Microcanonical versus Canonical Analysis of Protein Folding

The microcanonical analysis is shown to be a powerful tool to characterize the protein folding transition and to neatly distinguish between good and bad folders. An off-lattice model with parameter chosen to represent polymers of these two types is used to illustrate this approach. Both canonical and microcanonical ensembles are employed. The required calculations were performed using parallel tempering Monte Carlo simulations. The most revealing features of the folding transition are related to its first-order-like character, namely, the S-bend pattern in the caloric curve, which gives rise to negative microcanonical specific heats, and the bimodality of the energy distribution function at the transition temperatures. Models for a good folder are shown to be quite robust against perturbations in the interaction potential parameters.Comment: 4 pages, 4 figure

Pattern-induced anchoring transitions in nematic liquid crystals

In this paper we revisit the problem of a nematic liquid crystal in contact with patterned substrates. The substrate is modelled as a periodic array of parallel infinite grooves of well-defined cross section sculpted on a chemically homogeneous substrate which favors local homeotropic anchoring of the nematic. We consider three cases: a sawtooth, a crenellated and a sinusoidal substrate. We analyse this problem within the modified Frank-Oseen formalism. We argue that, for substrate periodicities much larger than the extrapolation length, the existence of different nematic textures with distinct far-field orientations, as well as the anchoring transitions between them, are associated with the presence of topological defects either on or close to the substrate. For the sawtooth and sinusoidal case, we observe a homeotropic to planar anchoring transition as the substrate roughness is increased. On the other hand, a homeotropic to oblique anchoring transition is observed for crenellated substrates. In this case, the anchoring phase diagram shows a complex dependence on the substrate roughness and substrate anchoring strength.Comment: 36 pages, 15 figures, revised version submitted to Journal of Physics: Condensed Matte

Binding of SARS-CoV-2 to cell receptors: a tale of molecular evolution

The magnified infectious power of the SARS-CoV-2 virus compared to its precursor SARS-CoV is intimately linked to an enhanced ability in the mutated virus to find available hydrogen bond sites in the host cells. This characteristic is acquired during virus evolution because of the selective pressure exerted at the molecular level. We pinpoint the specific residue (in the virus) to residue (in the cell) contacts during the initial recognition and binding and show that the virus\ub7 \ub7 \ub7 cell interaction is mainly due to an extensive network of hydrogen bonds and to a large surface of non-covalent interactions. In addition to the formal quantum characterization of bonding interactions, computation of absorption spectra for the specific virus\ub7 \ub7 \ub7 cell interacting residues yields significant shifts of 06\u3bb max = 47 and 66 nm in the wavelength for maximum absorption in the complex with respect to the isolated host and virus, respectively

Inverting Adversarially Robust Networks for Image Synthesis

Recent research in adversarially robust classifiers suggests their representations tend to be aligned with human perception, which makes them attractive for image synthesis and restoration applications. Despite favorable empirical results on a few downstream tasks, their advantages are limited to slow and sensitive optimization-based techniques. Moreover, their use on generative models remains unexplored. This work proposes the use of robust representations as a perceptual primitive for feature inversion models, and show its benefits with respect to standard non-robust image features. We empirically show that adopting robust representations as an image prior significantly improves the reconstruction accuracy of CNN-based feature inversion models. Furthermore, it allows reconstructing images at multiple scales out-of-the-box. Following these findings, we propose an encoding-decoding network based on robust representations and show its advantages for applications such as anomaly detection, style transfer and image denoising

Ring Vibrations to Sense Anionic Ibuprofen in Aqueous Solution as Revealed by Resonance Raman

We unravel the potentialities of resonance Raman spectroscopy to detect ibuprofen in diluted aqueous solutions. In particular, we exploit a fully polarizable quantum mechanics/molecular mechanics (QM/MM) methodology based on fluctuating charges coupled to molecular dynamics (MD) in order to take into account the dynamical aspects of the solvation phenomenon. Our findings, which are discussed in light of a natural bond orbital (NBO) analysis, reveal that a selective enhancement of the Raman signal due to the normal mode associated with the C–C stretching in the ring, νC=C, can be achieved by properly tuning the incident wavelength, thus facilitating the recognition of ibuprofen in water samples

Making Vision Transformers Truly Shift-Equivariant

For computer vision, Vision Transformers (ViTs) have become one of the go-to deep net architectures. Despite being inspired by Convolutional Neural Networks (CNNs), ViTs' output remains sensitive to small spatial shifts in the input, i.e., not shift invariant. To address this shortcoming, we introduce novel data-adaptive designs for each of the modules in ViTs, such as tokenization, self-attention, patch merging, and positional encoding. With our proposed modules, we achieve true shift-equivariance on four well-established ViTs, namely, Swin, SwinV2, CvT, and MViTv2. Empirically, we evaluate the proposed adaptive models on image classification and semantic segmentation tasks. These models achieve competitive performance across three different datasets while maintaining 100% shift consistency

SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer

Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with compromised semantic information, ultimately impacting downstream performance. To overcome this, we propose SASSL: Style Augmentations for Self Supervised Learning, a novel augmentation technique based on Neural Style Transfer. SASSL decouples semantic and stylistic attributes in images and applies transformations exclusively to the style while preserving content, generating diverse samples that better retain semantics. Our technique boosts top-1 classification accuracy on ImageNet by up to 2$\%$ compared to established self-supervised methods like MoCo, SimCLR, and BYOL, while achieving superior transfer learning performance across various datasets

Empyema associated with Lemierre syndrome: case report and literature review

Postanginal septicemia, also called Lemierre syndrome, is a metastatic infection that can have multiple complications, including empyema. Therefore, the natural history of the disease begins with an infection of the oropharynx by microbiota from the digestive system, which causes a thrombophlebitis of the jugular vein with septic infiltrations, including into the lungs causing pneumonia, which in turn can generate parapneumonic effusions and/or empyemas. Furthermore, it is a syndrome that was thought to have been forgotten by the era of antibiotics, but with resistance to these antibiotics it has begun to re-emerge. Next, we will talk about a case of a 41-year-old man with no significant pathological history, who entered secondary to a peritonsillar abscess which turned into Lemierre syndrome with a treatment based on broad-spectrum antibiotics and then performed of lung decortication by thoracotomy. Empyema as a complication of Lemierre syndrome is rare and even more so in this post-antibiotic era, so health personnel should have a high clinical suspicion since adequate and timely treatment will help reduce the complications of this disease, as well as like his mortality

Hemoglobin Mass, Blood Volume and VO₂max of Trained and Untrained Children and Adolescents Living at Different Altitudes

Introduction: To a considerable extent, the magnitude of blood volume (BV) and hemoglobin mass (Hbmass) contribute to the maximum O(2)-uptake (VO(2)max), especially in endurance-trained athletes. However, the development of Hbmass and BV and their relationships with VO(2)max during childhood are unknown. The aim of the present cross-sectional study was to investigate Hbmass and BV and their relationships with VO(2)max in children and adolescents. In addition, the possible influence of endurance training and chronic hypoxia was evaluated. Methods: A total of 475 differently trained children and adolescents (girls n = 217, boys n = 258; untrained n = 171, endurance trained n = 304) living at two different altitudes (∼1,000 m, n = 204, ∼2,600 m, n = 271) and 9–18 years old participated in the study. The stage of puberty was determined according to Tanner; Hbmass and BV were determined by CO rebreathing; and VO(2)max was determined by cycle ergometry and for runners on the treadmill. Results: Before puberty, there was no association between training status and Hbmass or BV. During and after puberty, we found 7–10% higher values in the trained groups. Living at a moderate altitude had a uniformly positive effect of ∼7% on Hbmass in all groups and no effect on BV. The VO(2)max before, during and after puberty was strongly associated with training (pre/early puberty: boys +27%, girls +26%; mid puberty: +42% and +45%; late puberty: +43% and +47%) but not with altitude. The associated effects of training in the pre/early pubertal groups were independent of Hbmass and BV, while in the mid- and late pubertal groups, 25% of the training effect could be attributed to the elevated Hbmass. Conclusions: The associated effects of training on Hbmass and BV, resulting in increased VO(2)max, can only be observed after the onset of puberty

Effects of coffee with different roasting degrees on obesity and related metabolic disorders

This study aimed to assess the effect of unroasted, dark and very dark roasted coffee on obesity and metabolic disorders in obese rats. All coffee samples significantly reduced weight gain (∼17%) compared to obese control. Coffee reduced glucose levels (∼17%) upon a glucose tolerance test in all cases compared to the control, while fasting glucose only decreased (∼26%) with very dark coffee. Insulin levels and insulin resistance significantly decreased (∼77% and 65% respectively) with all coffee samples compared to the control. Unroasted and dark roasted coffee decreased triglycerides (∼21% and ∼ 11%, respectively), and unroasted coffee also reduced free fatty acids (∼43%) and adipocyte size. Coffee decreased liver steatosis (∼55%) and Caspase-3 levels (∼27%), regardless of the roasting degree. Overall, coffee plays a positive role in restraining obesity and related metabolic disorders but, depending on the metabolic pathway and relevant marker, an effect of roasting could be either found or not