Managed Query Processing within the SAP HANA Database Platform

The SAP HANA database extends the scope of traditional database engines as it supports data models beyond regular tables, e.g. text, graphs or hierarchies. Moreover, SAP HANA also provides developers with a more fine-grained control to define their database application logic, e.g. exposing specific operators which are difficult to express in SQL. Finally, the SAP HANA database implements efficient communication to dedicated client applications using more effective communication mechanisms than available with standard interfaces like JDBC or ODBC. These features of the HANA database are complemented by the extended scripting engine–an application server for server-side JavaScript applications–that is tightly integrated into the query processing and application lifecycle management. As a result, the HANA platform offers more concise models and code for working with the HANA platform and provides superior runtime performance. This paper describes how these specific capabilities of the HANA platform can be consumed and gives a holistic overview of the HANA platform starting from query modeling, to the deployment, and efficient execution. As a distinctive feature, the HANA platform integrates most steps of the application lifecycle, and thus makes sure that all relevant artifacts stay consistent whenever they are modified. The HANA platform also covers transport facilities to deploy and undeploy applications in a complex system landscape

Smart Grid Economics: Policy Guidance through Competitive Simulation

Sustainable energy systems of the future will need more than efficient, clean, low-cost, renewable energy sources; they will also need efficient price signals that motivate sustainable energy consumption as well as a better real-time alignment of energy demand and supply

A Multi-Agent Energy Trading Competition

The energy sector will undergo fundamental changes over the next ten years. Prices for fossil energy resources are continuously increasing, there is an urgent need to reduce CO2 emissions, and the United States and European Union are strongly motivated to become more independent from foreign energy imports. These factors will lead to installation of large numbers of distributed renewable energy generators, which are often intermittent in nature. This trend conflicts with the current power grid control infrastructure and strategies, where a few centralized control centers manage a limited number of large power plants such that their output meets the energy demands in real time. As the proportion of distributed and intermittent generation capacity increases, this task becomes much harder, especially as the local and regional distribution grids where renewable energy generators are usually installed are currently virtually unmanaged, lack real time metering and are not built to cope with power flow inversions (yet). All this is about to change, and so the control strategies must be adapted accordingly. While the hierarchical command-and-control approach served well in a world with a few large scale generation facilities and many small consumers, a more flexible, decentralized, and self-organizing control infrastructure will have to be developed that can be actively managed to balance both the large grid as a whole, as well as the many lower voltage sub-grids. We propose a competitive simulation test bed to stimulate research and development of electronic agents that help manage these tasks. Participants in the competition will develop intelligent agents that are responsible to level energy supply from generators with energy demand from consumers. The competition is designed to closely model reality by bootstrapping the simulation environment with real historic load, generation, and weather data. The simulation environment will provide a low-risk platform that combines simulated markets and real-world data to develop solutions that can be applied to help building the self-organizing intelligent energy grid of the future

Plasma Uromodulin Correlates With Kidney Function and Identifies Early Stages in Chronic Kidney Disease Patients

Uromodulin, released from tubular cells of the ascending limb into the blood, may be associated with kidney function. This work studies the relevance of plasma uromodulin as a biomarker for kidney function in an observational cohort of chronic kidney disease (CKD) patients and subjects without CKD (CKD stage 0). It should be further evaluated if uromodulin allows the identification of early CKD stages. Plasma uromodulin, serumcreatinine, cystatin C, blood-urea-nitrogen (BUN) concentrations, and estimated glomerular filtration rate (eGFR CKD-EPIcrea-cystatin) were assessed in 426 individuals of whom 71 were CKD stage 0 and 355 had CKD. Besides descriptive statistics, univariate correlations between uromodulin and biomarkers/eGFR were calculated using Pearson-correlation coefficient. Multiple linear regression modeling was applied to establish the association between uromodulin and eGFR adjusted for demographic parameters and pharmacologic treatment. Receiver-operating-characteristic (ROC) analysis adjusted for demographic parameters was performed to test if uromodulin allows differentiation of subjects with CKD stage 0 and CKD stage I. Mean uromodulin plasma levels were 85.7 +/- 60.5 ng/mL for all CKD stages combined. Uromodulin was correlated with all biomarkers/eGFR in univariate analysis (eGFR: r = 0.80, creatinine: r = +/- 0.76, BUN: r = +/- 0.72, and cystatin C: r = +/- 0.79). Multiple linear regression modeling showed significant association between uromodulin and eGFR (coefficient estimate beta = 0.696, 95% confidence interval [CI] 0.603-0.719, P<0.001). In ROC analysis uromodulin was the only parameter that significantly improved a model containing demographic parameters to differentiate between CKD 0 degrees and I degrees (area under the curve [AUC] 0.831, 95% CI 0.746-0.915, P = 0.008) compared to creatinine, cystatin C, BUN, and eGFR (AUC for creatinine: 0.722, P = 0.056, cystatin C: 0.668, P = 0.418, BUN: 0.653, P = 0.811, and eGFR: 0.634, P = 0.823). Plasma uromodulin serves as a robust biomarker for kidney function and uniquely allows the identification of early stages of CKD. As a marker of tubular secretion it might represent remaining nephron mass and therefore intrinsic "kidney function'' rather than just glomerular filtration, the latter only being of limited value to represent kidney function as a whole. It therefore gives substantial information on the renal situation in addition to glomerular filtration and potentially solves the problem of creatinine-blind range of CKD, in which kidney impairment often remains undetected

Plasma Uromodulin Correlates With Kidney Function and Identifies Early Stages in Chronic Kidney Disease Patients

Uromodulin, released from tubular cells of the ascending limb into the blood, may be associated with kidney function. This work studies the relevance of plasma uromodulin as a biomarker for kidney function in an observational cohort of chronic kidney disease (CKD) patients and subjects without CKD (CKD stage 0). It should be further evaluated if uromodulin allows the identification of early CKD stages. Plasma uromodulin, serumcreatinine, cystatin C, blood-urea-nitrogen (BUN) concentrations, and estimated glomerular filtration rate (eGFR CKD-EPIcrea-cystatin) were assessed in 426 individuals of whom 71 were CKD stage 0 and 355 had CKD. Besides descriptive statistics, univariate correlations between uromodulin and biomarkers/eGFR were calculated using Pearson-correlation coefficient. Multiple linear regression modeling was applied to establish the association between uromodulin and eGFR adjusted for demographic parameters and pharmacologic treatment. Receiver-operating-characteristic (ROC) analysis adjusted for demographic parameters was performed to test if uromodulin allows differentiation of subjects with CKD stage 0 and CKD stage I. Mean uromodulin plasma levels were 85.7 +/- 60.5 ng/mL for all CKD stages combined. Uromodulin was correlated with all biomarkers/eGFR in univariate analysis (eGFR: r = 0.80, creatinine: r = +/- 0.76, BUN: r = +/- 0.72, and cystatin C: r = +/- 0.79). Multiple linear regression modeling showed significant association between uromodulin and eGFR (coefficient estimate beta = 0.696, 95% confidence interval [CI] 0.603-0.719, P<0.001). In ROC analysis uromodulin was the only parameter that significantly improved a model containing demographic parameters to differentiate between CKD 0 degrees and I degrees (area under the curve [AUC] 0.831, 95% CI 0.746-0.915, P = 0.008) compared to creatinine, cystatin C, BUN, and eGFR (AUC for creatinine: 0.722, P = 0.056, cystatin C: 0.668, P = 0.418, BUN: 0.653, P = 0.811, and eGFR: 0.634, P = 0.823). Plasma uromodulin serves as a robust biomarker for kidney function and uniquely allows the identification of early stages of CKD. As a marker of tubular secretion it might represent remaining nephron mass and therefore intrinsic "kidney function'' rather than just glomerular filtration, the latter only being of limited value to represent kidney function as a whole. It therefore gives substantial information on the renal situation in addition to glomerular filtration and potentially solves the problem of creatinine-blind range of CKD, in which kidney impairment often remains undetected

Повторный гидравлический разрыв пласта в горизонтальных скважинах с нецементируемым хвостовиком

Myocardial perfusion imaging with 99mTc-tetrofosmin is based on the assumption of a linear correlation between myocardial blood flow (MBF) and tracer uptake. However, it is known that 99mTc-tetrofosmin uptake is directly related to energy-depen-dent transport processes, such as Na/H ion channel activity, as well as cellular and mitochondrial membrane potentials. Therefore, cellular alterations that affect these energy-depen-dent transport processes ought to influence 99mTc-tetrofosmin uptake independently of blood flow. Because metabolism (18F-FDG)–perfusion (99mTc-tetrofosmin) mismatch myocardium (MPMM) reflects impaired but viable myocardium showing cel-lular alterations, MPMM was chosen to quantify the blood flow– independent effect of cellular alterations on 99mTc-tetrofosmin uptake. Therefore, we compared microsphere-equivalent MBF (MBF_micr; 15O-water PET) and 99mTc-tetrofosmin uptake i

Effects of Early Life Stress on Bone Homeostasis in Mice and Humans

Bone pathology is frequent in stressed individuals. A comprehensive examination of mechanisms linking life stress, depression and disturbed bone homeostasis is missing. In this translational study, mice exposed to early life stress (MSUS) were examined for bone microarchitecture (μCT), metabolism (qPCR/ELISA), and neuronal stress mediator expression (qPCR) and compared with a sample of depressive patients with or without early life stress by analyzing bone mineral density (BMD) (DXA) and metabolic changes in serum (osteocalcin, PINP, CTX-I). MSUS mice showed a significant decrease in NGF, NPYR1, VIPR1 and TACR1 expression, higher innervation density in bone, and increased serum levels of CTX-I, suggesting a milieu in favor of catabolic bone turnover. MSUS mice had a significantly lower body weight compared to control mice, and this caused minor effects on bone microarchitecture. Depressive patients with experiences of childhood neglect also showed a catabolic pattern. A significant reduction in BMD was observed in depressive patients with childhood abuse and stressful life events during childhood. Therefore, future studies on prevention and treatment strategies for both mental and bone disease should consider early life stress as a risk factor for bone pathologies

A Computational Systems Biology Software Platform for Multiscale Modeling and Simulation: Integrating Whole-Body Physiology, Disease Biology, and Molecular Reaction Networks

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach

Variability and magnitude of brain glutamate levels in schizophrenia:a meta and mega-analysis

Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-individual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR); (2) standardised mean differences (SMD) using Hedges g; (3) modal distribution of individual-level glutamate data (Hartigan’s unimodality dip test). MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx in schizophrenia. 123 studies reporting on 8256 patients and 7532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex (MFC, glutamate: CVR = 0.15, p &lt; 0.001; glutamine: CVR = 0.15, p = 0.003; Glx: CVR = 0.11, p = 0.002), dorsolateral prefrontal cortex (glutamine: CVR = 0.14, p = 0.05; Glx: CVR = 0.25, p &lt; 0.001) and thalamus (glutamate: CVR = 0.16, p = 0.008; Glx: CVR = 0.19, p = 0.008). Studies in younger, more symptomatic patients were associated with greater variability in the basal ganglia (BG glutamate with age: z = −0.03, p = 0.003, symptoms: z = 0.007, p = 0.02) and temporal lobe (glutamate with age: z = −0.03, p = 0.02), while studies with older, more symptomatic patients associated with greater variability in MFC (glutamate with age: z = 0.01, p = 0.02, glutamine with symptoms: z = 0.01, p = 0.02). For individual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower MFC glutamate (g = −0.15, p = 0.03), higher thalamic glutamine (g = 0.53, p &lt; 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p &lt; 0.001). Proportion of males was negatively associated with MFC glutamate (z = −0.02, p &lt; 0.001) and frontal white matter Glx (z = −0.03, p = 0.02) in patients relative to controls. Patient PANSS total score was positively associated with glutamate SMD in BG (z = 0.01, p = 0.01) and temporal lobe (z = 0.05, p = 0.008). Further research into the mechanisms underlying greater glutamatergic metabolite variability in schizophrenia and their clinical consequences may inform the identification of patient subgroups for future treatment strategies.</p

Variability and magnitude of brain glutamate levels in schizophrenia:a meta and mega-analysis

Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-individual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR); (2) standardised mean differences (SMD) using Hedges g; (3) modal distribution of individual-level glutamate data (Hartigan’s unimodality dip test). MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx in schizophrenia. 123 studies reporting on 8256 patients and 7532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex (MFC, glutamate: CVR = 0.15, p &lt; 0.001; glutamine: CVR = 0.15, p = 0.003; Glx: CVR = 0.11, p = 0.002), dorsolateral prefrontal cortex (glutamine: CVR = 0.14, p = 0.05; Glx: CVR = 0.25, p &lt; 0.001) and thalamus (glutamate: CVR = 0.16, p = 0.008; Glx: CVR = 0.19, p = 0.008). Studies in younger, more symptomatic patients were associated with greater variability in the basal ganglia (BG glutamate with age: z = −0.03, p = 0.003, symptoms: z = 0.007, p = 0.02) and temporal lobe (glutamate with age: z = −0.03, p = 0.02), while studies with older, more symptomatic patients associated with greater variability in MFC (glutamate with age: z = 0.01, p = 0.02, glutamine with symptoms: z = 0.01, p = 0.02). For individual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower MFC glutamate (g = −0.15, p = 0.03), higher thalamic glutamine (g = 0.53, p &lt; 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p &lt; 0.001). Proportion of males was negatively associated with MFC glutamate (z = −0.02, p &lt; 0.001) and frontal white matter Glx (z = −0.03, p = 0.02) in patients relative to controls. Patient PANSS total score was positively associated with glutamate SMD in BG (z = 0.01, p = 0.01) and temporal lobe (z = 0.05, p = 0.008). Further research into the mechanisms underlying greater glutamatergic metabolite variability in schizophrenia and their clinical consequences may inform the identification of patient subgroups for future treatment strategies.</p