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Background: Several randomized controlled trials assessed the outcomes of patients treated with neoadjuvant hormonal therapy (NHT) before radical prostatectomy (RP). The majority of them included mainly low and intermediate risk prostate cancer (PCa) without specifically assessing PCa-related death (PCRD). Thus, there is a lack of knowledge regarding a possible effect of NHT on PCRD in the high-risk PCa population. We aimed to analyze the effect of NHT on PCRD in a multicenter high-risk PCa population treated with RP, using a propensity-score adjustment. Methods: This is a retrospective multi-institutional study including patients with high-risk PCa defined as: clinical stage T3–4, PSA >20 ng ml −1 or biopsy Gleason score 8–10. We compared PCRD between RP and NHT+RP using competing risks analysis. Correction for group differences was performed by propensity-score adjustment. Results: After application of the inclusion/exclusion criteria, 1573 patients remained for analysis; 1170 patients received RP and 403 NHT+RP. Median follow-up was 56 months (interquartile range 29–88). Eighty-six patients died of PCa and 106 of other causes. NHT decreased the risk of PCRD (hazard ratio (HR) 0.5; 95% confidence interval (CI) 0.32–0.80; P =0.0014). An interaction effect between NHT and radiotherapy (RT) was observed (HR 0.3; 95% CI 0.21–0.43; P <0.0008). More specifically, of patients who received adjuvant RT, those who underwent NHT+RP had decreased PCRD rates (2.3% at 5 year) compared to RP (7.5% at 5 year). The retrospective design and lack of specific information about NHT are possible limitations. Conclusions: In this propensity-score adjusted analysis from a large high-risk PCa population, NHT before surgery significantly decreased PCRD. This effect appeared to be mainly driven by the early addition of RT post-surgery. The specific sequence of NHT+RP and adjuvant RT merits further study in the high-risk PCa population.

Taste receptors on enteroendocrine cells sense nutrients and transmit signals that control gut hormone release. This study aimed to investigate the amino acid (AA) sensing mechanisms of the ghrelin cell in a gastric ghrelinoma cell line, tissue segments and mice. Peptone and specific classes of amino acids stimulate ghrelin secretion in the ghrelinoma cell line. Sensing of L-Phe occurs via the CaSR, monosodium glutamate via the TAS1R1-TAS1R3 while L-Ala and peptone act via 2 different amino acid taste receptors: CaSR & TAS1R1-TAS1R3 and CaSR & GPRC6A, respectively. The stimulatory effect of peptone on ghrelin release was mimicked ex vivo in gastric but not in jejunal tissue segments, where peptone inhibited ghrelin release. The latter effect could not be blocked by receptor antagonists for CCK, GLP-1 or somatostatin. In vivo , plasma ghrelin levels were reduced both upon intragastric (peptone or L-Phe) or intravenous (L-Phe) administration, indicating that AA- sensing is not polarized and is due to inhibition of ghrelin release from the stomach or duodenum respectively. In conclusion, functional AA taste receptors regulate AA-induced ghrelin release in vitro . The effects differ between stomach and jejunum but these local nutrient sensing mechanisms are overruled in vivo by indirect mechanisms inhibiting ghrelin release.

PIONEER is a European network of excellence for big data in prostate cancer consisting of 37 private and public stakeholders from 9 countries across Europe. Many progresses have been done in prostate cancer management, but unanswered questions in the field still exist, and big data could help to answer these questions. The PIONEER consortium conducted a two-round modified Delphi survey aiming at building consensus between two stakeholder groups — health-care professionals and patients with prostate cancer — about the most important questions in the field of prostate cancer to be answered using big data. Respondents were asked to consider what would be the effect of answering the proposed questions on improving diagnosis and treatment outcomes for patients with prostate cancer and to score these questions on a scale of 1 (not important) to 9 (critically important). The mean percentage of participants who scored each of the proposed questions as critically important was calculated across the two stakeholder groups and used to rank the questions and identify the highest scoring questions in the critically important category. The identification of questions in prostate cancer that are important to various stakeholders will help the PIONEER consortium to provide answers to these questions to improve the clinical care of patients with prostate cancer.In this Consensus Statement, the authors present results from an international multi-stakeholder consensus conducted by the PIONEER consortium to identify the most important questions in the field of prostate cancer that could be addressed using big data.

Summarises and discusses findings of roundtable discussions on the opinions of the citizens of two Belgian (Flemish) cities about the policing and security policy in their cities. Citizens question the organisational and cultural readiness of their local police forces for the full‐scale development of community policing. In practice, problem‐oriented policing tends to dominate, whereby it is the police who define the problems to be tackled. Despite decentralisation of policy and participation procedures, the public complains about the lack of citizen democracy in government. Problems of transparency and participation are related to the plethora of projects and initiatives which have been launched by different authorities at different policy levels. Finally, the consensual vision of community policing is discussed since geographically decentralised policing and the encouragement of community involvement will logically confront the police with ever diverging socio‐economic and cultural interests in the neighbourhood.

SHAP explanations are a popular feature-attribution mechanism for explainable AI. They use game-theoretic notions to measure the influence of individual features on the prediction of a machine learning model. Despite a lot of recent interest from both academia and industry, it is not known whether SHAP explanations of common machine learning models can be computed efficiently. In this paper, we establish the complexity of computing the SHAP explanation in three important settings. First, we consider fully-factorized data distributions, and show that the complexity of computing the SHAP explanation is the same as the complexity of computing the expected value of the model. This fully-factorized setting is often used to simplify the SHAP computation, yet our results show that the computation can be intractable for commonly used models such as logistic regression. Going beyond fully-factorized distributions, we show that computing SHAP explanations is already intractable for a very simple setting: computing SHAP explanations of trivial classifiers over naive Bayes distributions. Finally, we show that even computing SHAP over the empirical distribution is #P-hard.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is not always confined to the respiratory system, as it impacts people on a broad clinical spectrum from asymptomatic to severe systemic manifestations resulting in death. Further, accumulation of intra-host single nucleotide variants during prolonged SARS-CoV-2 infection may lead to emergence of variants of concern (VOCs). Still, information on virus infectivity and intra-host evolution across organs is sparse. We report a detailed virological analysis of thirteen postmortem coronavirus disease 2019 (COVID-19) cases that provides proof of viremia and presence of replication-competent SARS-CoV-2 in extrapulmonary organs of immunocompromised patients, including heart, kidney, liver, and spleen (NCT04366882). In parallel, we identify organ-specific SARS-CoV-2 genome diversity and mutations of concern N501Y, T1027I, and Y453F, while the patient had died long before reported emergence of VOCs. These mutations appear in multiple organs and replicate in Vero E6 cells, highlighting their infectivity. Finally, we show two stages of fatal disease evolution based on disease duration and viral loads in lungs and plasma. Our results provide insights about the pathogenesis and intra-host evolution of SARS-CoV-2 and show that COVID-19 treatment and hygiene measures need to be tailored to specific needs of immunocompromised patients, even when respiratory symptoms cease. Here the authors provide a detailed virological analysis of thirteen postmortem COVID-19 cases, including presence of replication-competent SARS-CoV-2 in extrapulmonary organs and tissue-specific patterns of SARS-CoV-2 genome diversity of an immunocompromised patient.

Increasing natural resistance and resilience in plants is key for ensuring food security within a changing climate. Breeders improve these traits by crossing cultivars with their wild relatives and introgressing specific alleles through meiotic recombination. However, some genomic regions are devoid of recombination especially in crosses between divergent genomes, limiting the combinations of desirable alleles. Here, we used pooled-pollen sequencing to build a map of recombinant and non-recombinant regions between tomato and five wild relatives commonly used for introgressive tomato breeding. We detected hybrid-specific recombination coldspots that underscore the role of structural variations in modifying recombination patterns and maintaining genetic linkage in interspecific crosses. Crossover regions and coldspots show strong association with specific TE superfamilies exhibiting differentially accessible chromatin between somatic and meiotic cells. About two-thirds of the genome are conserved coldspots, located mostly in the pericentromeres and enriched with retrotransposons. The coldspots also harbor genes associated with agronomic traits and stress resistance, revealing undesired consequences of linkage drag and possible barriers to breeding. We presented examples of linkage drag that can potentially be resolved by pairing tomato with other wild species. Overall, this catalogue will help breeders better understand crossover localization and make informed decisions on generating new tomato varieties.

Interpreting high-energy, astrophysical phenomena, such as supernova explosions or neutron-star collisions, requires a robust understanding of matter at supranuclear densities. However, our knowledge about dense matter explored in the cores of neutron stars remains limited. Fortunately, dense matter is not probed only in astrophysical observations, but also in terrestrial heavy-ion collision experiments. Here we use Bayesian inference to combine data from astrophysical multi-messenger observations of neutron stars 1 – 9 and from heavy-ion collisions of gold nuclei at relativistic energies 10 , 11 with microscopic nuclear theory calculations 12 – 17 to improve our understanding of dense matter. We find that the inclusion of heavy-ion collision data indicates an increase in the pressure in dense matter relative to previous analyses, shifting neutron-star radii towards larger values, consistent with recent observations by the Neutron Star Interior Composition Explorer mission 5 – 8 , 18 . Our findings show that constraints from heavy-ion collision experiments show a remarkable consistency with multi-messenger observations and provide complementary information on nuclear matter at intermediate densities. This work combines nuclear theory, nuclear experiment and astrophysical observations, and shows how joint analyses can shed light on the properties of neutron-rich supranuclear matter over the density range probed in neutron stars. The physics of dense matter extracted from neutron star collision data is demonstrated to be consistent with information obtained from heavy-ion collisions, and analyses incorporating both data sources as well as information from nuclear theory provide new constraints for neutron star matter.

After the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting in late summer 2020 that was deadlier and more difficult to contain 1 . Relaxed intervention measures and summer travel have been implicated as drivers of the second wave 2 . Here we build a phylogeographical model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the resurgence of COVID-19 in Europe. We inform this model using genomic, mobility and epidemiological data from 10 European countries and estimate that in many countries more than half of the lineages circulating in late summer resulted from new introductions since 15 June 2020. The success in onward transmission of newly introduced lineages was negatively associated with the local incidence of COVID-19 during this period. The pervasive spread of variants in summer 2020 highlights the threat of viral dissemination when restrictions are lifted, and this needs to be carefully considered in strategies to control the current spread of variants that are more transmissible and/or evade immunity. Our findings indicate that more effective and coordinated measures are required to contain the spread through cross-border travel even as vaccination is reducing disease burden. In many European countries, more than half of the SARS-CoV-2 lineages circulating in late summer 2020 resulted from new introductions, highlighting the threat of viral dissemination when restrictions are lifted.