@article{BenderBertheauKoerppenetal.2022,
  author    = {Bender, Benedict and Bertheau, Clementine and K{\"o}rppen, Tim and Lauppe, Hannah and Gronau, Norbert},
  title     = {A proposal for future data organization in enterprise systems},
  series = {Information systems and e-business management},
  volume    = {20},
  journal   = {Information systems and e-business management},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1617-9846},
  doi       = {10.1007/s10257-022-00555-6},
  pages     = {441 -- 494},
  year      = {2022},
  abstract  = {The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.},
  language  = {en}
}
@phdthesis{Benson2024,
  author    = {Benson, Lawrence},
  title     = {Efficient state management with persistent memory},
  doi       = {10.25932/publishup-62563},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-625637},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 124},
  year      = {2024},
  abstract  = {Efficiently managing large state is a key challenge for data management systems. Traditionally, state is split into fast but volatile state in memory for processing and persistent but slow state on secondary storage for durability. Persistent memory (PMem), as a new technology in the storage hierarchy, blurs the lines between these states by offering both byte-addressability and low latency like DRAM as well persistence like secondary storage. These characteristics have the potential to cause a major performance shift in database systems. Driven by the potential impact that PMem has on data management systems, in this thesis we explore their use of PMem. We first evaluate the performance of real PMem hardware in the form of Intel Optane in a wide range of setups. To this end, we propose PerMA-Bench, a configurable benchmark framework that allows users to evaluate the performance of customizable database-related PMem access. Based on experimental results obtained with PerMA-Bench, we discuss findings and identify general and implementation-specific aspects that influence PMem performance and should be considered in future work to improve PMem-aware designs. We then propose Viper, a hybrid PMem-DRAM key-value store. Based on PMem-aware access patterns, we show how to leverage PMem and DRAM efficiently to design a key database component. Our evaluation shows that Viper outperforms existing key-value stores by 4-18x for inserts while offering full data persistence and achieving similar or better lookup performance. Next, we show which changes must be made to integrate PMem components into larger systems. By the example of stream processing engines, we highlight limitations of current designs and propose a prototype engine that overcomes these limitations. This allows our prototype to fully leverage PMem's performance for its internal state management. Finally, in light of Optane's discontinuation, we discuss how insights from PMem research can be transferred to future multi-tier memory setups by the example of Compute Express Link (CXL). Overall, we show that PMem offers high performance for state management, bridging the gap between fast but volatile DRAM and persistent but slow secondary storage. Although Optane was discontinued, new memory technologies are continuously emerging in various forms and we outline how novel designs for them can build on insights from existing PMem research.},
  language  = {en}
}
@article{LiStomaLottaetal.2020,
  author    = {Li, Chen and Stoma, Svetlana and Lotta, Luca A. and Warner, Sophie and Albrecht, Eva and Allione, Alessandra and Arp, Pascal P. and Broer, Linda and Buxton, Jessica L. and Boeing, Heiner and Langenberg, Claudia and Codd, Veryan},
  title     = {Genome-wide association analysis in humans links nucleotide metabolism to leukocyte telomere length},
  series = {American Journal of Human Genetics},
  volume    = {106},
  journal   = {American Journal of Human Genetics},
  number    = {3},
  publisher = {Elsevier},
  address   = {Amsterdam},
  pages     = {16},
  year      = {2020},
  abstract  = {Leukocyte telomere length (LTL) is a heritable biomarker of genomic aging. In this study, we perform a genome-wide meta-analysis of LTL by pooling densely genotyped and imputed association results across large-scale European-descent studies including up to 78,592 individuals. We identify 49 genomic regions at a false dicovery rate (FDR) < 0.05 threshold and prioritize genes at 31, with five highlighting nucleotide metabolism as an important regulator of LTL. We report six genome-wide significant loci in or near SENP7, MOB1B, CARMIL1 , PRRC2A, TERF2, and RFWD3, and our results support recently identified PARP1, POT1, ATM, and MPHOSPH6 loci. Phenome-wide analyses in >350,000 UK Biobank participants suggest that genetically shorter telomere length increases the risk of hypothyroidism and decreases the risk of thyroid cancer, lymphoma, and a range of proliferative conditions. Our results replicate previously reported associations with increased risk of coronary artery disease and lower risk for multiple cancer types. Our findings substantially expand current knowledge on genes that regulate LTL and their impact on human health and disease.},
  language  = {en}
}
@misc{LiStomaLottaetal.2020,
  author    = {Li, Chen and Stoma, Svetlana and Lotta, Luca A. and Warner, Sophie and Albrecht, Eva and Allione, Alessandra and Arp, Pascal P. and Broer, Linda and Buxton, Jessica L. and Boeing, Heiner and Langenberg, Claudia and Codd, Veryan},
  title     = {Genome-wide association analysis in humans links nucleotide metabolism to leukocyte telomere length},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {3},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-52684},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-526843},
  pages     = {18},
  year      = {2020},
  abstract  = {Leukocyte telomere length (LTL) is a heritable biomarker of genomic aging. In this study, we perform a genome-wide meta-analysis of LTL by pooling densely genotyped and imputed association results across large-scale European-descent studies including up to 78,592 individuals. We identify 49 genomic regions at a false dicovery rate (FDR) < 0.05 threshold and prioritize genes at 31, with five highlighting nucleotide metabolism as an important regulator of LTL. We report six genome-wide significant loci in or near SENP7, MOB1B, CARMIL1 , PRRC2A, TERF2, and RFWD3, and our results support recently identified PARP1, POT1, ATM, and MPHOSPH6 loci. Phenome-wide analyses in >350,000 UK Biobank participants suggest that genetically shorter telomere length increases the risk of hypothyroidism and decreases the risk of thyroid cancer, lymphoma, and a range of proliferative conditions. Our results replicate previously reported associations with increased risk of coronary artery disease and lower risk for multiple cancer types. Our findings substantially expand current knowledge on genes that regulate LTL and their impact on human health and disease.},
  language  = {en}
}
@phdthesis{RianoPachon2008,
  author    = {Ria{\~n}o-Pach{\´o}n, Diego Mauricio},
  title     = {Identification of transcription factor genes in plants},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-27009},
  school      = {Universit{\"a}t Potsdam},
  year      = {2008},
  abstract  = {In order to function properly, organisms have a complex control mechanism, in which a given gene is expressed at a particular time and place. One way to achieve this control is to regulate the initiation of transcription. This step requires the assembly of several components, i.e., a basal/general machinery common to all expressed genes, and a specific/regulatory machinery, which differs among genes and is the responsible for proper gene expression in response to environmental or developmental signals. This specific machinery is composed of transcription factors (TFs), which can be grouped into evolutionarily related gene families that possess characteristic protein domains. In this work we have exploited the presence of protein domains to create rules that serve for the identification and classification of TFs. We have modelled such rules as a bipartite graph, where families and protein domains are represented as nodes. Connections between nodes represent that a protein domain should (required rule) or should not (forbidden rule) be present in a protein to be assigned into a TF family. Following this approach we have identified putative complete sets of TFs in plant species, whose genome is completely sequenced: Cyanidioschyzon merolae (red algae), Chlamydomonas reinhardtii (green alga), Ostreococcus tauri (green alga), Physcomitrella patens (moss), Arabidopsis thaliana (thale cress), Populus trichocarpa (black cottonwood) and Oryza sativa (rice). The identification of the complete sets of TFs in the above-mentioned species, as well as additional information and reference literature are available at http://plntfdb.bio.uni-potsdam.de/. The availability of such sets allowed us performing detailed evolutionary studies at different levels, from a single family to all TF families in different organisms in a comparative genomics context. Notably, we uncovered preferential expansions in different lineages, paving the way to discover the specific biological roles of these proteins under different conditions. For the basic leucine zipper (bZIP) family of TFs we were able to infer that in the most recent common ancestor (MRCA) of all green plants there were at least four bZIP genes functionally involved in oxidative stress and unfolded protein responses that are bZIP-mediated processes in all eukaryotes, but also in light-dependent regulations. The four founder genes amplified and diverged significantly, generating traits that benefited the colonization of new environments. Currently, following the approach described above, up to 57 TF and 11 TR families can be identified, which are among the most numerous transcription regulatory families in plants. Three families of putative TFs predate the split between rhodophyta (red algae) and chlorophyta (green algae), i.e., G2-like, PLATZ, and RWPRK, and may have been of particular importance for the evolution of eukaryotic photosynthetic organisms. Nine additional families, i.e., ABI3/VP1, AP2-EREBP, ARR-B, C2C2-CO-like, C2C2-Dof, PBF-2-like/Whirly, Pseudo ARR-B, SBP, and WRKY, predate the split between green algae and streptophytes. The identification of putative complete list of TFs has also allowed the delineation of lineage-specific regulatory families. The families SBP, bHLH, SNF2, MADS, WRKY, HMG, AP2-EREBP and FHA significantly differ in size between algae and land plants. The SBP family of TFs is significantly larger in C. reinhardtii, compared to land plants, and appears to have been lost in the prasinophyte O. tauri. The families bHLH, SNF2, MADS, WRKY, HMG, AP2-EREBP and FHA preferentially expanded with the colonisation of land, and might have played an important role in this great moment in evolution. Later, after the split of bryophytes and tracheophytes, the families MADS, AP2-EREBP, NAC, AUX/IAA, PHD and HRT have significantly larger numbers in the lineage leading to seed plants. We identified 23 families that are restricted to land plants and that might have played an important role in the colonization of this new habitat. Based on the list of TFs in different species we have started to develop high-throughput experimental platforms (in rice and C. reinhardtii) to monitor gene expression changes of TF genes under different genetic, developmental or environmental conditions. In this work we present the monitoring of Arabidopsis thaliana TFs during the onset of senescence, a process that leads to cell and tissue disintegration in order to redistribute nutrients (e.g. nitrogen) from leaves to reproductive organs. We show that the expression of 185 TF genes changes when leaves develop from half to fully expanded leaves and finally enter partial senescence. 76\% of these TFs are down-regulated during senescence, the remaining are up-regulated. The identification of TFs in plants in a comparative genomics setup has proven fruitful for the understanding of evolutionary processes and contributes to the elucidation of complex developmental programs.},
  language  = {en}
}
@phdthesis{Steinhauser2004,
  author    = {Steinhauser, Dirk},
  title     = {Inferring hypotheses from complex profile data - by means of CSB.DB, a comprehensive systems-biology database},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-2467},
  school      = {Universit{\"a}t Potsdam},
  year      = {2004},
  abstract  = {The past decades are characterized by various efforts to provide complete sequence information of genomes regarding various organisms. The availability of full genome data triggered the development of multiplex high-throughput assays allowing simultaneous measurement of transcripts, proteins and metabolites. With genome information and profiling technologies now in hand a highly parallel experimental biology is offering opportunities to explore and discover novel principles governing biological systems. Understanding biological complexity through modelling cellular systems represents the driving force which today allows shifting from a component-centric focus to integrative and systems level investigations. The emerging field of systems biology integrates discovery and hypothesis-driven science to provide comprehensive knowledge via computational models of biological systems. Within the context of evolving systems biology, investigations were made in large-scale computational analyses on transcript co-response data through selected prokaryotic and plant model organisms. CSB.DB - a comprehensive systems-biology database - (http://csbdb.mpimp-golm.mpg.de/) was initiated to provide public and open access to the results of biostatistical analyses in conjunction with additional biological knowledge. The database tool CSB.DB enables potential users to infer hypothesis about functional interrelation of genes of interest and may serve as future basis for more sophisticated means of elucidating gene function. The co-response concept and the CSB.DB database tool were successfully applied to predict operons in Escherichia coli by using the chromosomal distance and transcriptional co-responses. Moreover, examples were shown which indicate that transcriptional co-response analysis allows identification of differential promoter activities under different experimental conditions. The co-response concept was successfully transferred to complex organisms with the focus on the eukaryotic plant model organism Arabidopsis thaliana. The investigations made enabled the discovery of novel genes regarding particular physiological processes and beyond, allowed annotation of gene functions which cannot be accessed by sequence homology. GMD - the Golm Metabolome Database - was initiated and implemented in CSB.DB to integrated metabolite information and metabolite profiles. This novel module will allow addressing complex biological questions towards transcriptional interrelation and extent the recent systems level quest towards phenotyping.},
  subject      = {Datenbank},
  language  = {en}
}
@misc{Matzk2016,
  type      = {Master Thesis},
  author    = {Matzk, S{\"o}ren},
  title     = {Predictive analysis of metabolic and preventive patient data},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-406103},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XI, 63},
  year      = {2016},
  abstract  = {Every day huge amounts of medical records are stored by means of hospitals' and medical offices' software. These data are generally unconsidered in research. In this work anonymized everyday medical records ascertained in a physician's office, cov- ering holistic internal medicine in combination with orthomolecular medicine, are analyzed. Due to the lack of cooperation by the provider of the medical practice software a selection of diagnoses and anthropometric parameters was extracted manually. Information about patients' treatment are not available in this study. Nevertheless, data mining approaches in- cluding machine learning techniques are used to enable research, prevention and monitoring of patients' course of treatment. The potential of these everyday medical data is demonstrated by investigating co-morbidity and pyroluria which is a metabolic dysfunction indicated by increased levels of hydroxy- hemopyrrolin-2-one (HPL). It points out that the metabolic syndrome forms a cluster of its components and cancer, as well as mental disorders are grouped with thyroid diseases including autoimmune thyroid diseases. In contrast to prevailing assumptions in which it was estimated that approximately 10 \% of the population show increased levels of HPL, in this analysis 84.9 \% of the tested patients have an increased concentration of HPL. Prevention is illustrated by using decision tree models to predict diseases. Evaluation of the obtained model for Hashimoto's disease yield an accuracy of 87.5 \%. The model generated for hypothyroidism (accuracy of 60.9 \%) reveals shortcomings due to missing information about the treatment. Dynamics in the biomolecular status of 20 patients who have visited the medical office at least one time a year between 2010 and 2014 for laboratory tests are visualized by STATIS, a consensus analysis based on an extension to principal component analysis. Thereby, one can obtain patterns which are predestinated for specific diseases as hypertension. This study demonstrates that these often overlooked everyday data are challenging due to its sparsity and heterogeneity but its analysis is a great possibility to do research on disease profiles of real patients.},
  language  = {de}
}
@article{HavingaKoolAchilleetal.2016,
  author    = {Havinga, Reinout and Kool, Anneleen and Achille, Frederic and Bavcon, Joze and Berg, Christian and Bonomi, Costantino and Burkart, Michael and De Meyere, Dirk and Havstrom, Mats and Kessler, Paul and Knickmann, Barbara and Koester, Nils and Martinez, Remy and Ostgaard, Havard and Ravnjak, Blanka and Scheen, Anne-Cathrine and Smith, Pamela and Smith, Paul and Socher, Stephanie A. and Vange, Vibekke},
  title     = {The Index Seminum: Seeds of change for seed exchange},
  series = {Taxon},
  volume    = {65},
  journal   = {Taxon},
  publisher = {International Association for Plant Taxonomy},
  address   = {Bratislava},
  issn      = {0040-0262},
  doi       = {10.12705/652.9},
  pages     = {333 -- 336},
  year      = {2016},
  abstract  = {Botanic gardens have been exchanging seeds through seed catalogues for centuries. In many gardens, these catalogues remain an important source of plant material. Living collections have become more relevant for genetic analysis and derived research, since genomics of non-model organisms heavily rely on living material. The range of species that is made available annually on all seed lists combined, provides an unsurpassed source of instantly accessible plant material for research collections. Still, the Index Seminum has received criticism in the past few decades. The current exchange model dictates that associated data is manually entered into each database. The amount of time involved and the human errors occurring in this process are difficult to justify when the data was initially produced as a report from another database. The authors propose that an online marketplace for seed exchange should be established, with enhanced search possibilities and downloadable accession data in a standardised format. Such online service should preferably be supervised and coordinated by Botanic Gardens Conservation International (BGCI). This manuscript is the outcome of a workshop on July 9th, 2015, at the European botanic gardens congress "Eurogard VII" in Paris, where the first two authors invited members of the botanic garden community to discuss how the anachronistic Index Seminum can be transformed into an improved and modern tool for seed exchange.},
  language  = {en}
}
@phdthesis{Kossmann2023,
  author    = {Koßmann, Jan},
  title     = {Unsupervised database optimization},
  doi       = {10.25932/publishup-58949},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-589490},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 203},
  year      = {2023},
  abstract  = {The amount of data stored in databases and the complexity of database workloads are ever- increasing. Database management systems (DBMSs) offer many configuration options, such as index creation or unique constraints, which must be adapted to the specific instance to efficiently process large volumes of data. Currently, such database optimization is complicated, manual work performed by highly skilled database administrators (DBAs). In cloud scenarios, manual database optimization even becomes infeasible: it exceeds the abilities of the best DBAs due to the enormous number of deployed DBMS instances (some providers maintain millions of instances), missing domain knowledge resulting from data privacy requirements, and the complexity of the configuration tasks. Therefore, we investigate how to automate the configuration of DBMSs efficiently with the help of unsupervised database optimization. While there are numerous configuration options, in this thesis, we focus on automatic index selection and the use of data dependencies, such as functional dependencies, for query optimization. Both aspects have an extensive performance impact and complement each other by approaching unsupervised database optimization from different perspectives. Our contributions are as follows: (1) we survey automated state-of-the-art index selection algorithms regarding various criteria, e.g., their support for index interaction. We contribute an extensible platform for evaluating the performance of such algorithms with industry-standard datasets and workloads. The platform is well-received by the community and has led to follow-up research. With our platform, we derive the strengths and weaknesses of the investigated algorithms. We conclude that existing solutions often have scalability issues and cannot quickly determine (near-)optimal solutions for large problem instances. (2) To overcome these limitations, we present two new algorithms. Extend determines (near-)optimal solutions with an iterative heuristic. It identifies the best index configurations for the evaluated benchmarks. Its selection runtimes are up to 10 times lower compared with other near-optimal approaches. SWIRL is based on reinforcement learning and delivers solutions instantly. These solutions perform within 3 \% of the optimal ones. Extend and SWIRL are available as open-source implementations. (3) Our index selection efforts are complemented by a mechanism that analyzes workloads to determine data dependencies for query optimization in an unsupervised fashion. We describe and classify 58 query optimization techniques based on functional, order, and inclusion dependencies as well as on unique column combinations. The unsupervised mechanism and three optimization techniques are implemented in our open-source research DBMS Hyrise. Our approach reduces the Join Order Benchmark's runtime by 26 \% and accelerates some TPC-DS queries by up to 58 times. Additionally, we have developed a cockpit for unsupervised database optimization that allows interactive experiments to build confidence in such automated techniques. In summary, our contributions improve the performance of DBMSs, support DBAs in their work, and enable them to contribute their time to other, less arduous tasks.},
  language  = {en}
}
@misc{KumarGoodwinUhouseetal.2015,
  author    = {Kumar, Kevin K. and Goodwin, Cody R. and Uhouse, Michael A. and Bornhorst, Julia and Schwerdtle, Tanja and Aschner, Michael A. and McLean, John A. and Bowman, Aaron B.},
  title     = {Untargeted metabolic profiling identifies interactions between Huntington's disease and neuronal manganese status},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-94314},
  pages     = {363 -- 370},
  year      = {2015},
  abstract  = {Manganese (Mn) is an essential micronutrient for development and function of the nervous system. Deficiencies in Mn transport have been implicated in the pathogenesis of Huntington's disease (HD), an autosomal dominant neurodegenerative disorder characterized by loss of medium spiny neurons of the striatum. Brain Mn levels are highest in striatum and other basal ganglia structures, the most sensitive brain regions to Mn neurotoxicity. Mouse models of HD exhibit decreased striatal Mn accumulation and HD striatal neuron models are resistant to Mn cytotoxicity. We hypothesized that the observed modulation of Mn cellular transport is associated with compensatory metabolic responses to HD pathology. Here we use an untargeted metabolomics approach by performing ultraperformance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS) on control and HD immortalized mouse striatal neurons to identify metabolic disruptions under three Mn exposure conditions, low (vehicle), moderate (non-cytotoxic) and high (cytotoxic). Our analysis revealed lower metabolite levels of pantothenic acid, and glutathione (GSH) in HD striatal cells relative to control cells. HD striatal cells also exhibited lower abundance and impaired induction of isobutyryl carnitine in response to increasing Mn exposure. In addition, we observed induction of metabolites in the pentose shunt pathway in HD striatal cells after high Mn exposure. These findings provide metabolic evidence of an interaction between the HD genotype and biologically relevant levels of Mn in a striatal cell model with known HD by Mn exposure interactions. The metabolic phenotypes detected support existing hypotheses that changes in energetic processes underlie the pathobiology of both HD and Mn neurotoxicity.},
  language  = {en}
}
@article{KumarGoodwinUhouseetal.2015,
  author    = {Kumar, Kevin K. and Goodwin, Cody R. and Uhouse, Michael A. and Bornhorst, Julia and Schwerdtle, Tanja and Aschner, Michael A. and McLean, John A. and Bowman, Aaron B.},
  title     = {Untargeted metabolic profiling identifies interactions between Huntington's disease and neuronal manganese status},
  series = {Metallomics},
  volume    = {7},
  journal   = {Metallomics},
  publisher = {RSC Publ.},
  address   = {Cambridge},
  issn      = {1756-591X},
  doi       = {10.1039/C4MT00223G},
  pages     = {363 -- 370},
  year      = {2015},
  abstract  = {Manganese (Mn) is an essential micronutrient for development and function of the nervous system. Deficiencies in Mn transport have been implicated in the pathogenesis of Huntington's disease (HD), an autosomal dominant neurodegenerative disorder characterized by loss of medium spiny neurons of the striatum. Brain Mn levels are highest in striatum and other basal ganglia structures, the most sensitive brain regions to Mn neurotoxicity. Mouse models of HD exhibit decreased striatal Mn accumulation and HD striatal neuron models are resistant to Mn cytotoxicity. We hypothesized that the observed modulation of Mn cellular transport is associated with compensatory metabolic responses to HD pathology. Here we use an untargeted metabolomics approach by performing ultraperformance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS) on control and HD immortalized mouse striatal neurons to identify metabolic disruptions under three Mn exposure conditions, low (vehicle), moderate (non-cytotoxic) and high (cytotoxic). Our analysis revealed lower metabolite levels of pantothenic acid, and glutathione (GSH) in HD striatal cells relative to control cells. HD striatal cells also exhibited lower abundance and impaired induction of isobutyryl carnitine in response to increasing Mn exposure. In addition, we observed induction of metabolites in the pentose shunt pathway in HD striatal cells after high Mn exposure. These findings provide metabolic evidence of an interaction between the HD genotype and biologically relevant levels of Mn in a striatal cell model with known HD by Mn exposure interactions. The metabolic phenotypes detected support existing hypotheses that changes in energetic processes underlie the pathobiology of both HD and Mn neurotoxicity.},
  language  = {en}
}