Rotatum of Light

Vortices are ubiquitous in nature and can be observed in fluids, condensed matter, and even in the formation of galaxies. Light, too, can evolve like a vortex. Optical vortices are exploited in light-matter interaction, free-space communications, and imaging. Here, we introduce optical rotatum; a new degree-of-freedom of light in which an optical vortex experiences a quadratic chirp in its orbital angular momentum along the optical path. We show that such an adiabatic deformation of topology is associated with the accumulation of a Berry phase factor which in turn perturbs the propagation constant (spatial frequency) of the beam. Remarkably, the spatial structure of optical rotatum follows a logarithmic spiral; a signature that is commonly seen in the pattern formation of seashells and galaxies. Our work expands previous literature on structured light, offers new modalities for light-matter interaction, communications, and sensing, and hints to analogous effects in condensed matter physics and Bose-Einstein condensates.Comment: 24 Pages, 4 Main Figures, 2 Extended Figure

A CD3-Specific Antibody Reduces Cytokine Production and Alters Phosphoprotein Profiles in Intestinal Tissues From Patients With Inflammatory Bowel Disease

NOTICE: this is the author’s version of a work that was accepted for publication in Gastroenterology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in GASTROENTEROLOGY, 10.1053/j.gastro.2014.03.04

Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems

Most current methods for identifying coherent structures in spatially-extended systems rely on prior information about the form which those structures take. Here we present two new approaches to automatically filter the changing configurations of spatial dynamical systems and extract coherent structures. One, local sensitivity filtering, is a modification of the local Lyapunov exponent approach suitable to cellular automata and other discrete spatial systems. The other, local statistical complexity filtering, calculates the amount of information needed for optimal prediction of the system's behavior in the vicinity of a given point. By examining the changing spatiotemporal distributions of these quantities, we can find the coherent structures in a variety of pattern-forming cellular automata, without needing to guess or postulate the form of that structure. We apply both filters to elementary and cyclical cellular automata (ECA and CCA) and find that they readily identify particles, domains and other more complicated structures. We compare the results from ECA with earlier ones based upon the theory of formal languages, and the results from CCA with a more traditional approach based on an order parameter and free energy. While sensitivity and statistical complexity are equally adept at uncovering structure, they are based on different system properties (dynamical and probabilistic, respectively), and provide complementary information.Comment: 16 pages, 21 figures. Figures considerably compressed to fit arxiv requirements; write first author for higher-resolution version

A Strong B-cell Response Is Part of the Immune Landscape in Human High-Grade Serous Ovarian Metastases

In high-grade serous ovarian cancer (HGSOC), higher densities of both B cells and the CD8 + T-cell infiltrate were associated with a better prognosis. However, the precise role of B cells in the antitumor response remains unknown. As peritoneal metastases are often responsible for relapse, our aim was to characterize the role of B cells in the antitumor immune response in HGSOC metastases. Unmatched pre and post-chemotherapy HGSOC metastases were studied. B-cell localization was assessed by immunostaining. Their cytokines and chemokines were measured by a multiplex assay, and their phenotype was assessed by flow cytometry. Further in vitro and in vivo assays highlighted the role of B cells and plasma cell IgGs in the development of cytotoxic responses and dendritic cell activation. B cells mainly infiltrated lymphoid structures in the stroma of HGSOC metastases. There was a strong B-cell memory response directed at a restricted repertoire of antigens and production of tumor-specific IgGs by plasma cells. These responses were enhanced by chemotherapy. Interestingly, transcript levels of CD20 correlated with markers of immune cytolytic responses and immune complexes with tumor-derived IgGs stimulated the expression of the costimulatory molecule CD86 on antigen-presenting cells. A positive role for B cells in the antitumor response was also supported by B-cell depletion in a syngeneic mouse model of peritoneal metastasis. Our data showed that B cells infiltrating HGSOC omental metastases support the development of an antitumor response. Clin Cancer Res; 1-13. ©2016 AACR

Agricultural by-products with bioactive effects: A multivariate approach to evaluate microbial and physicochemical changes in a fresh pork sausage enriched with phenolic compounds from olive vegetation water

The use of phenolic compounds derived from agricultural by-products could be considered as an eco-friendly strategy for food preservation. In this study a purified phenol extract from olive vegetation water (PEOVW) was explored as a potential bioactive ingredient for meat products using Italian fresh sausage as food model. The research was developed in two steps: first, an in vitro delineation of the extract antimicrobial activities was performed, then, the PEOVW was tested in the food model to investigate the possible application in food manufacturing. The in vitro tests showed that PEOVW clearly inhibits the growth of food-borne pathogens such as Listeria monocytogenes and Staphylococcus aureus. The major part of Gram-positive strains was inhibited at the low concentrations (0.375–3 mg/mL). In the production of raw sausages, two concentrates of PEOVW (L1:0.075% and L2: 0.15%) were used taking into account both organoleptic traits and the bactericidal effects. A multivariate statistical approach allowed the definition of the microbial and physicochemical changes of sausages during the shelf life (14 days). In general, the inclusion of the L2 concentration reduced the growth of several microbial targets, especially Staphylococcus spp. and LABs (2 log10 CFU/g reduction),while the increasing the growth of yeasts was observed. The reduction of microbial growth could be involved in the reduced lipolysis of raw sausages supplemented with PEOVWas highlighted by the lower amount of diacylglycerols. Moisture and aw had a significant effect on the variability of microbiological features,while food matrix (the sausages' environment) can mask the effects of PEOVW on other targets (e.g. Pseudomonas). Moreover, the molecular identification of the main representative taxa collected during the experimentation allowed the evaluation of the effects of phenols on the selection of bacteria. Genetic data suggested a possible strain selection based on storage time and the addition of phenol compounds especially on LABs and Staphylococcus spp. The modulation effects on lipolysis and the reduction of several microbial targets in a naturally contaminated product indicates that PEOVW may be useful as an ingredient in fresh sausages for improving food safety and quality

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry

Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells' refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method