3D-druckbarer Normalbeton mit grober Gesteinskörnung

Angetrieben von vielversprechenden Effizienzsteigerungen wird der Beton-3D-Druck stetig weiterentwickelt. Um die gewonnenen Erkenntnisse niederschwellig in die Baupraxis zu überführen, empfehlen sich druckbare Betone im Rahmen des geltenden Regelwerks. Dabei stellt die Limitierung des Mehlkorngehalts eine Herausforderung dar. Um diese zu meistern, wird eine verallgemeinerbare, numerisch unterstützte Anwendung der Korngrößenverteilung nach Andreasen und Andersen als Basis für den Betonentwurf vorgeschlagen. Experimentelle Untersuchungen haben eine gute Verbaubarkeit und hinreichende Extrudierbarkeit eines Betons mit einem 16 mm Größtkorn und einem Mehlkorngehalt von 500 kg/m³ demonstriert

Calculations of current densities for neutral and doubly charged persubstituted benzenes using effective core potentials

Magnetically induced current density susceptibilities and ring-current strengths have been calculated for neutral and doubly charged persubstituted benzenes C6X6 and C6X62+ with X = F, Cl, Br, I, At, SeH, SeMe, TeH, TeMe, and SbH2. The current densities have been calculated using the gauge-including magnetically induced current (GIMIC) method, which has been interfaced to the Gaussian electronic structure code rendering current density calculations using effective core potentials (ECP) feasible. Relativistic effects on the ring-current strengths have been assessed by employing ECP calculations of the current densities. Comparison of the ring-current strengths obtained in calculations on C6At6 and C6At62+ using relativistic and non-relativistic ECPs show that scalar relativistic effects have only a small influence on the ring-current strengths. Comparisons of the ring-current strengths and ring-current profiles show that the C6I62+, C6At62+, C-6(SeH)(6)(2+), C-6(SeMe)(6)(2+), C-6(TeH)(6)(2+), C-6(TeMe)(6)(2+), and C-6(SbH2)(6)(2+) dications are doubly aromatic sustaining spatially separated ring currents in the carbon ring and in the exterior of the molecule. The C6I6+ radical cation is also found to be doubly aromatic with a weaker ring current than obtained for the dication.Peer reviewe

Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations

We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, Propulate omits the synchronization after each generation as done in conventional genetic algorithms. Instead, it steers the search with the complete population present at time of breeding new individuals. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration and is easy to extend with custom functionality. We compare Propulate to the established optimization tool Optuna. We find that Propulate is up to three orders of magnitude faster without sacrificing solution accuracy, demonstrating the efficiency and efficacy of our lazy synchronization approach. Code and documentation are available at https://github.com/Helmholtz-AI-Energy/propulateComment: 18 pages, 5 figures submitted to ISC High Performance 202

Learning tree structures from leaves for particle decay reconstruction

In this work, we present a neural approach to reconstructing rooted tree graphs describing hierarchical interactions, using a novel representation we term the lowest common ancestor generations (LCAG) matrix. This compact formulation is equivalent to the adjacency matrix, but enables learning a tree\u27s structure from its leaves alone without the prior assumptions required if using the adjacency matrix directly. Employing the LCAG therefore enables the first end-to-end trainable solution which learns the hierarchical structure of varying tree sizes directly, using only the terminal tree leaves to do so. In the case of high-energy particle physics, a particle decay forms a hierarchical tree structure of which only the final products can be observed experimentally, and the large combinatorial space of possible trees makes an analytic solution intractable. We demonstrate the use of the LCAG as a target in the task of predicting simulated particle physics decay structures using both a Transformer encoder and a neural relational inference encoder graph neural network. With this approach, we are able to correctly predict the LCAG purely from leaf features for a maximum tree-depth of 8 in 92.5% of cases for trees up to 6 leaves (including) and 59.7% for trees up to 10 in our simulated dataset

Formale wissenschaftliche Kommunikation

Taubert NC. Formale wissenschaftliche Kommunikation. In: Bonfadelli H, Fähnrich B, Lüthje C, Milde J, Rhombert M, Schäfer M, eds. Forschungsfeld Wissenschaftskommunikation. Wiesbaden: VS Verlag; 2017: 1

Expression of GP88 (Progranulin) Protein Is an Independent Prognostic Factor in Prostate Cancer Patients

Prostate cancer, the second most common cancer, is still a major cause of morbidity and mortality among men worldwide. The expression of the survival and proliferation factor progranulin (GP88) has not yet been comprehensively studied in PCa tumors. The aim of this study was to characterize GP88 protein expression in PCa by immunohistochemistry and to correlate the findings to the clinico-pathological data and prognosis. Immunohistochemical staining for GP88 was performed by TMA with samples from 442 PCa patients using an immunoreactive score (IRS). Altogether, 233 cases (52.7%) with negative GP88 staining (IRS < 2) and 209 cases (47.3%) with positive GP88 staining (IRS ≥ 2) were analyzed. A significant positive correlation was found for the GP88 IRS with the PSA value at prostatectomy and the cytoplasmic cytokeratin 20 IRS, whereas it was negatively associated with follow-up times. The association of GP88 staining with prognosis was further studied by survival analyses (Kaplan–Meier, univariate and multivariate Cox’s regression analysis). Increased GP88 protein expression appeared as an independent prognostic factor for overall, disease-specific and relapse-free survival in all PCa patients. Interestingly, in the subgroup of younger PCa patients (≤65 years), GP88 positivity was associated with a 3.8-fold (p = 0.004), a 6.0-fold (p = 0.008) and a 3.7-fold (p = 0.003) increased risk for death, disease-specific death and occurrence of a relapse, respectively. In the PCa subgroup with negative CK20 staining, GP88 positivity was associated with a 1.8-fold (p = 0.018) and a 2.8-fold increased risk for death and disease-specific death (p = 0.028). Altogether, GP88 protein positivity appears to be an independent prognostic factor for PCa patients

CCL2 Expression in Tumor Cells and Tumor-Infiltrating Immune Cells Shows Divergent Prognostic Potential for Bladder Cancer Patients Depending on Lymph Node Stage

Bladder cancer (BCa) is the ninth most commonly diagnosed cancer worldwide. Although there are several well-established molecular and immunological classifications, markers for tumor cells and immune cells that are associated with prognosis are still needed. The chemokine CC motif ligand 2 (CCL2) could be such a marker. We analyzed the expression of CCL2 by immunohistochemistry (IHC) in 168 muscle invasive BCa samples using a tissue microarray. Application of a single cut-off for the staining status of tumor cells (TCs; positive vs. negative) and immune cells (ICs; ≤6% of ICs vs. >6% of ICs) revealed 57 cases (33.9%) and 70 cases (41.7%) with CCL2-positive TCs or ICs, respectively. IHC results were correlated with clinicopathological and survival data. Positive CCL2 staining in TCs was associated with shorter overall survival (OS), disease-specific survival (DSS), and relapse-free survival (RFS) (p = 0.004, p = 0.036, and p = 0.047; log rank test) and appeared to be an independent prognostic factor for OS (RR = 1.70; p = 0.007; multivariate Cox’s regression analysis). In contrast, positive CCL2 staining in the ICs was associated with longer OS, DSS, and RFS (p = 0.032, p = 0.001, and p = 0.001; log rank test) and appeared to be an independent prognostic factor for DSS (RR = 1.77; p = 0.031; multivariate Cox’s regression analysis). Most interestingly, after separating the patients according to their lymph node status (N0 vs. N1+2), CCL2 staining in the ICs was differentially associated with prognosis. In the N0 group, CCL2 positivity in the ICs was a positive independent prognostic factor for OS (RR = 1.99; p = 0.014), DSS (RR = 3.17; p = 0.002), and RFS (RR = 3.10; p = 0.002), whereas in the N1+2 group, CCL2 positivity was a negative independent factor for OS (RR = 3.44; p = 0.019)) and RFS (RR = 4.47; p = 0.010; all multivariate Cox’s regression analyses). In summary, CCL2 positivity in TCs is a negative prognostic factor for OS, and CCL2 can mark ICs that are differentially associated with prognosis depending on the nodal stage of BCa patients. Therefore, CCL2 staining of TCs and ICs is suggested as a prognostic biomarker for BCa patients

Brain Exposure to Piperacillin in Acute Hemorrhagic Stroke Patients Assessed by Cerebral Microdialysis and Population Pharmacokinetics

Background The broad antibacterial spectrum of piperacillin/tazobactam makes the combination suitable for the treatment of nosocomial bacterial central nervous system (CNS) infections. As limited data are available regarding piperacillin CNS exposure in patients without or with low-grade inflammation, a clinical study was conducted (1) to quantify CNS exposure of piperacillin by cerebral microdialysis and (2) to evaluate different dosing regimens in order to improve probability of target attainment (PTA) in brain. Methods Ten acute hemorrhagic stroke patients (subarachnoid hemorrhage, n = 6; intracerebral hemorrhage, n = 4) undergoing multimodality neuromonitoring received 4 g piperacillin/0.5 g tazobactam every 8 h by 30-min infusions for the management of healthcare-associated pneumonia. Cerebral microdialysis was performed as part of the clinical neuromonitoring routine, and brain interstitial fluid samples were retrospectively analyzed for piperacillin concentrations after the first and after multiple doses for at least 5 days and quantified by high-performance liquid chromatography. Population pharmacokinetic modeling and Monte Carlo simulations with various doses and types of infusions were performed to predict exposure. A T->MIC of 50% was selected as pharmacokinetic/pharmacodynamic target parameter. Results Median peak concentrations of unbound piperacillin in brain interstitial space fluid were 1.16 (range 0.08-3.59) and 2.78 (range 0.47-7.53) mg/L after the first dose and multiple doses, respectively. A one-compartment model with a transit compartment and a lag time (for the first dose) between systemic and brain exposure was appropriate to describe the brain concentrations. Bootstrap median estimates of the parameters were: transfer rate from plasma to brain (0.32 h(-1)), transfer rate from brain to plasma (7.31 h(-1)), and lag time [2.70 h (coefficient of variation 19.7%)]. The simulations suggested that PTA would exceed 90% for minimum inhibitory concentrations (MICs) up to 0.5 mg/L and 1 mg/L at a dose of 12-16 and 24 g/day, respectively, regardless of type of infusion. For higher MICs, PTA dropped significantly. Conclusion Limited CNS exposure of piperacillin might be an obstacle in treating patients without general meningeal inflammation except for infections with highly susceptible pathogens. Brain exposure of piperacillin did not improve significantly with a prolongation of infusions

RNA contact prediction by data efficient deep learning

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps") as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction

In Vitro Profiling of Commonly Used Post-transplant Immunosuppressants Reveals Distinct Impact on Antiviral T-cell Immunity Towards CMV

Infectious complications, including widespread human cytomegalovirus (CMV) disease, frequently occur after hematopoietic stem cell and solid organ transplantation due to immunosuppressive treatment causing impairment of T-cell immunity. Therefore, in-depth analysis of the impact of immunosuppressants on antiviral T cells is needed. We analyzed the impact of mTOR inhibitors sirolimus (SIR/S) and everolimus (EVR/E), calcineurin inhibitor tacrolimus (TAC/T), purine synthesis inhibitor mycophenolic acid (MPA/M), glucocorticoid prednisolone (PRE/P) and common double (T+S/E/M/P) and triple (T+S/E/M+P) combinations on antiviral T-cell functionality. T-cell activation and effector molecule production upon antigenic stimulation was impaired in presence of T+P and triple combinations. SIR, EVR and MPA exclusively inhibited T-cell proliferation, TAC inhibited activation and cytokine production and PRE inhibited various aspects of T-cell functionality including cytotoxicity. This was reflected in an in vitro infection model, where elimination of CMV-infected human fibroblasts by CMV-specific T cells was reduced in presence of PRE and all triple combinations. CMV-specific memory T cells were inhibited by TAC and PRE, which was also reflected with double (T+P) and triple combinations. EBV- and SARS-CoV-2-specific T cells were similarly affected. These results highlight the need to optimize immune monitoring to identify patients who may benefit from individually tailored immunosuppression