Gold Open-Access
Refine
Year of publication
Document Type
- Article (1314)
- Review (60)
- Monograph/Edited Volume (40)
- Part of Periodical (29)
- Other (22)
- Master's Thesis (15)
- Doctoral Thesis (14)
- Report (7)
- Bachelor Thesis (6)
- Working Paper (4)
Language
- English (1138)
- German (335)
- Spanish (22)
- French (17)
- Italian (2)
- Portuguese (2)
- Russian (2)
- Multiple languages (1)
Keywords
- random point processes (19)
- statistical mechanics (19)
- stochastic analysis (19)
- Jewish Studies (12)
- Jüdische Studien (12)
- Genisa (11)
- Geniza (11)
- Ländliches Judentum (11)
- Rural Jewry (11)
- USA (11)
Institute
- Institut für Biochemie und Biologie (243)
- Extern (236)
- Institut für Romanistik (135)
- Institut für Geowissenschaften (133)
- Institut für Physik und Astronomie (101)
- Historisches Institut (100)
- Institut für Umweltwissenschaften und Geographie (80)
- Strukturbereich Kognitionswissenschaften (71)
- Vereinigung für Jüdische Studien e. V. (63)
- Hasso-Plattner-Institut für Digital Engineering GmbH (60)
Its properties make copper one of the world’s most important functional metals. Numerous megatrends are increasing the demand for copper. This requires the prospection and exploration of new deposits, as well as the monitoring of copper quality in the various production steps. A promising technique to perform these tasks is Laser Induced Breakdown Spectroscopy (LIBS). Its unique feature, among others, is the ability to measure on site without sample collection and preparation. In this work, copper-bearing minerals from two different deposits are studied. The first set of field samples come from a volcanogenic massive sulfide (VMS) deposit, the second part from a stratiform sedimentary copper (SSC) deposit. Different approaches are used to analyze the data. First, univariate regression (UVR) is used. However, due to the strong influence of matrix effects, this is not suitable for the quantitative analysis of copper grades. Second, the multivariate method of partial least squares regression (PLSR) is used, which is more suitable for quantification. In addition, the effects of the surrounding matrices on the LIBS data are characterized by principal component analysis (PCA), alternative regression methods to PLSR are tested and the PLSR calibration is validated using field samples.
Long COVID patients show symptoms, such as fatigue, muscle weakness and pain. Adequate diagnostics are still lacking. Investigating muscle function might be a beneficial approach. The holding capacity (maximal isometric Adaptive Force; AFisomax) was previously suggested to be especially sensitive for impairments. This longitudinal, non-clinical study aimed to investigate the AF in long COVID patients and their recovery process. AF parameters of elbow and hip flexors were assessed in 17 patients at three time points (pre: long COVID state, post: immediately after first treatment, end: recovery) by an objectified manual muscle test. The tester applied an increasing force on the limb of the patient, who had to resist isometrically for as long as possible. The intensity of 13 common symptoms were queried. At pre, patients started to lengthen their muscles at ~50% of the maximal AF (AFmax), which was then reached during eccentric motion, indicating unstable adaptation. At post and end, AFisomax increased significantly to ~99% and 100% of AFmax, respectively, reflecting stable adaptation. AFmax was statistically similar for all three time points. Symptom intensity decreased significantly from pre to end. The findings revealed a substantially impaired maximal holding capacity in long COVID patients, which returned to normal function with substantial health improvement. AFisomax might be a suitable sensitive functional parameter to assess long COVID patients and to support therapy process
The effect of two types of scanning strategies on the grain structure and build-up of Residual Stress (RS) has been investigated in an as-built IN718 alloy produced by Laser Powder Bed Fusion (LPBF). The RS state has been investigated by X-ray diffraction techniques. The microstructural characterization was performed principally by Electron Backscatter Diffraction (EBSD), where the application of a post-measurement refinement technique enables small misorientations (< 2 degrees) to be resolved. Kernel average misorientation (KAM) distributions indicate that preferably oriented columnar grains contain higher levels of misorientation, when compared to elongated grains with lower texture. The KAM distributions combined with X-ray diffraction stress maps infer that the increased misorientation is induced via plastic deformation driven by the thermal stresses, acting to self-relieve stress. The possibility of obtaining lower RS states in the build direction as a consequence of the influence of the microstructure should be considered when envisaging scanning strategies aimed at the mitigation of RS.
Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data. By employing the data fromEscherichia coliandSaccharomyces cerevisiaeas well as synthetic networks from the DREAM4 and five network inference challenges, we demonstrate that our GRADIS approach outperforms the state-of-the-art supervised and unsupervided approaches. This holds when predictions about target genes for individual transcription factors as well as for the entire network are considered. We employ experimentally verified GRNs fromE. coliandS. cerevisiaeto validate the predictions and obtain further insights in the performance of the proposed approach. Our GRADIS approach offers the possibility for usage of other network-based representations of large-scale data, and can be readily extended to help the characterisation of other cellular networks, including protein-protein and protein-metabolite interactions.
The Colorado Learning Attitudes about Science Survey (CLASS) is an instrument which is widely used in physics education to characterize students' attitudes toward physics and learning physics and compare them with those of experts. While CLASS has been extensively validated for use in the context of higher education institutions in the United States, there has been less information about its use with European students. We have studied the structural, content, and substantive aspects of validity of CLASS by first doing a confirmatory factor analysis of N = 642 sets of student answers from the University of Helsinki, Finland. The students represented a culturally and demographically different subset of university physics students than in previous studies. The confirmatory factor analysis used a 3-factor, 15-item factor structure as a starting point and the resulting factor structure was similar to the original. Just minor modifications were needed for fit parameters to be in the acceptable range. We explored the differences by student interviews and consultation of experts. With the exception of one item, they supported the new 14-item, 3-factor structure. The results show that the interpretations made from CLASS results are mostly transferable, and CLASS remains a useful instrument for a wide variety of populations.
STEP.De study
(2020)
Introduction Although exercise therapy has widely been shown to be an efficacious treatment modality for depression, evidence for its effectiveness and cost efficiency is lacking. The Sport/Exercise Therapy for Depression study is a multicentre cluster-randomised effectiveness trial that aims to compare the effectiveness and cost efficiency of exercise therapy and psychotherapy as antidepressant treatment. <br /> Methods and analysis 480 patients (aged 18-65) with an International Classification of Diseases diagnosis associated with depressive symptoms are recruited. Up to 30 clusters (psychotherapists) are randomly assigned to allocate patients to either an exercise or a psychotherapy treatment as usual in a 2: 1 ratio. The primary outcome (depressive symptoms) and the secondary outcomes (work and social adjustment, quality of life) will be assessed at six measurement time points (t0: baseline, t1: 8 weeks after treatment initiation, t2: 16 weeks after treatment initiation, t3/ 4/5: 2, 6, 12 months after treatment). Linear regression analyses will be used for the primary endpoint data analysis. For the secondary endpoints, mixed linear and logistic regression models with fixed and random factors will be added. For the cost efficiency analysis, expenditures in the 12 months before and after the intervention and the outcome difference will be compared between groups in a multilevel model. Recruitment start date was 1 July 2018 and the planned recruitment end date is 31 December 2020. <br /> Ethics and dissemination The study protocol was approved by the ethics committee of the University of Potsdam (No. 17/2018) and the Freie Universitat Berlin (No. 206/2018) and registered in the ISRCTN registry. Informed written consent will be obtained from all participants. The study will be reported in accordance with the Consolidated Standards of Reporting Trials and the Recommendations for Interventional Trials statements. The results will be published in peer-reviewed academic journals and disseminated to the public.
There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these "imported" techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed.
Mathematical analysis of partial differential equations (PDEs) has led to many insights regarding the effect of organism movements on spatial population dynamics. However, their use has mainly been confined to the community of mathematical biologists, with less attention from statistical and empirical ecologists. We conjecture that this is principally due to the inherent difficulties in fitting PDEs to data. To help remedy this situation, in the context of movement ecology, we show how the popular technique of step selection analysis (SSA) can be used to parametrize a class of PDEs, calleddiffusion-taxismodels, from an animal's trajectory. We examine the accuracy of our technique on simulated data, then demonstrate the utility of diffusion-taxis models in two ways. First, for non-interacting animals, we derive the steady-state utilization distribution in a closed analytic form. Second, we give a recipe for deriving spatial pattern formation properties that emerge from interacting animals: specifically, do those interactions cause heterogeneous spatial distributions to emerge and if so, do these distributions oscillate at short times or emerge without oscillations? The second question is applied to data on concurrently tracked bank volesMyodes glareolus. Our results show that SSA can accurately parametrize diffusion-taxis equations from location data, providing the frequency of the data is not too low. We show that the steady-state distribution of our diffusion-taxis model, where it is derived, has an identical functional form to the utilization distribution given by resource selection analysis (RSA), thus formally linking (fine scale) SSA with (broad scale) RSA. For the bank vole data, we show how our SSA-PDE approach can give predictions regarding the spatial aggregation and segregation of different individuals, which are difficult to predict purely by examining results of SSA. Our methods provide a user-friendly way into the world of PDEs, via a well-used statistical technique, which should lead to tighter links between the findings of mathematical ecology and observations from empirical ecology. By providing a non-speculative link between observed movement behaviours and space use patterns on larger spatio-temporal scales, our findings will also aid integration of movement ecology into understanding spatial species distributions.
Iron-sulfur (Fe-S) clusters are essential protein cofactors. In enzymes, they are present either in the rhombic [2Fe-2S] or the cubic [4Fe-4S] form, where they are involved in catalysis and electron transfer and in the biosynthesis of metal-containing prosthetic groups like the molybdenum cofactor (Moco). Here, we give an overview of the assembly of Fe-S clusters in bacteria and humans and present their connection to the Moco biosynthesis pathway. In all organisms, Fe-S cluster assembly starts with the abstraction of sulfur froml-cysteine and its transfer to a scaffold protein. After formation, Fe-S clusters are transferred to carrier proteins that insert them into recipient apo-proteins. In eukaryotes like humans and plants, Fe-S cluster assembly takes place both in mitochondria and in the cytosol. Both Moco biosynthesis and Fe-S cluster assembly are highly conserved among all kingdoms of life. Moco is a tricyclic pterin compound with molybdenum coordinated through its unique dithiolene group. Moco biosynthesis begins in the mitochondria in a Fe-S cluster dependent step involving radical/S-adenosylmethionine (SAM) chemistry. An intermediate is transferred to the cytosol where the dithiolene group is formed, to which molybdenum is finally added. Further connections between Fe-S cluster assembly and Moco biosynthesis are discussed in detail.
The article describes a systematic investigation of the effects of an aqueous NaOH treatment of 3D printed poly(lactic acid) (PLA) scaffolds for surface activation. The PLA surface undergoes several morphology changes and after an initial surface roughening, the surface becomes smoother again before the material dissolves. Erosion rates and surface morphologies can be controlled by the treatment. At the same time, the bulk mechanical properties of the treated materials remain unaltered. This indicates that NaOH treatment of 3D printed PLA scaffolds is a simple, yet viable strategy for surface activation without compromising the mechanical stability of PLA scaffolds.