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The outstanding mechanical properties, light weight, and biodegradability of cellulose nanofibrils (CNFs) make them promising components of renewable and sustainable next-generation reinforced composite biomaterials and bioplastics. Manufacturing CNFs at a pilot scale requires disc-refining fibrillation of dilute cellulose fibers in aqueous pulp suspensions to shear the fibers apart into their nanodimensional forms, which is, however, an energy-intensive process. Here, we used atomistic molecular dynamics (MD) simulation to examine media that might facilitate the reduction of interactions between cellulose fibers, thereby reducing energy consumption in fibrillation. The most suitable medium found by the simulations was an aqueous solution with 0.007:0.012 wt.% NaOH:urea, and indeed this was found in pilot-scale experiments to reduce the fibrillation energy by ~21% on average relative to water alone. The NaOH:urea-mediated CNFs have similar crystallinity, morphology, and mechanical strength to those formed in water. The NaOH and urea act synergistically on CNFs to aid fibrillation but at different length scales. NaOH deprotonates hydroxyl groups leading to mesoscale electrostatic repulsion between fibrils, whereas urea forms hydrogen bonds with protonated hydroxyl groups thus disrupting interfibril hydrogen bonds. This suggests a general mechanism in which an aqueous medium that contains a strong base and a small organic molecule acting as a hydrogen-bond acceptor and/or donor may be effectively employed in materials processes where dispersion of deprotonable polymers is required. The study demonstrates how atomic-detail computer simulation can be integrated with pilot-scale experiments in the rational design of materials processes for the circular bioeconomy. [ABSTRACT FROM AUTHOR]

GPRC6A, a member of the Family C G‐protein coupled receptors, regulates energy metabolism and sex hormone production and is activated by diverse ligands, including cations, L‐amino acids, the osteocalcin (Ocn) peptide and the steroid hormone testosterone. We sought a structural framework for the ability of multiple distinct classes of ligands to active GPRC6A. We created a structural model of GPRC6A using Alphafold2. Using this model we explored a putative orthosteric ligand binding site in the bilobed Venus fly trap (VFT) domain of GPRC6A and two positive allosteric modulator (PAM) sites, one in the VFT and the other in the 7 transmembrane (7TM) domain. We provide evidence that Ocn peptides act as a PAM for GPRC6A by binding to a site in the VFT that is distinct from the orthosteric site for calcium and L‐amino acids. In agreement with this prediction, alternatively spliced GPRC6A isoforms 2 and 3, which lack regions of the VFT, and mutations in the computationally predicted Ocn binding site, K352E and H355P, prevent Ocn activation of GPRC6A. These observations explain how dissimilar ligands activate GPRC6A and set the stage to develop novel molecules to activate and inhibit this previously poorly understood receptor. [ABSTRACT FROM AUTHOR]

Artificial-intelligence and machine-learning (AI/ML) approaches to predicting Tcell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are 'unseen' during training of the MLmodel.We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope 'hard splitting' (i.e., on test sets with all epitopes unseen duringML training), 0.92 for TCR hard splitting and 0.89 for 'strict splitting' in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore,we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for post hoc interrogation to interpret the models trained with different hard splits, shedding light on the key physiochemical features driving model predictions. TCR-H thus represents a significant step towards general applicability and explainability of epitope:TCR specificity prediction. [ABSTRACT FROM AUTHOR]

Pretreatment facilitates cost-effective operations on lignocellulosic biomass ranging from densification to deconstruction and bioproduct development. However, determining molecular-level mechanisms behind pretreatment and their effects has remained elusive. Here, we combine computational simulation and experiment to investigate the effects on wheat straw agricultural residue densification of an emerging pretreatment solvent, namely, a deep eutectic solvent (DES) consisting of choline chloride (ChCl) and oxalic acid (OA). Ab initio molecular dynamics indicates that dissociation of lignin from cellulose in lignin–carbohydrate complexes, which does not occur to a significant extent in aqueous solution, is favorable in the DES and occurs via cleavage of the guaiacyl : xylose ether bond linkage by OA. The ensuing hemicellulose removal exposes lignin to the DES which, molecular dynamics simulation indicates, leads to lignin expansion. The resulting changes in wheat straw fiber structure, lignin distribution, and functional group modifications upon DES treatment by scanning electron and fluorescence microscopy along with Fourier-transform infrared spectroscopy. The molecular expansion of lignin enhances inter-particle binding in wheat straw, leading to denser structures under pelletization. The resulting high mechanical stability and combustion properties make the wheat straw a suitable precursor of high-quality densified solids (e.g., solid biofuel). Overall, we shed light on the molecular-level mechanisms involved in DES pretreatment for biomass densification, demonstrated here in the development of a solid biofuel. The approach here illuminates the rational design from first chemical principles of methods to convert lignocellulosic resources into advanced materials. [ABSTRACT FROM AUTHOR]

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators. [ABSTRACT FROM AUTHOR]

Contemporary research into community music programmes highlights diverse contexts for collaborative music making. This article contributes a case of the collaboration in music making to a growing literature on creative practice in community music initiatives led by major orchestras. The authors reflect on field research involving an after-school programme that provides free, classical instrumental music instruction to primary school-age students who would not normally have an opportunity due to their socio-economic circumstances. The Pizzicato Effect, run by the Melbourne Symphony Orchestra (MSO) in the City of Hume, Australia has operated in the northern suburbs of Melbourne since 2009. Through focus group interviews with students, family members and teaching artists, the researchers examined community experiences of accruing cultural capital in conditions of social inequality and multicultural practice. Using qualitative methods, the case study aimed to position respondents as experts in their own lives, who own their knowledge of their worlds. Collaboration was central to a model of improvisation developed locally by teaching artists and applied on site. Furthermore, improvisation took the programme in different directions to the El Sistema model of youth orchestra training in Venezuela, which had been the original source of inspiration for the programme. [ABSTRACT FROM AUTHOR]

Classical and contemporary civilizational analysis has not sat comfortably with theoretical constructions of the intercultural or their empirical applications. A 'classical era' of civilizations analysis generated a program of research problems that was productive in critical and multidisciplinary ways and limited in scope and vision in others, but this failed to generate a provisional notion of the intercultural. Contemporary civilizational analysis improves on this position significantly in respect of the intercultural, particularly in the development of a current around 'intercivilizational encounters'. This essay examines this current especially in the work of Benjamin Nelson, Marshall Hodgson and Johann P. Arnason. Arguing that this approach represents vital advances for theoretical constructions of the intercultural in civilizational analysis and more generally in the human sciences, the essay also identifies limitations in latter-day approaches. [ABSTRACT FROM AUTHOR]

The outstanding mechanical properties, light weight, and biodegradability of cellulose nanofibrils (CNFs) make them promising components of renewable and sustainable next-generation reinforced composite biomaterials and bioplastics. Manufacturing CNFs at a pilot scale requires disc-refining fibrillation of dilute cellulose fibers in aqueous pulp suspensions to shear the fibers apart into their nanodimensional forms, which is, however, an energy-intensive process. Here, we used atomistic molecular dynamics (MD) simulation to examine media that might facilitate the reduction of interactions between cellulose fibers, thereby reducing energy consumption in fibrillation. The most suitable medium found by the simulations was an aqueous solution with 0.007:0.012 wt.% NaOH:urea, and indeed this was found in pilot-scale experiments to reduce the fibrillation energy by ~21% on average relative to water alone. The NaOH:urea-mediated CNFs have similar crystallinity, morphology, and mechanical strength to those formed in water. The NaOH and urea act synergistically on CNFs to aid fibrillation but at different length scales. NaOH deprotonates hydroxyl groups leading to mesoscale electrostatic repulsion between fibrils, whereas urea forms hydrogen bonds with protonated hydroxyl groups thus disrupting interfibril hydrogen bonds. This suggests a general mechanism in which an aqueous medium that contains a strong base and a small organic molecule acting as a hydrogen-bond acceptor and/or donor may be effectively employed in materials processes where dispersion of deprotonable polymers is required. The study demonstrates how atomic-detail computer simulation can be integrated with pilot-scale experiments in the rational design of materials processes for the circular bioeconomy., Competing Interests: Competing interests statement:The authors declare no competing interest.

Understanding the mechanism of metformin actions in treating type 2 diabetes is limited by an incomplete knowledge of the specific protein targets mediating its metabolic effects. Metformin has structural similarities to L-Arginine (2-amino-5-guanidinopentanoic acid), which is a ligand for GPRC6A, a Family C G-protein coupled receptor that regulates energy metabolism. Ligand activation of GPRC6A results in lowering of blood glucose and other metabolic changes resembling the therapeutic effect of metformin. In the current study, we tested if metformin activates GPRC6A. We used Alphafold2 to develop a structural model for L-Arginine (L-Arg) binding to the extracellu-lar bilobed venus flytrap domain (VFT) of GPRC6A. We found that metformin docked to the site in the VFT that overlaps the binding site for L-Arg. Metformin resulted in a dose-dependent stimulation of GPRC6A activity in HEK-293 cells transfected with full-length wild-type GPRC6A but not in untransfected control cells. In addition, metformin failed to activate an alternatively spliced GPRC6A isoform lacking the putative binding site in the VFT. More specifically, mutation of the predicted metformin key binding residues Glu170 and Asp303 in the GPRC6A VFT resulted in loss of metformin receptor activation in vitro. The in vivo role of GPRC6A in mediating the effects of metformin was tested in Gprc6a-/- mice. Administration of therapeutic doses of metformin lowered blood glucose levels following a glucose tolerance test in wild-type but not Gprc6a-/- mice. Finally, we EN300, created by adding a carboxymethyl group from L-Arg to the biguanide backbone of metformin. EN300 showed dose-dependent stimulation of GPRC6A activity in vitro with greater potency than L-Arginine, but less than metformin. Thus, we suggest that GPRC6A is a potential molecular target for metformin which may be used to understand the therapeutic actions of metformin and develop novel small molecules to treat T2D., Competing Interests: CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest.

Artificial-intelligence and machine-learning (AI/ML) approaches to predicting T-cell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are 'unseen' during training of the ML model. We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope 'hard splitting' (i.e., on test sets with all epitopes unseen during ML training), 0.92 for TCR hard splitting and 0.89 for 'strict splitting' in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore, we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for post hoc interrogation to interpret the models trained with different hard splits, shedding light on the key physiochemical features driving model predictions. TCR-H thus represents a significant step towards general applicability and explainability of epitope:TCR specificity prediction., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 T., Demerdash and Smith.)