Elite opinion and foreign policy in post-communist Russia

Russian elite opinion on matters of foreign policy may be classified as ‘Liberal Westerniser’, ‘Pragmatic Nationalist’ and ‘Fundamentalist Nationalist’, terms that reflect longstanding debates about the country’s relationship with the outside world. An analysis of press statements and election manifestoes together with a programme of elite interviews between 2004 and 2006 suggests a clustering of opinion on a series of strategic issues. Liberal Westernisers seek the closest possible relationship with Europe, and favour eventual membership of the EU and NATO. Pragmatic Nationalists are more inclined to favour practical co-operation, and do not assume an identity of values or interests with the Western countries. Fundamentalist Nationalists place more emphasis on the other former Soviet republics, and on Asia as much as Europe, and see the West as a threat to Russian values as well as to its state interests. Each of these positions, in turn, draws on an identifiable set of domestic constituencies: Liberal Westernisers on the promarket political parties, Pragmatic Nationalists on the presidential administration and defence and security ministries, and Fundamentalist Nationalists on the Orthodox Church and Communists

Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics

Introduction There is a cumulative risk of 20-40% of developing brain metastases (BM) in solid cancers. Stereotactic radiotherapy (SRT) enables the application of high focal doses of radiation to a volume and is often used for BM treatment. However, SRT can cause adverse radiation effects (ARE), such as radiation necrosis, which sometimes cause irreversible damage to the brain. It is therefore of clinical interest to identify patients at a high risk of developing ARE. We hypothesized that models trained with radiomics features, deep learning (DL) features, and patient characteristics or their combination can predict ARE risk in patients with BM before SRT. Methods Gadolinium-enhanced T1-weighted MRIs and characteristics from patients treated with SRT for BM were collected for a training and testing cohort (N = 1,404) and a validation cohort (N = 237) from a separate institute. From each lesion in the training set, radiomics features were extracted and used to train an extreme gradient boosting (XGBoost) model. A DL model was trained on the same cohort to make a separate prediction and to extract the last layer of features. Different models using XGBoost were built using only radiomics features, DL features, and patient characteristics or a combination of them. Evaluation was performed using the area under the curve (AUC) of the receiver operating characteristic curve on the external dataset. Predictions for individual lesions and per patient developing ARE were investigated. Results The best-performing XGBoost model on a lesion level was trained on a combination of radiomics features and DL features (AUC of 0.71 and recall of 0.80). On a patient level, a combination of radiomics features, DL features, and patient characteristics obtained the best performance (AUC of 0.72 and recall of 0.84). The DL model achieved an AUC of 0.64 and recall of 0.85 per lesion and an AUC of 0.70 and recall of 0.60 per patient. Conclusion Machine learning models built on radiomics features and DL features extracted from BM combined with patient characteristics show potential to predict ARE at the patient and lesion levels. These models could be used in clinical decision making, informing patients on their risk of ARE and allowing physicians to opt for different therapies

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

peer reviewedDetection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours

The ‘rising power’ status and the evolution of international order : conceptualising Russia’s Syria policies

Taking Syria’s armed conflict as a case study to illustrate processes of normative contestation in international relations, this paper is interested in re-examining the typology of Russia as a ‘rising power’ to account for ‘rise’ in a non-material dimension. The article embeds the ‘rising power’ label in the literature on international norm dynamics to reflect on the rationale for Russia’s engagement in Syria despite adverse material preconditions. It will be argued that Russian norm divergence from alleged ‘Western’ norms illustrates the ambition to co-define conditions for legitimate transgressions of state sovereignty

Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.

peer reviewedOBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features

The truth about lend-lease

Lend-lease, according to E.M. Primakov, is a unique project of the XX century. It was implemented by the states with different socio-political structures, different ideology and way of life, with a unique style of thinking of their leaders. But they were united by the main goal - the destruction of Nazi fascism once and for all. The author analyzes the events and circumstances surrounding the implementation of contracts under lendlease, as well as comments on the book by N.I. Ryzhkov «The Great Patriotic War: the battle of the economies and the weapons of Victory»