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- Phillips, Helen R. P., et al.
(författare)
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Global distribution of earthworm diversity
- 2019
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 366:6464, s. 480-
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Tidskriftsartikel (refereegranskat)abstract
- Soil organisms, including earthworms, are a key component of terrestrial ecosystems. However, little is known about their diversity, their distribution, and the threats affecting them. We compiled a global dataset of sampled earthworm communities from 6928 sites in 57 countries as a basis for predicting patterns in earthworm diversity, abundance, and biomass. We found that local species richness and abundance typically peaked at higher latitudes, displaying patterns opposite to those observed in aboveground organisms. However, high species dissimilarity across tropical locations may cause diversity across the entirety of the tropics to be higher than elsewhere. Climate variables were found to be more important in shaping earthworm communities than soil properties or habitat cover. These findings suggest that climate change may have serious implications for earthworm communities and for the functions they provide.
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| 2. |
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| 3. |
- Bessani, A., et al.
(författare)
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BiobankCloud : A platform for the secure storage, sharing, and processing of large biomedical data sets
- 2016
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Ingår i: 1st International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2015 and Workshop on Big-Graphs Online Querying, Big-O(Q) 2015 held in conjunction with 41st International Conference on Very Large Data Bases, VLDB 2015. - Cham : Springer. - 9783319415758 - 9783319415765 ; , s. 89-105
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Konferensbidrag (refereegranskat)abstract
- Biobanks store and catalog human biological material that is increasingly being digitized using next-generation sequencing (NGS). There is, however, a computational bottleneck, as existing software systems are not scalable and secure enough to store and process the incoming wave of genomic data from NGS machines. In the BiobankCloud project, we are building a Hadoop-based platform for the secure storage, sharing, and parallel processing of genomic data. We extended Hadoop to include support for multi-tenant studies, reduced storage requirements with erasure coding, and added support for extensible and consistent metadata. On top of Hadoop, we built a scalable scientific workflow engine featuring a proper workflow definition language focusing on simple integration and chaining of existing tools, adaptive scheduling on Apache Yarn, and support for iterative dataflows. Our platform also supports the secure sharing of data across different, distributed Hadoop clusters. The software is easily installed and comes with a user-friendly web interface for running, managing, and accessing data sets behind a secure 2-factor authentication. Initial tests have shown that the engine scales well to dozens of nodes. The entire system is open-source and includes pre-defined workflows for popular tasks in biomedical data analysis, such as variant identification, differential transcriptome analysis using RNA-Seq, and analysis of miRNA-Seq and ChIP-Seq data.
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| 5. |
- Gholami, Omid, 1979-, et al.
(författare)
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Heuristic Algorithms to Maximize Revenue and the Number of Jobs Processed on Parallel Machines
- 2019
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Ingår i: Automation and remote control. - : MAIK NAUKA/INTERPERIODICA/SPRINGER. - 0005-1179 .- 1608-3032. ; 80:2, s. 297-316
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Tidskriftsartikel (refereegranskat)abstract
- A set of jobs has to be processed on parallel machines. For each job, there are given a release time and a due date and the job must be processed no later than its due date. If the job will be completed no later than the given due date, a benefit will be earned. Otherwise, this job will be rejected and the benefit will be discarded. The criterion under consideration is to maximize the weighted sum of the benefits and the number of jobs processed in time. Some properties of the objective function are found which allow to construct a optimal schedule. We develop a simulated annealing algorithm, a tabu search algorithm, and a genetic algorithm for solving this problem. The developed algorithms were tested on moderate and large instances with up to 500 jobs and 50 machines. Some recommendations are given showing how to use the obtained results and developed algorithms in production planning.
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