The EU has long been suffering a legitimacy crisis. In this article we argue that multilingual Europarties, that is, European political parties operating in all the various languages spoken by their members via interpreting and translation, rather than resorting to a lingua franca, could contribute to providing an effective democratic linkage between EU citizens and EU institutions. Moreover, by drawing inspiration from an analysis of Belgium, Canada and Switzerland, we argue that centripetal institutions such as an EU-wide electoral district, presidentialism, and direct democracy could provide favourable institutional conditions for the development of such multilingual Europarties.

Abstract This article reframes the formation of the Ottoman-Habsburg frontier after 1699 in social historical terms. By going beyond diplomatic and military factors, it identifies how the contraction of Ottoman borders affected taxation, landholding, and Muslim-Christian relations in Bosnia. The article argues that peasants in Ottoman Bosnia experienced the mounting pressures of increasing taxation, manipulation over landownership, and religiously inflected hostility, often driven by those Muslim noblemen who tried to capitalize on the destabilizing wake of several wars that the Ottoman Empire fought with the Habsburg, Venetian, and Russian states in the eighteenth century. Through these processes, by the end of the century the meaning of the reaya or raya—an Ottoman term for taxpaying “subjects” that theoretically applied to all denominations, including Muslims—had become synonymous with “Christians,” acquiring a new political significance.

IrO 2 is the most stable oxygen evolution reaction (OER) catalyst in acidic media and it has been widely used as co-catalyst to mitigate cell reversal damages in the anode of PEM fuel cells (PEMFCs). In this study, a mechanistic understanding of the degradation of an IrO 2 anode co-catalyst under transient operation of a PEMFC is provided. Thermogravimetric analysis (TGA) in reductive atmosphere (3.3 vol.% H 2 /Ar) shows that IrO 2 is not stable in H 2 containing atmosphere at operational temperatures of PEMFCs. By conducting a series of physical-chemical and electrochemical analyses, it is proven that H 2 under the operating conditions in a PEMFC anode can chemically reduce a few outer monolayers of the surface of IrO 2 nanoparticles to metallic Ir. The metallic Ir formed on the IrO 2 surface can then dissolve during fuel cell start-up/shut-down (SUSD) cycles. At least part of the dissolved Ir species formed in the anode electrode are shown to diffuse through the membrane to the cathode electrode, where they lead to a deterioration of the oxygen reduction reaction (ORR) activity of the Pt cathode catalyst. The consequences of Ir dissolution on the cell reversal tolerance of the anode are also discussed.

This paper presents an analysis of the depolarization effect in off-body channels, based on a previously developed geometry-based channel model for polarized communications with dynamic users. The model considers Line-of-Sight propagation and components reflected from scatterers distributed on cylinders centered around the user. A mobility model for wearable antennas based on Fourier series is employed to take the effects of user’s motion into account. The focus is on scattered signal components, where the impact of a scatter’s position, its material properties, and the influence of user dynamics on signal depolarization are investigated. It is observed that the wearable antenna motion has a strong impact on the channel’s polarization characteristics, particularly for dynamic on-body placements, such as arms and legs. If the antenna motion is neglected, the error in cross-polarization ratio is greater than 23dB compared to a static approach. The antenna rotation during motion is found to be the dominant factor, while the corresponding displacement can be neglected, with the error not exceeding 1dB. This result justifies the channel model simplification proposed in this paper.

Although thousands of solid tumors have been sequenced to date, a fundamental under-sampling bias is inherent in current methodologies. This is caused by a tissue sample input of fixed dimensions (e.g., 6 mm biopsy), which becomes grossly under-powered as tumor volume scales. Here, we demonstrate representative sequencing (Rep-Seq) as a new method to achieve unbiased tumor tissue sampling. Rep-Seq uses fixed residual tumor material, which is homogenized and subjected to next-generation sequencing. Analysis of intratumor tumor mutation burden (TMB) variability shows a high level of misclassification using current single-biopsy methods, with 20% of lung and 52% of bladder tumors having at least one biopsy with high TMB but low clonal TMB overall. Misclassification rates by contrast are reduced to 2% (lung) and 4% (bladder) when a more representative sampling methodology is used. Rep-Seq offers an improved sampling protocol for tumor profiling, with significant potential for improved clinical utility and more accurate deconvolution of clonal structure.

Background Immune checkpoint inhibitors (ICIs) have significantly improved the outcome in metastatic cutaneous melanoma (CM). However, therapy response is limited to subgroups of patients and clinically useful predictive biomarkers are lacking. Methods To discover treatment-related systemic changes in plasma and potential biomarkers associated with treatment outcome, we analyzed serial plasma samples from 24 patients with metastatic CM, collected before and during ICI treatment, with mass-spectrometry-based global proteomics (high-resolution isoelectric focusing liquid chromatography–mass spectrometry (HiRIEF LC-MS/MS)) and targeted proteomics with proximity extension assays (PEAs). In addition, we analyzed plasma proteomes of 24 patients with metastatic CM treated with mitogen-activated protein kinase inhibitors (MAPKis), to pinpoint changes in protein plasma levels specific to the ICI treatment. To detect plasma proteins associated with treatment response, we performed stratified analyses in anti-programmed cell death protein 1 (anti-PD-1) responders and non-responders. In addition, we analyzed the association between protein plasma levels and progression-free survival (PFS) by Cox proportional hazards models. Results Unbiased HiRIEF LC-MS/MS-based proteomics showed plasma levels’ alterations related to anti-PD-1 treatment in 80 out of 1160 quantified proteins. Circulating PD-1 had the highest increase during anti-PD-1 treatment (log2-FC=2.03, p=0.0008) and in anti-PD-1 responders (log2-FC=2.09, p=0.005), but did not change in the MAPKis cohort. Targeted, antibody-based proteomics by PEA confirmed this observation. Anti-PD-1 responders had an increase in plasma proteins involved in T-cell response, neutrophil degranulation, inflammation, cell adhesion, and immune suppression. Furthermore, we discovered new associations between plasma proteins (eg, interleukin 6, interleukin 10, proline-rich acidic protein 1, desmocollin 3, C-C motif chemokine ligands 2, 3 and 4, vascular endothelial growth factor A) and PFS, which may serve as predictive biomarkers. Conclusions We detected an increase in circulating PD-1 during anti-PD-1 treatment, as well as diverse immune plasma proteomic signatures in anti-PD-1 responders. This study demonstrates the potential of plasma proteomics as a liquid biopsy method and in discovery of putative predictive biomarkers for anti-PD-1 treatment in metastatic CM.

The reliability of the operations of the high-voltage circuit breaker is the key to the stable power system, so it’s fault diagnosis and condition assessment it is of great significance. Considering that high-voltage circuit breaker vibration fingerprints contain valuable information about its mechanical integrity and that the vibration measurements are non-invasive, this paper presents the algorithm for the analysis of residual life of a high-voltage circuit breaker. The algorithm is based on the variational mode decomposition (VMD) and Shannon information entropy mean (EM) in order to obtain indices that are used as an indicator of the circuit breaker structural deterioration.

Abstract Introduction. Bladder cancer is the most common malignancy involving the urinary system. Recent research tends to emphasize the role of oxidative stress products in the carcinogenesis of bladder cancer. The level of oxidative stress can be measured by assessing the MDA levels. This study aimed to evaluate serum MDA levels in patients with bladder cancer, as well as to determine its potential role as a biomarker in the diagnosis of the disease and progression risk considerations. Methods. The study was designed as a cross-sectional study and included 90 patients, divided into three groups with 30 patients each: Ta, T1and T2–T4 group, based on histopathological findings after transurethral resection of the tumor. The control group included 30 healthy volunteers. MDA level was determined using the spectrophotometric method. Results. Serum MDA level in patients with bladder cancer [0.86 (0.78–1.05) μmol/L] was significantly higher than the serum MDA level in control group [0.70 (0.69–0.72) μmol/L] (p < 0.001). Serum MDA level in Ta group [0.73 (0.70–1.05) μmol/L], T1 group [0.85 (0.80–1.12) μmol/L] and in T2–T4 group [0.91 (0.84–1.04) μmol/L] was significantly higher than the serum MDA level in control group [0.70 (0.69–0.72) μmol/L] (p < 0.01). MDA level in T1 and T2–T4 group was significantly higher than the MDA level in Ta group (p < 0.01). No significant difference was observed in MDA level between T1 and T2–T4 group (p = NS). A statistically significant positive correlation was found between tumor size and serum MDA level in patients with bladder cancer (rho = 0.254 p < 0.01). Conclusions. The results of the present study suggest that MDA serum level might play a significant role as a biomarker in the diagnosis of bladder cancer, as well as in the monitoring of its progression.

Under the AKP government, Turkey’s foreign policy towards the Western Balkans, and Bosnia and Herzegovina in particular, has led many analysts to suspect it of possessing neo-imperial, or so-called neo-Ottoman, objectives. These suspicions have been compounded by the repeated declarations of former Prime Minister Davutoğlu and current President Erdoğan that the history and religious identity shared by Turks and Western Balkan Muslims forms the basis of both Turkish-Balkan relations and a common future. Critical examination of official Ankara’s attitudes toward the Western Balkans in general, and especially Bosnia and Herzegovina, identifies four distinct phases in which cultural, historical, and religious appeals morphed into the set of distinctive foreign policies. These policies have also been shaped by pragmatic pursuits of regional influence, the effects of internal (Turkish) transformations, and more recently, the ad hoc policies of President Erdoğan. This article will reconstruct the development of Turkish foreign policy since 1990, from multilateral and soft power efforts to religious and economic objectives, and will analyse the limits of this policy.

Machine learning, particularly in the form of deep learning (DL), has driven most of the recent fundamental developments in artificial intelligence (AI). DL is based on computational models that are, to a certain extent, bio‐inspired, as they rely on networks of connected simple computing units operating in parallel. The success of DL is supported by three factors: availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. The approaching demise of Moore's law, and the consequent expected modest improvements in computing power that can be achieved by scaling, raises the question of whether the progress will be slowed or halted due to hardware limitations. This article reviews the case for a novel beyond‐complementary metal–oxide–semiconductor (CMOS) technology—memristors—as a potential solution for the implementation of power‐efficient in‐memory computing, DL accelerators, and spiking neural networks. Central themes are the reliance on non‐von‐Neumann computing architectures and the need for developing tailored learning and inference algorithms. To argue that lessons from biology can be useful in providing directions for further progress in AI, an example‐based reservoir computing is briefly discussed. At the end, speculation is given on the “big picture” view of future neuromorphic and brain‐inspired computing systems.