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In temperate Europe alley cropping systems which integrate strips of short rotation coppices into conventional agricultural fields (ACS) are receiving increasing attention. These systems can be used for crops and woody biomass production at the same time, enabling farmers to diversify the provision of market goods. Adding trees into the agricultural land creates various additional benefits for the farmer and society, also known as ecosystem services. However, tree-crop interactions in the temperate region have not been adequately substantiated which is identified as a drawback to the practical implementation of such systems. In order to bridge this gap, the current paper aims to present a comprehensive overview of selected ecosystem services provided by agroforestry with focus on ACS in the temperate region. The literature indicates that compared with conventional agriculture ACS have the potential to increase carbon sequestration, improve soil fertility and generally optimize the utilization of resources. Furthermore, due to their structural flexibility, ACS may help to regulate water quality, enhance biodiversity, and increase the overall productivity. ACS are shown as suitable land use systems especially for marginal sites. Based on the available data collected, we conclude that ACS are advantageous compared to conventional agriculture in many aspects, and therefore suggest that they should be implemented at a larger scale in temperate regions.

Subjects with mild cognitive impairment (MCI) have an increased risk to develop Alzheimer's disease (AD). Voxel-based MRI studies have demonstrated that widely distributed cortical and subcortical brain areas show atrophic changes in MCI, preceding the onset of AD-type dementia. Here we developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. MRI was collected in 32 AD patients, 24 MCI subjects and 18 healthy controls (HC). Nine out of 24 MCI subjects converted to AD after an average follow-up interval of 2.5 years. Using feature selection algorithms, brain regions showing the highest accuracy for the discrimination between AD and HC were identified, reaching a classification accuracy of up to 92%. The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD. The present results suggest that novel multivariate methods of pattern matching reach a clinically relevant accuracy for the a priori prediction of the progression from MCI to AD.

The cultivation of fast growing trees on agricultural sites is an area undergoing a growth in interest due to the rising demand for woody biomass as a source of bioenergy. Short rotation alley cropping systems (SRACS) represent a promising possibility to combine annual crops for food, fodder or bioenergy with woody plants for biomass production, doing so through an integration of hedgerows of fast growing trees into conventional agricultural sites. Against such developments, the question has arisen as to what extent hedgerows in SRACS can act as an effective windbreak despite their management-related low height of only a few meters. On the basis of multiannual recorded wind velocity data in high resolution at two sites in Germany, it could be shown that the wind speed on crop alleys was reduced significantly by such hedgerows. At the central point of 24 m wide crop alleys, the wind speed decreased on an annual average basis by more than 50 % when compared to the wind speeds of open field. The overall amount of reduction was strongly dependent on the location within the crop alleys, the height of trees, the distance between two hedgerows, and their orientation. In reflection upon these results, it was concluded that the establishment of SRACS could lead to enhanced soil protection against wind erosion and thus to ecological and economic benefits for agricultural sites.

The idea of combining the high representational power of deep learning techniques with clustering methods has gained much attention in recent years. Optimizing a clustering objective and the dataset representation simultaneously has been shown to be advantageous over separately optimizing them. So far, however, all proposed methods have been using a flat clustering strategy, with the actual number of clusters known a priori. In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method. The cluster tree does not need to know the actual number of clusters during optimization. Instead, the level of detail to be analyzed can be chosen afterward and for each sub-tree separately. An optional data-augmentation-based extension allows DeepECT to ignore prior-known invariances of the dataset, such as affine transformations in image data. We evaluate and show the advantages of DeepECT in extensive experiments.

Liver-Specific Peroxisome Proliferator–Activated Receptor α Target Gene Regulation by the Angiotensin Type 1 Receptor Blocker Telmisartan Markus Clemenz 1 , Nikolaj Frost 1 , Michael Schupp 2 , Sandrine Caron 3 , Anna Foryst-Ludwig 1 , Christian Böhm 1 , Martin Hartge 1 , Ronald Gust 4 , Bart Staels 4 , Thomas Unger 1 and Ulrich Kintscher 1 1 Center for Cardiovascular Research, Institute of Pharmacology, Charité-Universitätsmedizin Berlin, Berlin, Germany 2 Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, University of Pennsylvania, School of Medicine, Philadelphia, Pennsylvania 3 Unité de Recherche 545 Institut National de la Santé et de la Recherche Médicale, Institute Pasteur de Lille, Université de Lille 2, Lille, France 4 Institute of Pharmacy, Free University of Berlin, Berlin, Germany Corresponding author: Prof. Ulrich Kintscher, MD, Center for Cardiovascular Research, Institute of Pharmacology, Charité-Universitätsmedizin Berlin, Hessische Str. 3-4, 10115 Berlin, Germany. E-mail: ulrich.kintscher{at}charite.de Abstract OBJECTIVE— The angiotensin type 1 receptor blocker (ARB) and peroxisome proliferator–activated receptor (PPAR) γ modulator telmisartan has been recently demonstrated to reduce plasma triglycerides in nondiabetic and diabetic hypertensive patients. The present study investigates the molecular mechanisms of telmisartans hypolipidemic actions, in particular its effect on the PPARα pathway. RESEARCH DESIGN AND METHODS— Regulation of PPARα target genes by telmisartan was studied by real-time PCR and Western immunoblotting in vitro and in vivo in liver/skeletal muscle of mice with diet-induced obesity. Activation of the PPARα ligand binding domain (LBD) was investigated using transactivation assays. RESULTS— Telmisartan significantly induced the PPARα target genes carnitine palmitoyl transferase 1A (CPT1A) in human HepG2 cells and acyl-CoA synthetase long-chain family member 1 (ACSL1) in murine AML12 cells in the micromolar range. Telmisartan-induced CPT1A stimulation was markedly reduced after small interfering RNA–mediated knockdown of PPARα. Telmisartan consistently activated the PPARα-LBD as a partial PPARα agonist. Despite high in vitro concentrations required for PPARα activation, telmisartan (3 mg · kg −1 · day −1 ) potently increased ACSL1 and CPT1A expression in liver from diet-induced obese mice associated with a marked decrease of hepatic and serum triglycerides. Muscular CPT1B expression was not affected. Tissue specificity of telmisartan-induced PPARα target gene induction may be the result of previously reported high hepatic concentrations of telmisartan. CONCLUSIONS— The present study identifies the ARB/PPARγ modulator telmisartan as a partial PPARα agonist. As a result of its particular pharmacokinetic profile, PPARα activation by telmisartan seems to be restricted to the liver. Hepatic PPARα activation may provide an explanation for telmisartan's antidyslipidemic actions observed in recent clinical trials. ACSL1, acyl-CoA synthetase long-chain family member 1 ALT, alanine aminotransferase ARB, angiotensin type 1 receptor blocker AST, aspartate aminotransferase CPT1A, carnitine palmitoyl transferase 1A hPPARα, human peroxisome proliferator–activated receptor α LBD, ligand binding domain; NASH, nonalcoholic steatohepatitis PPAR, peroxisome proliferator–activated receptor siRNA, small interfering RNA Footnotes Published ahead of print at http://diabetes.diabetesjournals.org on 9 January 2008. DOI: 10.2337/db07-0839. Additional information for this article can be found in an online appendix at http://dx.doi.org/10.2337/db07-0839 . M.C. and N.F. contributed equally to this article. T.U. is a member of the speakers bureau of and has received grant/research support from Boehringer Ingelheim and Bayer Schering Pharma. U.K. is a member of the speakers bureau of Bayer Schering Pharma and has received grant/research support from Boehringer Ingelheim and Bayer Schering Pharma. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Accepted January 2, 2008. Received June 20, 2007. DIABETES

Successful reduction of body weight (BW) is often followed by recidivism to obesity. BW-changes including BW-loss and -regain is associated with marked alterations in energy expenditure (EE) and adipose tissue (AT) metabolism. Since these processes are sex-specifically controlled, we investigated sexual dimorphisms in metabolic processes during BW-dynamics (gain-loss-regain). Obesity was induced in C57BL/6J male (m) and female (f) mice by 15 weeks high-fat diet (HFD) feeding. Subsequently BW was reduced (-20%) by caloric restriction (CR) followed by adaptive feeding, and a regain-phase. Measurement of EE, body composition, blood/organ sampling were performed after each feeding period. Lipolysis was analyzed ex-vivo in gonadal AT. Male mice exhibited accelerated BW-gain compared to females (relative BW-gain m:140.5±3.2%; f:103.7±6.5%; p<0.001). In consonance, lean mass-specific EE was significantly higher in females compared to males during BW-gain. Under CR female mice reached their target-BW significantly faster than male mice (m:12.2 days; f:7.6 days; p<0.001) accompanied by a sustained sex-difference in EE. In addition, female mice predominantly downsized gonadal AT whereas the relation between gonadal and total body fat was not altered in males. Accordingly, only females exhibited an increased rate of forskolin-stimulated lipolysis in AT associated with significantly higher glycerol concentrations, lower RER-values, and increased AT expression of adipose triglyceride lipase (ATGL) and hormone sensitive lipase (HSL). Analysis of AT lipolysis in estrogen receptor alpha (ERα)-deficient mice revealed a reduced lipolytic rate in the absence of ERα exclusively in females. Finally, re-feeding caused BW-regain faster in males than in females. The present study shows sex-specific dynamics during BW-gain-loss-regain. Female mice responded to CR with an increase in lipolytic activity, and augmented lipid-oxidation leading to more efficient weight loss. These processes likely involve ERα-dependent signaling in AT and sexual dimorphic regulation of genes involved in lipid metabolism.

Background: The “digital era” in the field of medicine is the new “here and now”. Artificial intelligence has entered many fields of medicine and is recently emerging in the field of organ transplantation. Solid organs remain a scarce resource. Being able to predict the outcome after liver transplantation promises to solve one of the long-standing problems within organ transplantation. What is the perfect donor recipient match? Within this work we developed and validated a novel deep-learning-based donor–recipient allocation system for liver transplantation. Method: In this study we used data collected from all liver transplant patients between 2004 and 2019 at the university transplantation centre in Munich. We aimed to design a transparent and interpretable deep learning framework to predict the outcome after liver transplantation. An individually designed neural network was developed to meet the unique requirements of transplantation data. The metrics used to determine the model quality and its level of performance are accuracy, cross-entropy loss, and F1 score as well as AUC score. Results: A total of 529 transplantations with a total of 1058 matching donor and recipient observations were added into the database. The combined prediction of all outcome parameters was 95.8% accurate (cross-entropy loss of 0.042). The prediction of death within the hospital was 94.3% accurate (cross-entropy loss of 0.057). The overall F1 score was 0.899 on average, whereas the overall AUC score was 0.940. Conclusion: With the achieved results, the network serves as a reliable tool to predict survival. It adds new insight into the potential of deep learning to assist medical decisions. Especially in the field of transplantation, an AUC Score of 94% is very valuable. This neuronal network is unique as it utilizes transparent and easily interpretable data to predict the outcome after liver transplantation. Further validation must be performed prior to utilization in a clinical context.

Many clustering algorithms suffer from scalability problems on massive datasets and do not support any user interaction during runtime. To tackle these problems, anytime clustering algorithms are proposed. They produce a fast approximate result which is continuously refined during the further run. Also, they can be stopped or suspended anytime to provide an intermediate answer. In this paper, we propose a novel anytime clustering algorithm modeled on the density-based clustering paradigm. Our algorithm called A-DBSCAN is applicable to many complex data such as trajectory and medical data. The general idea of our algorithm is to use a sequence of lower bounding functions (LBs) of the true distance function to produce multiple approximate results of the true density-based clusters. A-DBSCAN operates in multiple levels w.r.t. the LBs and is mainly based on two algorithmic schemes: (1) an efficient distance upgrade scheme which restricts distance calculations to core objects at each level of the LBs and (2) a local reclustering scheme which restricts update operations to the relevant objects only. To further improve the performance, we propose a significant extension version of A-DBSCAN called A-DBSCAN-XS which is built upon the anytime scheme of A-DBSCAN and the μ -range query scheme of a data structure called extended Xseedlist. A-DBSCAN-XS requires less distance calculations at each level than A-DBSCAN and thus is more efficient. Extensive experiments demonstrate that A-DBSCAN and A-DBSCAN-XS acquire very good clustering results at very early stages of execution and thus save a large amount of computational time. Even if they run to the end, A-DBSCAN and A-DBSCAN-XS are still orders of magnitude faster than the original algorithm DBSCAN and its variants. We also introduce a novel application for our algorithms for the segmentation of the white matter fiber tracts in human brain which is an important tool for studying the brain structure and various diseases such as Alzheimer.

This book presents innovative approaches from database researchers supporting the challenging process of knowledge discovery in biomedicine. Ranging from how to effectively store and organize biomedical data via data quality and case studies to sophisticated data mining methods, this book provides the state-of-the-art of database technology for life sciences and medicine.

The properties of preferential flow path surfaces may affect transport processes in structured soil. Wetting and sorption depends on the organic matter (OM) at the effective outermost surface of flow paths, which is largely unknown locally with respect to OM composition and distribution. The objectives are to adapt Diffuse Reflectance infrared Fourier Transform (DRIFT) spectral analysis for the in situ characterization of coated crack surfaces and linings of earthworm burrow walls. The Kubelka-Munk (KM) transformed DRIFT spectra are used to analyze OM composition in terms of the ratio between CH- (Band A) and C=O groups (Band B). The A/B ratio indicates a relation between hydrophobic and hydrophilic groups in OM. Disturbed soil samples are arranged (i) in standard cup (CUP) and (ii) larger box (BOX) devices; intact aggregate and burrow surfaces are prepared from clods with cracks and earthworm burrows. The disturbed samples with relatively homogeneous surfaces are used to compare data obtained from CUP with those from the BOX device. The A/B ratios are determined in a stepwise mapping-type procedure and compared with pictures of the surfaces for inferring a spatial distribution of OM composition along transects. The DRIFT mapping results indicate negligibly small texture effects for the finer-textural subsoil samples for which the applicability of KM is not restricted. For intact surfaces like soil aggregates, cracks, and earthworm burrow walls, the A/B ratios are relatively higher near a root residue and relatively lower near a quartz grain. The millimeter-scale variability of OM composition obtained here by an in situ analysis of A/B ratios at structural surfaces seems promising for determination of locally distributed OM properties. The DRIFT mapping may improve understanding the development of soil structural properties, from which descriptions of preferential flow and transport may benefit.