Assessment of ionospheric Joule heating by GUMICS-4 MHD simulation, AMIE, and satellite-based statistics: towards a synthesis

We investigate the Northern Hemisphere Joule heating from several observational and computational sources with the purpose of calibrating a previously identified functional dependence between solar wind parameters and ionospheric total energy consumption computed from a global magnetohydrodynamic (MHD) simulation (Grand Unified Magnetosphere Ionosphere Coupling Simulation, GUMICS-4). In this paper, the calibration focuses on determining the amount and temporal characteristics of Northern Hemisphere Joule heating. Joule heating during a substorm is estimated from global observations, including electric fields provided by Super Dual Auroral Network (SuperDARN) and Pedersen conductances given by the ultraviolet (UV) and X-ray imagers on board the Polar satellite. Furthermore, Joule heating is assessed from several activity index proxies, large statistical surveys, assimilative data methods (AMIE), and the global MHD simulation GUMICS-4. We show that the temporal and spatial variation of the Joule heating computed from the GUMICS-4 simulation is consistent with observational and statistical methods. However, the different observational methods do not give a consistent estimate for the magnitude of the global Joule heating. We suggest that multiplying the GUMICS-4 total Joule heating by a factor of 10 approximates the observed Joule heating reasonably well. The lesser amount of Joule heating in GUMICS-4 is essentially caused by weaker Region 2 currents and polar cap potentials. We also show by theoretical arguments that multiplying independent measurements of averaged electric fields and Pedersen conductances yields an overestimation of Joule heating.<br><br> <b>Keywords.</b> Ionosphere (Auroral ionosphere; Modeling and forecasting; Electric fields and currents

Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.Comment: 18 pages, 12 Figures. Accepted on IEEE Acces

Performance of Different Diagnostic PD-L1 Clones in Head and Neck Squamous Cell Carcinoma

Background: The approval of immune checkpoint inhibitors in combination with specific diagnostic biomarkers presents new challenges to pathologists as tumor tissue needs to be tested for expression of programmed death-ligand 1 (PD-L1) for a variety of indications. As there is currently no requirement to use companion diagnostic assays for PD-L1 testing in Germany different clones are used in daily routine. While the correlation of staining results has been tested in various entities, there is no data for head and neck squamous cell carcinomas (HNSCC) so far. Methods: We tested five different PD-L1 clones (SP263, SP142, E1L3N, 22-8, 22C3) on primary HNSCC tumor tissue of 75 patients in the form of tissue microarrays. Stainings of both immune and tumor cells were then assessed and quantified by pathologists to simulate real-world routine diagnostics. The results were analyzed descriptively and the resulting staining pattern across patients was further investigated by principal component analysis and non-negative matrix factorization clustering. Results: Percentages of positive immune and tumor cells varied greatly. Both the resulting combined positive score as well as the eligibility for certain checkpoint inhibitor regimens was therefore strongly dependent on the choice of the antibody. No relevant co-clustering and low similarity of relative staining patterns across patients was found for the different antibodies. Conclusions: Performance of different diagnostic anti PD-L1 antibody clones in HNSCC is less robust and interchangeable compared to reported data from other tumor entities. Determination of PD-L1 expression is critical for therapeutic decision making and may be aided by back-to-back testing of different PD-L1 clones

Improved diagnostics targeting c-MET in non-small cell lung cancer: expression, amplification and activation?

Background: Several c-MET targeting inhibitory molecules have already shown promising results in the treatment of patients with Non-small Cell Lung Cancer (NSCLC). Combination of EGFR-and c-MET-specific molecules may overcome EGFR tyrosine kinase inhibitor (TKI) resistance. The aim of this study was to allow for the identification of patients who might benefit from TKI treatments targeting MET and to narrow in on the diagnostic assessment of MET. Methods: 222 tumor tissues of patients with NSCLC were analyzed concerning c-MET expression and activation in terms of phosphorylation (Y1234/1235 and Y1349) using a microarray format employing immunohistochemistry (IHC). Furthermore, protein expression and MET activation was correlated with the amplification status by Fluorescence in Situ Hybridization (FISH). Results: Correlation was observed between phosphorylation of c-MET at Y1234/1235 and Y1349 (spearman correlation coefficient r(s) = 0.41;p 0.05). c-MET gene amplification was detected in eight of 214 patients (3.7 %). No significant association was observed between c-MET amplification, c-MET protein expression and phosphorylation. Conclusion: Our data indicate, that neither expression of c-MET nor the gene amplification status might be the best way to select patients for MET targeting therapies, since no correlation with the activation status of MET was observed. We propose to take into account analyzing the phosphorylation status of MET by IHC to select patients for MET targeting therapies. Signaling of the receptor and the activation of downstream molecules might be more crucial for the benefit of therapeutics targeting MET receptor tyrosine kinases than expression levels alone

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the onboarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.Comment: 5 pages, 6 figures. Accepted for presentation at IEEE conference VTC2023-Spring. Available dataset at https://ieee-dataport.org/open-access/berlin-v2

Implementing a system of quality-of-life diagnosis and therapy for breast cancer patients: results of an exploratory trial as a prerequisite for a subsequent RCT

A system for quality-of-life diagnosis and therapy (QoL system) was implemented for breast cancer patients. The system fulfilled the criteria for complex interventions (Medical Research Council). Following theory and modeling, this study contains the exploratory trial as a next step before the randomised clinical trial (RCT) answering three questions: (1) Are there differences between implementation sample and general population? (2) Which amount and type of disagreement exist between patient and coordinating practitioners (CPs) in assessed global QoL? (3) Are there empirical reasons for a cutoff of 50 points discriminating between healthy and diseased QoL? Implementation was successful: 74% of CPs worked along the care pathway. However, CPs showed preferences for selecting patients with lower age and UICC prognostic staging. Patients and CPs disagreed considerably in values of global QoL, despite education in QoL assessment by outreach visits, opinion leaders and CME: Zero values of QoL were only expressed by patients. Finally, the cutoff of 50 points was supported by the relationship between QoL in single items and global QoL: no patients with values above 50 dropped global QoL below 50, but values below 50 and especially at 0 points in single items, induced a dramatic fall of global QoL down to below 50. The exploratory trial was important for defining the complex intervention in the definitive RCT: control for age and prognostic stage grading, support for a QoL unit combining patient's and CP's assessment of QoL and support for the 50-point cutoff criterion between healthy and diseased QoL

The significance of trust in the political system and motivation for pupils' learning progress in politics lessons

Very little research has been conducted on the contribution of political education to learning progress in Germany. Hence, there is a need for intervention studies measuring performance against the theoretical background of a political competence model. This model comprises three constructs: subject knowledge, motivation and attitudes. According to this model, politics lessons should not only convey knowledge but also arouse subject interest, promote political attitudes and develop problem-solving skills. This study investigates how knowledge acquisition is influenced by intervention using theory-oriented teaching materials on the European Union, intervention using conventional textbooks on the European Union and politics lessons without any reference to the European Union. It further asks how the performance-related self-concept and subject interest in political issues impact political knowledge and whether civic virtue and trust in the system are related to it. The sample comprises 1071 pupils. Theory-oriented politics classes lead to greater growth of pupils’ knowledge than in the control group. As anticipated, this study proves that a positive subject-specific self-concept impacts knowledge. The examination of political attitudes reveals a positive correlation between civic virtue and knowledge. There is no connection between trust in the political system and knowledge

The development of study-specific self-efficacy during grammar school.(Zur Entwicklung der studienspezifischen Selbstwirksamkeit in der Oberstufe)

Article is in German. Even if more and more German adolescents acquire a university entrance qualification, not all of them finally enrol at a university. In particular, the transition from school to university strongly depends on parent’s education. Even with the same marks in school, adolescents from non-academic households are less likely to enrol in universities than adolescents from academic housholds. One important reason is their lower belief to master a university study. This study analyses a specific intervention in grammar school to improve study-specific self- efficacy, the belief in one’s capabilities to master a university study, using a longitudinal design. We apply a difference-in-difference framework and show that programme participation significantly improves the study-specific self-efficacy for puplis from non- academic families but not for those from academic families. Hence, such a programme could reduce social disparities between both groups

Entropy Measures Quantify Global Splicing Disorders in Cancer

Most mammalian genes are able to express several splice variants in a phenomenon known as alternative splicing. Serious alterations of alternative splicing occur in cancer tissues, leading to expression of multiple aberrant splice forms. Most studies of alternative splicing defects have focused on the identification of cancer-specific splice variants as potential therapeutic targets. Here, we examine instead the bulk of non-specific transcript isoforms and analyze their level of disorder using a measure of uncertainty called Shannon's entropy. We compare isoform expression entropy in normal and cancer tissues from the same anatomical site for different classes of transcript variations: alternative splicing, polyadenylation, and transcription initiation. Whereas alternative initiation and polyadenylation show no significant gain or loss of entropy between normal and cancer tissues, alternative splicing shows highly significant entropy gains for 13 of the 27 cancers studied. This entropy gain is characterized by a flattening in the expression profile of normal isoforms and is correlated to the level of estimated cellular proliferation in the cancer tissue. Interestingly, the genes that present the highest entropy gain are enriched in splicing factors. We provide here the first quantitative estimate of splicing disruption in cancer. The expression of normal splice variants is widely and significantly disrupted in at least half of the cancers studied. We postulate that such splicing disorders may develop in part from splicing alteration in key splice factors, which in turn significantly impact multiple target genes