A Strategic Location Model of Stationary Production Units: A Case study in the Albacora Leste field

The imminent interest in issues related to the oil and gas sector has always proved to be a profitable source of investment and research, with incremental gains and innovations in the various sectors of the offshore industry. Particularly in the context of resource localization, the adoption of mathematical models presents itself as a challenging theme. In this context, the research has the purpose of proposing a localization model of Stationary Production Units (SPU) of an oilfield located in the Campos Basin, Rio de Janeiro (Brazil). The computational tests were conducted using the Lingo software, based on data from the Albacora Leste field. The results of the proposed model demonstrated a reduction of approximately 12% in the configuration costs, compared to the current location

Subgingival Microbiota Dysbiosis in Systemic Lupus Erythematosus: Association with Periodontal Status

Background Periodontitis results from the interaction between a subgingival biofilm and host immune response. Changes in biofilm composition are thought to disrupt homeostasis between the host and subgingival bacteria resulting in periodontal damage. Chronic systemic inflammatory disorders have been shown to affect the subgingival microbiota and clinical periodontal status. However, this relationship has not been examined in subjects with systemic lupus erythematosus (SLE). The objective of our study was to investigate the influence of SLE on the subgingival microbiota and its connection with periodontal disease and SLE activity. Methods We evaluated 52 patients with SLE compared to 52 subjects without SLE (control group). Subjects were classified as without periodontitis and with periodontitis. Oral microbiota composition was assessed by amplifying the V4 region of 16S rRNA gene from subgingival dental plaque DNA extracts. These amplicons were examined by Illumina MiSeq sequencing. Results SLE patients exhibited higher prevalence of periodontitis which occurred at a younger age compared to subjects of the control group. More severe forms of periodontitis were found in SLE subjects that had higher bacterial loads and decreased microbial diversity. Bacterial species frequently detected in periodontal disease were observed in higher proportions in SLE patients, even in periodontal healthy sites such as Fretibacterium, Prevotella nigrescens, and Selenomonas. Changes in the oral microbiota were linked to increased local inflammation, as demonstrated by higher concentrations of IL-6, IL-17, and IL-33 in SLE patients with periodontitis. Conclusions SLE is associated with differences in the composition of the microbiota, independently of periodontal status. Electronic supplementary material The online version of this article (doi:10.1186/s40168-017-0252-z) contains supplementary material, which is available to authorized users

Quantum-secured time transfer between precise timing facilities: a field trial with simulated satellite links

Global Navigation Satellite Systems (GNSSs), such as GPS and Galileo, provide precise time and space coordinates globally and constitute part of the critical infrastructure of modern society. To reliably operate GNSS, a highly accurate and stable system time is required, such as the one provided by several independent clocks hosted in Precise Timing Facilities (PTFs) around the world. The relative clock offset between PTFs is periodically measured to have a fallback system to synchronize the GNSS satellite clocks. The security and integrity of the communication between PTFs is of paramount importance: if compromised, it could lead to disruptions to the GNSS service. Therefore, securing the communication between PTFs is a compelling use-case for protection via Quantum Key Distribution (QKD), since this technology provides information-theoretic security. We have performed a field trial demonstration of such a use-case by sharing encrypted time synchronization information between two PTFs, one located in Oberpfaffenhofen (Germany) and one in Matera (Italy)—more than 900 km apart. To bridge this large distance, a satellite-QKD system is required, plus a “last-mile” terrestrial link to connect the optical ground station (OGS) to the actual location of the PTF. In our demonstration, we have deployed two full QKD systems to protect the last-mile connection at both locations and have shown via simulation that upcoming QKD satellites will be able to distribute keys between Oberpfaffenhofen and Matera, exploiting already existing OGSs

Tissue Invasion by Entamoeba histolytica: Evidence of Genetic Selection and/or DNA Reorganization Events in Organ Tropism

Entamoeba histolytica infection may have various clinical manifestations. Nine out of ten E. histolytica infections remain asymptomatic, while the remainder become invasive and cause disease. The most common form of invasive infection is amebic diarrhea and colitis, whereas the most common extra-intestinal disease is amebic liver abscess. The underlying reasons for the different outcomes are unclear, but a recent study has shown that the parasite genotype is a contributor. To investigate this link further we have examined the genotypes of E. histolytica in stool- and liver abscess-derived samples from the same patients. Analysis of all 18 paired samples (16 from Bangladesh, one from the United States of America, and one from Italy) revealed that the intestinal and liver abscess amebae are genetically distinct. The results suggest either that E. histolytica subpopulations in the same infection show varying organ tropism, or that a DNA reorganization event takes place prior to or during metastasis from intestine to liver

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology