006 reports in the training dataset. There was a significant difference in error scores between reports of incidents categorised by experts as arising from errors (p<0.001, d=0.73 (large)) and other incidents. The accuracy, recall, precision and F-score values were 0.8, 0.82, 0.85 and 0.84, respectively. Group error scores were positively associated with expert ratings (correlation coefficient, 0.66; 95% CI 0.54 to 0.75, p<0.001) for all departments. Our error scoring system could provide insights to improve patient safety using aggregated incident report data.

A multi-directional advection based moment of fluid method for phase change problems

A multi-directional face-flux based Moment of Fluid (MoF) advection method is developed to simulate two-phase flows with and without phase change. The material volume and centroid are advected in a face-flux based approach which guarantees the conservation of the advected quantities. The face-flux based advection avoids material overlap and void formation between the advected material volumes. The centroid advection of a material volume is computed by advecting the individual centroids of smaller subvolumes. Hence, the proposed procedure reduces the centroid advection error which is proportional to the size of the material volume. Also, the multi-directional advection enables two-phase simulations over unstructured meshes. A two-phase flow solver with the proposed method is used successfully to study film boiling and condensation phenomena.

The Removal of Phosphorus from Wastewater Using a Sewage Sludge Biochar: A Column Study

This scientific study investigated the adsorption capabilities of biochar samples derived from municipal sewage sludge pellets. Sewage sludge was pyrolyzed at various temperatures (400, 500, and 600 °C), and the biochar’s properties, including specific surface area, pore volume, and pore size distribution, were assessed. The results indicate that the sewage sludge biochar samples are mesoporous materials with significant potential for good adsorption performance. Despite showing a decrease in specific surface area compared to that achieved with pyrolysis at 400 °C, samples pyrolyzed at 600 °C demonstrated an increase in mesopore surface area, enhancing their adsorption potential. Two filtration experiments, conducted at a flow rate of 8 mL/min, revealed that the column containing sewage sludge pyrolyzed at 600 °C retained phosphorus the most effectively during the first and second experiments (with retention efficiencies of 87% and 78%, respectively). The study concludes that municipal sewage sludge biochar could be a promising material for the removal of phosphorus from wastewater and represents a viable solution for sustainable environmental development.

An incoherent Thomson scattering system for measurements near plasma boundaries.

Laser Thomson scattering (LTS) is a minimally invasive measurement technique used for determining electron properties in plasma systems. Sheath model closure validation requires minimally invasive measurements of the electron properties that traverse the boundaries between the bulk plasma, the presheath, and the plasma sheath. Several studies have probed the radial properties along the surface of discharge electrodes with laser-based diagnostics and electrostatic probes. These measurements provide valuable insight into the electron properties in this dynamic region. However, sheath model calibration requires plasma property measurements perpendicular to plasma bounding surfaces, in this case, along the electrode normal vector between discharge electrodes. This work presents the development of a discharge plasma cell and laser Thomson scattering system with a measurement volume step of 1mm normal to plasma bounding surfaces. The laser Thomson scattering measurements are made between a set of discharge electrodes separated by ∼25mm that are used to generate a pulsed argon plasma. The spatial distribution of electron temperature and density is measured at several discharge voltages between 8 and 20kV at a pressure of 8 Torr-Ar. It is determined that the system is statistically stationary and resembles a classic DC discharge plasma. The results are some of the first laser diagnostic-based "between electrode" measurements made along the plasma bounding electrode normal vector. A one-dimensional sheath model is applied to determine the near cathode electron properties, and it is determined that the edge of the presheath is probed in the high-voltage cases. As the lengths of the presheath and sheath decrease with decreasing voltage, the region recedes below the closest probed point to the cathode. To improve the performance of the diagnostic, the step size of the interrogation volume should decrease by an order of magnitude from 1mm to less than 100 μm, and the data acquisition strategy should be revised to increase the signal-to-noise ratio.

Best practices in the characterization of bulk catalyst properties

Bulk characterization of a solid provides us with a spatially-averaged description of its physical and chemical properties. These properties are not site-specific, but they are critical for categorizing materials and understanding their catalytic performance. Important bulk chemical properties include elemental composition, crystal structure, atomic coordination environment, and the oxidation states of elements comprising the catalyst. Significant physical properties include accessible surface area, pore volume, pore diameter, and particle size distributions that exist at the various length scales of catalysis. Each physicochemical property is sensitive to the conditions employed for catalyst synthesis, storage, pre-treatment, activation, and characterization. Thus, it is imperative that authors fully describe materials and methods in publications as this metadata provides context for observed phenomena and allows others to recreate the exact conditions that are essential for reproducibility. Such protocols are necessary for a meaningful interpretation of differences in catalyst performance observed across laboratories. This perspective article defines bulk characterization, provides guidance on bulk characterization methods, and emphasizes important considerations and best practices. Our aim is to build a more nuanced understanding of catalyst characterization. We hope that awareness and application of the principles outlined herein will improve rigor and reproducibility in catalysis science.

Hyperparameter tuning for deep learning semantic image segmentation of micro computed tomography scanned fiber-reinforced composites

Image segmentation with deep learning models has significantly improved the accuracy of the pixel-wise labeling of scientific imaging which is critical for many quantitative image analyses. This has been feasible through U-Net and related architecture convolutional neural network models. Although the adoption of these models has been widespread, their training data pool and hyperparameters have been mostly determined by educated guesses through trial and error. In this study, we present observations of how training data volume, data augmentation, and patch size affect deep learning performance within a limited data set. Here we study U-Net model training on four different samples of x-ray CT images of fiber-reinforced composites. Because the training process is not deterministic, we relied on seven-fold replication of each experimental condition to avoid under-sampling and observe model training variance. Unsurprisingly, we find greater training data volume strongly benefits individual models’ final accuracy and learning speed while depressing variance among replicates. Importantly, data augmentation has a profound benefit to model performance, especially in cases with a low abundance of ground truth, and we conclude that high coefficients of data augmentation should be used in scientific imaging semantic segmentation models. Future work to describe and measure image complexity is warranted and likely to ultimately guide researchers on the minimum required training data volume for particular scientific imaging deep learning tasks.

Significantly enhanced low-temperature activity and SO2/H2O tolerance of Mn–Ce-Ox/TiO2 catalyst prepared by a facile citric acid assisted impregnation method

Two different Mn–Ce-Ox/TiO2 catalysts were prepared by ordinary impregnation (denoted as MCT) and citric acid assisted impregnation (denoted as MCT-CA) methods, respectively. Excellent NOx removal is achieved over MCT-CA for selective catalytic reduction with NH3 (NH3-SCR), and 100% NOx conversion is obtained at 125 °C under weight hour space velocity (WHSV) of 80000 mL/(gcat·h). Particularly, 100% NOx is converted on MCT-CA in the presence of 10 vol% H2O at 175 °C. As H2O and SO2 coexist in the reaction system for 9 h, NOx conversion can still be maintained >90%, much higher than that (22%) of MCT. A series of characterization results indicates that MCT-CA exhibits a larger BET specific surface area, pore volume, and pore size, which enhances the dispersion of Mn and Ce oxides and promotes the rapid adsorption of reactants and desorption of products. Additionally, MCT-CA possesses more Mn4+, Ce3+, chemisorbed oxygen species, and stronger reducibility, facilitating the conversion of NO to NO2. Specially, the amount of active NH3 species and active nitrate species on MCT-CA is much more than that over MCT. The combined effect of the aforementioned factors devotes to the excellent low-temperature SCR performance and tolerance to H2O/SO2 over MCT-CA.

Numerical and experimental investigation of stratified water storage tanks: An enhanced adaptive-grid model

Stratified water storage tanks are key in thermal energy systems, effectively balancing energy supply with heat demand, thus facilitating operational flexibility. Accurately modeling both energy balance and thermocline evolution with minimal computational effort is essential for models implementation in real-time control applications. One-dimensional, static-grid models are favored for their easy implementation; however, they require dense grids in order to minimize numerical diffusion and to represent mixing effect correctly, resulting in rather expensive computational effort. This study introduces an adaptive-grid model that comprehensively represents the water storage tank’s geometrical features, including inlet and outlet duct heights and dead volume sizes. It also details the spatially discretized heat diffusion and characterizes the inflow region’s mixing using an empirical equation. Implemented in both TRaNsient SYstem Simulation (TRNSYS) and Modelica environments, the model is validated through laboratory experiments with a hot water storage tank operating under various mass flow and temperature conditions, representative of heat pump-driven heating systems. The results indicate improvements in computational efficiency compared to static-grid models, specifically a 55% reduction in simulation duration with the same level of uncertainty.

An examination of point-particle Lagrangian simulations for assessing time-resolved hydroacoustic particle flux measurements in sediment-laden flows.

Accurate modelling and prediction of sediment transport in aquatic environments is essential for sustainable coastal and riverine management. Current capabilities rely on physical process-based numerical models and fine-scale sediment flux measurements. High-resolution hydroacoustic instrumentation has emerged as a promising tool for such measurements. However, challenges arise due to the inherent complexity of ultrasound scattering processes. This study introduces a numerical modelling using a point-particle approach to simulate the echoes backscattered by such instrumentation in sediment-laden flow conditions. The model considers geometric, statistical, particle cloud, and flow-induced effects on sediment velocity, concentration, and flux estimates using an acoustic concentration and velocity profiler as a reference. The model performance is assessed here under unidirectional constant flow conditions in terms of velocity, concentration, and time-resolved sediment flux estimates for a large range of the particles' advection speed and sampled volume sizes. Application to the estimation of the measurement accuracy of sediment flux in these flows is also considered, with a final error on the flux seen to be partially controlled by the residence time of particles within the sampled volumes. The proposed model provides insights into scattering processes and offers a tool for investigating robust sediment flux estimation techniques in various flow conditions.

Geometric modeling and performance analysis of a combined algebraic spiral scroll compressor

This study proposed a combined algebraic spiral (CAS) scroll compressor with a profile that consists of two algebraic spirals with different parameters smoothly connected by a higher-order curve. The advantage lies in the ability of the ending algebraic spiral to regulate the stroke volume and wrap size, while the starting algebraic spiral can control the discharge volume and the center part of the wrap, thus improving the geometric performance of the scroll compressor. The research established the geometric model of CAS scroll wrap and conducted parametric studies. The results indicate that CAS scroll compressor exhibits the minimum discharge volume, maximum stroke volume, and compression ratio when both the discharge volume chamber and the stroke volume chamber are entirely constructed using the algebraic spiral. Moreover, it was observed that the radial leakage curve length decreases as the polar angle at the connection point increases. Under identical wrap diameters and adequate minimum wall thickness, a larger spiral coefficient may be chosen for the ending algebraic spiral in order to enhance geometric performance. Conversely, a smaller spiral coefficient and spiral index for the starting algebraic spiral can improve compression and reduce leakage. Moreover, in comparison to a single algebraic spiral scroll compressor with an equivalent wrap size, CAS scroll compressor exhibits a 20.62% decrease in discharge volume, a 31.62% rise in compression ratio, a 30.75% reduction in radial leakage curve length, and superior geometric performance when compared to other algebraic spiral scroll compressors.

Abstract 4044: Potentiated antitumor activity of poly-γ-glutamic acid combined with anti PD-L1 blockade on murine models of colorectal cancer

Abstract Objective: Immune checkpoint inhibitors(ICIs) have been shown to be key role in cancer therapy. Especially, antibodies targeting programmed cell death ligand1(PD-L1) promoted T cells to activate immune response against cancer cells. However, the current ICIs still have insignificant to motivate effective antitumor immunity by blocking PD-1/PD-L1 pathway and the regulatory network of PD-L1 by cytokines in the tumor microenvironment remains to be uncovered. Previously, we have reported the anti-cancer effects of combination treatment with a natural product, poly-γ-glutamic acid(γ-PGA), and PD-1 blockade. Now, we show that combination therapy with anti PD-L1 and γ-PGA can overcome the limits of ICIs and enhance anticancer effect in murine colorectal cancer. Design: In vivo efficacy studies were performed by intraperitoneal injection of antibody targeting PD-L1 and oral administration of γ-PGA in C57BL/6 model transplanted with mouse tumor cell lines. Systemic changes in immunophenotype was measured by tumor volume size and body weight. The splenocyte was isolated from mice and the populations of myeloid-driven suppressor cells(MDSCs) and CD4/CD8 T cells were analyzed by FACS. Also, the significant cytokines activated by T- cell, IFN-γ and IL-2, were measured by ELISA. Results and Conclusions: In murine model of colorectal cancer, γ-PGA and anti PD-L1 blockade combination therapy significantly improved anti-tumor activity compared with single agents. In addition, tumoral infiltration of CD8+ T cells and IFN-γ productions were increased by γ-PGA, and its antitumoral activity was attenuated by antibody-mediated depletion of these cells, indicating that g-PGA suppressed tumor growth in a CD8 T cell-dependent manner. Therefore, our results suggest that γ-PGA promote ICI-mediated antitumor responses by the transformation of "cold tumors" into “hot tumors” Citation Format: Jae-Pyung Jang, Chan-Hee Yoo, Kyung-Soo Hahm, Do Young Lee, Young-Chul Park, Jae Chul Choi. Potentiated antitumor activity of poly-γ-glutamic acid combined with anti PD-L1 blockade on murine models of colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4044.

Effects of early respacing on physico-mechanical properties of naturally regenerated Picea sitchensis in Great Britain

Natural regeneration can reduce costs compared with replanting. However, its use requires knowledge about how either active or passive management will affect the balance between quality and quantity in the timber supply. This study aimed to quantify the effects of respacing on volume recovery and wood properties. Two British forest experiments using Picea sitchensis with various respacing distances and an un-respaced control were assessed 21-22 years after the treatments were applied. Tree dimensions were measured and used to quantify slenderness, merchantable volume and sawlog-volume. Wood properties were assessed on a sub-sample using mechanical testing. Generalised linear mixed models were used to examine differences between treatments and sites. Respacing decreased slenderness and increased relative sawlog volume and branch size. Wider respacing reduced wood strength and the widest respacing reduced wood stiffness. Respacing did not affect wood density. However, at the relatively low productivity sites considered here, respacing to 2.1m represented the best compromise for current markets. In summary, not respacing improved some wood properties, but reduced tree stability and the proportion and volume of sawlogs, which will negatively affect forest value.

A Critical Exploration of Bargaining in Purchasing and Supply Management: A Systematic Literature Review

AbstractBargaining with suppliers is a key Purchasing and Supply Management (PSM) activity but there is considerable ambiguity over what bargaining entails and the concept currently lacks a systematic treatment, despite its significant interest to PSM professionals. The literature shows that bargaining can be seen as an adversarial approach to negotiation (in contrast to more integrative/collaborative ones) and also the back-and-forth discussion over price and other variables between buying and supplying organisations to reach an agreement. In addition, many will move between fundamentally distributive and integrative approaches as the discussions play out. A systematic literature review of the Scopus, ProQuest, ScienceDirect, JSTOR and Web of Science databases was undertaken to address this gap, identifying 427 relevant journal papers that were systematically analysed. First, descriptive techniques identified the trajectory of published papers, methods, theories and their industrial context. Second, content analysis identified the key constructs and associated operational measures/variables of bargaining. Third, the constructs have then been ordered temporally and by areas of location (organisational/departmental and individual levels) to generate a model and inform a series of practice-based recommendations at different stages of the bargaining process. The findings will allow future researchers to use the constructs either directly in developing focused hypotheses to test relationships or as a basis for refinement and extension in cumulative theory building and testing. In addition, a series of focused research gaps have been identified, such as addressing the current contradictory findings of the effect of purchasing volume or organisational size on bargaining power.

Insights into primary carbides and nanoparticles in an additively manufactured high-alloy steel

This paper reports the use of combined electron microscopy and small-angle neutron scattering (SANS) to probe into the compositional and size evolution of the primary carbides and nanoparticles (defined by a size threshold of 100 nm) in an additively manufactured high-alloy steel before and after heat treatment. The primary carbides exhibit marginal changes in their total volume fraction and size after the austenitising and tempering. However, those carbides located at the prior-austenite boundaries provide a pinning effect to impede grain growth during the austenitising. The grain size increases from 1.7 µm in the as-manufactured to 2.8 µm in the as-tempered conditions, resulting in a limited strength loss of 33–126 MPa as estimated by using the Hall-Petch relationship. Atom probe tomography, which offers atomic spatial resolution examining a sample volume of 6.6 × 105 nm3, reveals the presence of numerous V and Cr-enriched nanoparticles with sizes of 1 to 10 nm within the steel matrix after the tempering. In contrast, the complementary SANS which examines a significantly larger sample volume of 0.9 mm3 but lacking spatial resolution, provides nanoparticle size information. It reveals a radius reduction from 7.6 to 1.0 nm and a volume fraction increase from 1.6 % to 2.3 %, in the as-manufactured and as-tempered conditions, respectively. The nanoparticles induced during tempering can contribute to a strength enhancement of 691 MPa, primarily through the Orowan bypass mechanism. This suggests that the combination of limited prior-austenite grain growth and the presence of nanoparticles is the key factor responsible for the unprecedented material strength.