Refined conformal spectra in the dimer model

Working with Lieb's transfer matrix for the dimer model, we point out that the full set of dimer configurations may be partitioned into disjoint subsets (sectors) closed under the action of the transfer matrix. These sectors are labelled by an integer or half-integer quantum number we call the variation index. In the continuum scaling limit, each sector gives rise to a representation of the Virasoro algebra. We determine the corresponding conformal partition functions and their finitizations, and observe an intriguing link to the Ramond and Neveu-Schwarz sectors of the critical dense polymer model as described by a conformal field theory with central charge c=-2.Comment: 44 page

Enhanced detection of antigen-specific T cells by a multiplexed AIM assay.

Broadly applicable methods to identify and characterize antigen-specific CD4 <sup>+</sup> and CD8 <sup>+</sup> T cells are key to immunology research, including studies of vaccine responses and immunity to infectious diseases. We developed a multiplexed activation-induced marker (AIM) assay that presents several advantages compared to single pairs of AIMs. The simultaneous measurement of four AIMs (CD69, 4-1BB, OX40, and CD40L) creates six AIM pairs that define CD4 <sup>+</sup> T cell populations with partial and variable overlap. When combined in an AND/OR Boolean gating strategy for analysis, this approach enhances CD4 <sup>+</sup> T cell detection compared to any single AIM pair, while CD8 <sup>+</sup> T cells are dominated by CD69/4-1BB co-expression. Supervised and unsupervised clustering analyses show differential expression of the AIMs in defined T helper lineages and that multiplexing mitigates phenotypic biases. Paired and unpaired comparisons of responses to infections (HIV and cytomegalovirus [CMV]) and vaccination (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) validate the robustness and versatility of the method

Extracellular DNA release, quorum sensing, and PrrF1/F2 small RNAs are key players in Pseudomonas aeruginosa tobramycin-enhanced biofilm formation

Biofilms are structured microbial communities that are the leading cause of numerous chronic infections which are difficult to eradicate. Within the lungs of individuals with cystic fibrosis (CF), Pseudomonas aeruginosa causes persistent biofilm infection that is commonly treated with aminoglycoside antibiotics such as tobramycin. However, sublethal concentrations of this aminoglycoside were previously shown to increase biofilm formation by P. aeruginosa, but the underlying adaptive mechanisms still remain elusive. Herein, we combined confocal laser scanning microscope analyses, proteomics profiling, gene expression assays and phenotypic studies to unravel P. aeruginosa potential adaptive mechanisms in response to tobramycin exposure during biofilm growth. Under this condition, we show that the modified biofilm architecture is related at least in part to increased extracellular DNA (eDNA) release, most likely as a result of biofilm cell death. Furthermore, the activity of quorum sensing (QS) systems was increased, leading to higher production of QS signaling molecules. We also demonstrate upon tobramycin exposure an increase in expression of the PrrF small regulatory RNAs, as well as expression of iron uptake systems. Remarkably, biofilm biovolumes and eDNA relative abundances in pqs and prrF mutant strains decrease in the presence of tobramycin. Overall, our findings offer experimental evidences for a potential adaptive mechanism linking PrrF sRNAs, QS signaling, biofilm cell death, eDNA release, and tobramycin-enhanced biofilm formation in P. aeruginosa. These specific adaptive mechanisms should be considered to improve treatment strategies against P. aeruginosa biofilm establishment in CF patients’ lungs

Radio observations of the merging galaxy cluster system Abell 3391-Abell 3395

The pre-merging system of galaxy clusters Abell 3391-Abell 3395 located at a mean redshift of 0.053 has been observed at 1 GHz in an ASKAP/EMU Early Science observation as well as in X-rays with eROSITA. The projected separation of the X-ray peaks of the two clusters is $\sim$50$'$ or $\sim$ 3.1 Mpc. Here we present an inventory of interesting radio sources in this field around this cluster merger. While the eROSITA observations provide clear indications of a bridge of thermal gas between the clusters, neither ASKAP nor MWA observations show any diffuse radio emission coinciding with the X-ray bridge. We derive an upper limit on the radio emissivity in the bridge region of $\langle J \rangle_{1\,{\rm GHz}}< 1.2 \times 10^{-44} {\rm W}\, {\rm Hz}^{-1} {\rm m}^{-3}$. A non-detection of diffuse radio emission in the X-ray bridge between these two clusters has implications for particle-acceleration mechanisms in cosmological large-scale structure. We also report extended or otherwise noteworthy radio sources in the 30 deg$^2$ field around Abell 3391-Abell 3395. We identified 20 Giant Radio Galaxies, plus 7 candidates, with linear projected sizes greater than 1 Mpc. The sky density of field radio galaxies with largest linear sizes of $>0.7$ Mpc is $\approx 1.7$ deg$^{-2}$, three times higher than previously reported. We find no evidence for a cosmological evolution of the population of Giant Radio Galaxies. Moreover, we find seven candidates for cluster radio relics and radio halos.Comment: Astronomy & Astrophysics, in pres

Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra ‘group regularization’ to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study

A direct test of the diathesis-stress model for depression

The diathesis–stress theory for depression states that the effects of stress on the depression risk are dependent on the diathesis or vulnerability, implying multiplicative interactive effects on the liability scale. We used polygenic risk scores for major depressive disorder (MDD) calculated from the results of the most recent analysis from the Psychiatric Genomics Consortium as a direct measure of the vulnerability for depression in a sample of 5221 individuals from 3083 families. In the same we also had measures of stressful life events and social support and a depression symptom score, as well as DSM-IV MDD diagnoses for most individuals. In order to estimate the variance in depression explained by the genetic vulnerability, the stressors and their interactions, we fitted linear mixed models controlling for relatedness for the whole sample as well as stratified by sex. We show a significant interaction of the polygenic risk scores with personal life events (0.12% of variance explained, P-value=0.0076) contributing positively to the risk of depression. Additionally, our results suggest possible differences in the aetiology of depression between women and men. In conclusion, our findings point to an extra risk for individuals with combined vulnerability and high number of reported personal life events beyond what would be expected from the additive contributions of these factors to the liability for depression, supporting the multiplicative diathesis–stress model for this disease

The absence of the Pseudomonas aeruginosa OprF protein leads to increased biofilm formation through variation in c-di-GMP level

OprF is the major outer membrane porin in bacteria belonging to the Pseudomonas genus. In previous studies, we have shown that OprF is required for full virulence expression of the opportunistic pathogen Pseudomonas aeruginosa. Here, we describe molecular insights on the nature of this relationship and report that the absence of OprF leads to increased biofilm formation and production of the Pel exopolysaccharide. Accordingly, the level of c-di-GMP, a key second messenger in biofilm control, is elevated in an oprF mutant. By decreasing c-di-GMP levels in this mutant, both biofilm formation and pel gene expression phenotypes were restored to wild-type levels. We further investigated the impact on two small RNAs, which are associated with the biofilm lifestyle, and found that expression of rsmZ but not of rsmY was increased in the oprF mutant and this occurs in a c-di-GMP-dependent manner. Finally, the extracytoplasmic function (ECF) sigma factors AlgU and SigX displayed higher activity levels in the oprF mutant. Two genes of the SigX regulon involved in c-di-GMP metabolism, PA1181 and adcA (PA4843), were up-regulated in the oprF mutant, partly explaining the increased c-di-GMP level. We hypothesized that the absence of OprF leads to a cell envelope stress that activates SigX and results in a c-di-GMP elevated level due to higher expression of adcA and PA1181. The c-di-GMP level can in turn stimulate Pel synthesis via increased rsmZ sRNA levels and pel mRNA, thus affecting Pel-dependent phenotypes such as cell aggregation and biofilm formation. This work highlights the connection between OprF and c-di-GMP regulatory networks, likely via SigX (ECF), on the regulation of biofilm phenotypes