@article{TalebRohrerBergneretal.2022, author = {Taleb, Aiham and Rohrer, Csaba and Bergner, Benjamin and De Leon, Guilherme and Rodrigues, Jonas Almeida and Schwendicke, Falk and Lippert, Christoph and Krois, Joachim}, title = {Self-supervised learning methods for label-efficient dental caries classification}, series = {Diagnostics : open access journal}, volume = {12}, journal = {Diagnostics : open access journal}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {2075-4418}, doi = {10.3390/diagnostics12051237}, pages = {15}, year = {2022}, abstract = {High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce >= 45\% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive.}, language = {en} } @misc{TangNakamotoSternetal.2022, author = {Tang, Mitchell and Nakamoto, Carter H. and Stern, Ariel Dora and Mehrotra, Ateev}, title = {Trends in remote patient monitoring use in traditional medicare}, series = {JAMA internal medicine}, volume = {182}, journal = {JAMA internal medicine}, number = {9}, publisher = {American Medical Association}, address = {Chicago, Ill.}, issn = {2168-6106}, doi = {10.1001/jamainternmed.2022.3043}, pages = {1005 -- 1006}, year = {2022}, language = {en} } @article{AltenburgGieseWangetal.2022, author = {Altenburg, Tom and Giese, Sven Hans-Joachim and Wang, Shengbo and Muth, Thilo and Renard, Bernhard Y.}, title = {Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides}, series = {Nature machine intelligence}, volume = {4}, journal = {Nature machine intelligence}, number = {4}, publisher = {Springer Nature Publishing}, address = {London}, issn = {2522-5839}, doi = {10.1038/s42256-022-00467-7}, pages = {378 -- 388}, year = {2022}, abstract = {Fragmentation of peptides leaves characteristic patterns in mass spectrometry data, which can be used to identify protein sequences, but this method is challenging for mutated or modified sequences for which limited information exist. Altenburg et al. use an ad hoc learning approach to learn relevant patterns directly from unannotated fragmentation spectra. Mass spectrometry-based proteomics provides a holistic snapshot of the entire protein set of living cells on a molecular level. Currently, only a few deep learning approaches exist that involve peptide fragmentation spectra, which represent partial sequence information of proteins. Commonly, these approaches lack the ability to characterize less studied or even unknown patterns in spectra because of their use of explicit domain knowledge. Here, to elevate unrestricted learning from spectra, we introduce 'ad hoc learning of fragmentation' (AHLF), a deep learning model that is end-to-end trained on 19.2 million spectra from several phosphoproteomic datasets. AHLF is interpretable, and we show that peak-level feature importance values and pairwise interactions between peaks are in line with corresponding peptide fragments. We demonstrate our approach by detecting post-translational modifications, specifically protein phosphorylation based on only the fragmentation spectrum without a database search. AHLF increases the area under the receiver operating characteristic curve (AUC) by an average of 9.4\% on recent phosphoproteomic data compared with the current state of the art on this task. Furthermore, use of AHLF in rescoring search results increases the number of phosphopeptide identifications by a margin of up to 15.1\% at a constant false discovery rate. To show the broad applicability of AHLF, we use transfer learning to also detect cross-linked peptides, as used in protein structure analysis, with an AUC of up to 94\%.}, language = {en} } @article{GevayRablBressetal.2022, author = {G{\´e}vay, G{\´a}bor E. and Rabl, Tilmann and Breß, Sebastian and Madai-Tahy, Lor{\´a}nd and Quian{\´e}-Ruiz, Jorge-Arnulfo and Markl, Volker}, title = {Imperative or functional control flow handling}, series = {SIGMOD record / Association for Computing Machinery, Special Interest Group on Management of Data}, volume = {51}, journal = {SIGMOD record / Association for Computing Machinery, Special Interest Group on Management of Data}, number = {1}, publisher = {Association for Computing Machinery}, address = {New York}, issn = {0163-5808}, doi = {10.1145/3542700.3542715}, pages = {60 -- 67}, year = {2022}, abstract = {Modern data analysis tasks often involve control flow statements, such as the iterations in PageRank and K-means. To achieve scalability, developers usually implement these tasks in distributed dataflow systems, such as Spark and Flink. Designers of such systems have to choose between providing imperative or functional control flow constructs to users. Imperative constructs are easier to use, but functional constructs are easier to compile to an efficient dataflow job. We propose Mitos, a system where control flow is both easy to use and efficient. Mitos relies on an intermediate representation based on the static single assignment form. This allows us to abstract away from specific control flow constructs and treat any imperative control flow uniformly both when building the dataflow job and when coordinating the distributed execution.}, language = {en} } @misc{KonigorskiWernickeSlosareketal.2022, author = {Konigorski, Stefan and Wernicke, Sarah and Slosarek, Tamara and Zenner, Alexander M. and Strelow, Nils and Ruether, Darius F. and Henschel, Florian and Manaswini, Manisha and Pottb{\"a}cker, Fabian and Edelman, Jonathan A. and Owoyele, Babajide and Danieletto, Matteo and Golden, Eddye and Zweig, Micol and Nadkarni, Girish N. and B{\"o}ttinger, Erwin}, title = {StudyU: a platform for designing and conducting innovative digital N-of-1 trials}, series = {Journal of medical internet research}, volume = {24}, journal = {Journal of medical internet research}, number = {7}, publisher = {Healthcare World}, address = {Richmond, Va.}, issn = {1439-4456}, doi = {10.2196/35884}, pages = {12}, year = {2022}, abstract = {N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. Digital tools have the potential to initiate a new era of N-of-1 trials in terms of scale and scope, but fully functional platforms are not yet available. Here, we present the open source StudyU platform, which includes the StudyU Designer and StudyU app. With the StudyU Designer, scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials. The StudyU app is a smartphone app with innovative user-centric elements for participants to partake in trials published through the StudyU Designer to assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. We envision that StudyU can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice.}, language = {en} } @misc{DellepianeVaidJaladankietal.2021, author = {Dellepiane, Sergio and Vaid, Akhil and Jaladanki, Suraj K. and Coca, Steven and Fayad, Zahi A. and Charney, Alexander W. and B{\"o}ttinger, Erwin and He, John Cijiang and Glicksberg, Benjamin S. and Chan, Lili and Nadkarni, Girish}, title = {Acute kidney injury in patients hospitalized with COVID-19 in New York City}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t}, number = {5}, issn = {2590-0595}, doi = {10.25932/publishup-58541}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-585415}, pages = {5}, year = {2021}, language = {en} } @article{LangenhanJaegerBaumetal.2022, author = {Langenhan, Jennifer and Jaeger, Carsten and Baum, Katharina and Simon, Mareike and Lisec, Jan}, title = {A flexible tool to correct superimposed mass isotopologue distributions in GC-APCI-MS flux experiments}, series = {Metabolites}, volume = {12}, journal = {Metabolites}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {2218-1989}, doi = {10.3390/metabo12050408}, pages = {10}, year = {2022}, abstract = {The investigation of metabolic fluxes and metabolite distributions within cells by means of tracer molecules is a valuable tool to unravel the complexity of biological systems. Technological advances in mass spectrometry (MS) technology such as atmospheric pressure chemical ionization (APCI) coupled with high resolution (HR), not only allows for highly sensitive analyses but also broadens the usefulness of tracer-based experiments, as interesting signals can be annotated de novo when not yet present in a compound library. However, several effects in the APCI ion source, i.e., fragmentation and rearrangement, lead to superimposed mass isotopologue distributions (MID) within the mass spectra, which need to be corrected during data evaluation as they will impair enrichment calculation otherwise. Here, we present and evaluate a novel software tool to automatically perform such corrections. We discuss the different effects, explain the implemented algorithm, and show its application on several experimental datasets. This adjustable tool is available as an R package from CRAN.}, language = {en} } @article{SinnGieseStuiveretal.2022, author = {Sinn, Ludwig R. and Giese, Sven Hans-Joachim and Stuiver, Marchel and Rappsilber, Juri}, title = {Leveraging parameter dependencies in high-field asymmetric waveform ion-mobility spectrometry and size exclusion chromatography for proteome-wide cross-linking mass spectrometry}, series = {Analytical chemistry : the authoritative voice of the analytical community}, volume = {94}, journal = {Analytical chemistry : the authoritative voice of the analytical community}, number = {11}, publisher = {American Chemical Society}, address = {Columbus, Ohio}, issn = {0003-2700}, doi = {10.1021/acs.analchem.1c04373}, pages = {4627 -- 4634}, year = {2022}, abstract = {Ion-mobility spectrometry shows great promise to tackle analytically challenging research questions by adding another separation dimension to liquid chromatography-mass spectrometry. The understanding of how analyte properties influence ion mobility has increased through recent studies, but no clear rationale for the design of customized experimental settings has emerged. Here, we leverage machine learning to deepen our understanding of field asymmetric waveform ion-mobility spectrometry for the analysis of cross-linked peptides. Knowing that predominantly m/z and then the size and charge state of an analyte influence the separation, we found ideal compensation voltages correlating with the size exclusion chromatography fraction number. The effect of this relationship on the analytical depth can be substantial as exploiting it allowed us to almost double unique residue pair detections in a proteome-wide cross-linking experiment. Other applications involving liquid- and gas-phase separation may also benefit from considering such parameter dependencies.}, language = {en} } @article{GevayRablBressetal.2022, author = {Gevay, Gabor E. and Rabl, Tilmann and Bress, Sebastian and Maclai-Tahy, Lorand and Quiane-Ruiz, Jorge-Arnulfo and Markl, Volker}, title = {Imperative or Functional Control Flow Handling: Why not the Best of Both Worlds?}, series = {SIGMOD record}, volume = {51}, journal = {SIGMOD record}, number = {1}, publisher = {Association for Computing Machinery}, address = {New York}, issn = {0163-5808}, doi = {10.1109/ICDE51399.2021.00127}, pages = {60 -- 67}, year = {2022}, abstract = {Modern data analysis tasks often involve control flow statements, such as the iterations in PageRank and K-means. To achieve scalability, developers usually implement these tasks in distributed dataflow systems, such as Spark and Flink. Designers of such systems have to choose between providing imperative or functional control flow constructs to users. Imperative constructs are easier to use, but functional constructs are easier to compile to an efficient dataflow job. We propose Mitos, a system where control flow is both easy to use and efficient. Mitos relies on an intermediate representation based on the static single assignment form. This allows us to abstract away from specific control flow constructs and treat any imperative control flow uniformly both when building the dataflow job and when coordinating the distributed execution.}, language = {en} } @article{VerweijNeyThompson2022, author = {Verweij, Marco and Ney, Steven and Thompson, Michael}, title = {Cultural Theory's contributions to climate science}, series = {European journal for philosophy of science}, volume = {12}, journal = {European journal for philosophy of science}, number = {2}, publisher = {Springer}, address = {Dordrecht}, issn = {1879-4912}, doi = {10.1007/s13194-022-00464-y}, pages = {13}, year = {2022}, abstract = {In his article, 'Social constructionism and climate science denial', Hansson claims to present empirical evidence that the cultural theory developed by Dame Mary Douglas, Aaron Wildavsky and ourselves (among others) leads to (climate) science denial. In this reply, we show that there is no validity to these claims. First, we show that Hansson's empirical evidence that cultural theory has led to climate science denial falls apart under closer inspection. Contrary to Hansson's claims, cultural theory has made significant contributions to understanding and addressing climate change. Second, we discuss various features of Douglas' cultural theory that differentiate it from other constructivist approaches and make it compatible with the scientific method. Thus, we also demonstrate that cultural theory cannot be accused of epistemic relativism.}, language = {en} }