International Consensus Conference for Advanced Breast Cancer, Lisbon 2019: ABC5 Consensus – Assessment by a German Group of Experts

The 5th International Consensus Conference for Advanced Breast Cancer (ABC5) took place on November 14–16, 2019, in Lisbon, Portugal. Its aim is to standardize the treatment of advanced breast cancer based on the available evidence and to ensure that all breast cancer patients worldwide receive adequate treatment and access to new therapies. This year, the conference focused on developments and study results in the treatment of patients with hormone receptor-positive/HER2-negative breast cancer as well as precision medicine. As in previous years, patient advocates from around the world were integrated into the ABC conference and had seats on the ABC consensus panel. In the present paper, a working group of German breast cancer experts comments on the results of the on-site ABC5 consensus votes by ABC panelists regarding their applicability for routine treatment in Germany. These comments take the recommendations of the Breast Committee of the Gynecological Oncology Working Group (Arbeitsgemeinschaft Gynäkologische Onkologie; AGO) into account. The report and assessment presented here pertain to the preliminary results of the ABC5 consensus. The final version of the statements will be published in Annals of Oncology and The Breast

The genomic and transcriptional landscape of primary central nervous system lymphoma

Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Molecular drivers of PCNSL have not been fully elucidated. Here, we profile and compare the whole-genome and transcriptome landscape of 51 CNS lymphomas (CNSL) to 39 follicular lymphoma and 36 DLBCL cases outside the CNS. We find recurrent mutations in JAK-STAT, NFkB, and B-cell receptor signaling pathways, including hallmark mutations in MYD88 L265P (67%) and CD79B (63%), and CDKN2A deletions (83%). PCNSLs exhibit significantly more focal deletions of HLA-D (6p21) locus as a potential mechanism of immune evasion. Mutational signatures correlating with DNA replication and mitosis are significantly enriched in PCNSL. TERT gene expression is significantly higher in PCNSL compared to activated B-cell (ABC)-DLBCL. Transcriptome analysis clearly distinguishes PCNSL and systemic DLBCL into distinct molecular subtypes. Epstein-Barr virus (EBV)+ CNSL cases lack recurrent mutational hotspots apart from IG and HLA-DRB loci. We show that PCNSL can be clearly distinguished from DLBCL, having distinct expression profiles, IG expression and translocation patterns, as well as specific combinations of genetic alterations

Wellbeing and resilience:Mechanisms of transmission of health and risk in parents with complex mental health problems and their offspring—The WARM Study

The WARM study is a longitudinal cohort study following infants of mothers with schizophrenia, bipolar disorder, depression and control from pregnancy to infant 1 year of age. Background: Children of parents diagnosed with complex mental health problems including schizophrenia, bipolar disorder and depression, are at increased risk of developing mental health problems compared to the general population. Little is known regarding the early developmental trajectories of infants who are at ultra-high risk and in particular the balance of risk and protective factors expressed in the quality of early caregiver-interaction. Methods/Design: We are establishing a cohort of pregnant women with a lifetime diagnosis of schizophrenia, bipolar disorder, major depressive disorder and a non-psychiatric control group. Factors in the parents, the infant and the social environment will be evaluated at 1, 4, 16 and 52 weeks in terms of evolution of very early indicators of developmental risk and resilience focusing on three possible environmental transmission mechanisms: stress, maternal caregiver representation, and caregiver-infant interaction. Discussion: The study will provide data on very early risk developmental status and associated psychosocial risk factors, which will be important for developing targeted preventive interventions for infants of parents with severe mental disorder

Middle East - North Africa and the millennium development goals : implications for German development cooperation

          Closed-loop controlled combustion is a promising technique to improve the overall performance of internal combustion engines and Diesel engines in particular. In order for this technique to be implemented some form of feedback from the combustion process is required. The feedback signal is processed and from it combustionrelated parameters are computed. These parameters are then fed to a control process which drives a series of outputs (e.g. injection timing in Diesel engines) to control their values. This paper’s focus lies on the processing and computation that is needed on the feedback signal before this is ready to be fed to the control process as well as on the electronics necessary to support it. A number of feedback alternatives are briefly discussed and for one of them, the in-cylinder pressure sensor, the CA50 (crank angle in which the integrated heat release curve reaches its 50% value) and the IMEP (Indicated Mean Effective Pressure) are identified as two potential control variables. The hardware architecture of a system capable of calculating both of them on-line is proposed and necessary feasibility size and speed considerations are made by implementing critical blocks in VHDL targeting a flash-based Actel ProASIC3 automotive-grade FPGA

Data from: Odor diversity decreases with inbreeding in the ant Hypoponera opacior

Reduction in heterozygosity can lead to inbreeding depression. This loss of genetic variability especially affects diverse loci, such as immune genes or those encoding recognition cues. In social insects, nestmates are recognized by their odor, i.e. their cuticular hydrocarbon profile. Genes underlying hydrocarbon production are thought to be under balancing selection. If so, inbreeding should result in a loss of chemical diversity. We show here that cuticular hydrocarbon diversity decreases with inbreeding. Studying an ant with a facultative inbreeding lifestyle we found inbred workers to exhibit both a lower number of hydrocarbons and less diverse, that is, less evenly-proportioned profiles. The association with inbreeding was strong for methyl-branched alkanes, which play a major role in nestmate recognition, and for n-alkanes, whereas unsaturated compounds were unaffected. Shifts in allocation strategies with inbreeding in our focal species indicate that these ants can detect their inbreeding level and use this information to adjust their reproductive strategy. Our study is the first to demonstrate that odor profiles can encode information on inbreeding, with broad implications not only for social insects, but for sexual selection and mate choice in general. Odor profiles may constitute an honest signal of inbreeding, a fitness-relevant trait in many species

Cuticular hydrocarbon and genetic data

The individual data show diversity indices, percentages of each cuticular hydrocarbon, and HL data of individual Hypoponera opacior workers as used in this study. The colony-level data show average HL per colony (colony HL), relatedness between nestmates, and Bray-Curtis distances for the different hydrocarbon classes

Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data

Detection of atmospheric features in gridded datasets from numerical simulation models is typically done by means of rule-based algorithms. Recently, also the feasibility of learning feature detection tasks using supervised learning with convolutional neural networks (CNNs) has been demonstrated. This approach corresponds to semantic segmentation tasks widely investigated in computer vision. However, while in recent studies the performance of CNNs was shown to be comparable to human experts, CNNs are largely treated as a “black box”, and it remains unclear whether they learn the features for the correct reasons. Here we build on the recently published “ClimateNet” dataset that contains features of tropical cyclones and atmospheric rivers as detected by human experts. We adapt the explainable artificial intelligence technique “Layer-wise Relevance Propagation” (LRP) to the feature detection task and investigate which input information CNNs with the Context-Guided Network (CG-Net) and U-Net architectures use for feature detection. We find that both CNNs indeed consider plausible patterns in the input fields of atmospheric variables, which helps to build trust in the approach. We also demonstrate application of the approach for finding the most relevant input variables and evaluating detection robustness when changing the input domain. However, LRP in its current form cannot explain shape information used by the CNNs, and care needs to be taken regarding the normalization of input values, as LRP cannot explain the contribution of bias neurons, accounting for inputs close to zero. These shortcomings need to be addressed by future work to obtain a more complete explanation of CNNs for geoscientific feature detection