Using Graph Transformation Systems to Specify and Verify Data Abstractions

This paper proposes an approach for the specification of the behavior of software components that implement data abstractions. By generalizing the approach of behavior models using graph transformation, we provide a concise specification for data abstractions that describes the relationship between the internal state, represented in a canonical form, and the observers of the component. Graph transformation also supports the generation of behavior models that are amenable to verification. To this end, we provide a translation approach into an LTL model on which we can express useful properties that can be model-checked with a SAT solver

Insight into the Molecular Model in Carbon Dots through Experimental and Theoretical Analysis of Citrazinic Acid in Aqueous Solution

The molecular emission model is the most accredited one to explain the emission properties of carbon dots (CDs) in a low-temperature bottom-up synthesis approach. In the case of citric acid and urea, the formation of a citrazinic acid (CZA) single monomer and oligomers is expected to affect the optical properties of the CDs. It is therefore mandatory to elucidate the possible role of weak bonding interactions in determining the UV absorption spectrum of some molecular aggregates of CZA. Although this carboxylic acid is largely exploited in the synthesis of luminescent CDs, a full understanding of its role in determining the final emission spectra of the produced CDs is still very far to be achieved. To this aim, by relying on purely first-principles density functional theory calculations combined with experimental optical characterization, we built and checked the stability of some molecular aggregates, which could possibly arise from the formation of oligomers of CZA, mainly dimers, trimers, and some selected tetramers. The computed vibrational fingerprint of the formation of aggregates is confirmed by surface-enhanced Raman spectroscopy. The comparison of experimental data with calculated UV absorption spectra showed a clear impact of the final morphology of the aggregates on the position of the main peaks in the UV spectra, with particular regard to the 340 nm peak associated with n-π∗ transition

Prediction of steel coils mechanical properties and microstructure by using deep learning and advanced data preprocessing techniques

In the production of steel strips, the fulfillment of required product properties is a key factor to improve the company’s productivity and competitiveness. Product characteristics can be evaluated online throughout the length of the strip by means of non–destructive tests such as the IMPOC whose output signal is related to mechanical properties and their uniformity. In this work, a novel approach based on the use of deep–neural–networks and advanced analytics is used to develop a model for the prediction of IMPOC signal from process parameters. The model provides plant managers with an insight into the relationships among process conditions, product characteristics and mechanical properties in order to suitably set up process parameters to meet product requirements. In this work, different model architectures and data processing techniques are evaluated leading an overall prediction error lower than 5% that puts the basis for their integration into the plant

Formation of citrazinic acid ions and their contribution to optical and magnetic features of carbon nanodots: A combined experimental and computational approach

The molecular model is one of the most appealing to explain the peculiar optical properties of Carbon nanodots (CNDs) and was proven to be successful for the bottom up synthesis, where a few molecules were recognized. Among the others, citrazinic acid is relevant for the synthesis of citric acid-based CNDs. Here we report a combined experimental and computational approach to discuss the formation of different protonated and deprotonated species of citrazinic acid and their contribution to vibrational and magnetic spectra. By computing the free energy formation in water solution, we selected the most favoured species and we retrieved their presence in the experimental surface enhanced Raman spectra. As well, the chemical shifts are discussed in terms of tautomers and rotamers of most favoured species. The expected formation of protonated and de-protonated citrazinic acid ions under extreme pH conditions was proven by evaluating specific interactions with H2 SO4 and NaOH molecules. The reported results confirm that the presence of citrazinic acid and its ionic forms should be considered in the interpretation of the spectroscopic features of CNDs

Using graph transformation systems to specify and verify data abstractions

This paper proposes an approach for the specification of the behavior of software components that implement data abstractions. By generalizing the approach of behavior models using graph transformation, we provide a concise specification for data abstractions that describes the relationship between the internal state, represented in a canonical form, and the observers of the component. Graph transformation also supports the generation of behavior models that are amenable to verification. To this end, we provide a translation approach into an LTL model on which we can express useful properties that can be model-checked with a SAT solver

Continuous-Flow Synthesis of Arylthio-Cyclopropyl Carbonyl Compounds

The straightforward, continuous-flow synthesis of cyclopropyl carbaldehydes and ketones has been developed starting from 2-hydroxycyclobutanones and aryl thiols. This acid-catalyzed mediated procedure allows access to the multigram and easily scalable synthesis of cyclopropyl adducts under mild conditions, using reusable Amberlyst-35 as a catalyst. The resins, suitably ground and used for filling steel columns, have been characterized via TGA, ATR, SEM and BET analyses to describe the physical-chemical properties of the packed bed and the continuous-flow system in detail. To highlight the synthetic versatility of the arylthiocyclopropyl carbonyl compounds, a series of selective oxidation reactions have been performed to access sulfoxide and sulfone carbaldehyde cyclopropanes, oxiranes and carboxylic acid derivatives

Biomarker dynamics affecting neoadjuvant therapy response and outcome of HER2-positive breast cancer subtype

HER2+ breast cancer (BC) is an aggressive subtype genetically and biologically heterogeneous. We evaluate the predictive and prognostic role of HER2 protein/gene expression levels combined with clinico-pathologic features in 154 HER2+ BCs patients who received trastuzumab-based neoadjuvant chemotherapy (NACT). The tumoral pathological complete response (pCR) rate was 40.9%. High tumoral pCR show a scarce mortality rate vs subjects with a lower response. 93.7% of ypT0 were HER2 IHC3+ BC, 6.3% were HER2 IHC 2+/SISH+ and 86.7% of ypN0 were HER2 IHC3+, the remaining were HER2 IHC2+/SISH+. Better pCR rate correlate with a high percentage of infiltrating immune cells and right-sided tumors, that reduce distant metastasis and improve survival, but no incidence difference. HER2 IHC score and laterality emerge as strong predictors of tumoral pCR after NACT from machine learning analysis. HER2 IHC3+ and G3 are poor prognostic factors for HER2+ BC patients, and could be considered in the application of neoadjuvant therapy. Increasing TILs concentrations, lower lymph node ratio and lower residual tumor cellularity are associated with a better outcome. The immune microenvironment and scarce lymph node involvement have crucial role in clinical outcomes. The combination of all predictors might offer new options for NACT effectiveness prediction and stratification of HER2+ BC during clinical decision-making

Long-term outcomes of acute severe ulcerative colitis in the rescue therapy era: A multicentre cohort study

BACKGROUND: The long‐term course of ulcerative colitis after a severe attack is poorly understood. Second‐line rescue therapy with cyclosporine or infliximab is effective for reducing short‐term colectomy but the impact in the long‐term is controversial. OBJECTIVE: The purpose of this study was to evaluate the long‐term course of acute severe ulcerative colitis patients who avoid early colectomy either because of response to steroids or rescue therapy. METHODS: This was a multicentre retrospective cohort study of adult patients with acute severe ulcerative colitis admitted to Italian inflammatory bowel disease referral centres from 2005 to 2017. All patients received intravenous steroids, and those who did not respond received either rescue therapy or colectomy. For patients who avoided early colectomy (within 3 months from the index attack), we recorded the date of colectomy, last follow‐up visit or death. The primary end‐point was long‐term colectomy rate in patients avoiding early colectomy. RESULTS: From the included 372 patients with acute severe ulcerative colitis, 337 (90.6%) avoided early colectomy. From those, 60.5% were responsive to steroids and 39.5% to the rescue therapy. Median follow‐up was 44 months (interquartile range, 21–85). Colectomy‐free survival probability was 93.5%, 81.5% and 79.4% at 1, 3 and 5 years, respectively. Colectomy risk was higher among rescue therapy users than in steroid‐responders (log‐rank test, p = 0.02). At multivariate analysis response to steroids was independently associated with a lower risk of long‐term colectomy (adjusted odds ratio = 0.5; 95% confidence interval, 0.2–0.8), while previous exposure to antitumour necrosis factor‐α agents was associated with an increased risk (adjusted odds ratio = 3.0; 95% confidence interval, 1.5–5.7). Approximately 50% of patients required additional therapy or new hospitalisation within 5 years due to a recurrent flare. Death occurred in three patients (0.9%). CONCLUSIONS: Patients with acute severe ulcerative colitis avoiding early colectomy are at risk of long‐term colectomy, especially if previously exposed to antitumour necrosis factor‐α agents or if rescue therapy during the acute attack was required because of steroid refractoriness

Electron and ion spectroscopy of azobenzene in the valence and core shells

Azobenzene is a prototype and a building block of a class of molecules of extreme technological interest as molecular photo-switches. We present a joint experimental and theoretical study of its response to irradiation with light across the UV to x-ray spectrum. The study of valence and inner shell photo-ionization and excitation processes combined with measurement of valence photoelectron-photoion coincidence and mass spectra across the core thresholds provides a detailed insight into the site- and state-selected photo-induced processes. Photo-ionization and excitation measurements are interpreted via the multi-configurational restricted active space self-consistent field method corrected by second order perturbation theory. Using static modeling, we demonstrate that the carbon and nitrogen K edges of azobenzene are suitable candidates for exploring its photoinduced dynamics thanks to the transient signals appearing in background-free regions of the NEXAFS and XPS

Genome-Wide Significant Risk Loci for Mood Disorders in the Old Order Amish Founder Population

Genome-wide association studies (GWAS) of mood disorders in large case-control cohorts have identified numerous risk loci, yet pathophysiological mechanisms remain elusive, primarily due to the very small effects of common variants. We sought to discover risk variants with larger effects by conducting a genome-wide association study of mood disorders in a founder population, the Old Order Amish (OOA, n = 1,672). Our analysis revealed four genome-wide significant risk loci, all of which were associated with \u3e2-fold relative risk. Quantitative behavioral and neurocognitive assessments (n = 314) revealed effects of risk variants on sub-clinical depressive symptoms and information processing speed. Network analysis suggested that OOA-specific risk loci harbor novel risk-associated genes that interact with known neuropsychiatry-associated genes via gene interaction networks. Annotation of the variants at these risk loci revealed population-enriched, non-synonymous variants in two genes encoding neurodevelopmental transcription factors, CUX1 and CNOT1. Our findings provide insight into the genetic architecture of mood disorders and a substrate for mechanistic and clinical studies