Energy Metabolites as Biomarkers in Ischemic and Dilated Cardiomyopathy

With more than 25 million people affected, heart failure (HF) is a global threat. As energy production pathways are known to play a pivotal role in HF, we sought here to identify key metabolic changes in ischemic- and non-ischemic HF by using a multi-OMICS approach. Serum metabolites and mRNAseq and epigenetic DNA methylation profiles were analyzed from blood and left ventricular heart biopsy specimens of the same individuals. In total we collected serum from n = 82 patients with Dilated Cardiomyopathy (DCM) and n = 51 controls in the screening stage. We identified several metabolites involved in glycolysis and citric acid cycle to be elevated up to 5.7-fold in DCM (p = 1.7 × 10⁻⁶). Interestingly, cardiac mRNA and epigenetic changes of genes encoding rate-limiting enzymes of these pathways could also be found and validated in our second stage of metabolite assessment in n = 52 DCM, n = 39 ischemic HF and n = 57 controls. In conclusion, we identified a new set of metabolomic biomarkers for HF. We were able to identify underlying biological cascades that potentially represent suitable intervention targets.     

Structure-Activity Relationships of Benzamides and Isoindolines Designed as SARS-CoV Protease Inhibitors Effective against SARS-CoV-2

Inhibition of coronavirus (CoV)-encoded papain-like cysteine proteases (PL^pro) represents an attractive strategy to treat infections by these important human pathogens. Herein we report on structure-activity relationships (SAR) of the noncovalent active-site directed inhibitor (R)-5-amino-2-methyl-N-(1-(naphthalen-1-yl)ethyl) benzamide ( 2 b ), which is known to bind into the S3 and S4 pockets of the SARS-CoV PL^pro. Moreover, we report the discovery of isoindolines as a new class of potent PL^pro inhibitors. The studies also provide a deeper understanding of the binding modes of this inhibitor class. Importantly, the inhibitors were also confirmed to inhibit SARS-CoV-2 replication in cell culture suggesting that, due to the high structural similarities of the target proteases, inhibitors identified against SARS-CoV PL^pro are valuable starting points for the development of new pan-coronaviral inhibitors.     

Polyunsaturated monoglycerides and a pregnadiene in defensive glands of the water beetle Agabus affinis

In addition to the C₂₁ steroid 15α-hydroxypregna-4,6-dien-3,20-dione, four 1- or 2-acylated polyunsaturated monoglycerides, 1- or 2-(cis-5,8,11,14-eicosatetraenoyl)-glycerol and 1- or 2-(cis-5,8,11,14,17-eicosapentaenoyl)glycerol were identified as constituents of the prothoracic defensive gland secretion of the dytiscid beetle Agabus affinis by gas chromatography-mass spectrometry of trimethylsilylated gland extracts. In a feeding assay with minnows, synthetic samples of the two 2-acylated monoglycerides showed only a weak activity as a feeding deterrent. For that reason, other possible functions of the monoglycerides are discussed, such as roles as emulsifiers of cannabimimetics.     

Empirische Methodologien und Methoden der angewandten und der empirischen Ethik

Definition of the problem This paper deals with the question of adequate methodologies and methods to conduct empirical research and integrate empirical knowledge in the field of biomedical ethics. Arguments It starts with reflections about the main subject matter of ethics, the human being as a bio-psycho-social creature and its morals, and argues that social sciences are the adequate basic empirical science in the field of ethics in general. The next paragraph considers theory of science perspectives of the relationship of ethics, empirical findings, methodologies and theories and examples of the integration of empirical findings into ethical deliberations. Reasons for the tension between social science and ethics are scrutinized which are different in the yet to be defined field of empirical ethics compared to traditional applied ethics. Conclusion How a bio-psycho-social ethics that incorporates these criteria into its own body of work could look like is finally depicted.     

Mass transfer in 3D-printed electrolyzers: The importance of inlet effects

This article investigates the effect of inlet shape, entrance length, and turbulence promoters on mass transfer by using 3D-printed electrolyzers. Our results show that the inlet design can promote turbulence and lead to an earlier transition to turbulent flow. The Reynolds number at which the transition occurs can be predicted by the ratio of the cross-sectional area of the inlet to the cross-sectional area of the electrolyzer channel. A longer entrance length results in more laminar behavior and a later transition to turbulent flow. With an entrance length of 550 mm, the inlet design did no longer affect the mass transfer performance significantly. The addition of gyroid type turbulence promoters resulted in a factor of 2 to 4 increase in mass transfer depending on inlet design, entrance length, and the type of promoter. From one configuration to another, there was a minimal variation in pressure drop (<1600 Pa).     

Business Intelligence Acceptance: The Prominence of Organizational Factors

The specifics of business intelligence systems compared to operational IS motivate the necessity to research the business intelligence systems acceptance determinants separately. The authors followed an exploratory approach in order to conceptualize a business intelligence acceptance model. Their findings show that in the Business Intelligence Systems context, there is a significant emphasis on organizational factors, such as result demonstrability, social influence, and facilitating conditions with sufficient resources that help build an adequate information culture all substantially influencing the effective acceptance of business intelligence systems.     

Dynamic admission control for two customer classes with stochastic demands and strict due dates

We study a dynamic capacity allocation problem with admission control decisions of a company that caters for two demand classes with random arrivals, capacity requirements and strict due dates. We formulate the problem as a Markov decision process (MDP) in order to find the optimal admission control policy that maximises the expected profit of the company. Such a formulation suffers a state-space explosion. Moreover, it involves an additional dimension arising from the multiple possible order sizes that customers can request which further increases the complexity of the problem. To reduce the cardinality of possible policies, and, thus, the computational requirements, we propose a threshold-based policy. We formulate an MDP to generate such a policy. To deal with the curse of dimensionality, we develop threshold-based approximate algorithms based on the state-reduction heuristics with aggregation proposed previously. Our results reveal that for the majority of instances considered the optimal policy has a threshold structure. We then demonstrate the superiority of the proposed threshold-based approximate algorithms over two benchmark policies in terms of the generated profits and the robustness of the solutions to changes in operational conditions. Finally, we show that our proposed policies are also robust to changes in actual demand from its estimation.     

Knowledge and information awareness for initiating transactive memory system processes of computer-supported collaborating ad hoc groups

This paper presents an innovative approach for initiating processes of a transactive memory system in newly formed groups of experts collaborating computer-supported in a complex problem-solving task. Our empirical study compared 15 experimental and 15 control groups, each consisting of triads. In the experimental condition, the triads were provided with a tool for fostering knowledge and information awareness, that is, being informed about the knowledge and the underlying information of the collaboration partners in form of digital concept maps. In the control condition, the groups had no access to this tool. Results confirmed the potential of the tool to initiate processes of a transactive memory system: shared agreement of the knowledge of the other group members’ knowledge proved to influence group performance positively. In addition, previous findings of the tool’s potential to establish knowledge and information awareness and to augment group performance could be replicated. However, the postulated mediating effect of processes of a transactive memory system concerning the impact of knowledge and information awareness on group performance did not reach statistical significance. Aspects for future studies and implications of these findings regarding their practical implementation, for example, in teams of organizations, are discussed.     

Real-time hybrid predictive modeling of the Teniente Converter

The Teniente Converter (TC) is an important technology for smelting and converting copper concentrates. Due to the complexities of its autogenous operation, which combines continuous input flows with intermittent product extraction, efforts to model and control this dynamic have only been partially successful. This work presents a model of TC operation that combines phenomenological equations with empirical expressions and includes both continuous and discrete variables. Six different operating modes are described. To improve predictive capabilities, an adaptive compensation technique takes account of changes in concentrate mix characteristics. The model predicts five important variables: white metal copper concentration, slag magnetite concentration, white metal temperature, and white metal and slag levels. It is calibrated and validated with real operating data from Codelco’s Potrerillos smelter. Once the model is implemented, it delivers online estimates of process variables that are measured intermittently.Based on the model, a hybrid predictive controller is developed to control the quantities and characteristics of the TC products as a function of the number of dependent units such as Peirce-Smith converters and slag-cleaning furnaces. The controller’s performance is evaluated via simulation under a variety of operating conditions. It is currently employed in an open-loop configuration to provide suggestions on set points for the local flow controllers and on the product extraction sequences.