@article{WenderingNikoloski2022, author = {Wendering, Philipp and Nikoloski, Zoran}, title = {COMMIT}, series = {PLoS Computational Biology : a new community journal / publ. by the Public Library of Science (PLoS) in association with the International Society for Computational Biology (ISCB)}, volume = {18}, journal = {PLoS Computational Biology : a new community journal / publ. by the Public Library of Science (PLoS) in association with the International Society for Computational Biology (ISCB)}, number = {3}, publisher = {Public Library of Science}, address = {San Fransisco}, issn = {1553-734X}, doi = {10.1371/journal.pcbi.1009906}, pages = {24}, year = {2022}, abstract = {Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.
Author summaryMicrobial communities are important in ecology, human health, and crop productivity. However, detailed information on the interactions within natural microbial communities is hampered by the community size, lack of detailed information on the biochemistry of single organisms, and the complexity of interactions between community members. Metabolic models are comprised of biochemical reaction networks based on the genome annotation, and can provide mechanistic insights into community functions. Previous analyses of microbial community models have been performed with high-quality reference models or models generated using a single reconstruction pipeline. However, these models do not contain information on the composition of the community that determines the metabolites exchanged between the community members. In addition, the quality of metabolic models is affected by the reconstruction approach used, with direct consequences on the inferred interactions between community members. Here, we use fully automated consensus reconstructions from four approaches to arrive at functional models with improved genomic support while considering the community composition. We applied our pipeline to two soil communities from the Arabidopsis thaliana culture collection, providing only genome sequences. Finally, we show that the obtained models have 90\% genomic support and demonstrate that the derived interactions are corroborated by independent computational predictions.}, language = {en} } @misc{BenlianWienerCrametal.2022, author = {Benlian, Alexander and Wiener, Martin and Cram, W. Alec and Krasnova, Hanna and Maedche, Alexander and Mohlmann, Mareike and Recker, Jan and Remus, Ulrich}, title = {Algorithmic management}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, number = {6}, issn = {2363-7005}, doi = {10.25932/publishup-60711}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-607112}, pages = {17}, year = {2022}, language = {en} } @article{BenlianWienerCrametal.2022, author = {Benlian, Alexander and Wiener, Martin and Cram, W. Alec and Krasnova, Hanna and Maedche, Alexander and Mohlmann, Mareike and Recker, Jan and Remus, Ulrich}, title = {Algorithmic management}, series = {Business and information systems engineering}, volume = {64}, journal = {Business and information systems engineering}, number = {6}, publisher = {Springer Gabler}, address = {Wiesbaden}, issn = {2363-7005}, doi = {10.1007/s12599-022-00764-w}, pages = {825 -- 839}, year = {2022}, language = {en} } @article{BensonMakaitRabl2021, author = {Benson, Lawrence and Makait, Hendrik and Rabl, Tilmann}, title = {Viper}, series = {Proceedings of the VLDB Endowment}, volume = {14}, journal = {Proceedings of the VLDB Endowment}, number = {9}, publisher = {Association for Computing Machinery}, address = {New York}, issn = {2150-8097}, doi = {10.14778/3461535.3461543}, pages = {1544 -- 1556}, year = {2021}, abstract = {Key-value stores (KVSs) have found wide application in modern software systems. For persistence, their data resides in slow secondary storage, which requires KVSs to employ various techniques to increase their read and write performance from and to the underlying medium. Emerging persistent memory (PMem) technologies offer data persistence at close-to-DRAM speed, making them a promising alternative to classical disk-based storage. However, simply drop-in replacing existing storage with PMem does not yield good results, as block-based access behaves differently in PMem than on disk and ignores PMem's byte addressability, layout, and unique performance characteristics. In this paper, we propose three PMem-specific access patterns and implement them in a hybrid PMem-DRAM KVS called Viper. We employ a DRAM-based hash index and a PMem-aware storage layout to utilize the random-write speed of DRAM and efficient sequential-write performance PMem. Our evaluation shows that Viper significantly outperforms existing KVSs for core KVS operations while providing full data persistence. Moreover, Viper outperforms existing PMem-only, hybrid, and disk-based KVSs by 4-18x for write workloads, while matching or surpassing their get performance.}, language = {en} } @inproceedings{SultanowChircuWuestemannetal.2022, author = {Sultanow, Eldar and Chircu, Alina and W{\"u}stemann, Stefanie and Schwan, Andr{\´e} and Lehmann, Andreas and Sept, Andr{\´e} and Szymaski, Oliver and Venkatesan, Sripriya and Ritterbusch, Georg David and Teichmann, Malte Rolf}, title = {Metaverse opportunities for the public sector}, series = {International Conference on Information Systems 2022 : Special Interest Group on Big Data : Proceedings}, booktitle = {International Conference on Information Systems 2022 : Special Interest Group on Big Data : Proceedings}, publisher = {AIS}, address = {Atlanta}, year = {2022}, abstract = {The metaverse is envisioned as a virtual shared space facilitated by emerging technologies such as virtual reality (VR), augmented reality (AR), the Internet of Things (IoT), 5G, artificial intelligence (AI), big data, spatial computing, and digital twins (Allam et al., 2022; Dwivedi et al., 2022; Ravenscraft, 2022; Wiles, 2022). While still a nascent concept, the metaverse has the potential to "transform the physical world, as well as transport or extend physical activities to a virtual world" (Wiles, 2022). Big data technologies will also be essential in managing the enormous amounts of data created in the metaverse (Sun et al., 2022). Metaverse technologies can offer the public sector a host of benefits, such as simplified information exchange, stronger communication with citizens, better access to public services, or benefiting from a new virtual economy. Implementations are underway in several cities around the world (Geraghty et al., 2022). In this paper, we analyze metaverse opportunities for the public sector and explore their application in the context of Germany's Federal Employment Agency. Based on an analysis of academic literature and practical examples, we create a capability map for potential metaverse business capabilities for different areas of the public sector (broadly defined). These include education (virtual training and simulation, digital campuses that offer not just online instruction but a holistic university campus experience, etc.), tourism (virtual travel to remote locations and museums, virtual festival participation, etc.), health (employee training - as for emergency situations, virtual simulations for patient treatment - for example, for depression or anxiety, etc.), military (virtual training to experience operational scenarios without being exposed to a real-world threats, practice strategic decision-making, or gain technical knowledge for operating and repairing equipment, etc.), administrative services (document processing, virtual consultations for citizens, etc.), judiciary (AI decision-making aids, virtual proceedings, etc.), public safety (virtual training for procedural issues, special operations, or unusual situations, etc.), emergency management (training for natural disasters, etc.), and city planning (visualization of future development projects and interactive feedback, traffic management, attraction gamification, etc.), among others. We further identify several metaverse application areas for Germany's Federal Employment Agency. These applications can help it realize the goals of the German government for digital transformation that enables faster, more effective, and innovative government services. They include training of employees, training of customers, and career coaching for customers. These applications can be implemented using interactive learning games with AI agents, virtual representations of the organizational spaces, and avatars interacting with each other in these spaces. Metaverse applications will both use big data (to design the virtual environments) and generate big data (from virtual interactions). Issues related to data availability, quality, storage, processing (and related computing power requirements), interoperability, sharing, privacy and security will need to be addressed in these emerging metaverse applications (Sun et al., 2022). Special attention is needed to understand the potential for power inequities (wealth inequity, algorithmic bias, digital exclusion) due to technologies such as VR (Egliston \& Carter, 2021), harmful surveillance practices (Bibri \& Allam, 2022), and undesirable user behavior or negative psychological impacts (Dwivedi et al., 2022). The results of this exploratory study can inform public sector organizations of emerging metaverse opportunities and enable them to develop plans for action as more of the metaverse technologies become a reality. While the metaverse body of research is still small and research agendas are only now starting to emerge (Dwivedi et al., 2022), this study offers a building block for future development and analysis of metaverse applications.}, language = {en} } @inproceedings{KrauseBaumann2021, author = {Krause, Hannes-Vincent and Baumann, Annika}, title = {The devil in disguise}, series = {ICIS 2021: user behaviors, engagement, and consequences}, booktitle = {ICIS 2021: user behaviors, engagement, and consequences}, publisher = {AIS Electronic Library (AISeL)}, address = {[Erscheinungsort nicht ermittelbar]}, year = {2021}, abstract = {Envy constitutes a serious issue on Social Networking Sites (SNSs), as this painful emotion can severely diminish individuals' well-being. With prior research mainly focusing on the affective consequences of envy in the SNS context, its behavioral consequences remain puzzling. While negative interactions among SNS users are an alarming issue, it remains unclear to which extent the harmful emotion of malicious envy contributes to these toxic dynamics. This study constitutes a first step in understanding malicious envy's causal impact on negative interactions within the SNS sphere. Within an online experiment, we experimentally induce malicious envy and measure its immediate impact on users' negative behavior towards other users. Our findings show that malicious envy seems to be an essential factor fueling negativity among SNS users and further illustrate that this effect is especially pronounced when users are provided an objective factor to mask their envy and justify their norm-violating negative behavior.}, language = {en} } @misc{SeewannVerwiebeBuderetal.2022, author = {Seewann, Lena and Verwiebe, Roland and Buder, Claudia and Fritsch, Nina-Sophie}, title = {"Broadcast your gender."}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe}, number = {152}, issn = {1867-5808}, doi = {10.25932/publishup-56628}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-566287}, pages = {16}, year = {2022}, abstract = {Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Na{\"i}ve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.}, language = {en} } @article{SeewannVerwiebeBuderetal.2022, author = {Seewann, Lena and Verwiebe, Roland and Buder, Claudia and Fritsch, Nina-Sophie}, title = {"Broadcast your gender."}, series = {Frontiers in Big Data}, journal = {Frontiers in Big Data}, number = {5}, publisher = {Frontiers}, address = {Lausanne, Schweiz}, issn = {2624-909X}, doi = {10.3389/fdata.2022.908636}, pages = {16}, year = {2022}, abstract = {Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Na{\"i}ve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.}, language = {en} } @article{WrightWachsHarper2018, author = {Wright, Michelle F. and Wachs, Sebastian and Harper, Bridgette D.}, title = {The moderation of empathy in the longitudinal association between witnessing cyberbullying, depression, and anxiety}, series = {Journal of Psychosocial Research on Cyberspace}, volume = {12}, journal = {Journal of Psychosocial Research on Cyberspace}, number = {4}, publisher = {Masrykova Univ.}, address = {Brno}, issn = {1802-7962}, doi = {10.5817/CP2018-4-6}, pages = {14}, year = {2018}, abstract = {While the role of and consequences of being a bystander to face-to-face bullying has received some attention in the literature, to date, little is known about the effects of being a bystander to cyberbullying. It is also unknown how empathy might impact the negative consequences associated with being a bystander of cyberbullying. The present study focused on examining the longitudinal association between bystander of cyberbullying depression, and anxiety, and the moderating role of empathy in the relationship between bystander of cyberbullying and subsequent depression and anxiety. There were 1,090 adolescents (M-age = 12.19; 50\% female) from the United States included at Time 1, and they completed questionnaires on empathy, cyberbullying roles (bystander, perpetrator, victim), depression, and anxiety. One year later, at Time 2, 1,067 adolescents (M-age = 13.76; 51\% female) completed questionnaires on depression and anxiety. Results revealed a positive association between bystander of cyberbullying and depression and anxiety. Further, empathy moderated the positive relationship between bystander of cyberbullying and depression, but not for anxiety. Implications for intervention and prevention programs are discussed.}, language = {en} } @article{XuRazaghiMoghadamNikoloski2021, author = {Xu, Rudan and Razaghi-Moghadam, Zahra and Nikoloski, Zoran}, title = {Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli}, series = {Bioinformatics}, volume = {37}, journal = {Bioinformatics}, number = {21}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btab575}, pages = {3848 -- 3855}, year = {2021}, abstract = {Motivation: Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms. Results: Here, we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data from Escherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation > 0.9). We also find that the maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes.}, language = {en} }