TY - JOUR A1 - Konigorski, Stefan A1 - Wernicke, Sarah A1 - Slosarek, Tamara A1 - Zenner, Alexander M. A1 - Strelow, Nils A1 - Ruether, Darius F. A1 - Henschel, Florian A1 - Manaswini, Manisha A1 - Pottbäcker, Fabian A1 - Edelman, Jonathan A. A1 - Owoyele, Babajide A1 - Danieletto, Matteo A1 - Golden, Eddye A1 - Zweig, Micol A1 - Nadkarni, Girish N. A1 - Böttinger, Erwin T1 - StudyU: a platform for designing and conducting innovative digital N-of-1 trials JF - Journal of medical internet research N2 - 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. KW - digital interventions KW - N-of-1 trial KW - SCED KW - single-case experimental design KW - web application KW - mobile application KW - app KW - digital health Y1 - 2022 U6 - https://doi.org/10.2196/35884 SN - 1439-4456 SN - 1438-8871 VL - 24 IS - 7 PB - Healthcare World CY - Richmond, Va. ER - TY - GEN A1 - Dellepiane, Sergio A1 - Vaid, Akhil A1 - Jaladanki, Suraj K. A1 - Coca, Steven A1 - Fayad, Zahi A. A1 - Charney, Alexander W. A1 - Böttinger, Erwin A1 - He, John Cijiang A1 - Glicksberg, Benjamin S. A1 - Chan, Lili A1 - Nadkarni, Girish T1 - Acute kidney injury in patients hospitalized with COVID-19 in New York City BT - Temporal Trends From March 2020 to April 2021 T2 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 21 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-585415 SN - 2590-0595 IS - 5 ER - TY - JOUR A1 - Langenhan, Jennifer A1 - Jaeger, Carsten A1 - Baum, Katharina A1 - Simon, Mareike A1 - Lisec, Jan T1 - A flexible tool to correct superimposed mass isotopologue distributions in GC-APCI-MS flux experiments JF - Metabolites N2 - 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. KW - mass isotopologue distribution KW - enrichment calculation KW - flux KW - experiments KW - atmospheric pressure chemical ionization KW - R package KW - CorMID Y1 - 2022 U6 - https://doi.org/10.3390/metabo12050408 SN - 2218-1989 VL - 12 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Sinn, Ludwig R. A1 - Giese, Sven Hans-Joachim A1 - Stuiver, Marchel A1 - Rappsilber, Juri T1 - Leveraging parameter dependencies in high-field asymmetric waveform ion-mobility spectrometry and size exclusion chromatography for proteome-wide cross-linking mass spectrometry JF - Analytical chemistry : the authoritative voice of the analytical community N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1021/acs.analchem.1c04373 SN - 0003-2700 SN - 1520-6882 VL - 94 IS - 11 SP - 4627 EP - 4634 PB - American Chemical Society CY - Columbus, Ohio ER - TY - JOUR A1 - Gevay, Gabor E. A1 - Rabl, Tilmann A1 - Bress, Sebastian A1 - Maclai-Tahy, Lorand A1 - Quiane-Ruiz, Jorge-Arnulfo A1 - Markl, Volker T1 - Imperative or Functional Control Flow Handling: Why not the Best of Both Worlds? JF - SIGMOD record N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1109/ICDE51399.2021.00127 SN - 0163-5808 SN - 1943-5835 VL - 51 IS - 1 SP - 60 EP - 67 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Verweij, Marco A1 - Ney, Steven A1 - Thompson, Michael T1 - Cultural Theory’s contributions to climate science BT - reply to Hansson JF - European journal for philosophy of science N2 - 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. KW - Mary Douglas KW - Aaron Wildavsky KW - Cultural theory KW - Climate change Y1 - 2022 U6 - https://doi.org/10.1007/s13194-022-00464-y SN - 1879-4912 SN - 1879-4920 VL - 12 IS - 2 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Boissier, Martin T1 - Robust and budget-constrained encoding configurations for in-memory database systems JF - Proceedings of the VLDB Endowment N2 - Data encoding has been applied to database systems for decades as it mitigates bandwidth bottlenecks and reduces storage requirements. But even in the presence of these advantages, most in-memory database systems use data encoding only conservatively as the negative impact on runtime performance can be severe. Real-world systems with large parts being infrequently accessed and cost efficiency constraints in cloud environments require solutions that automatically and efficiently select encoding techniques, including heavy-weight compression. In this paper, we introduce workload-driven approaches to automaticaly determine memory budget-constrained encoding configurations using greedy heuristics and linear programming. We show for TPC-H, TPC-DS, and the Join Order Benchmark that optimized encoding configurations can reduce the main memory footprint significantly without a loss in runtime performance over state-of-the-art dictionary encoding. To yield robust selections, we extend the linear programming-based approach to incorporate query runtime constraints and mitigate unexpected performance regressions. KW - General Earth and Planetary Sciences KW - Water Science and Technology KW - Geography, Planning and Development Y1 - 2021 U6 - https://doi.org/10.14778/3503585.3503588 SN - 2150-8097 VL - 15 IS - 4 SP - 780 EP - 793 PB - Association for Computing Machinery (ACM) CY - [New York] ER - TY - JOUR A1 - Björk, Jennie A1 - Hölzle, Katharina A1 - Boer, Harry T1 - ‘What will we learn from the current crisis?’ JF - Creativity and innovation management Y1 - 2021 U6 - https://doi.org/10.1111/caim.12442 SN - 0963-1690 SN - 1467-8691 VL - 30 IS - 2 SP - 231 EP - 232 PB - Wiley-Blackwell CY - Oxford [u.a.] ER - TY - JOUR A1 - Bonifati, Angela A1 - Mior, Michael J. A1 - Naumann, Felix A1 - Noack, Nele Sina T1 - How inclusive are we? BT - an analysis of gender diversity in database venues JF - SIGMOD record / Association for Computing Machinery, Special Interest Group on Management of Data N2 - ACM SIGMOD, VLDB and other database organizations have committed to fostering an inclusive and diverse community, as do many other scientific organizations. Recently, different measures have been taken to advance these goals, especially for underrepresented groups. One possible measure is double-blind reviewing, which aims to hide gender, ethnicity, and other properties of the authors.
We report the preliminary results of a gender diversity analysis of publications of the database community across several peer-reviewed venues, and also compare women's authorship percentages in both single-blind and double-blind venues along the years. We also obtained a cross comparison of the obtained results in data management with other relevant areas in Computer Science. Y1 - 2022 U6 - https://doi.org/10.1145/3516431.3516438 SN - 0163-5808 SN - 1943-5835 VL - 50 IS - 4 SP - 30 EP - 35 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Reimann, Max A1 - Buchheim, Benito A1 - Semmo, Amir A1 - Döllner, Jürgen A1 - Trapp, Matthias T1 - Controlling strokes in fast neural style transfer using content transforms JF - The Visual Computer N2 - Fast style transfer methods have recently gained popularity in art-related applications as they make a generalized real-time stylization of images practicable. However, they are mostly limited to one-shot stylizations concerning the interactive adjustment of style elements. In particular, the expressive control over stroke sizes or stroke orientations remains an open challenge. To this end, we propose a novel stroke-adjustable fast style transfer network that enables simultaneous control over the stroke size and intensity, and allows a wider range of expressive editing than current approaches by utilizing the scale-variance of convolutional neural networks. Furthermore, we introduce a network-agnostic approach for style-element editing by applying reversible input transformations that can adjust strokes in the stylized output. At this, stroke orientations can be adjusted, and warping-based effects can be applied to stylistic elements, such as swirls or waves. To demonstrate the real-world applicability of our approach, we present StyleTune, a mobile app for interactive editing of neural style transfers at multiple levels of control. Our app allows stroke adjustments on a global and local level. It furthermore implements an on-device patch-based upsampling step that enables users to achieve results with high output fidelity and resolutions of more than 20 megapixels. Our approach allows users to art-direct their creations and achieve results that are not possible with current style transfer applications. Y1 - 2022 U6 - https://doi.org/10.1007/s00371-022-02518-x SN - 0178-2789 SN - 1432-2315 VL - 38 IS - 12 SP - 4019 EP - 4033 PB - Springer CY - New York ER - TY - JOUR A1 - Borchert, Florian A1 - Mock, Andreas A1 - Tomczak, Aurelie A1 - Hügel, Jonas A1 - Alkarkoukly, Samer A1 - Knurr, Alexander A1 - Volckmar, Anna-Lena A1 - Stenzinger, Albrecht A1 - Schirmacher, Peter A1 - Debus, Jürgen A1 - Jäger, Dirk A1 - Longerich, Thomas A1 - Fröhling, Stefan A1 - Eils, Roland A1 - Bougatf, Nina A1 - Sax, Ulrich A1 - Schapranow, Matthieu-Patrick T1 - Correction to: Knowledge bases and software support for variant interpretation in precision oncology JF - Briefings in bioinformatics Y1 - 2021 U6 - https://doi.org/10.1093/bib/bbab246 SN - 1467-5463 SN - 1477-4054 VL - 22 IS - 6 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Borchert, Florian A1 - Mock, Andreas A1 - Tomczak, Aurelie A1 - Hügel, Jonas A1 - Alkarkoukly, Samer A1 - Knurr, Alexander A1 - Volckmar, Anna-Lena A1 - Stenzinger, Albrecht A1 - Schirmacher, Peter A1 - Debus, Jürgen A1 - Jäger, Dirk A1 - Longerich, Thomas A1 - Fröhling, Stefan A1 - Eils, Roland A1 - Bougatf, Nina A1 - Sax, Ulrich A1 - Schapranow, Matthieu-Patrick T1 - Knowledge bases and software support for variant interpretation in precision oncology JF - Briefings in bioinformatics N2 - Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process. KW - HiGHmed KW - personalized medicine KW - molecular tumor board KW - data integration KW - cancer therapy Y1 - 2021 U6 - https://doi.org/10.1093/bib/bbab134 SN - 1467-5463 SN - 1477-4054 VL - 22 IS - 6 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Prill, Robert A1 - Walter, Marina A1 - Królikowska, Aleksandra A1 - Becker, Roland T1 - A systematic review of diagnostic accuracy and clinical applications of wearable movement sensors for knee joint rehabilitation JF - Sensors N2 - In clinical practice, only a few reliable measurement instruments are available for monitoring knee joint rehabilitation. Advances to replace motion capturing with sensor data measurement have been made in the last years. Thus, a systematic review of the literature was performed, focusing on the implementation, diagnostic accuracy, and facilitators and barriers of integrating wearable sensor technology in clinical practices based on a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. For critical appraisal, the COSMIN Risk of Bias tool for reliability and measurement of error was used. PUBMED, Prospero, Cochrane database, and EMBASE were searched for eligible studies. Six studies reporting reliability aspects in using wearable sensor technology at any point after knee surgery in humans were included. All studies reported excellent results with high reliability coefficients, high limits of agreement, or a few detectable errors. They used different or partly inappropriate methods for estimating reliability or missed reporting essential information. Therefore, a moderate risk of bias must be considered. Further quality criterion studies in clinical settings are needed to synthesize the evidence for providing transparent recommendations for the clinical use of wearable movement sensors in knee joint rehabilitation. KW - wearable movement sensor KW - IMU KW - motion capture KW - reliability KW - clinical KW - orthopedic Y1 - 2021 U6 - https://doi.org/10.3390/s21248221 SN - 1424-8220 VL - 21 IS - 24 PB - MDPI CY - Basel ER - TY - JOUR A1 - Chan, Lili A1 - Jaladanki, Suraj K. A1 - Somani, Sulaiman A1 - Paranjpe, Ishan A1 - Kumar, Arvind A1 - Zhao, Shan A1 - Kaufman, Lewis A1 - Leisman, Staci A1 - Sharma, Shuchita A1 - He, John Cijiang A1 - Murphy, Barbara A1 - Fayad, Zahi A. A1 - Levin, Matthew A. A1 - Böttinger, Erwin A1 - Charney, Alexander W. A1 - Glicksberg, Benjamin A1 - Coca, Steven G. A1 - Nadkarni, Girish N. T1 - Outcomes of patients on maintenance dialysis hospitalized with COVID-19 JF - Clinical journal of the American Society of Nephrology : CJASN KW - chronic dialysis KW - COVID-19 KW - end-stage kidney disease Y1 - 2021 U6 - https://doi.org/10.2215/CJN.12360720 SN - 1555-9041 SN - 1555-905X VL - 16 IS - 3 SP - 452 EP - 455 PB - American Society of Nephrology CY - Washington ER - TY - JOUR A1 - Datta, Suparno A1 - Sachs, Jan Philipp A1 - Freitas da Cruz, Harry A1 - Martensen, Tom A1 - Bode, Philipp A1 - Morassi Sasso, Ariane A1 - Glicksberg, Benjamin S. A1 - Böttinger, Erwin T1 - FIBER BT - enabling flexible retrieval of electronic health records data for clinical predictive modeling JF - JAMIA open N2 - Objectives: The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. Materials and Methods: FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. Results: Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case. Conclusion: FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process. KW - databases KW - factual KW - electronic health records KW - information storage and KW - retrieval KW - workflow KW - software/instrumentation Y1 - 2021 U6 - https://doi.org/10.1093/jamiaopen/ooab048 SN - 2574-2531 VL - 4 IS - 3 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - De Freitas, Jessica K. A1 - Johnson, Kipp W. A1 - Golden, Eddye A1 - Nadkarni, Girish N. A1 - Dudley, Joel T. A1 - Böttinger, Erwin A1 - Glicksberg, Benjamin S. A1 - Miotto, Riccardo T1 - Phe2vec BT - Automated disease phenotyping based on unsupervised embeddings from electronic health records JF - Patterns N2 - Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts. Y1 - 2021 U6 - https://doi.org/10.1016/j.patter.2021.100337 SN - 2666-3899 VL - 2 IS - 9 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Adnan, Hassan Sami A1 - Srsic, Amanda A1 - Venticich, Pete Milos A1 - Townend, David M.R. T1 - Using AI for mental health analysis and prediction in school surveys JF - European journal of public health N2 - Background: Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods: For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results: We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions: This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects. KW - ethics KW - artificial intelligence KW - adolescent KW - child KW - confidentiality KW - health personnel KW - mental disorders KW - mental health KW - personal satisfaction KW - privacy KW - school (environment) KW - statutes and laws KW - public health medicine KW - surveillance KW - medical KW - prevention KW - datasets KW - machine learning KW - supervised machine learning KW - data analysis Y1 - 2020 U6 - https://doi.org/10.1093/eurpub/ckaa165.336 SN - 1101-1262 SN - 1464-360X VL - 30 SP - V125 EP - V125 PB - Oxford Univ. Press CY - Oxford [u.a.] ER - TY - JOUR A1 - Chan, Lili A1 - Chaudhary, Kumardeep A1 - Saha, Aparna A1 - Chauhan, Kinsuk A1 - Vaid, Akhil A1 - Zhao, Shan A1 - Paranjpe, Ishan A1 - Somani, Sulaiman A1 - Richter, Felix A1 - Miotto, Riccardo A1 - Lala, Anuradha A1 - Kia, Arash A1 - Timsina, Prem A1 - Li, Li A1 - Freeman, Robert A1 - Chen, Rong A1 - Narula, Jagat A1 - Just, Allan C. A1 - Horowitz, Carol A1 - Fayad, Zahi A1 - Cordon-Cardo, Carlos A1 - Schadt, Eric A1 - Levin, Matthew A. A1 - Reich, David L. A1 - Fuster, Valentin A1 - Murphy, Barbara A1 - He, John C. A1 - Charney, Alexander W. A1 - Böttinger, Erwin A1 - Glicksberg, Benjamin A1 - Coca, Steven G. A1 - Nadkarni, Girish N. T1 - AKI in hospitalized patients with COVID-19 JF - Journal of the American Society of Nephrology : JASN N2 - Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associatedwith worse outcomes. However, AKI among hospitalized patients with COVID19 in the United States is not well described. Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged >= 18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patientswith AKI required dialysis. The proportionswith stages 1, 2, or 3 AKIwere 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge. KW - acute renal failure KW - clinical nephrology KW - dialysis KW - COVID-19 Y1 - 2021 U6 - https://doi.org/10.1681/ASN.2020050615 SN - 1046-6673 SN - 1533-3450 VL - 32 IS - 1 SP - 151 EP - 160 PB - American Society of Nephrology CY - Washington ER - TY - JOUR A1 - Oliveira-Ciabati, Livia A1 - Loures dos Santos, Luciane A1 - Hsiou Schmaltz, Annie A1 - Sasso, Ariane Morassi A1 - Castro, Margaret de A1 - Souza, João Paulo T1 - Scientific sexism BT - the gender bias in the scientific production of the Universidade de São Paulo JF - Revista de saúde pública : publication of the Faculdade de Saúde Pública da Universidade de São Paulo = Journal of public health N2 - OBJECTIVE: To investigate gender inequity in the scientific production of the University of Sao Paulo. METHODS: Members of the University of Sao Paulo faculty are the study population. The Web of Science repository was the source of the publication metrics. We selected the measures: total publications and citations, average of citations per year and item, H-index, and history of citations between 1950 and 2019. We used the name of the faculty member as a proxy to the gender identity. We use descriptive statistics to characterize the metrics. We evaluated the scissors effect by selecting faculty members with a high H-index. The historical series of citations was projected until 2100. We carry out analyses for the general population and working time subgroups: less than 10 years, 10 to 20 years, and 20 years or more. RESULTS: Of the 8,325 faculty members, we included 3,067 (36.8%). Among those included, 1,893 (61.7%) were male and 1,174 (38.28%) female. The male gender presented higher values in the publication metrics (average of articles: M = 67.0 versus F = 49.7; average of citations/year: M = 53.9 versus F = 35.9), and H-index (M = 14.5 versus F = 12.4). Among the 100 individuals with the highest H-index (>= 37), 83% are male. The male curve grows faster in the historical series of citations, opening a difference between the groups whose separation is confirmed by the projection. DISCUSSION: Scientific production at the Universidade de Sao Paulo is subject to a gender bias. Two-thirds of the faculty are male, and hiring over the past few decades perpetuates this pattern. The large majority of high impact faculty members are male. CONCLUSION: Our analysis suggests that the Universidade de Sao Paulo will not overcome gender inequality in scientific production without substantive affirmative action. Development does not happen by chance but through choices that are affirmative, decisive, and long-term oriented. KW - Sexism KW - Scientific Publication Indicators KW - Gender Inequality Y1 - 2021 U6 - https://doi.org/10.11606/s1518-8787.2021055002939 SN - 1518-8787 VL - 55 PB - Faculdade de Saúde Pública da Universidade de São Paulo CY - São Paulo ER - TY - JOUR A1 - Long, Xiang A1 - de Melo, Gerard A1 - He, Dongliang A1 - Li, Fu A1 - Chi, Zhizhen A1 - Wen, Shilei A1 - Gan, Chuang T1 - Purely attention based local feature integration for video classification JF - IEEE Transactions on Pattern Analysis and Machine Intelligence N2 - Recently, substantial research effort has focused on how to apply CNNs or RNNs to better capture temporal patterns in videos, so as to improve the accuracy of video classification. In this paper, we investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we first propose Basic Attention Clusters (BAC), which concatenates the output of multiple attention units applied in parallel, and introduce a shifting operation to capture more diverse signals. Experiments show that BAC can achieve excellent results on multiple datasets. However, BAC treats all feature channels as an indivisible whole, which is suboptimal for achieving a finer-grained local feature integration over the channel dimension. Additionally, it treats the entire local feature sequence as an unordered set, thus ignoring the sequential relationships. To improve over BAC, we further propose the channel pyramid attention schema by splitting features into sub-features at multiple scales for coarse-to-fine sub-feature interaction modeling, and propose the temporal pyramid attention schema by dividing the feature sequences into ordered sub-sequences of multiple lengths to account for the sequential order. Our final model pyramidxpyramid attention clusters (PPAC) combines both channel pyramid attention and temporal pyramid attention to focus on the most important sub-features, while also preserving the temporal information of the video. We demonstrate the effectiveness of PPAC on seven real-world video classification datasets. Our model achieves competitive results across all of these, showing that our proposed framework can consistently outperform the existing local feature integration methods across a range of different scenarios. KW - Feature extraction KW - Convolution KW - Computational modeling KW - Plugs KW - Three-dimensional displays KW - Task analysis KW - Two dimensional displays KW - Video classification KW - action recognition KW - attention mechanism KW - computer vision KW - Algorithms KW - Neural Networks KW - Computer Y1 - 2020 U6 - https://doi.org/10.1109/TPAMI.2020.3029554 SN - 0162-8828 SN - 1939-3539 SN - 2160-9292 VL - 44 IS - 4 SP - 2140 EP - 2154 PB - Inst. of Electr. and Electronics Engineers CY - Los Alamitos ER -