Multiple Batches Research Articles

Inductive reasoning with type-constrained encoding for emerging entities

Knowledge graph reasoning, vital for addressing incompleteness and supporting applications, faces challenges with the continuous growth of graphs. To address this challenge, several inductive reasoning models for encoding emerging entities have been proposed. However, they do not consider the multi-batch emergence scenario, where new entities and new facts are usually added to knowledge graphs (KGs) in multiple batches in the order of their emergence. To simulate the continuous growth of knowledge graphs, a novel multi-batch emergence (MBE) scenario has recently been proposed. We propose a path-based inductive model to handle multi-batch entity growth, enhancing entity encoding with type information. Specifically, we observe a noteworthy pattern in which entity types at the head and tail of the same relation exhibit relative regularity. To utilize this regularity, we introduce a pair of learnable parameters for each relation, representing entity type features linked to the relation. The type features are dedicated to encoding and updating the features of entities. Meanwhile, our model incorporates a novel attention mechanism, combining statistical co-occurrence and semantic similarity of relations effectively for contextual information capture. After generating embeddings, we employ reinforcement learning for path reasoning. To reduce sparsity and expand the action space, our model generates soft candidate facts by grounding a set of soft path rules. Meanwhile, we incorporate the confidence scores of these facts in the action space to facilitate the agent to better distinguish between original facts and rule-generated soft facts. Performances on three multi-batch entity growth datasets demonstrate robust performance, consistently outperforming state-of-the-art models.

Pretreatment of multiple lignocellulosic batches with recoverable and fermentable deep eutectic solvent (FDES) for co-production of sugar, lignin and lipid

Addressing the high cost of comprehensive utilization of lignocellulose, an efficient and green lignocellulosic biorefinery process was developed in this study, which can achieve the co-production of lignin, sugar and lipid. A series of FDES were prepared using glycerin, choline chloride (ChCl), and H2SO4 for the pretreatment of lignocellulose and ultimately for fermentation. After optimization, the hemicellulose removal reaches 98 %, and enzymatic hydrolysis rate of pretreated solids exceeds 90 %. The FDES was then recovered and reused for pretreatment, in total five batches of lignocellulose were pretreated, only a slight decrease in lignin and hemicellulose removal rate occurred, no significant decrease in lignin yield, and a decrease in solid enzyme hydrolysis from 83.26 % to 78.25 %. Finally, FDES after using five batches was used as a substrate for Rhodotorula glutinis with biomass up to 40.25 g/L and lipid concentration up to 8.33 g/L. In addition, the cellulose was probed with high solid-loading enzymatic hydrolysis, obtaining high concentration of glucose (137.4 g/L) for ethanol fermentation. In conclusion, this study developed a biorefinery technology for the efficient utilization of lignocellulosic fractions and pretreatment solvents, which reduces the cost of DES pretreatments of lignocellulose, and achieves the cogeneration of lignin, sugar and lipids.

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

GSNR-aware resource re-provisioning for C to C+L-bands upgrade in optical backbone networks

Efficient network management in optical backbone networks is essential to manage continuous traffic growth. To accommodate this growth, network operators need to upgrade their infrastructure at appropriate times. Given the cost constraint of upgrading the entire network at once, upgrading the network periodically in multiple batches is a more pragmatic approach to meet the growing demands. While multi-period, batch-upgrade strategies to increase network capacity from the conventional C band to C+L bands have been proposed, they did not consider so far the possibility to re-provision existing traffic. In this work, we investigate how to selectively re-provision connections from C band to L band during a batch upgrade. This is to ensure greater availability of C-band resources which can help to delay network upgrade and hence reduce upgrade cost, while limiting the number of disrupted connections in the network. This study proposes two re-provisioning strategies, namely, Budget-Based (BB) and Margin-Aware (MA) re-provisioning, which rely on the Quality of Transmission (QoT) of lightpaths. These strategies leverage the knowledge of Generalized Signal-to-Noise Ratio (GSNR) to choose which lightpaths to re-provision. We compare these strategies with a baseline distance-based strategy that uses path length to select and re-provision lightpaths. We also incorporate Machine Learning techniques for QoT estimation of lightpaths to reduce the computational time required for optical-path feasibility check. Numerical results show that, compared to distance-based strategy, BB and MA strategies reduce disruption by about 22% and 27%, respectively, in representative network topologies.

Simultaneous determination of maltol and seventeen saponins in red and black ginseng by UPLC-PDA wavelength switching method

This study developed a UPLC-PDA wavelength switching method to simultaneously determine the content of maltol and seventeen saponins in red and black ginseng and compared the quality differences of two different processed products of red and black ginseng. A Waters HSS T3 column(2. 1 mm×100 mm, 1. 8 μm) at 30 ℃ was adopted, with the mobile phase of acetonitrile(A) and water containing 0. 1% phosphoric acid(B) under gradient elution, the flow rate of 0. 3 m L·min~(-1), and the injection volume of 2 μL.The wavelength switching was set at 273 nm within 0-11 min and 203 nm within 11-60 min. The content results of multiple batches of red and black ginseng samples were analyzed by the hierarchical cluster analysis(HCA) and principal component analysis(PCA) to evaluate the quality difference. The results showed that the 18 constituents exhibited good linear relationships within certain concentration ranges, with the correlation coefficients(r) greater than 0. 999 1. The relative standard deviations(RSDs) of precision,repeatability, and stability were all less than 5. 0%. The average recoveries ranged from 95. 93% to 104. 2%, with an RSD of 1. 8%-4. 2%. The content determination results showed that the quality of red and black ginseng samples was different, and the two types of processed products were intuitively distinguished by HCA and PCA. The method is accurate, reliable, and reproducible. It can be used to determine the content of maltol and seventeen saponins in red and black ginseng and provide basic information for the quality evaluation and comprehensive utilization of red and black ginseng.

Optimization of aerospace engine bolt tightening based on Kriging model

Bolt tightening is widely used in various types of aerospace engine assembly process. To ensure the tightness of the aerospace engine, it is extremely important to reduce the deformation of the engine and achieve uniform bolt preload in the tightening process. The initial clearance value between nozzle and chamber of different types of aerospace engine is different, which makes the tightening target of low deformation and even load difficult. Besides, the impact of elastic interaction makes the tightening of aerospace engine very challenging. And aerospace engine bolt tightening is too time-consuming. A method is proposed in this paper to solve the above puzzle. The influence of elastic interaction in the tightening process can be weakened by optimizing the tightening sequence, and the optimal tightening process of different types of aerospace engines under different clearance values can be predicted based on kriging model. The experiment shows that the deformation of the engine deduce by 82% and 72%, the average preload deduce by 3.8% and 5.1%. Then kriging model is used to predict the best tightening sequence under different types and tightening clearance. The genetic algorithm is used to obtain the best sequence in the design domain, and the deviation between the final predicted value and the simulation value is less than 3%. After comparing, the research also improves the efficiency of process planning of and medium-sized batches and multiple batches of aerospace engines.

An integrated multi-system to screen quality markers of blossom of Citrus aurantium L. var. amara Engl. via combining lipid-lowering and expectorant assays.

The present research demonstrated that an integrated multi-system based on the assays of lipid-lowering and expectorant effects was used to screen quality markers of an edible and medical material-the blossom of Citrus aurantium L. var. amara Engl. (BCAVA)-and a portion of active constituents were quantified in multiple batches to provide scientific data to establish a quality standard for BCAVA. Mouse models were developed to evaluate the lipid-lowering and expectorant effects, facilitating the investigation of medicinal parts through different polar extractions of BCAVA. Subsequently, ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was utilized for the in vivo and in vitro identification of chemical profiles within the medicinal parts of BCAVA. This methodological approach led to the selection and quantification of several active compounds from 21 batches of BCAVA sourced from different geographical regions samples. Notably, the ethanol extract of BCAVA exhibited significant lipid-lowering and expectorant effects while 183 compounds were identified in vitro and 109 in vivo, respectively. Then, five key ingredients were quantified, and the quantitative data were subjected to statistical analysis to discriminate between samples from various geographical regions. Overall, the findings underscore the significance of an integrated, assay-based approach for the characterization and quality assessment of BCAVA.

Research on quality marker based on the processing from Aconiti lateralis radix praeparata to Heishunpian.

Aconiti lateralis radix praeparata (ALRP), the sub root of Aconitum carmichaelii Debx., is a traditional Chinese medicine with good pharmacological effects. Heishunpian (HSP), prepared through the process of brine immersing, boiling, rinsing, dyeing, and steaming ALRP is one of the most widely used forms of decoction pieces in clinical practice. This study aims to investigate the mechanisms of component changes and transformations during the processing from ALRP to HSP, and to screen for their quality markers through UHPLC-QTOF-MS analysis. Samples from ALRP to HSP during processing were prepared and analyzed by UHPLC-QTOF-MS. By comparing the differences between before and after each processing step, the purpose of processing and the transformation of components during processing were studied. In addition, multiple batches of ALRP and HSP were determined, and potential quality markers were screened. Through the analysis of ALRP and five key processing samples, 55 components were identified. Immersing in brine, rinsing, and dyeing were the main factors of component loss, and boiling caused a slight loss of components. Some components were enhanced during the steaming process. Combining the screened differences components between multiple ALRP and HSP, 10 components were considered as potential quality biomarkers. This study found that the adjacent hydroxyl groups of the ester group may have a positive impact on the hydrolysis of the ester group, and 10 quality markers were preliminarily screened. It provides a reference for quality control and clinical application of ALRP and HSP.

Size Matters in Conjugated Polymer Chirality-Selective SWCNT Extraction.

Carbon-based nanomaterials have catalyzed breakthroughs across various scientific and engineering disciplines. The key to unlocking a new generation of tailor-made nanomaterials based on single-walled carbon nanotubes (SWCNTs) lies in the precise sorting of raw material into individual chiralities, each possessing unique properties. This can be achieved using conjugated polymer extraction (CPE), but to a very limited extent since the process generates only a few chirality-enriched suspensions. Therefore, it is imperative to comprehend the mechanism of the wrapping of SWCNTs by polymers to unleash CPE's full potential. However, the lack of a diverse palette of chirality-selective polymers with varying macromolecular parameters has hindered a comprehensive understanding of how the nature of the polymer affects the performance and selectivity of SWCNT isolation. To address this gap, multiple batches of such polymers are synthesized to elucidate the impact of molecular weight and dispersity on the purity and concentrations of the generated SWCNT suspensions. The obtained results explain the inconsistent outcomes reported in the literature, greatly improving the application potential of this promising SWCNT sorting approach. Concomitantly, the discovered significant influence of the macromolecular characteristics of conjugated polymers on the SWCNT isolation efficacy sheds considerable insight into the unresolved mechanism of this sorting technique.

Investigating the Effect of Trace Levels of Manganese Ions During Solvothermal Synthesis of Massey University Framework-16 on CO2 Uptake Capacity.

The effects of impurities on reaction precursors for metal-organic framework (MOF) synthesis have not been studied in extensive detail. The impact of these impurities can be an important factor while considering scale-up of these materials. In this work, we study the apparently positive impact of the presence of manganese ions for the synthesis of a Co-based MOF, Massey University Framework-16 (MUF-16). The presence of a trace amount of manganese in the reaction mixture led to consistently high CO2 uptake across multiple batches. Characterization including X-ray diffraction, scanning electron microscopy, Fourier transform infrared-attenuated total reflectance, ultraviolet-visible spectroscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy, and extended X-ray absorption fine structure spectroscopy led us to hypothesize that the differences in CO2 adsorption among materials with differing synthesis routes arise from variations in the local environment around the cobalt metal center. Aided by density functional theory calculations, we speculate that manganese ions get inserted into the structure during crystallization and act as catalysts for ligand substitution, improving the possibility for octahedral coordination of cobalt with the ligand, thus leading to Co-based pristine structures with higher CO2 uptakes.

A non-destructive predictive model for estimating the freshness/spoilage of packaged chicken meat using changes in drip metabolites

This study investigates the possibility of utilizing drip as a non-destructive method for assessing the freshness and spoilage of chicken meat. The quality parameters [pH, volatile base nitrogen (VBN), and total aerobic bacterial counts (TAB)] of chicken meat were evaluated over a 13-day storage period in vacuum packaging at 4 °C. Simultaneously, the metabolites in the chicken meat and its drip were measured by nuclear magnetic resonance. Correlation (Pearson's and Spearman's rank) and pathway analyses were conducted to select the metabolites for model training. Binary logistic regression (model 1 and model 2) and multiple linear regression models (model 3-1 and model 3-2) were trained using selected metabolites, and their performance was evaluated using receiver operating characteristic (ROC) curves. As a result, the chicken meat was spoiled after 7 days of storage, exceeding 20 mg/100 g VBN and 5.7 log CFU/g TAB. The correlation analysis identified one organic acid, eight free amino acids, and five nucleic acids as highly correlated with chicken meat and its drip during storage. Pathway analysis revealed tyrosine and purine metabolism as metabolic pathways highly correlated with spoilage. Based on these findings, specific metabolites were selected for model training: ATP, glutamine, hypoxanthine, IMP, tyrosine, and tyramine. To predict the freshness and spoilage of chicken meat, model 1, trained using tyramine, ATP, tyrosine, and IMP from chicken meat, achieved a 99.9 % accuracy and had an ROC value of 0.884 when validated using drip metabolites. This model 1 was improved by training with tyramine and IMP from both chicken meat and its drip (model 2), which increased the ROC value for drip metabolites from 0.884 to 0.997. Finally, selected two metabolites (tyramine and IMP) can predict TAB and VBN quantitatively through models 3-1 and 3-2, respectively. Therefore, the model developed using metabolic changes in drip demonstrated the capability to non-destructively predict the freshness and spoilage of chicken meat at 4 °C. To make generic predictions, it is necessary to expand the model's applicability to various conditions, such as different temperatures, and validate its performance across multiple chicken batches.

Abstract PO3-13-11: Quantitative Multiplex Immunofluorescence Assay for TROP2 and HER2 Expression in Breast Cancer: Towards Guiding Patient Selection for Antibody Drug Conjugate Therapies

Abstract Emerging antibody drug conjugate (ADC) therapies targeting human trophoblast cell-surface antigen (TROP2) and human epidermal growth factor receptor 2 (HER2) are transforming the treatment landscape for breast cancer. Sacituzumab govitecan (SG) and trastuzumab deruxtecan (T-DXd) have gained approval for an overlapping set of "HER2-low" metastatic breast cancers, including hormone receptor (HR)-positive HER2 non-amplified and "triple-negative" subtypes. Nevertheless, the optimal selection of patients and treatment sequencing for these ADC therapies remains a clinical challenge. Clinical trial objective response rates to SG are approximately 30%, compared to 30-90% for T-DXd depending on HER2 expression levels. While both drugs are thought to be targeted therapies, the value of measuring the target and the best methods to do so are still not established. We believe that quantitative measurement of TROP2 and HER2 antigen expression levels could establish thresholds for responders, enabling more effective patient selection for ADC therapies. Here, we present a TROP2, high-sensitivity HER2, and cytokeratin (CK) quantitative immunofluorescence (QIF) multiplex assay. Using a ten-cell line standard array and proteomic mass spectrometry, we can convert tumor compartment QIF intensity to protein concentration in fmol/mm2 for tissue specimens. Anti-HER2, anti-TROP2 antibodies, and fluorescence detection systems were titrated and combined to maximize signal-to-noise ratio on our cell line standard array and breast cancer tissue microarrays (TMA). The multiplex assay was designed for automated slide stainers (Leica BOND Rx) and fluorescence slide scanning (Rarecyte CyteFinder II HT). We perform our analyses in QuPath using an image processing plugin developed for automated QIF/IHC analysis (Qymia). Reproducible TROP2 and HER2 QIF scoring (R² > 0.95) was achieved across multiple staining batches using serial sections of breast cancer TMAs and cell standard arrays. This assay has a TROP2 linear range between 0.63 - 9.17 fmol/mm2 (about 1 million TROP2 receptors/cell) and HER2 linear range between 0.09 - 0.565 fmol/mm2 (about 60,000 HER2 receptors/cell). We then applied this multiplex assay to two serial retrospective primary breast cancer cohorts from Yale University to quantitatively measure TROP2 and HER2 expression (338 clinical cases). We find a weak negative correlation between TROP2 and HER2 expression in our breast cancer cohorts (Pearson r = -0.14, p = 0.0097, n = 338). TROP2 expression levels were above the limit of detection (LOD) in 90.2% of cases, with 4.1% exceeding the limit of linearity (LOL), and a mean TROP2 expression of 4.05 fmol/mm2. For HER2, 67.2% of cases were above the LOD, with 7.1% exceeding the LOL, and a mean HER2 expression of 0.186 fmol/mm2. Both TROP2 and HER2 were below the LOD in 3.0% of cases, which we define as “negative”. We found 29.9% expressed TROP2 and were HER2-negative, and 6.8% expressed HER2 and were TROP2-negative. Our future studies will aim to quantitatively define expression thresholds for T-DXd and SG response with the goal to produce a clinical grade assay for ADC patient selection and determine the value of the assay to help select which ADC to give first. HER2 and TROP2 protein expression summary in Yale breast cancer cohort Table 1: Summary of HER2 and TROP2 protein expression levels in serial retrospective primary breast cancer cohort of 338 cases using our high-sensitivity HER2 and TROP2 multiplex immunofluorescent assay Citation Format: Charles Robbins, Mengni He, Revekka Khaimova, Katherine Bates, Nay Nwe Nyein Chan, Daniel Liebler, Regan Fulton, David Rimm. Quantitative Multiplex Immunofluorescence Assay for TROP2 and HER2 Expression in Breast Cancer: Towards Guiding Patient Selection for Antibody Drug Conjugate Therapies [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 PO3-13-11.

Testing of ultrasonic vibration to speed up the remuage operation in sparkling wine production

The research aims at providing an innovative method for remuage, testing the use of ultrasonic vibrations induced directly into the bottles to accelerate the process. This ultrasonic technique generates vibrations that cause the lees to move more swiftly along the slope of the tilted bottle, leading to a quicker accumulation of lees in the bottleneck. The efficacy of the method was measured using also image analysis and the effects on the wine were evaluated using sensory analysis. The implementation of the tested system showed significant time savings during the remuage process (2.5 h for the remuage process), without negatively impacting the sensory qualities of the wine produced (no statistically significant differences at 99.9% compared to traditional remuage methods). Moreover, the manuscript discussed the internal strengths and weaknesses, along with external opportunities and threats, that the research outputs encounter while the potential implementation of the prototype for ultrasonic remuage. In the future, a remuage prototype can be envisaged that can handle multiple batches of bottles, such as a conversion kit for existing riddling machines. This would provide a cost-effective and efficient solution for wineries looking to optimize their sparkling wine production.

Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture

With the development and changes of industry and market demand, the personalized customization production mode with small batch and multiple batches has gradually become a new production mode. This makes production environment become more complex and dynamic. However, traditional production workshops cannot effectively adapt to this environment. Combining with new technologies, transforming traditional workshops into intelligent workshop to cope with new production mode become an urgent problem. Therefore, this paper proposes a multi-agent manufacturing system based on IoT for intelligent workshop. Meanwhile, this paper takes flexible job shop scheduling problem (FJSP) as a specific production scenario and establishes relevant mathematics model. To build the agent in intelligent workshop, this paper proposes a data-based with combination of virtual and physical agent (DB-VPA) which has information layer, software layer and physical layer. Then, based on the manufacturing system, this paper designs a dynamic scheduling mechanism with deep reinforcement learning (DRL) for intelligent workshop. This method contains three aspects: (1) Modeling production process based on Markov decision process (MDP). (2) Designing communication mechanism for DB-VPAs. (3) Designing scheduling model combining with improved genetic programming and proximal policy optimization (IGP-PPO) which is a DRL method. Finally, relevant experiments are executed in a prototype experiment platform. The experiments indicate that the proposed method has superiority and generality in solving scheduling problem with dynamic events.

Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

BackgroundThe integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity.ResultsTo expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific genes.ConclusionscDisco is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

Effects of Multiple Batches of Stripped Eggs on the Reproductive Performances of Heterobranchus longifilis

Studying the effects of multiple batches of stripped eggs contributes to the sustainability and viability of aquaculture practices by improving our understanding of reproductive performance and informing best practices in breeding programs, hence, the need to investigate on the effects of multiple batches of stripped egg on reproductive performances of Heterobranchus logifilis became necessary. The aim was to provide valuable information for decision-making processes in aquaculture operations. Nine brood stocks of H. longifilis (6 male & 3 female) with average body weight of 2.3 kg and average length of 64.2 cm where carefully selected for the study. Six matured males were sacrificed for the removal of testis without hormonal inducements. The milt collected from the six males were pulled together and divided into twelve portions each diluted with 2ml of normal saline solution. Three female brood stocks where separately induced at single dosage of 0.5ml/kg body weight and allowed for a period of 10 hours before stripping. Stripping of eggs from each brood stock where in four batches, each batch measured 50g and labeled A, B, C & D. 3g of eggs containing approximately 2000 oocytes (eggs) where measured out from each batch and mixed with the diluted milt and incubated in a 2 x 1 x 10cm3 of water in a concrete pond at temperature of 26oC in three replicates. The result revealed that the reproductive performances considered in this study decreased as the batches of stripped eggs increased from 1- 4 batches. The first batch of stripped eggs produced 90.17 ± 0.44%, 97.78±0.86%, 89.48 ±1.08% fertilization, hatchability and survival respectively, while the least batch of stripped eggs (batch 4) produced the least value of fertilization (5.83 ± 1.69%); 45.00±4.90% hatchability and survival value of 5.59 ± 0.61%. Sequel to the findings of the present study, the least batch of stripped eggs should be avoided for use in fertilization since the unhatched or dead eggs has some detrimental effects on the fertilized eggs including fry at the hatchery level.

Isolation and Characterization of Bacterial Contaminants from Bone Marrow-Derived Mesenchymal Stem Cell Cultures

This study aimed to assess and manage bacterial contamination in multiple batches of mesenchymal stem cell (MSC) cultures derived from rabbit bone marrow. Routine visual inspection and microscopic examination were employed for the detection of the contaminated cultures. The contaminated cultures were inoculated on Nutrient agar and multiple isolated colonies were subjected to Gram staining and biochemical characterization. Further, molecular identification of the bacterial isolates was performed using polymerase chain reaction. The determination of antibiotic susceptibility patterns was conducted using the Kirby-Bauer disc diffusion method. Among the 351 mesenchymal stem cell culture (SCC) flasks monitored, only 1.42% were found to be contaminated. Based on the phenotypic and biochemical characterization, the major bacterial contaminants were identified as Staphylococcus aureus, Bacillus spp., and Escherichia coli infiltrating during various stages of cell processing. Antibiotic susceptibility patterns revealed varying responses among isolates, crucial for effective antimicrobial strategies and maintaining aseptic conditions in SCCs. The study emphasizes the importance of regular monitoring to maintain sterile environments, validate culture quality, and uphold safety standards. The findings indicate the need to establish stringent quality control measures, crucial for the successful translation of MSC research into clinical applications. The research advocates for continuous monitoring, adherence to SOPs, and further investigations into preventive strategies for ensuring the safety and efficacy of MSC-based therapies.

Bayesian estimation of equipment reliability with normal-type life distribution based on multiple batch tests

Abstract The test of new equipment is usually carried out in multiple batches according to the task schedule and test results. Constrained by the test environment, cost, and other factors, the amount of reliability test data in each batch is relatively limited, which brings difficulties to the accurate equipment reliability estimation work. For the reliability simulation tests conducted before each batch tests, it is particularly important to make full use of each batch tests information and simulation tests information to estimate the reliability of the equipment for small sample tests. This study takes the common normal-type life distribution equipment as the research object, and selects the normal-inverse gamma distribution as the equipment life parameters prior distribution based on the Bayesian method. Combined with the system contribution, the fusion weights of each batch tests information are determined and all the batch tests information is fused. Finally, the estimation of equipment reliability based on multiple batch tests is completed. The research results show that this method can integrate the information of each batch test and simulation test, overcome the problem of insufficient information of single batch tests, and provide an effective analytical tool for equipment reliability estimation.

Quantifying Infiltration for Quality Control in Printed Mesoscopic Perovskite Solar Cells: A Microscopic Perspective.

Mesoscopic carbon-based perovskite solar cells (CPSCs) are often cited as a potential frontrunner to perovskite commercialization. Infiltration, the extent to which perovskite fills the mesoporous scaffold, is critical for optimum performance and stability. However, infiltration data are usually presented as qualitative photographic comparisons of samples with extreme infiltration variation. This work examines how small infiltration defects impact performance using an optical microscopy examination of the base TiO2 layer to identify issues and develop targeted techniques for infiltration enhancement. Critically, the uninfiltrated area at the base of the stack was found to correlate well with PCE across multiple batches of varied print quality and ZrO2 thickness. Through reduction of mesh mark defects and improvement of print quality in the ZrO2 and carbon layers, a champion PCE of 15.01% is attained. It follows that this facile, multiscaled, nondestructive technique could enable targeted performance enhancement and quality control in future scale-up initiatives.

MetabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.