Analysis of information exchange activities to actualize and validate situation awareness during shift changeovers in nuclear power plants

Shift work situations occur in almost all safety‐critical organizations, and the investigations of some catastrophes like Chernobyl, Exxon Valdez, and the Gol/Legacy mid‐air collision indicated that shift work information exchange played an important role during the evolution of the situation before the accidents. Inadequate communications during shift changeovers challenged operators' work in the moments that preceded these accidents, because they got inadequate information about the current situation. Our research focuses on the information exchange activities (verbal, written, and nonverbal) of nuclear power plant control operators during shift changeovers. Our aim is to investigate how verbal exchanges and other representations enable operator crews to share information regarding the events that occurred in the previous shift to achieve adequate situation awareness. Our findings indicated the importance and richness of the information exchange during the shift changeover process to update and validate individual and collective situation awareness, showing that information adequately shared enables the ad hoc configurations of regulation loops and a safer use of simplified strategies that can be understood and be validated by other operators, reducing the occurrence of cognitive overloads and contributing to the construction of a common cognitive ground that enhances system resilience. © 2010 Wiley Periodicals, Inc.     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

Constructive model-based analysis for safety assessment

The aerospace industry still uses fault trees to perform reliability analysis. This is because fault-tree modeling and analysis (FTA) seems easier to practical engineers when compared with Markov models, even though FTA provides a weaker form of analysis. In this paper, we propose an automatic strategy for generating Markov-based models and corresponding analysis formulations, according to ARP 4761, directly from Simulink diagrams annotated with failure information. The generated Markov-based models are expressed in the formal language PRISM, and the analysis is carried out by the PRISM model checker. The strategy is compositional and based on a comprehensive set of translation rules from Simulink to PRISM. We briefly address soundness and completeness of the rules and, to illustrate the application of the strategy, we apply it to a classical avionics case study: an actuator control system.     

The Landscape of microRNAs in βCell: Between Phenotype Maintenance and Protection

Diabetes mellitus is a group of heterogeneous metabolic disorders characterized by chronic hyperglycaemia mainly due to pancreatic β cell death and/or dysfunction, caused by several types of stress such as glucotoxicity, lipotoxicity and inflammation. Different patho-physiological mechanisms driving β cell response to these stresses are tightly regulated by microRNAs (miRNAs), a class of negative regulators of gene expression, involved in pathogenic mechanisms occurring in diabetes and in its complications. In this review, we aim to shed light on the most important miRNAs regulating the maintenance and the robustness of β cell identity, as well as on those miRNAs involved in the pathogenesis of the two main forms of diabetes mellitus, i.e., type 1 and type 2 diabetes. Additionally, we acknowledge that the understanding of miRNAs-regulated molecular mechanisms is fundamental in order to develop specific and effective strategies based on miRNAs as therapeutic targets, employing innovative molecules.     

The GAUGAA Motif Is Responsible for the Binding between circSMARCA5 and SRSF1 and Related Downstream Effects on Glioblastoma Multiforme Cell Migration and Angiogenic Potential

Circular RNAs (circRNAs) are a large class of RNAs with regulatory functions within cells. We recently showed that circSMARCA5 is a tumor suppressor in glioblastoma multiforme (GBM) and acts as a decoy for Serine and Arginine Rich Splicing Factor 1 (SRSF1) through six predicted binding sites (BSs). Here we characterized RNA motifs functionally involved in the interaction between circSMARCA5 and SRSF1. Three different circSMARCA5 molecules (Mut1, Mut2, Mut3), each mutated in two predicted SRSF1 BSs at once, were obtained through PCR-based replacement of wild-type (WT) BS sequences and cloned in three independent pcDNA3 vectors. Mut1 significantly decreased its capability to interact with SRSF1 as compared to WT, based on the RNA immunoprecipitation assay. In silico analysis through the “Find Individual Motif Occurrences” (FIMO) algorithm showed GAUGAA as an experimentally validated SRSF1 binding motif significantly overrepresented within both predicted SRSF1 BSs mutated in Mut1 (q-value = 0.0011). U87MG and CAS-1, transfected with Mut1, significantly increased their migration with respect to controls transfected with WT, as revealed by the cell exclusion zone assay. Immortalized human brain microvascular endothelial cells (IM-HBMEC) exposed to conditioned medium (CM) harvested from U87MG and CAS-1 transfected with Mut1 significantly sprouted more than those treated with CM harvested from U87MG and CAS-1 transfected with WT, as shown by the tube formation assay. qRT-PCR showed that the intracellular pro- to anti-angiogenic Vascular Endothelial Growth Factor A (VEGFA) mRNA isoform ratio and the amount of total VEGFA mRNA secreted in CM significantly increased in Mut1-transfected CAS-1 as compared to controls transfected with WT. Our data suggest that GAUGAA is the RNA motif responsible for the interaction between circSMARCA5 and SRSF1 as well as for the circSMARCA5-mediated control of GBM cell migration and angiogenic potential.     

P16 and HPV Genotype Significance in HPV-Associated Cervical Cancer—A Large Cohort of Two Tertiary Referral Centers

The expression of p16 is a good surrogate of human papillomavirus (HPV) infection in HPV-associated cancers. The significance of p16 expression, HPV genotype and genera in the outcome of patients with HPV-associated cervical cancer (CC) is unclear. Our aim is to ascertain the prognostic significance of these factors. Data from 348 patients (median age: 47.5 years old) with CC, diagnosed in two referral centers, were retrospectively collected. Advanced disease (FIGO2018 IB2-IV) was present in 68% of patients. A single HPV genotype was identified in 82.8% of patients. The most common HPVs were HPV16 (69%) and HPV18 (14%). HPV genera reflected this distribution. HPV16 tumors presented at an earlier stage. P16 was negative in 18 cases (5.2%), 83.3% of which were squamous cell carcinomas. These cases occurred in older patients who tended to have advanced disease. In the univariate analysis, HPV16 (HR: 0.58; p = 0.0198), α-9 genera (HR: 0.37; p = 0.0106) and p16 overexpression (HR: 0.54; p = 0.032) were associated with better survival. HPV16 (HR: 0.63; p = 0.0174) and α-9 genera (HR: 0.57; p = 0.0286) were associated with less relapse. In the multivariate analysis, only the International Federation of Gynecology and Obstetrics (FIGO) stage retained an independent prognostic value. HPV16, α-9 genera and p16 overexpression were associated with better survival, although not as independent prognostic factors. Patients with p16-negative HPV-associated CC were older, presented with advanced disease and had worse prognosis.     

Peptides to Tackle Leishmaniasis: Current Status and Future Directions

Peptide-based drugs are an attractive class of therapeutic agents, recently recognized by the pharmaceutical industry. These molecules are currently being used in the development of innovative therapies for diverse health conditions, including tropical diseases such as leishmaniasis. Despite its socioeconomic influence on public health, leishmaniasis remains long-neglected and categorized as a poverty-related disease, with limited treatment options. Peptides with antileishmanial effects encountered to date are a structurally heterogeneous group, which can be found in different natural sources—amphibians, reptiles, insects, bacteria, marine organisms, mammals, plants, and others—or inspired by natural toxins or proteins. This review details the biochemical and structural characteristics of over one hundred peptides and their potential use as molecular frameworks for the design of antileishmanial drug leads. Additionally, we detail the main chemical modifications or substitutions of amino acid residues carried out in the peptide sequence, and their implications in the development of antileishmanial candidates for clinical trials. Our bibliographic research highlights that the action of leishmanicidal peptides has been evaluated mainly using in vitro assays, with a special emphasis on the promastigote stage. In light of these findings, and considering the advances in the successful application of peptides in leishmaniasis chemotherapy, possible approaches and future directions are discussed here.     

Syndecan-1 Depletion Has a Differential Impact on Hyaluronic Acid Metabolism and Tumor Cell Behavior in Luminal and Triple-Negative Breast Cancer Cells

Glycosaminoglycans (GAGs) and proteoglycans (PGs) are major components of the glycocalyx. The secreted GAG and CD44 ligand hyaluronic acid (HA), and the cell surface PG syndecan-1 (Sdc-1) modulate the expression and activity of cytokines, chemokines, growth factors, and adhesion molecules, acting as critical regulators of tumor cell behavior. Here, we studied the effect of Sdc-1 siRNA depletion and HA treatment on hallmark processes of cancer in breast cancer cell lines of different levels of aggressiveness. We analyzed HA synthesis, and parameters relevant to tumor progression, including the stem cell phenotype, Wnt signaling constituents, cell cycle progression and apoptosis, and angiogenic markers in luminal MCF-7 and triple-negative MDA-MB-231 cells. Sdc-1 knockdown enhanced HAS-2 synthesis and HA binding in MCF-7, but not in MDA-MB-231 cells. Sdc-1-depleted MDA-MB-231 cells showed a reduced CD24-/CD44+ population. Furthermore, Sdc-1 depletion was associated with survival signals in both cell lines, affecting cell cycle progression and apoptosis evasion. These changes were linked to the altered expression of KLF4, MSI2, and miR-10b and differential changes in Erk, Akt, and PTEN signaling. We conclude that Sdc-1 knockdown differentially affects HA metabolism in luminal and triple-negative breast cancer model cell lines and impacts the stem phenotype, cell survival, and angiogenic factors.