Discordant Supramolecular Fibres Reversibly Depolymerised by Temperature and Light

Synthetic stimuli responsive supramolecular polymers attract increasing interest for their ability to mimic the unique properties of natural assemblies. Here we focus on the well-studied benzene-1,3,5-tricarboxamide (BTA) motif, and substitute it with two (S)-3,7-dimethyloctyl groups and an azobenzene photoswitch. We demonstrate the UV (λ=365 nm) induced depolymerisation of the helical hydrogen-bonded polymers in methylcyclohexane (MCH) through circular dichroism and UV-vis spectroscopy in dilute solution (15 μm), and NMR and iPAINT super-resolution microscopy in concentrated solution (300 μm). The superstructure can be regenerated after thermal depolymerization, whilst repeated depolymerisation can be reversed without degradation by irradiating at λ=455 nm. Molecular dynamics simulations show that the most energetically favourable configuration for these polymers in MCH is a left-handed helical network of hydrogen-bonds between the BTA cores surrounded by two right-handed helices of azobenzenes. The responsiveness to two orthogonal triggers across a broad concentration range holds promise for use in, for example, photo-responsive gelation

An investigation of motor memory deficits in Parkinson’s disease

People with Parkinson’s disease (PD) display motor memory deficits when tested on motor adaptation tasks involving visuomotor rotations, while the process of adaptation itself seems largely unaffected. Other forms of adaptation are unexplored and the mechanisms underlying their motor memory deficits unknown. Previous research has suggested reinforcement mechanisms to be affected in PD, but whether defective reinforcement is underlying motor memory impairments has never been directly investigated. Firstly, we investigated if the motor memory deficits shown by earlier studies also hold for force-field adaptation, where the participant learns to compensate for a perturbation caused by an external force acting on the limb. We then explored if adaptation to such force-fields was possible when adaptation was dependent on contextual cues, i.e. if people with PD were able to make context-motor associations, and in addition we investigated whether augmentation of reward and punishment improved reinforcement in PD. To increase our understanding of the effect of reward and punishment feedback on context-dependent motor learning their separate effects were investigated in a group of young adults. Results showed intact recall of the learned adapted state in people with PD, suggesting intact consolidation, but motor memory as tested with interference, where memory of the initial adaptation impairs learning of an opposite adaptation, to be strongly reduced in PD. We found evidence that people with PD were less able to learn context-motor associations in comparison to older Controls and these deficits became more pronounced when success-based feedback was strengthened suggesting reduced sensitivity to augmentation of reward and/or punishment. In young adults, reward and punishment feedback did not influence context-dependent motor adaptation itself, but it had some effect on movement velocity. We conclude that PD pathology leads to weaker context-motor associations and defective reinforcement processes, which may be underlying impaired recall of certain motor states

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance

Effect of H-Bonding on Order Amplification in the Growth of a Supramolecular Polymer in Water

While a great deal of knowledge on the roles of hydrogen bonding and hydrophobicity in proteins has resulted in the creation of rationally designed and functional peptidic structures, the roles of these forces on purely synthetic supramolecular architectures in water have proven difficult to ascertain. Focusing on a 1,3,5-benzenetricarboxamide (BTA)-based supramolecular polymer, we have designed a molecular modeling strategy to dissect the energetic contributions involved in the self-assembly (electrostatic, hydrophobic, etc.) upon growth of both ordered BTA stacks and random BTA aggregates. Utilizing this set of simulations, we have unraveled the cooperative mechanism for polymer growth, where a critical size must be reached in the aggregates before emergence and amplification of order into the experimentally observed fibers. Furthermore, we have found that the formation of ordered fibers is favored over disordered aggregates solely on the basis of electrostatic interactions. Detailed analysis of the simulation data suggests that H-bonding is a major source of this stabilization energy. Experimental and computational comparison with a newly synthesized 1,3,5-benzenetricarboxyester (BTE) derivative, lacking the ability to form the H-bonding network, demonstrated that this BTE variant is also capable of fiber formation, albeit at a reduced persistence length. This work provides unambiguous evidence for the key 1D driving force of hydrogen bonding in enhancing the persistency of monomer stacking and amplifying the level of order into the growing supramolecular polymer in water. Our computational approach provides an important relationship directly linking the structure of the monomer to the structure and properties of the supramolecular polymer

Munc18-1 promotes larger dense-core vesicle docking.

AbstractSecretory vesicles dock at the plasma membrane before Ca2+ triggers their exocytosis. Exocytosis requires the assembly of SNARE complexes formed by the vesicle protein Synaptobrevin and the membrane proteins Syntaxin-1 and SNAP-25. We analyzed the role of Munc18-1, a cytosolic binding partner of Syntaxin-1, in large dense-core vesicle (LDCV) secretion. Calcium-dependent LDCV exocytosis was reduced 10-fold in mouse chromaffin cells lacking Munc18-1, but the kinetic properties of the remaining release, including single fusion events, were not different from controls. Concomitantly, mutant cells displayed a 10-fold reduction in morphologically docked LDCVs. Moreover, acute overexpression of Munc18-1 in bovine chromaffin cells increased the amount of releasable vesicles and accelerated vesicle supply. We conclude that Munc18-1 functions upstream of SNARE complex formation and promotes LDCV docking

Electrostatic hierarchical co-assembly in aqueous solutions of two oppositevely charged double hydrophilic diblock copolymers

peer reviewedThe formation of spherical micelles in aqueous solutions of poly(N-methyl-2-vinyl pyridinium iodide)-block-poly(ethylene oxide), P2MVP-b-PEO and poly(acrylic acid)-block-poly(vinyl alcohol), PAA-b-PVOH has been investigated with light scattering-titrations, dynamic and static light scattering, and 1H 2D Nuclear Overhauser Effect Spectroscopy. Complex coacervate core micelles, also called PIC micelles, block ionomer complexes, and interpolyelectrolyte complexes, are formed in thermodynamic equilibrium under charge neutral conditions (pH 8, 1 mM NaNO3, T = 25 °C) through electrostatic interaction between the core-forming P2MVP and PAA blocks. 2D 1H NOESY NMR experiments show no cross-correlations between PEO and PVOH blocks, indicating their segregation in the micellar corona. Self-consistent field calculations support the conclusion that these C3Ms are likely to resemble a ‘patched micelle’; that is, micelles featuring a ‘spheres-on-sphere’ morphology

Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification

Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains as one of the leading causes of blindness worldwide. Computational models based on Convolutional Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images. Most of the current work address this problem as a binary classification task. However, including the grade estimation and quantification of predictions uncertainty can potentially increase the robustness of the model. In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented. This method combines the representational power of deep learning, with the ability to generalize from small datasets of Gaussian process models. The results show that uncertainty quantification in the predictions improves the interpretability of the method as a diagnostic support tool. The source code to replicate the experiments is publicly available at https://github.com/stoledoc/DLGP-DR-Diagnosis

International multicenter evaluation of the DiversiLab bacterial typing system for Escherichia coli and Klebsiella spp.

Successful multidrug-resistant clones are increasing in prevalence globally, which makes the ability to identify these clones urgent. However, adequate, easy-to-perform, and reproducible typing methods are lacking. We investigated whether DiversiLab (DL), an automated repetitive-sequence-based PCR bacterial typing system (bioMérieux), is suitable for comparing isolates analyzed at different geographic centers. A total of 39 Escherichia coli and 39 Klebsiella species isolates previously typed by the coordinating center were analyzed. Pulsed-field gel electrophoresis (PFGE) confirmed the presence of one cluster of 6 isolates, three clusters of 3 isolates, and three clusters of 2 isolates for each set of isolates. DL analysis was performed in 11 centers in six different countries using the same protocol. The DL profiles of 425 E. coli and 422 Klebsiella spp. were obtained. The DL system showed a lower discriminatory power for E. coli than did PFGE. The local DL data showed a low concordance, as indicated by the adjusted Rand and Wallace coefficients (0.132 to 0.740 and 0.070 to 1.0 [E. coli] and 0.091 to 0.864 and 0.056 to 1.0 [Klebsiella spp.], respectively). The central analysis showed a significantly improved concordance (0.473 to 1.0 and 0.290 to 1.0 [E. coli] and 0.513 to 0.965 and 0.425 to 1.0 [Klebsiella spp.], respectively). The misclassifications of profiles for individual isolates were mainly due to inconsistent amplification, which was most likely due to variations in the quality and amounts of the isolated DNA used for amplification. Despite local variations, the DL system has the potential to indicate the occurrence of clonal outbreaks in an international setting, provided there is strict adherence to standardized, reproducible DNA isolation methods and analysis protocols, all supported by a central database for profile comparisons