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Patterning along the apical-basal (A-B) axis is a crucial step during the early stages of plant embryogenesis and leads to the establishment of two poles of which each will develop their own stem cell niches. The activity of these meristems is responsible for post-embryonic growth, with the shoot apical meristem (SAM) generating the above-ground organs and the root apical meristem (RAM) producing the subterranean structures of the plant. While several transcriptional regulators governing A-B patterning have been identified, precisely how their regulatory function is orchestrated remains elusive. This study focuses on transcriptional co-regulators LEUNIG (LUG) and closely related LEUNIG_HOMOLOG (LUH) and their role in the formation of A-B patterning during embryogenesis as well as their post-embryonic maintenance. A link between the LUG regulatory complex and SAM formation and maintenance comes from the observation that lug mutants heterozygous for the luh allele (lug luh+/-) often have enlarged SAMs resulting from misregulated cell divisions. A more severe phenotype is observed in lug luh double mutants which are embryonically lethal. In this study, a detailed characterisation of lug luh embryo phenotype reveals that these mutants display aberrant cell divisions along the A-B axis, which correlates with defects in auxin distribution, complete loss of apical identity, and altered expression of transcription factors determining basal fate. Like other co-regulators, LUG and LUH lack intrinsic DNA-binding domains and instead must interact with DNA-binding cofactors to ensure recruitment to regulatory elements of target genes. This either involves direct contact between the co-regulators and transcription factors (TFs) or the formation of higher-order complexes with adaptor proteins such as SEUSS (SEU) or related SEUSS-LIKEs (SLKs), which facilitate binding to specific TFs. Results presented in this study provide insight into the molecular framework for the LUG regulatory complex activity during embryogenesis. Both yeast and in planta assays showed that LUG/LUH and SEU/SLKs physically associate with a variety of WUSCHEL-RELATED HOMEOBOX (WOX) TFs including members of the WOX2-module. Furthermore, genetic interactions between members of the WOX2-module and the LUG regulatory complex, support their mutual action during embryogenesis. Based on the reduced activity of HOMEODOMAIN LEUCINE-ZIPPER CLASS III (HD-ZIPIII) promoters in lug luh embryos, a model is proposed in which the LUG regulatory complex functions together with WOX2-module to promote apical identity and subsequent SAM initiation through regulation of the HD-ZIPIIIs. The activity of the LUG complex in promoting basal embryo identity through positive regulation of microRNA165/166 suggests that this complex also has functions that are independent of the WOX2-module. Preliminary work reported in this study further uncovered the role of the LUG regulatory complex in post-embryonic development. While the fasciated inflorescence meristems of lug luh+/- plants displayed defects in auxin transport and altered activity of stem cell markers, embryonically rescued lug luh mutants formed flat and differentiated SAMs. In addition, rescued lug luh mutants exhibited severely disorganised RAM and defects in quiescent center (QC) specification, supporting the involvement of the LUG complex in post-embryonic RAM maintenance.
Detection of the QRS complex is a long-standing topic in the context of electrocardiography and many algorithms build upon the knowledge of the QRS positions. Although the first solutions to this problem were proposed in the 1970s and 1980s, there is still potential for improvements. Advancements in neural network technology made in recent years also lead to the emergence of enhanced QRS detectors based on artificial neural networks. In this work, we propose a method for assessing the certainty that is in each of the detected QRS complexes, i.e. how confident the QRS detector is that there is, in fact, a QRS complex in the position where it was detected. We further show how this metric can be utilised to distinguish correctly detected QRS complexes from false detections.
As sessile organisms, plants have evolved sophisticated ways to constantly gauge and adapt to changing environmental conditions including extremes that may be harmful to their growth and development and are thus perceived as stress. In nature, stressful events are often chronic or recurring and thus an initial stress may prime a plant to respond more efficiently to a subsequent stress event. An epigenetic basis of such stress memory was long postulated and in recent years it has been shown that this is indeed the case. High temperature stress has proven an excellent system to unpick the molecular basis of somatic stress memory, which includes histone modifications and nucleosome occupancy. This review discusses recent findings and pinpoints open questions in the field.
Proceedings of the HPI Research School on Service-oriented Systems Engineering 2020 Fall Retreat
(2021)
Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application.
Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns.
The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the research school, this technical report covers a wide range of topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment.
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.
To predict how widely distributed species will perform under future climate change, it is crucial to understand and reveal their underlying phylogenetics. However, detailed information about plant adaptation and its genetic basis and history remains scarce and especially widely distributed species receive little attention despite their putatively high adaptability.
To examine the adaptation potential of a widely distributed species, we sampled the model plant Silene vulgaris across Europe. In a greenhouse experiment, we exposed the offspring of these populations to a climate change scenario for central Europe and revealed the population structure through whole-genome sequencing. Plants were grown under two temperatures (18°C and 21°C) and three precipitation regimes (65, 75, and 90 mm) to measure their response in biomass and fecundity-related traits. To reveal the population genetic structure, ddRAD sequencing was employed for a whole-genome approach. We found three major genetic clusters in S. vulgaris from Europe: one cluster comprising Southern European populations, one cluster of Western European populations, and another cluster containing central European populations. Population genetic diversity decreased with increasing latitude, and a Mantel test revealed significant correlations between FST and geographic distances as well as between genetic and environmental distances. Our trait analysis showed that the genetic clusters significantly differed in biomass-related traits and in the days to flowering. However, half of the traits showed parallel response patterns to the experimental climate change scenario. Due to the differentiated but parallel response patterns, we assume that phenotypic plasticity plays an important role for the adaptation of the widely distributed species S. vulgaris and its intraspecific genetic lineages.
Plants possess cell wall, a polysaccharide exoskeleton which encompasses all plant cells. Cell wall gives plant cells mechanical support, defines their shape, enables growth and water transport through a plant. It also has important role in communication with the external environment. Regulation of plant cell wall biosynthesis and cell and organ morphogenesis depends on cell’s ability to detect mechanical signals originating both from the external environment and from internal plant tissues. Thanks to the presence of the cell wall, all living plant cells develop constant internal pressure generated by the active water uptake, known as turgor pressure, which enables them to grow. Thus, actively growing cells in the tissue are exerting mechanical stress to each other. In order to properly coordinate cell growth, tissue morphogenesis and maintain cell-to-cell adhesion, plant cell have to detect these mechanical signals. That is performed by a group of still not well enough characterized plant mechanosensitive proteins. Mechanosensors are proteins capable of detecting changes in mechanical stress patterns and translating them into physiological and developmental outputs. One of plant mechanosensitive proteins, DEFECTIVE KERNEL1 (DEK1) has shown to be a very important in proper plant development. DEK1 bears similarity with animal cysteine proteases of Calpain superfamily. DEK1 is very important for plant development since all null alleles are embryo lethal. During the last 20 years of DEK1 studies, this protein has proven to be a very difficult for different molecular and biochemical manipulations. As a consequence, very little is known about its direct target proteins. Wang and co-workers (2003) and Johnson and co-workers (2008) have given a valuable contribution to biochemical understanding of DEK1 by determining that it functions as Cys-protease in similar way as animal calpains. However, a lot of indirect knowledge was gathered about the effects of disruption and modulation of DEK1 activity. DEK1 is important for proper organ development, epidermal specification, and maintenance. However, some studies have inferred that DEK1 affects expression of different cell wall related genes, and it regulates cell-to-cell adhesion in epidermal cells. This led to two extensive studies (Amanda et al., 2016, 2017) which demonstrated importance of DEK1 in regulation leaf epidermal cell walls in A. thaliana mature leaves and inflorescence stems. These studies demonstrated that DEK1 also influences cell wall thickness and cell-to-cell adhesion and that it could potentially regulate cell growth and expansion. Building up on this research, we decided to try to further characterize molecular and biomechanical aspects of DEK1 mediated cell wall regulation, with special emphasis on regulation of cellulose synthesis. We used two mutant lines, with modulated DEK1 activity, a constitutive overexpressor for DEK1 CALPAIN domain and a point mutant in CALPAIN domain, dek1-4. In Chapter 3 we demonstrated that DEK1 regulates dynamics of Cellulose Synthase Complexes (CSCs). Both lines showed decreased crystalline cellulose contents. This led us to investigate if velocity of CSCs in cotyledons, was affected, since it is known that changes in cellulose contents are often caused by defects in CSC. We found that bothDEK1 modulated lines we used have significantly decreased velocity of CSCs. We have also examined plasma membrane turnover rates of CSCs and found out that after photo-bleaching OE CALPAIN has much faster recovery rates compared to Col-0 wild type, while dek1-4 has lower exocytotic rates of CSCs, and much longer life-time of CSCs inserted into the plasma membrane. These results suggested that DEK1 regulates different aspects of CSC dynamics, possibly through interaction with different regulatory proteins. Decrease in cellulose contents we observed in DEK1 modulated lines, prompted us to investigate how this reflects biomechanics and structural properties of epidermal cotyledon cell walls of DEK1 modulated lines, which is described in Chapter 4. To achieve this, we developed a novel microdissection method for isolation and mechanical and structural characterization of native epidermal cell wall monolayers using atomic force microscopy (AFM). AFM force spectroscopy assays showed that both DEK1 modulated lines had stiffer cell walls compared to Col-0. This was awkward since we initially detected decrease in crystalline cellulose which implied decrease in cell wall stiffness. However, subsequent high-resolution AFM imaging has revealed that DEK1 modulate lines cells walls have their cellulose microfibrils organized in thicker bundles than Col-0. Also, polysaccharide composition analysis has revealed that DEK1 modulated lines have increased abundance of pectins, which could also be responsible for the observed increase in cell wall stiffness. Previous work has shown that different dek1 mutants and modulated lines have defects in cell-to-cell adhesion. This implied that DEK1 may be involved in sensing and/or maintaining cell wall integrity (CWI). We performed several growth assays to determine role of DEK1 in CWI, which is described in Chapter 5. We performed cellulose synthesis perturbation assays with cellulose synthesis inhibitor Isoxaben and obtained very interesting results. While OE CALPAIN plants were hypersensitive to Isoxaben, dek1-4 has shown complete insensitivity. Furthermore, a regular CWI maintenance response, reported in A. thaliana as result of compromised CWI, ectopic lignification in seedlings’ roots was absent in both DEK1 modulated lines we examined. We detected interesting growth response of DEK1 lines to NaCl and mannitol treatments as well. Although these findings are pointing out that DEK1 could be part of CWI signalling pathways, more experiments are necessary to fully elucidate possible role of DEK1 in CWI sensing and/or maintenance pathways, especially to check if DEK1 is interacting with Catharanthus roseus Receptor Like Kinase group of CWI sensors. Studies on 4-month old short day grown DEK1 modulated lines, have shown defects in branching, with development of fasciated stem branches in a DEK1 modulated line overexpressing CALPAIN domain (Amanda et al., 2017). This result pointed out to a possibility that DEK1 may regulate organ morphogenesis and patterning at the level of shoot apical meristem (SAM). Work towards elucidating role of DEK1 in SAM maintenance and organ patterning is detailed in Chapter 6. We determined that OE CALPAIN had significantly larger central zone of SAM as well as larger individual SAM cells in central zone, as well as higher distribution of cell sizes, implying possible cell expansion defects. dek1-4 did not exhibited changes in SAM central zone size or individual stem cell size, but it seemed that it had increased number of stem cells in SAM central zone. Both DEK1 lines had perturbation of phyllotaxis on SAM level, with disturbed divergence angles between floral primordia. Disturbed phyllotaxis was also observed between siliques, in mature plants. In addition to this, OE CALPAIN has exhibited occurrence of multiple (up to four) siliques growing from a single stem node. All this is pointing out that DEK1 might participate in hormone-signalling in the SAM.. DEK1 is a highly intriguing protein. However, since it is a unigene, and in addition to that, a regulatory protease, it probably participates in multiple signalling pathways, which makes understanding its function much more complicated.
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.
The life cycle of higher plants is based on recurring phases of growth and development based on repetitive sequences of cell division, cell expansion and cell differentiation. This dissertation deals with two projects, each of them investigating two different topics that are related to cell expansion. The first project is examining an Arabidopsis thaliana mutant exhibiting overall cell enlargement and the second project is analysing two naturally occurring floral morphs of Amsinckia spectabilis (Boraginaceae) differing (amongst others) in style length and anther heights due to differences in longitudinal cell elongation. The EMS-mutant eop1 was shown to exhibit a petal size increase of 26% caused by cell enlargement. Further phenotypes were detected, such as cotyledon size increase (based on larger cells) as well as increased carpel, sepal, leaf and pollen sizes. Plant height was shown to be increased and more highly branched trichomes explained the hairy eop1 phenotype. Fine mapping revealed the causal SNP to be a C to T transition at the last nucleotide of intron 7 of the INCURVATA11 (ICU11) gene, a 2-oxoglutarate /Fe(II)-dependant dioxygenase, and thus causing missplicing of the mRNA. Two T-DNA insertion lines (icu11-2 & icu11-4) confirmed ICU11 as causal gene by exhibiting increased petal size. A comparison of three icu11 alleles, which possessed different mutation-related changes, either overexpressing ICU11 or modified mRNAs, was the base for investigating the molecular mechanism that underlies the observed phenotype. Different approaches revealed contradictory results regarding ICU11 protein functionality in the icu11 mutants. A complementation assay proved the three mutants to be exchangeable and ICU11 overexpression in the wild-type led to an icu11-like phenotype, arguing for all three icu11 mutants to be GOF mutants. Contradicting this conclusion, the icu11-4 line could be rescued by a genomic ICU11 transgene. A model, based on the assumption that an overexpression of ICU11 is inhibiting the function of the protein, and thus causing the same effect as a LOF protein was proposed. Further, icu11-3 (eop1) mutants were shown to have an increased resistance towards paclobutrazol, a gibberellin (GA) inhibitor and an upregulation of AtGA20ox2, a main GA biosynthesis gene. Additionally, ICU11 subcellular localization was discovered to be cytoplasmic, supporting the assumption, that ICU11 affects GA biosynthesis and overall GA level, possibly explaining the observed (GA-overdose) phenotype.
The second project aimed to identify the genetic base of the S-locus in Amsinckia spectabilis, as the Amsinckia genus represents untypical characteristics for a heterostylous species, such as no obvious self-incompatibility (SI) and the repeated transition towards homostylous and fully selfing variants. The work was based on three Amsinckia spectabilis forms: a heterostylous form, consisting of two floral morphs with reciprocal positioning of sexual organs (S-morph: high anthers and a short style and L-morph: low anthers and a long style), and two homostylous forms, one large-flowered and partially selfing and the other small-flowered and fully selfing. The maintenance of the two floral morphs is genetically based on the S-locus region, containing genes that encode for the morph-specific traits, which are marked by a tight linkage due to suppressed recombination. Natural populations are found to possess a 1:1 S:L morph ratio, that can be explained by predominant disassortative mating of the two morphs, causing the occurrence of the dominant S-allele only in the heterozygous state (heterozygous (Ss) for the S-morph and homozygous recessive (ss) for the L-morph). Investigation of morph-specific phenotypes detected 56% elongated L-morph styles and 58% higher positioned S-morph anthers. Approximately 50% of the observed size differences were explained by an increase in cell elongation. Moreover, additional phenotypes were found, such as 21% enlarged S-morph pollen and no obvious SI, confirmed by hand pollinated seed counts, in vivo pollen tube growth and the development of homozygous dominant SS individuals via selfing. The Amsinckia spec. S-locus was assumed to at least consist of the G- (style length), the A- (anther height) and the P- (pollen size) locus. Comparative Transcriptomics of the two morphs revealed 22 differentially expressed markers that were found to be located within two contigs of a SS individual PacBio genome assembly, allowing the localization of the S-locus to be delimited to a region of approximately 23 Mb. Contradictory to revealed S-loci within the plant kingdom, no strong argument for a present hemizygous region was found to be causal for the suppressed recombination of the S-locus, so that an inversion was assumed to be the causal mechanism.
In Systems Medicine, in addition to high-throughput molecular data (*omics), the wealth of clinical characterization plays a major role in the overall understanding of a disease. Unique problems and challenges arise from the heterogeneity of data and require new solutions to software and analysis methods. The SMART and EurValve studies establish a Systems Medicine approach to valvular heart disease -- the primary cause of subsequent heart failure.
With the aim to ascertain a holistic understanding, different *omics as well as the clinical picture of patients with aortic stenosis (AS) and mitral regurgitation (MR) are collected. Our task within the SMART consortium was to develop an IT platform for Systems Medicine as a basis for data storage, processing, and analysis as a prerequisite for collaborative research. Based on this platform, this thesis deals on the one hand with the transfer of the used Systems Biology methods to their use in the Systems Medicine context and on the other hand with the clinical and biomolecular differences of the two heart valve diseases. To advance differential expression/abundance (DE/DA) analysis software for use in Systems Medicine, we state 21 general software requirements and features of automated DE/DA software, including a novel concept for the simple formulation of experimental designs that can represent complex hypotheses, such as comparison of multiple experimental groups, and demonstrate our handling of the wealth of clinical data in two research applications DEAME and Eatomics. In user interviews, we show that novice users are empowered to formulate and test their multiple DE hypotheses based on clinical phenotype. Furthermore, we describe insights into users' general impression and expectation of the software's performance and show their intention to continue using the software for their work in the future. Both research applications cover most of the features of existing tools or even extend them, especially with respect to complex experimental designs. Eatomics is freely available to the research community as a user-friendly R Shiny application.
Eatomics continued to help drive the collaborative analysis and interpretation of the proteomic profile of 75 human left myocardial tissue samples from the SMART and EurValve studies. Here, we investigate molecular changes within the two most common types of valvular heart disease: aortic valve stenosis (AS) and mitral valve regurgitation (MR). Through DE/DA analyses, we explore shared and disease-specific protein alterations, particularly signatures that could only be found in the sex-stratified analysis. In addition, we relate changes in the myocardial proteome to parameters from clinical imaging. We find comparable cardiac hypertrophy but differences in ventricular size, the extent of fibrosis, and cardiac function. We find that AS and MR show many shared remodeling effects, the most prominent of which is an increase in the extracellular matrix and a decrease in metabolism. Both effects are stronger in AS. In muscle and cytoskeletal adaptations, we see a greater increase in mechanotransduction in AS and an increase in cortical cytoskeleton in MR. The decrease in proteostasis proteins is mainly attributable to the signature of female patients with AS. We also find relevant therapeutic targets.
In addition to the new findings, our work confirms several concepts from animal and heart failure studies by providing the largest collection of human tissue from in vivo collected biopsies to date. Our dataset contributing a resource for isoform-specific protein expression in two of the most common valvular heart diseases. Apart from the general proteomic landscape, we demonstrate the added value of the dataset by showing proteomic and transcriptomic evidence for increased expression of the SARS-CoV-2- receptor at pressure load but not at volume load in the left ventricle and also provide the basis of a newly developed metabolic model of the heart.
Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
The scapula plays a significant role in efficient shoulder movement. Thus, alterations from typical scapular motion during upper limb movements are thought to be associated with shoulder pathologies. However, a clear understanding of the relationship is not yet obtained.. Scapular alterations may only represent physiological variability as their occurrence can appear equally as frequent in individuals with and without shoulder disorders. Evaluation of scapular motion during increased load might be a beneficial approach to detect clinically relevant alterations. However, functional motion adaptations in response to maximum effort upper extremity loading has not been established yet. Therefore, the overall purpose of this research project was to give further insight in physiological adaptations of scapular kinematics and their underlying scapular muscle activity in response to high demanding shoulder movements in healthy asymptomatic individuals. Prior to the investigation of the effect of various load situation, the reproducibility of scapular kinematics and scapular muscle activity were evaluated under maximum effort arm movements. Healthy asymptomatic adults performed unloaded and maximal loaded concentric and eccentric isokinetic shoulder flexion and extension movements in the scapular plane while scapular kinematics and scapular muscle activity were simultaneously assessed. A 3D motion capture system (infra-red cameras & reflective markers) was utilized to track scapular and humerus motion in relation to the thorax. 3D scapular position angles were given for arm raising and lowering between humerus positions of 20° and 120° flexion. To further characterize the scapular pattern, the scapular motion extent and scapulohumeral rhythm (ratio of scapular and humerus motion extent) were determined. Muscle activity of the upper and lower trapezius and the serratus anterior were assessed with surface electromyography. Amplitudes were calculated for the whole ROM and four equidistant movement phases. Reliability was characterized by overall moderate to good reproducibility across the load conditions. Irrespective of applied load, scapular kinematics followed a motion pattern of continuous upward rotation, posterior tilt and external rotation during arm elevation and a continuous downward rotation, anterior tilt and internal rotation during arm lowering. However, kinematics were altered between maximal loaded and unloaded conditions showing increased upward rotation, reduced posterior tilt and external rotation. Further, the scapulohumeral rhythm was decreased and scapular motion extent increased under maximal loaded movements. Muscle activity during maximum effort were of greater magnitude and differed in their pattern in comparison to the continuous increase and decrease of activity during unloaded shoulder flexion and extension. Relationships between scapular kinematics and their underlying scapular muscle activity could only be identified for a few isolated combinations, whereas the majority showed no associations. Scapular kinematics and scapular muscle activity pattern alter according to the applied load. Alterations between the load conditions comply in magnitude and partially in direction with differences seen between symptomatic and asymptomatic individuals. Even though long-term effects of identified adaptations in response to maximum load are so far unclear, deviations from typical scapular motion or muscle activation should not per se be seen as indicators of shoulder impairment. However, evaluation of alterations in scapular motion and activation in response to maximum effort may have the potential to identify individuals that are unable to cope with increased upper limb demands. Findings further challenge the understanding of scapular motion and stabilization by the trapezius and serratus anterior muscles, as clear relationships between the underlying scapular muscle activity and scapular kinematics were neither observed during unloaded nor maximal loaded shoulder movements.
TRIPOD
(2021)
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.
Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.
Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.
Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients' anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.
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.
Background:
More patient data are needed to improve research on rare liver diseases. Mobile health apps enable an exhaustive data collection. Therefore, the European Reference Network on Hepatological diseases (ERN RARE-LIVER) intends to implement an app for patients with rare liver diseases communicating with a patient registry, but little is known about which features patients and their healthcare providers regard as being useful.
Aims:
This study aimed to investigate how an app for rare liver diseases would be accepted, and to find out which features are considered useful.
Methods:
An anonymous survey was conducted on adult patients with rare liver diseases at a single academic, tertiary care outpatient-service. Additionally, medical experts of the ERN working group on autoimmune hepatitis were invited to participate in an online survey.
Results:
In total, the responses from 100 patients with autoimmune (n = 90) or other rare (n = 10) liver diseases and 32 experts were analyzed. Patients were convinced to use a disease specific app (80%) and expected some benefit to their health (78%) but responses differed signifi-cantly between younger and older patients (93% vs. 62%, p < 0.001; 88% vs. 64%, p < 0.01). Comparing patients' and experts' feedback, patients more often expected a simplified healthcare pathway (e.g. 89% vs. 59% (p < 0.001) wanted access to one's own medical records), while healthcare providers saw the benefit mainly in improving compliance and treatment outcome (e.g. 93% vs. 31% (p < 0.001) and 70% vs. 21% (p < 0.001) expected the app to reduce mistakes in taking medication and improve quality of life, respectively).
Despite advances in machine learning-based clinical prediction models, only few of such models are actually deployed in clinical contexts. Among other reasons, this is due to a lack of validation studies. In this paper, we present and discuss the validation results of a machine learning model for the prediction of acute kidney injury in cardiac surgery patients initially developed on the MIMIC-III dataset when applied to an external cohort of an American research hospital. To help account for the performance differences observed, we utilized interpretability methods based on feature importance, which allowed experts to scrutinize model behavior both at the global and local level, making it possible to gain further insights into why it did not behave as expected on the validation cohort. The knowledge gleaned upon derivation can be potentially useful to assist model update during validation for more generalizable and simpler models. We argue that interpretability methods should be considered by practitioners as a further tool to help explain performance differences and inform model update in validation studies.
Das Gewichtsstigma und insbesondere das internalisierte Gewichtsstigma sind bei Kindern und Jugendlichen mit negativen Folgen für die physische und psychische Gesundheit assoziiert. Da die Befundlage in diesem Altersbereich jedoch noch unzureichend ist, war es das Ziel der Dissertation, begünstigende Faktoren und Folgen von gewichtsbezogener Stigmatisierung und internalisiertem Gewichtsstigma bei Kindern und Jugendlichen zu untersuchen. Die Analysen basierten auf zwei großen Stichproben, die im Rahmen der prospektiven PIER-Studie an Schulen rekrutiert wurden. Die erste Publikation bezieht sich auf eine Stichprobe mit Kindern und Jugendlichen im Alter zwischen 9 und 19 Jahren (49.2 % weiblich) und untersuchte den prospektiven bidirektionalen Zusammenhang zwischen erlebter Gewichtsstigmatisierung und Gewichtsstatus anhand eines latenten Strukturgleichungsmodells über drei Messzeitpunkte hinweg. Die anderen beiden Publikationen beziehen sich auf eine Stichprobe mit Kindern und Jugendlichen im Alter zwischen 6 und 11 Jahren (51.1 % weiblich). Die zweite Publikation analysierte anhand einer hierarchischen Regression, welche intrapersonalen Risikofaktoren das internalisierte Gewichtsstigma prospektiv prädizieren. Die dritte Publikation untersuchte anhand von ROC-Kurven, ab welchem Ausmaß das internalisierte Gewichtsstigma mit einem erhöhten Risiko für psychosoziale Auffälligkeit und gestörtes Essverhalten einhergeht. Im Rahmen der ersten Publikation zeigte sich, dass ein höherer Gewichtsstatus mit einer höheren späteren Gewichtsstigmatisierung einhergeht und umgekehrt die Gewichtsstigmatisierung auch den späteren Gewichtsstatus prädiziert. Die zweite Publikation identifizierte Gewichtsstatus, gewichtsbezogene Hänseleien, depressive Symptome, Körperunzufriedenheit, Relevanz der eigenen Figur sowie das weibliche Geschlecht und einen niedrigeren Bildungsabschluss der Eltern als Prädiktoren des internalisierten Gewichtsstigmas. Die dritte Publikation verdeutlichte, dass das internalisierte Gewichtsstigma bereits ab einem geringen Ausmaß mit einem erhöhten Risiko für gestörtes Essverhalten einhergeht und mit weiteren psychosozialen Problemen assoziiert ist. Insgesamt zeigte sich, dass sowohl das erlebte als auch das internalisierte Gewichtsstigma bei Kindern und Jugendlichen über alle Gewichtsgruppen hinweg relevante Konstrukte sind, die im Entwicklungsverlauf ein komplexes Gefüge bilden. Es wurde deutlich, dass es essentiell ist, bidirektionale Wirkmechanismen einzubeziehen. Die vorliegende Dissertation liefert erste Ansatzpunkte für die Gestaltung von Präventions- und Interventionsmaßnahmen, um ungünstige Entwicklungsverläufe in Folge von Gewichtsstigmatisierung und internalisiertem Gewichtsstigma zu verhindern.
Background: Aggression-related sexual fantasies (ASF) are considered an important risk factor for sexual aggression, but empirical knowledge is limited, in part because previous research has been based on predominantly male, North-American college samples, and limited numbers of questions. <br /> Aim: The present study aimed to foster the knowledge about the frequency and correlates of ASF, while including a large sample of women and a broad range of ASF. <br /> Method: A convenience sample of N = 664 participants from Germany including 508 (77%) women and 156 (23%) men with a median age of 25 (21-27) years answered an online questionnaire. Participants were mainly recruited via social networks (online and in person) and were mainly students. We examined the frequencies of (aggression-related) sexual fantasies and their expected factor structure (factors reflecting affective, experimental, masochistic, and aggression-related contents) via exploratory factor analysis. We investigated potential correlates (eg, psychopathic traits, attitudes towards sexual fantasies) as predictors of ASF using multiple regression analyses. Finally, we examined whether ASF would positively predict sexual aggression beyond other pertinent risk factors using multiple regression analysis. <br /> Outcomes: The participants rated the frequency of a broad set of 56 aggression-related and other sexual fantasies, attitudes towards sexual fantasies, the Big Five (ie, broad personality dimensions including neuroticism and extraversion), sexual aggression, and other risk factors for sexual aggression. <br /> Results: All participants reported non-aggression-related sexual fantasies and 77% reported at least one ASF in their lives. Being male, frequent sexual fantasies, psychopathic traits, and negative attitudes towards sexual fantasies predicted more frequent ASF. ASF were the strongest predictor of sexual aggression beyond other risk factors, including general aggression, psychopathic traits, rape myth acceptance, and violent pornography consumption. <br /> Clinical Translation: ASF may be an important risk factor for sexual aggression and should be more strongly considered in prevention and intervention efforts. <br /> Strengths and Limitations: The strengths of the present study include using a large item pool and a large sample with a large proportion of women in order to examine ASF as a predictor of sexual aggression beyond important control variables. Its weaknesses include the reliance on cross-sectional data, that preclude causal inferences, and not continuously distinguishing between consensual and non-consensual acts. <br /> Conclusion: ASF are a frequent phenomenon even in in the general population and among women and show strong associations with sexual aggression. Thus, they require more attention by research on sexual aggression and its prevention.