Neuroendocrine carcinoma of the cervix (NEC) is a rare and highly aggressive cervical malignancy. Given that no targeted therapy has been approved specifically to NEC, we investigated the presence of novel, potentially targetable biomarkers in a large cohort of NEC. Sixty-two NEC were molecularly profiled for biomarkers of targeted therapies including antibody-drug conjugates [delta-like canonical notch ligand 3 (DLL3), a trophoblast cell surface antigen 2 (TROP-2), and folate receptor 1 (FOLR1)], NTRK1-3 gene fusions, and immune checkpoint inhibitors [programmed death-ligand 1 (PD-L1), tumor mutational burden, and microsatellite instability] using immunohistochemistry and DNA/RNA next-generation sequencing assays. A cohort of squamous cell carcinomas of the cervix (n=599) was used for comparison for immune-oncology biomarkers. DLL3 expression was observed in 81% of the cases. DLL3 expression was inversely correlated with commonly observed pathogenic mutations in PIK3CA (17%) (P=0.018) and PTEN (10%) (P=0.006). Other more frequently seen pathogenic mutations (TP53 17%, KRAS 11%, and CTNNB1 5%) were not associated with DLL3 expression. TROP-2 expression was detected in only 1 case and no case expressed FOLR1. Although NTRK protein expression was observed in 21% of the cases, none of these had an NTRK gene fusion. PD-L1 expression (10%) and high tumor mutational burden (3%) were significantly less frequent in NEC compared with the squamous cell carcinoma cohort (79% and 11%, respectively). None of the NEC exhibited high microsatellite instability status. Despite frequent DLL3 expression in NEC, a potential therapeutic benefit of DLL3-targeted drugs remains uncertain given the recent failure of the Rova-T therapeutic trial in small cell lung carcinomas. Small cohorts of NEC enriched in PIK3CA/PTEN/AKT and programmed cell death protein 1/PD-L1 alterations indicate therapeutic roles for their respective inhibitors.

The search for effective coronavirus disease (COVID-19) therapy has attracted a great deal of scientific interest due to its unprecedented health care system overload worldwide. We have carried out a study to investigate the in silico effects of the most abundant pomegranate peel extract constituents on the multi-step process of serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) internalization in the host cells. Binding affinities and interactions of ellagic acid, gallic acid, punicalagin and punicalin were studied on four selected protein targets with a significant and confirmed role in the process of the entry of virus into a host cell. The protein targets used in this study were: SARS-CoV-2 spike glycoprotein, angiotensin-converting enzyme 2, furin and transmembrane serine protease 2. The results showed that the constituents of pomegranate peel extracts, namely punicalagin and punicalin had very promising potential for significant interactions with the selected protein targets and were therefore deemed good candidates for further in vitro and in vivo evaluation.

High-harmonic generation by aligned diatomic molecules in orthogonally po-larized two-color laser fields is considered using the molecular strong-field approximation. Regions of the parameter space with large harmonic ellipticity are identified.

Due to its high lethality among older people, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures and vaccines becoming available at scale, nursing homes might relax prohibitory measures while controlling the spread of infections. By control we mean that each index case infects less than one other person on average. Here, we develop an agent-based epidemiological model for the spread of SARS-CoV-2 calibrated to Austrian nursing homes to identify optimal prevention strategies. We find that the effectiveness of mitigation testing depends critically on test turnover time (time until test result), the detection threshold of tests and mitigation testing frequencies. Under realistic conditions and in absence of vaccinations, we find that mitigation testing of employees only might be sufficient to control outbreaks if tests have low turnover times and detection thresholds. If vaccines that are 60% effective against high viral load and transmission are available, control is achieved if 80% or more of the residents are vaccinated, even without mitigation testing and if residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular testing and sequencing of virus genomes is advised to enable early identification of new variants of concern.

Future fifth generation (5G) networks are envisioned to provide improved Quality-of-Experience (QoE) for applications by means of higher data rates, low and ultra-reliable latency and very high reliability. Proving increasing beneficial for mobile devices running multimedia applications. However, there exist two main co-related challenges in multimedia delivery in 5G. Namely, balancing operator provisioning and client expectations. To this end, we investigate how to build a QoE-aware network that guarantees at run-time that the end-to-end user experience meets the end users' expectations at the same that the network's Quality of Service (QoS) varies. The contribution of this paper is twofold: first, we consider a Dynamic Adaptive Streaming over HTTP (DASH) video application in a realistic emulation environment derived from real 5G traces in static and mobility scenarios to assess the QoE performance of three state-of-art Adaptive Bitrate Streaming (ABS) algorithm categories: Hybrid - Elastic and Arbiter+; buffer-based - BBA and Logistic; and rate-based - Exponential and Conventional. Second, we propose a Machine Learning (ML) classifier to predict user satisfaction which considers network metrics, such as RTT, throughput, and number of packets. Our proposed model does not rely on knowledge about the application or specific traffic information. We show that our ML classifiers achieve a QoE prediction accuracy of 87.63 % and 79 % for static and mobility scenarios, respectively.

5G has opened up possibilities of introducing new use cases and business models that could not be perceived before. In the context of public safety, 5G offers immense opportunities towards enhancing mission success and situation awareness during emergency management. This paper introduces Back-Situation Awareness (BSA) application enabling early warning/notification to vehicles of an approaching emergency vehicle indicating its presence and the time it will arrive. Such an application is expected to give drivers enough time to create a safety corridor for the emergency vehicle to pass through safely and unhindered. We provide details on the system and application design of the BSA application leveraging Multi-Access Edge Computing (MEC) systems that complement the 5G mobile communication system. An evaluation of the application is provided by using data measurements and indicating the accuracy of the computation and notification of the Estimated Time of Arrival (ETA) based on the ETSI C-ITS protocol messages.

Abstract This article examines the social and political effects produced by the most recent wave of emigration in postwar Bosnia, widely understood to be the result of continued political instability and economic decline that followed the 1992–95 war. Drawing on ethnographic research in a deindustrialized Bosnian town and analysis of popular discourses seeking to make sense of this new wave of departures, I show how the phenomenon of postwar exit impacts those staying behind and inspires new forms of reflection that link past histories of violence to more recent forms of dispossession. The emergence of such forms of historical consciousness reveals that postwar migration is haunted both by the memory of wartime expulsions and ethnic cleansing, as well as by the often-unacknowledged violence of postwar economic restructuring glossed as the postsocialist transition. In asking what happens to nationalist regimes, as well as scholarship on nationalist politics, when the “people” leave, I demonstrate the need to analyze the ongoing out-migration both in terms of Bosnia’s historical specificity and global political-economic dynamics. In so doing, I show how absences created by these departures create new vantage points that bring to light and expose unsettling political configurations left behind by the Bosnian war.

ABSTRACT Research on the effect of school uniforms on school attendance in low income countries is scarce. Building on a meta-analysis of the available literature, this paper analyses primary survey data collected (n = 462) in Mongolia on students’ perceptions of school uniforms. The findings reveal that it is not only the cost of uniforms that matters, but also poor students’ feelings of exclusion when the majority of students in a school wear uniforms. The poor drop out from school when their symbolic association with the majority is visibly broken through their inability to afford and wear school uniforms. The study suggests that school uniform policies in low income countries are fraught with complications. Instead of creating cohesion, such policies are more likely to affect poor students’ negative perceptions of themselves and play a strong role in dropout rates.

Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram‐based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen‐detected cases and 1197 matched controls; and 354 younger‐diagnosis cases and 944 controls frequency‐matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually‐ and frequency‐matched studies, respectively. We estimated measure‐specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen‐detected and younger‐diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41‐2.31]) and Cirrus (1.72 [1.38‐2.14]); Cirrus (1.49 [1.32‐1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27‐1.68]), respectively. The AUCs were: 0.73 [0.68‐0.77], 0.63 [0.60‐0.66], and 0.72 [0.69‐0.75], respectively. Combined, our new mammogram‐based measures have twice the risk gradient for screen‐detected and younger‐diagnosis breast cancer (P ≤ 10−12), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk‐based personalised breast screening.

Fluid-Structure Interaction (FSI) simulations have applications to a wide range of engineering areas. One popular technique to solve FSI problems is the Arbitrary Lagrangian-Eulerian (ALE) method. Both academic and industry communities developed codes to implement the ALE method. One of them is Alya, a Finite Element Method (FEM) based code developed in Barcelona Supercomputing Center (BSC). By analyzing the application on a simplified artery case and compared to another commercial code, which is Finite Volume Method (FVM) based, this paper discusses the mathematical background of the solver for domains, and carries out verification work on Alya’s FSI capability. The results show that while both codes provide comparable FSI results, Alya has exhibited better robustness due to its Subgrid Scale (SGS) technique for stabilization of convective term and the subsequent numerical treatments. Thus this code opens the door for more extensive use of higher fidelity finite element based FSI methods in future.

Introduction: Postpartum depression is considered a public health problem with high prevalence and very underdiagnosed. Methods: In this context, the present research has the general objective of identifying the risk factors of postpartum depression through a literature review integrative. The articles were searched in the Scientific Electronic Library Online Scielo databases and in the LILACS Latin American and Caribbean Literature in Health Sciences database. The selected articles were chosen according to the following criteria: to be available in full, to answer the research question, to have a publication date of the last 5 years. After a complete reading of the works, 08 remained for analysis. Results: The analysis of the results showed that the most cited risk factors for postpartum depression in the literature of the last 5 (five) years were: lack of family or partner support, unplanned pregnancy, family or personal history of psychiatric illness and low education and being younger. Conclusion: The research concludes that social and emotional factors have more influence on the prevalence of postpartum depression than economic factors. There is a need for further studies on the clinical aspects of postpartum depression.