Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer

Data-driven methods have become increasingly more prominent for musculoskeletal modelling due to their conceptually intuitive simple and fast implementation. However, the performance of a pre-trained data-driven model using the data from specific subject(s) may be seriously degraded when validated using the data from a new subject, hindering the utility of the personalised musculoskeletal model in clinical applications. This paper develops an active physics-informed deep transfer learning framework to enhance the dynamic tracking capability of the musculoskeletal model on the unseen data. The salient advantages of the proposed framework are twofold: 1) For the generic model, physics-based domain knowledge is embedded into the loss function of the data-driven model as soft constraints to penalise/regularise the data-driven model. 2) For the personalised model, the parameters relating to the feature extraction will be directly inherited from the generic model, and only the parameters relating to the subject-specific inference will be finetuned by jointly minimising the conventional data prediction loss and the modified physics-based loss. In this paper, we use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Moreover, convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework, and the physics law between muscle forces and joint kinematics is utilised as the soft constraints. Results of comprehensive experiments on a self-collected dataset from eight healthy subjects indicate the effectiveness and great generalization of the proposed framework.Comment: arXiv admin note: text overlap with arXiv:2207.0143

A Novel Strategy to Construct Yeast Saccharomyces cerevisiae Strains for Very High Gravity Fermentation

Very high gravity (VHG) fermentation is aimed to considerably increase both the fermentation rate and the ethanol concentration, thereby reducing capital costs and the risk of bacterial contamination. This process results in critical issues, such as adverse stress factors (ie., osmotic pressure and ethanol inhibition) and high concentrations of metabolic byproducts which are difficult to overcome by a single breeding method. In the present paper, a novel strategy that combines metabolic engineering and genome shuffling to circumvent these limitations and improve the bioethanol production performance of Saccharomyces cerevisiae strains under VHG conditions was developed. First, in strain Z5, which performed better than other widely used industrial strains, the gene GPD2 encoding glycerol 3-phosphate dehydrogenase was deleted, resulting in a mutant (Z5ΔGPD2) with a lower glycerol yield and poor ethanol productivity. Second, strain Z5ΔGPD2 was subjected to three rounds of genome shuffling to improve its VHG fermentation performance, and the best performing strain SZ3-1 was obtained. Results showed that strain SZ3-1 not only produced less glycerol, but also increased the ethanol yield by up to 8% compared with the parent strain Z5. Further analysis suggested that the improved ethanol yield in strain SZ3-1 was mainly contributed by the enhanced ethanol tolerance of the strain. The differences in ethanol tolerance between strains Z5 and SZ3-1 were closely associated with the cell membrane fatty acid compositions and intracellular trehalose concentrations. Finally, genome rearrangements in the optimized strain were confirmed by karyotype analysis. Hence, a combination of genome shuffling and metabolic engineering is an efficient approach for the rapid improvement of yeast strains for desirable industrial phenotypes

Design of Rotor-Packing Tool for Dynamic Nuclear Polarization Experiment

From the Washington University Undergraduate Research Digest: WUURD, Volume 10, 2014-2015. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Stacy Ross, Editor; Kristin Sobotka, Editor; Jennifer Kohl.Mentor: Alexander Barne

Sequence Optimization for the Measurement of Longitudinal Relaxation Time of a Tissue Model: Cross-Linked Bovine Serum Albumin

From the Washington University Undergraduate Research Digest: WUURD, Volume 11, 2015-2016. Published by the Office of Undergraduate Research, Joy Zalis Kiefer Director of Undergraduate Research and Assistant Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Kristin Sobotka, Editor; Jennifer Kohl. Mentor: Joseph Ackerma

Interrogations of the Tumor Microenvironment Using Magnetic Resonance Imaging

Both tumor acidosis and tumor hypoxia are characteristics commonly found in the microenvironment of solid malignant tumors. Accurate characterization of the two phenomena could provide important information to clinicians for devising suitable treatment plans. Tumor acidosis and hypoxia are closely linked to each other. Tumor acidosis is caused by the inclination of cancer cells towards anaerobic respiration, and one of the main contributing factors for the avoidance of aerobic respiration is tumor hypoxia. Both phenomena can indicate the high metastatic potential of cancers and can also cause resistance of the cancer systems against anti-cancer therapies. Therefore, developing novel molecular imaging techniques is much needed for increasing the accuracy and the precision of the measurement of tumor acidosis and tumor hypoxia. These new molecular imaging methodologies will assist in achieving a better understanding of the cancer microenvironment and improving the quality of clinical care that cancer patients receive. In this dissertation, I present new methodologies for analyzing data from acidoCEST MRI, which expand the capability of acidoCEST MRI in producing accurate and precise pH measurements. I also present results from a small animal study where electron paramagnetic resonance imaging (EPRI) oximetry was employed to study the oxygenation states of tumor systems. Specifically, in chapter 2 of this dissertation, I present results from, to the best of our knowledge, the first study in which machine learning models were trained with acidoCEST MRI data to accurately and precisely predict the pH levels of iopamidol chemical solutions. The results from this study show that machine learning is a powerful method for analyzing acidoCEST MRI data in both pH classification and pH regression, although the random forest model achieves superior performance in pH regression than the LASSO model. In chapter 3, I optimized the Bloch fitting method and showed that the Bloch fitting algorithm fits for pH levels effectively both from phantom solutions and from in vivo tumor systems. The results demonstrate that no supplementary MR information is needed for the Bloch fitting process and adding potentially inaccurate supplementary MR information can be detrimental and reduce the accuracy of the fitting results. In chapter 4, I use EPRI oximetry to study the hypoxia conditions of three types of tumor models and demonstrate that a new biomarker that measures changes in ΔpO2 can be used to predict the early responses of cancers to radiation therapy as soon as 24 hours after the irradiation process is completed. The results from this study also demonstrate the importance of evaluating the oxygenation state of the cancer in each individual patient, as hypoxia conditions for the same tumor phenotype can vary significantly across subjects. The variation in intratumoral oxygenation can directly affect the efficacy of anti-cancer therapies

Permeation-enhancing effects and mechanisms of O-acylterpineol on isosorbide dinitrate: mechanistic insights based on ATR-FTIR spectroscopy, molecular modeling, and CLSM images

The present study aimed to evaluate the penetration activity of O-acylterpineol derivatives both in vitro and in vivo, and to investigate the enhancing mechanism of O-acylterpineol derivatives which were synthesized by α-terpineol and fatty acid. The promoting activities on the isosorbide dinitrate patch were tested across full thickness rabbit skin both in vitro and in vivo. In order to elucidate the permeation mechanism, attenuated total reflection Fourier transform infrared spectroscopy, molecular modeling, and confocal laser scanning microscopy were introduced to investigate the regulation of enhancers in the skin permeability and biophysical properties. With in vitro cytotoxicity test and in vivo erythema model, the skin irritation of enhancers was also evaluated. Permeation studies showed 2-(4-methylcyclohex-3-en-l-yl) propan-2-yl tetradecanoate produced the obvious enhancement activity for ISDN both in vitro and in vivo from patches. These results were supported by ATR-FTIR, molecular modeling, and CLSM studies which revealed that O-acylterpineol could decrease the order of the alkyl chains in the skin lipids. Additionally, it was found that TER-C14 produced a relatively low skin irritation, compared with the TER which was assumed to be a safe compound. The present research suggested that some newly designed acylterpineol derivatives are shown to be suitable permeation enhancers for transdermal drug delivery, and the chain length of C14 seem to be safe and more favorable for the penetration of ISDN from DIA patches

Diffusion, Separation, and Buffering of Non-Steady-State VOCs Flow on Activated Carbon

In this study, the diffusion, separation, and buffering of volatile organic compounds emitted in a non-steady state on activated carbon were studied. Ethanol and xylene, which have large differences in adsorption capacity and diffusion rate, were selected as the representative target pollutants of volatile organic compounds. In this paper, activated carbon with a certain intake concentration and adsorption equilibrium was chosen as the research object. The buffering effect of pulse load was studied. The buffering effect and influencing factors were analyzed. The Bangham equation proved to be a more effective tool in describing the dynamic processes of ethanol and xylene adsorption on activated carbon, indicating that pore diffusion was the rate-determining step in the adsorption process. R3 emerged as a more suitable criterion for evaluating non-steady-state emissions. Factors such as pulse time and pulse multiplier were influenced by Empty Bed Contact Time (EBCT), which collaborated with EBCT to impact the buffering performance of activated carbon. An EBCT of 4 cm was identified as the optimal bed height, with R3 reaching 1.48. Non-polar VOCs with chemically symmetric structures exhibited slower mass transfer rates compared to polar VOCs, resulting in larger adsorption capacities on activated carbon and better buffering performance

Effects of Metaxenia on Stone Cell Formation in Pear (Pyrus bretschneideri) Based on Transcriptomic Analysis and Functional Characterization of the Lignin-Related Gene PbC4H2

The deposition of lignin in flesh parenchyma cells for pear stone cells, and excessive stone cells reduce the taste and quality of the fruit. The effect of metaxenia on the quality of fruit has been heavily studied, but the effect of metaxenia on stone cell formation has not been fully elucidated to date. This study used P. bretschneideri (Chinese white pear) cv. ‘Yali’ (high-stone cell content) and P. pyrifolia (Sand pear) cv. ‘Cuiguan’ (low-stone cell content) as pollination trees to pollinate P. bretschneideri cv. ‘Lianglizaosu’ separately to fill this gap in the literature. The results of quantitative determination, histochemical staining and electron microscopy indicated that the content of stone cells and lignin in YL fruit (‘Yali’ (pollen parent) × ‘Lianglizaosu’ (seed parent)) was significantly higher than that in CL fruit (‘Cuiguan’ (pollen parent) × ‘Lianglizaosu’ (seed parent)). The transcriptome sequencing results that were obtained from the three developmental stages of the two types of hybrid fruits indicated that a large number of differentially expressed genes (DEGs) related to auxin signal transduction (AUX/IAAs and ARFs), lignin biosynthesis, and lignin metabolism regulation (MYBs, LIMs, and KNOXs) between the CL and YL fruits at the early stage of fruit development. Therefore, metaxenia might change the signal transduction process of auxin in pear fruit, thereby regulating the expression of transcription factors (TFs) related to lignin metabolism, and ultimately affecting lignin deposition and stone cell development. In addition, we performed functional verification of a differentially expressed gene, PbC4H2 (cinnamate 4-hydroxylase). Heterologous expression of PbC4H2 in the c4h mutant not only restored its collapsed cell wall, but also significantly increased the lignin content in the inflorescence stem. The results of our research help to elucidate the metaxenia-mediated regulation of pear stone cell development and clarify the function of PbC4H2 in cell wall development and lignin synthesis, which establishes a foundation for subsequent molecular breeding

Hydrogen Bond Networks Near Supported Lipid Bilayers from Vibrational Sum Frequency Generation Experiments and Atomistic Simulations

We report vibrational sum frequency generation (SFG) spectra in which the C–H stretches of lipid alkyl tails in fully hydrogenated single- and dual-component supported lipid bilayers are detected along with the O–H stretching continuum above the bilayer. As the salt concentration is increased from ~10 μM to 0.1 M, the SFG intensities in the O–H stretching region decrease by a factor of 2, consistent with significant absorptive-dispersive mixing between χ(2) and χ(3) contributions to the SFG signal generation process from charged interfaces. A method for estimating the surface potential from the second-order spectral lineshapes (in the OH stretching region) is presented and discussed in the context of choosing truly zero-potential reference states. Aided by atomistic simulations, we find that the strength and orientation distribution of the hydrogen bonds over the purely zwitterionic bilayers are largely invariant between sub-micromolar and hundreds of millimolar concentrations. However, specific interactions between water molecules and lipid headgroups are observed upon replacing phosphocholine (PC) lipids with negatively charged phosphoglycerol (PG) lipids, which coincides with SFG signal intensity reductions in the 3100 cm-1 to 3200 cm-1 frequency region. The atomistic simulations show that this outcome is consistent with a small, albeit statistically significant, decrease in the number of water molecules adjacent to both the lipid phosphate and choline moieties per unit area, supporting the SFG observations. Ultimately, the ability to probe hydrogen-bond networks over lipid bilayers holds the promise of opening paths for understanding, controlling, and predicting specific and non-specific interactions between membranes and ions, small molecules, peptides, polycations, proteins, and coated and uncoated nanomaterials.</div