Experimental Strategies Research Articles

Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C.difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Prospects for the computational humanization of antibodies and nanobodies

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the ‘humanness’ of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

Initial Study of Adaptive Threshold Cycle Slip Detection on BDS/GPS Kinematic Precise Point Positioning during Geomagnetic Storms

Global navigation satellite system (GNSS) provides users with all-weather, continuous, high-precision positioning, navigation, and timing (PNT) services. In the operation and use of GNSS, the influence of the space environment is a factor that must be considered. For example, during geomagnetic storms, a series of changes in the Earth’s magnetosphere, ionosphere, and upper atmosphere affect GNSS’s positioning performance. To investigate the positioning performance of global satellite navigation systems during geomagnetic storms, this study selected three geomagnetic storm events that occurred from September to December 2023. Utilizing the global positioning system (GPS)/Beidou navigation satellite system (BDS) dual-system, kinematic precise point positioning (PPP) experiments were conducted, and the raw observational data from 100 stations worldwide was analyzed. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry Free (GF) cycle-slip detection threshold during geomagnetic storms, which means that the GF is no longer applicable to high-precision positioning services. Meanwhile, there is no significant change in the satellite signal strengths received at the stations during the period of the decrease in positioning accuracy. Analyzing the cycle-slip rates for stations where abnormal accuracy occurred, it was observed that stations experiencing a significant decline in positioning accuracy exhibited serious cycle-slip misjudgments. To improve the kinematic PPP accuracy during magnetic storms, this paper proposes an adaptive threshold for cycle-slip detection and designs five experimental strategies. After using the GF adaptive threshold, the station positioning accuracy improved significantly. It achieved the accuracy level of the quiet period, while the cycle-slip incidence reached the average level. During magnetic storms, the ionosphere changes rapidly, and the use of the traditional GF constant threshold will cause serious cycle-slip misjudgments, which makes the dynamic accuracy in high latitude areas and some mid-latitude areas uncommon, while the use of the GF adaptive threshold can alleviate this phenomenon and improve the positioning accuracy in the high-latitude regions and some of the affected mid-latitude areas during the magnetic storms.

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review.

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

Effect of corrosion pit on fatigue damage and failure in powder bed fusion AlSi10Mg

AbstractThis paper is concerned with environmental corrosion during service of load‐bearing aluminum alloys fabricated by additive manufacturing (AM) with a focus on damage evolution characterization and identification of dominated failure mechanisms of pre‐corroded selective laser melted aluminum alloy. An experimental strategy for analysis of the damage and failure process is presented, combining 3D surface measurement, 3D digital image correlation, and scanning electron microscopy, to provide a multi‐source experimental characterization of corrosion morphology, strain field evolution, and fracture morphology. Statistics and machine learning methods were employed to process the measured multi‐source experimental data, showing that local average roughness has a strong influence on macro‐crack initiation position. The analysis focuses on four primary types of fatigue micro‐crack initiation, namely, internal defect, diffuse corrosion, corrosion micro‐pit, and corrosion jut; internal defect and diffuse corrosion are unique features for powder bed fusion (PBF) AlSi10Mg. The micro‐crack initiation mechanisms for all four types are (i) local stress concentrations due to adjacent material micro‐structural defects, (ii) interaction of underlying alloy microstructure and corrosion‐induced stress/strain, and (iii) local stress/strain concentration at corrosion micro‐pit and jut, respectively.

Transgenerational salinity tolerance plasticity in rice: Unraveling nongenetic phenotypic modifications and environmental influences.

Transgenerational plasticity in plants enables rapid adaptation to environmental changes, allowing organisms and their offspring to adapt to the environment without altering their underlying DNA. In this study, we investigated the plasticity of transgenerational salinity tolerance of rice plants using a reciprocal transplant experimental strategy. Our aim was to assess whether nongenetic environment-induced phenotypic modifications and transgenerational salinity affect the salinity tolerance of progeny while excluding nuclear genomic factors for two generations. Using salt-tolerant and salt-sensitive rice genotypes, we observed that the parentally salt-stressed salt-sensitive genotype displayed greater growth performance, photosynthetic activity, yield performance, and transcriptional responses than did the parentally nonstressed salt-sensitive plants under salt stress conditions. Surprisingly, salt stress-exposed salt-tolerant progeny did not exhibit as much salinity tolerance as salt stress-exposed salt-sensitive progeny under salt stress. Our findings indicate that the phenotypes of offspring plants differed based on the environment experienced by their ancestors, resulting in heritable transgenerational phenotypic modifications in salt-sensitive genotypes via maternal effects. These results elucidated the mechanisms underlying transgenerational plasticity in salinity tolerance, providing valuable insights into how plants respond to changing environmental conditions.

A new experimental strategy to enhance thermal stratification of solar hot water storage tank using different PCM layouts

ABSTRACT The study examines the effect of different positions and layouts of phase change material (PCM) inside the solar hot water storage tank experimentally. Three similar circular horizontal perforated plates similar to baffles are designed for placing some PCM cylindrical capsules. The thermal stratification performance and collector’s efficiency are sought. The circular plates are considered at the height of 100, 200, 300, and 400 mm from the top of tank. Various conditions including without PCM and with one, two, and three plates of PCM are investigated. The results indicate that the temperature difference improves 37.3% for the single-row of PCM at a height of 30 cm as compared to the without PCM. In addition, during the PCM discharging period, the tank shows better thermal stratification for a single row of PCM at a height of 300 mm. In discharging process, in one or two- row which are from middle or above middle of the tank, the arithmetic mean temperature at different heights is 3.6°C and 2.6°C higher than the water tank without PCM. However, this study shows that during charging and discharging one row and two rows perform better for charging and discharging process. Results show that the perforated plates act as stabilizers.

Ketamine as Treatment for Cluster Headache: A Systematic Review of Literature and a Case Series.

Cluster headache is a severe and debilitating neurological condition characterized by intense, excruciating pain with a significant impact on patients' wellbeing. Although different treatment options are available, many patients continue to experience inadequate relief. Therefore, experimental strategies are increasingly studied. One of the more promising approaches is the use of ketamine. We present the currently available evidence and our own data. In this mixed-methods paper, we first summarize the available evidence of ketamine for treatment of cluster headache based on a systematic review of literature in MEDLINE, EMBASE and the Cochrane library of systematic reviews. As the level of evidence is quite limited, we report our own cohort study with ten patients treated with ketamine infusions for cluster headache. They were followed up to investigate the patients' experience of treatment success and quality of life. The search and review of literature identified four reports with a total of 68 patients. All were uncontrolled case series. The current literature suggests that ketamine might decrease cluster headache. However, as the applied regimes and reported outcomes are highly heterogeneous, further analysis was futile. Our own data show high patient satisfaction with ketamine treatment. Despite the limited evidence, ketamine might be considered a potential therapeutic approach for cluster headache. Therefore, further research including randomized controlled trials should be encouraged.

Towards artificial intelligence empowered performance enhancement of EDM process using nano-graphene mixed bio-dielectric supporting the carbon neutrality and sustainable development

The growing population with every passing day sets an alarming situation with respect to the conservation climate protocols. The increasing needs of society also demand a significant enhancement in the manufacturing capacity to augment the situation. However, it's a stringent requirement of the hour to propose sustainable and clean manufacturing processes to realize the goal of carbon neutrality to support a healthy life on the earth. Specifically, the processes that are energy intensive like electric discharge machining (EDM) are of serious concern regarding sustainability viewpoint. The role of the said process cannot be essentially eliminated as advent of new materials of superior characteristics demand the application of EDM for accurate cutting of intricate profiles. Nevertheless, the commonly used oil-based dielectric (kerosene) in EDM releases aerosol, deposit particles, oxides of carbon (CO2 & CO), thus contributing to the environmental contamination. It is pertinent to mention that industries are compelled to tune their processes to achieve the goals of Net-Zero. Therefore, this study thoroughly investigates the potential of nano-graphene mixed rice bran oil to make the EDM process cleaner and sustainable which has never been investigated so far. Moreover, the process has been successfully modeled using artificial neural network (ANN) and optimized by non-dominated sorting genetic algorithm-II (NSGA-II) which is another novel aspect of this study as it eradicates the need of extensive experimentation. Experimentation has been performed via Taguchi's experimental strategy followed by a detailed explanation of the findings based on process physics. In comparison to the traditional dielectric an improvement of 98.8% in material removal rate (MRR) and 93.9% reduction in specific energy consumption (SEC) are realized if the said novel combination is applied without compromising the quality. CO2 emissions determined for both rice bran oil and kerosene oil have revealed that rice bran oil provides 99.96% lesser CO2 emission in comparison to its counterpart.

Exploring lipid-protein interactions in plant membranes.

Once regarded as mere membrane building blocks, lipids are now recognized as diverse and intricate players that mold the functions, identities, and responses of cellular membranes. Although the interactions of lipids with integral and peripheral membrane proteins are crucial for their localization, activity, and function, how proteins bind lipids is still far from being thoroughly explored. Describing and characterizing these dynamic protein-lipid interactions is thus essential to understanding the membrane-associated processes. Here we review the current repertoire of experimental techniques employed to study plant protein-lipid interactions, integrating various methods. We summarize the principles, advantages, and limitations of classical in vitro biochemical approaches, including protein-lipid overlays and various liposome binding assays, and complement them with in vivo microscopic techniques centered around the use of genetically encoded lipid sensors and pharmacological or genetical membrane lipid manipulation tools. We also highlight several emerging techniques still awaiting their advancement into plant membrane research and emphasize the need to use complementary experimental strategies as key for elucidating the mechanistic roles of protein-lipid interactions in plant cell biology.

Fiber Laser Cutting Technology: Pilot Case Study in Mild Steel Cutting

Upon different traditional and unconventional contour cutting technologies, fiber laser cutting technology is increasingly emerging as the most adequate choice for modern industrial production. In this paper the basics, specific characteristics and associated benefits and opportunities, as well as latest research and development directions in fiber laser cutting technology were discussed. In addition, the study presents results obtained by realization of two pilot experiments, based on the use of one factor at a time (OFAT) experimental strategy, in fiber laser cutting of mild steel. The first OFAT experiment considered the analysis and modelling of the effect of focus position on the kerf width and surface roughness, while the second OFAT experiment investigated the effect of the cutting speed. Based on the analysis of experimental results more adequate parameter settings than initial were identified which was one of the underlying motives and goals of the present study.

EnTdecker - A Machine Learning-Based Platform for Guiding Substrate Discovery in Energy Transfer Catalysis.

Due to the magnitude of chemical space, the discovery of novel substrates in energy transfer (EnT) catalysis remains a daunting task. Experimental and computational strategies to identify compounds that successfully undergo EnT-mediated reactions are limited by their time and cost efficiency. To accelerate the discovery process in EnT catalysis, we herein present the EnTdecker platform, which facilitates the large-scale virtual screening of potential substrates using machine-learning (ML) based predictions of their excited state properties. To achieve this, a data set is created containing more than 34,000 molecules aiming to cover a vast fraction of synthetically relevant compound space for EnT catalysis. Using this data predictive models are trained, and their aptitude for an in-lab application is demonstrated by rediscovering successful substrates from literature as well as experimental validation through luminescence-based screening. By reducing the computational effort needed to obtain excited state properties, the EnTdecker platform represents a tool to efficiently guide substrate selection and increase the experimental success rate for EnT catalysis. Moreover, through an easy-to-use web application, EnTdecker is made publicly accessible under entdecker.uni-muenster.de.

Constraint and corporeality: reading Brooke-Rose and Garréta’s gendered experiments

ABSTRACT This essay considers – and seeks to complicate – the critical tendency to gender constrained writing as ‘masculine’ and to read women’s experimental literature as a ‘feminine’ writing of the body. Via readings of Christine Brooke-Rose’s Between and Anne Garréta’s Sphinx (1986), it elucidates how the constraint in each novel actually produces the focus on the body – without thereby simulating a writing of the body – and on questions of gender and desire. In both novels, I suggest, gender itself emerges as a form of ‘constraint’. In so doing, the essay seeks to move beyond any facile polarisation of constraint and excess, the conceptual and the embodied, and to unpick the gender-political possibilities of certain experimental literary strategies.

Current advances in circular RNA detection and investigation methods: Are we running in circles?

Circular RNAs (circRNAs), characterized by their closed-loop structure, have emerged as significant transcriptomic regulators, with roles spanning from microRNA sponging to modulation of gene expression and potential peptide coding. The discovery and functional analysis of circRNAs have been propelled by advancements in both experimental and bioinformatics tools, yet the field grapples with challenges related to their detection, isoform diversity, and accurate quantification. This review navigates through the evolution of circRNA research methodologies, from early detection techniques to current state-of-the-art approaches that offer comprehensive insights into circRNA biology. We examine the limitations of existing methods, particularly the difficulty in differentiating circRNA isoforms and distinguishing circRNAs from their linear counterparts. A critical evaluation of various bioinformatics tools and novel experimental strategies is presented, emphasizing the need for integrated approaches to enhance our understanding and interpretation of circRNA functions. Our insights underscore the dynamic and rapidly advancing nature of circRNA research, highlighting the ongoing development of analytical frameworks designed to address the complexity of circRNAs and facilitate the assessment of their clinical utility. As such, this comprehensive overview aims to catalyze further advancements in circRNA study, fostering a deeper understanding of their roles in cellular processes and potential implications in disease. This article is categorized under: RNA Methods > RNA Nanotechnology RNA Methods > RNA Analyses in Cells RNA Methods > RNA Analyses In Vitro and In Silico.

Particle content of very inclined air showers for radio signal modeling

The reconstruction of very inclined air showers is a new challenge for next-generation radio experiments such as the AugerPrime radio upgrade, BEACON, and GRAND, which focus on the detection of ultra high energy particles. To tackle this, we study the electromagnetic particle content of very inclined air showers, which has scarcely been studied so far. Using the simulation tools CORSIKA and CoREAS, and analytical modeling, we explore the energy range of the particles that contribute most to the radio emission, quantify their lateral extent, and estimate the atmospheric depth at which the radio emission is strongest. We find that the spatial distribution of the electromagnetic component in very inclined air showers has characteristic features that could lead to clear signatures in the radio signal, and hence impact the reconstruction strategies of next-generation radio-detection experiments.

Data reduction strategy in the PandaX-4T experiment

The PandaX-4T experiment is designed for multiple purposes, including searches for solar neutrinos, weakly interacting massive particles, and rare double beta decays of xenon isotopes. The experiment produces a huge amount of raw data that needs to be stored for related physical analyses in a wide energy range. With the upgrading of the PandaX-4T experiment, the doubled sampling rate resulted in a larger data size, which challenges both the cost and the data processing speed. To address this issue, we propose a data reduction strategy by removing the noise tail of large signals and downsampling the remaining parts of them. This strategy reduces the requirement for storage by 65% while increasing data processing speed. The influences on physical analyses on different topics at different energy regions are negligible.

Automated optimal experimental design strategy for reduced order modeling of aerodynamic flow fields

Aerodynamic flow fields reveal essential physical insights (such as shocks) that substantially affect aerodynamic performance. Conventional flow field computations, however, adopt high-fidelity but time-consuming simulation models which prohibit real-time decision makings. Alternatively, reduced-order model (ROM) arises for fast flow field predictions but random sampling could result in an excessive training cost. In this paper, we propose a fully automated optimal experimental design (Auto-OED) strategy on proper orthogonal decomposition (POD) for ROM-based rapid flow field predictions. In particular, Auto-OED leverages two individual optimal experimental design (OED) strategies, automatically selects the number of POD bases for the first sampling strategy, and intelligently switches to the second strategy on the fly. We showcased the Auto-OED strategy on airfoil flow field predictions within a transonic regime where the Mach number and angle of attack varied within the ranges of [0.55, 0.75] and [-1, 3] deg, respectively. OED-based ROM's were constructed over 20 trials for robustness tests with a total of 40 training samples on each trial including 16 random initial samples by Latin hypercube sampling (LHS) and 24 OED samples. Results exhibited that the ROM predictions completely based on LHS had an error of 1.6×10−3 while the existing OED strategies-based ROM's over 20 trials obtained a mean error (μerror) of 7.53×10−4 with a standard deviation (σerror) of 9.00×10−5. In contrast, the best Auto-OED ROM achieved the lowest μerror of 7.49×10−4 and lowest σerror of 6.20×10−5. The self-evident error reductions confirm the viability of the proposed Auto-OED based ROM in fluid field predictions and potentially other engineering applications of the similar type.

Choline chloride-L-lactic acid mixtures as solvents for extraction of Coumarin from cinnamon-containing foods

The determination of coumarin in generally available cinnamon-containing foods such as biscuits, cereals is important to ensure that the populations consuming these foods more frequently are not exposed to higher levels and are not at risk. This study describes the development of an analytical method for determination of coumarin in foods containing cinnamon. Solid-liquid phase extraction with conventional stirring-assisted procedure and ultrasound-assisted extraction (UAE) procedures were used to prepare sample extracts for liquid chromatography analysis. The green solvent, a mixture of choline chloride:L-lactic acid (1:4, mol/mol)/water (50:50 v/v), as a deep eutectic solvent-like mixture, was selected as an extraction solvent. The extraction conditions were optimized by using the design of the experiment strategy that resulted in values, temperature 47 °C, time 36 min, solvent volume 4 mL, sample amount 0.8 g, stirring speed 20 rpm for rotary stirring-assisted extraction procedure, and temperature 30 °C, time 5 min, solvent volume 3.5 mL, sample amount 0.8 g for UAE. High-performance liquid chromatography separation with a total analysis time of 12 min was performed with a stationary phase of octadecyl type and a gradient of mobile phase of methanol-1 % acetic acid. The methods were successfully validated to show the detection limit of 0.13 μg.mL−1, the linearity range of 0.4–50 µg.mL−1 (R2 = 0.999), recovery rates of more than 80 %, and the RSDs of less than 7 %. Rotary stirring-assisted extraction showed moderately higher recovery values than UAE, while lower RSD % was observed for UAE.

Decoration of Ag nanoparticle on MXene sheets for SERS studies

MXene sheets with the unique electrical and optical properties show the excellent potential for surface-enhanced Raman spectroscopy (SERS) applications. In this study, we chose Ti3C2Tx, a type of MXene, to decorate silver nanoparticles (Ag NPs) on the ultrathin two-dimensional (2D) MXene sheets. The designed Ag-MXene substrates with SERS activity showed high sensitivity, high stability, and reproducibility. The SERS signal was enhanced by the synergistic contribution of both charge-transfer (CT) and surface plasmon resonance (SPR) involving the Ag NPs and the MXene sheets. Due to the strong interaction between the probe molecules and Ag NPs which provided the nanoscale gap, the substrate exhibited remarkable SERS performance. A novel experimental strategy was developed to facilitate the controlled synthesis of noble metal NPs and MXene sheets and provide insights for further improving the practical applications of these materials in SERS detection.

The interaction between gut microbiome and bone health.

This review critically examines interconnected health domains like gut microbiome, bone health, interleukins, chronic periodontitis, and coronavirus disease 2019 (COVID-19), offering insights into fundamental mechanisms and clinical implications, contributing significantly to healthcare and biomedical research. This review explores the relationship between gut microbiome and bone health, a growing area of study. It provides insights into skeletal integrity and potential therapeutic avenues. The review also examines interleukins, chronic periodontitis, and COVID-19, highlighting the complexity of viral susceptibility and immune responses. It highlights the importance of understanding genetic predispositions and immune dynamics in the context of disease outcomes. The review emphasizes experimental evidence and therapeutic strategies, aligning with evidence-based medicine and personalized interventions. This approach offers actionable insights for healthcare practitioners and researchers, paving the way for targeted therapeutic approaches and improved patient outcomes. The implications of these findings for clinical practice and research underscore the importance of a multidisciplinary approach to healthcare that considers the complex interactions between genetics, immune responses, oral health, and systemic diseases. By leveraging advances in biomedical research, clinicians can optimize patient care and improve health outcomes across diverse patient populations.