Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations

Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images

Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study

Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper we present GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. We show that Wasserstein Generative Adversarial Networks combined with linear Support Vector Machines enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to previous methods, our deep learning based approach does not require any annotation besides the one that is normally collected during the sample preparation process. We test our technique on the RxRx19a Sars-CoV-2 image collection. The dataset consists of fluorescent images that were generated to assess the ability of regulatory-approved or in late-stage clinical trials compound to modulate the in vitro infection from SARS-CoV-2 in both VERO and HRCE cell lines. We show that our technique can be exploited not only for classification tasks, but also to effectively derive a dose response curve for the tested treatments, in a self-supervised manner. Lastly, we demonstrate its generalization capabilities by successfully addressing a zero-shot learning task, consisting in the categorization of four different cell types of the RxRx1 fluorescent images collection

Imposing causality on a matrix model

We introduce a new matrix model that describes Causal Dynamical Triangulations (CDT) in two dimensions. In order to do so, we introduce a new, simpler definition of 2D CDT and show it to be equivalent to the old one. The model makes use of ideas from dually weighted matrix models, combined with multi-matrix models, and can be studied by the method of character expansion.Comment: 9 pages, 2 figures; slightly extended version with more details on the character expansio

DFT insights into the oxygen-assisted selective oxidation of benzyl alcohol on manganese dioxide catalysts

The reactivity pattern of the MnO2 catalyst in the selective aerobic oxidation of benzyl alcohol is assessed by density functional theory (DFT) analysis of adsorption energies and activation barriers on a model Mn4O8 cluster. DFT calculations predict high reactivity of defective Mn(IV) sites ruling a surface redox mechanism, L-H type, involving gas-phase oxygen. Bare and promoted (i.e., CeOx and FeOx) MnOx materials with high surface exposure of Mn(IV) sites were synthesized to assess kinetic and mechanistic issues of the selective aerobic oxidation of benzyl alcohol on real catalysts (T, 333- 363K). According to DFT predictions, the experimental study shows: i) comparable activity of bare and promoted catalysts due to surface Mn(IV) sites; ii) the catalytic role of oxygen-atoms in the neighboring of active Mn(IV) sites; and iii) a 0th-order dependence on alcohol concentration, diagnostic of remarkable influence of adsorption phenomena on the reactivity pattern. Evidences of catalyst deactivation due to the over-oxidation of benzyl alcohol to benzoic acid, acting as poison of the active sites, are discussed

Thermodynamic simulation of atmospheric DLI-CVD processes for the growth of chromium-based hard coatings using bis(benzene)chromium as molecular source

Direct liquid injection (DLI) is a new technology particularly convenient for feeding CVD reactors with low volatility molecular precursors. Thus DLI-CVD can operate under atmospheric pressure and is a promising process for industrial applications requiring high precursor flow rates such as continuous deposition. In order to help the experimenter, a thermodynamic approach is particularly suitable for determining the chemistry of the process, i.e. the influence of the main growth parameters such as temperature, total pressure and initial gas phase composition on the nature of the deposited phases. A choice of the most explicit representations of the thermodynamic modelling describing the great trends resulting from the variation of experimental parameters is presented. Thermodynamic calculations in the Cr–C–H, Cr–N–C–H and Cr–C–Cl–H chemical systems were made to predict the atmospheric CVD growth of carbides, nitrides andmetal chromium coatings, respectively. Bis(benzene)chromium (BBC) was used as metalorganic precursor and the calculations simulated respectively the reactive gas phase mixtures BBC/solvent, BBC/NH3/solvent and BBC/C6Cl6/solvent. Even if a satisfactory agreement was found between experimental and theoretical tendencies, the deposition of metastable phases reveals that kinetics can play amajor role in such processes. Based on these results, chromium carbides, nitrides and metal coatings have been successfully deposited by DLI-CVD under atmospheric pressure either as single phased or nanostructured multilayer hard coatings

Lipid profile changes in patients with rheumatic diseases receiving a treatment with TNF-α blockers: a meta-analysis of prospective studies.

Some studies showed an anti-atherogenic effect of TNF-α blockers on lipid profile, but these data have been challenged.To perform a meta-analysis on lipid profile changes induced by TNF-α blocker treatment.Prospective studies on rheumatic patients receiving TNF-α blockers and providing before-and-after treatment values of triglycerides (TGs), total cholesterol (TC), HDL-cholesterol (HDLc), LDL-cholesterol (LDLc), and atherogenic index (AI) were included. Standardized mean differences (SMD) in lipid profile were analyzed at short-term (2-12 weeks), middle-term (13-24 weeks), and long-term (25-52 weeks) assessments.Thirty articles (1707 patients) were included. TNF-α blockers determined an increase in TC at short-term, middle-term, and long-term assessments (SMD: 0.20 mmol/L [95% CI: 0.04, 0.35]; SMD: 0.27 mmol/L [95% CI: 0.08, 0.46]; SMD: 0.22 mmol/L [95% CI: 0.01, 0.43]). HDLc increased only at the short-term assessment (SMD: 0.19 mmol/L [95% CI: 0.10, 0.28]), and TGs achieved a significant increase at the long-term assessment (SMD: 0.19 mmol/L [95% CI: 0.04, 0.34]). LDLc and AI were not affected by TNF-α blocker treatment.Slight but significant increases in TC occurred without any significant change in LDLc and AI. Changes in HDLc and TGs were not consistent among the different time point assessments. These quantitative changes in lipid profile do not seem to be able to explain cardiovascular risk improvement reported in patients receiving TNF-α blockers. Further studies on other mechanisms are needed to address this issue

The Removal of β2-Microglobulin in Spent Dialysate Cannot Be Monitored by Spectrophotometric Analysis

We synthetically present here unpublished results on β2M removal during HD treatments with dialysis membranes having different flux and adsorption capacities to clarify if the spectrophotometric analysis of spent dialysate may allow the possibility to monitor the removal of β2M during HD. These results were obtained from the analyses of serum and spent dialysate samples of the 22 MHD patients (16 men, 6 women). Serum and spent dialysate concentrations of β2M were measured with an immunonephelometric method (Siemens, BNAII) and compared with absorbance and fluorescence values. We conclude that the removal of β2M cannot be evaluated by spectrophotometric analysis of spent dialysate

GTOC5: Results from the European Space Agency and University of Florence

http://www.esa.int/gsp/ACT/doc/ACTAFUTURA/AF08/papers/AF08.2014.45.pdfInternational audienc