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Mobile fitness applications provide the opportunity to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user’s current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of daily living activities. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning (ML) methods commonly used in HAR. Although CNN architectures achieve at least 94% test accuracy in all scenarios, two traditional ML architectures surpass them in the default scenario, with support vector machines (SVMs) achieving 99.00 ± 0.34% test accuracy. The removal of all sensors except one foot sensor reduced the performance of traditional ML architectures but improved the performance of CNN architectures on our data set, with our Scaling-FCN reaching the highest accuracy of 99.86 ± 0.11% on the test set. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively, although traditional ML architectures can still compete with or even surpass CNNs when favorable input data are utilized.

DNA methylation plays a critical role during development, particularly in repressing retrotransposons. The mammalian methylation landscape is dependent on the combined activities of the canonical maintenance enzyme Dnmt1 and the de novo Dnmts, 3a and 3b. Here, we demonstrate that Dnmt1 displays de novo methylation activity in vitro and in vivo with specific retrotransposon targeting. We used whole-genome bisulfite and long-read Nanopore sequencing in genetically engineered methylation-depleted mouse embryonic stem cells to provide an in-depth assessment and quantification of this activity. Utilizing additional knockout lines and molecular characterization, we show that the de novo methylation activity of Dnmt1 depends on Uhrf1, and its genomic recruitment overlaps with regions that enrich for Uhrf1, Trim28 and H3K9 trimethylation. Our data demonstrate that Dnmt1 can catalyze DNA methylation in both a de novo and maintenance context, especially at retrotransposons, where this mechanism may provide additional stability for long-term repression and epigenetic propagation throughout development. The canonical DNA methylation maintenance enzyme Dnmt1 displays global de novo methylation activity with greater targeting towards IAP transposons, which may contribute to their stable repression during early development.

Expansions of short tandem repeats are genetic variants that have been implicated in several neuropsychiatric and other disorders, but their assessment remains challenging with current polymerase-based methods1–4. Here we introduce a CRISPR–Cas-based enrichment strategy for nanopore sequencing combined with an algorithm for raw signal analysis. Our method, termed STRique for short tandem repeat identification, quantification and evaluation, integrates conventional sequence mapping of nanopore reads with raw signal alignment for the localization of repeat boundaries and a hidden Markov model-based repeat counting mechanism. We demonstrate the precise quantification of repeat numbers in conjunction with the determination of CpG methylation states in the repeat expansion and in adjacent regions at the single-molecule level without amplification. Our method enables the study of previously inaccessible genomic regions and their epigenetic marks.

Mammalian cells stably maintain high levels of DNA methylation despite expressing both positive (DNMT3A/B) and negative (TET1-3) regulators. Here, we analyzed the independent and combined effects of these regulators on the DNA methylation landscape using a panel of knockout human embryonic stem cell (ESC) lines. The greatest impact on global methylation levels was observed in DNMT3-deficient cells, including reproducible focal demethylation at thousands of normally methylated loci. Demethylation depends on TET expression and occurs only when both DNMT3s are absent. Dynamic loci are enriched for hydroxymethylcytosine and overlap with subsets of putative somatic enhancers that are methylated in ESCs and can be activated upon differentiation. We observe similar dynamics in mouse ESCs that were less frequent in epiblast stem cells (EpiSCs) and scarce in somatic tissues, suggesting a conserved pluripotency-linked mechanism. Taken together, our data reveal tightly regulated competition between DNMT3s and TETs at thousands of somatic regulatory sequences within pluripotent cells. Whole-genome bisulfite sequencing analysis of human embryonic stem cells shows that DNMT3 deficiency leads to global and local demethylation, which depends on TET activity. Dynamic loci overlap with putative somatic enhancers that are highly methylated in ESCs.

CaMKII was suggested to mediate ischemic myocardial injury and adverse cardiac remodeling. Here, we investigated the roles of different CaMKII isoforms and splice variants in ischemia/reperfusion (I/R) injury by the use of new genetic CaMKII mouse models. Although CaMKIIδC was upregulated 1 day after I/R injury, cardiac damage 1 day after I/R was neither affected in CaMKIIδ‐deficient mice, CaMKIIδ‐deficient mice in which the splice variants CaMKIIδB and C were re‐expressed, nor in cardiomyocyte‐specific CaMKIIδ/γ double knockout mice (DKO). In contrast, 5 weeks after I/R, DKO mice were protected against extensive scar formation and cardiac dysfunction, which was associated with reduced leukocyte infiltration and attenuated expression of members of the chemokine (C‐C motif) ligand family, in particular CCL3 (macrophage inflammatory protein‐1α, MIP‐1α). Intriguingly, CaMKII was sufficient and required to induce CCL3 expression in isolated cardiomyocytes, indicating a cardiomyocyte autonomous effect. We propose that CaMKII‐dependent chemoattractant signaling explains the effects on post‐I/R remodeling. Taken together, we demonstrate that CaMKII is not critically involved in acute I/R‐induced damage but in the process of post‐infarct remodeling and inflammatory processes. Synopsis CaMKII is critically involved in post‐infarct remodelling via the activation of inflammatory pathways. At variance with previous reports, however, CaMKII does not appear to be relevant in acute damage after ischemia/reperfusion (I/R) injury. Acute myocardial I/R‐induced damage is not mediated by CaMKII whereas post‐I/R remodelling and inflammatory processes are. Leukocyte infiltration and expression of members of the chemokine (C‐C motif) ligand family, in particular CCL3 (macrophage inflammatory protein‐1a, MIP‐1a), are reduced in mice lacking the two cardiac CaMKII isoforms delta and gamma upon I/R injury. CaMKII is sufficient and required for CCL3 expression in cardiomyocytes. Graphical Abstract CaMKII is critically involved in post‐infarct remodelling via the activation of inflammatory pathways. At variance with previous reports, however, CaMKII does not appear to be relevant in acute damage after ischemia/reperfusion injury.

DNA and Histone 3 Lysine 27 methylation typically function as repressive modifications and operate within distinct genomic compartments. In mammals, the majority of the genome is kept in a DNA methylated state, whereas the Polycomb repressive complexes regulate the unmethylated CpG-rich promoters of developmental genes. In contrast to this general framework, the extra-embryonic lineages display non-canonical, globally intermediate DNA methylation levels, including disruption of local Polycomb domains. Here, to better understand this unusual landscape’s molecular properties, we genetically and chemically perturbed major epigenetic pathways in mouse trophoblast stem cells. We find that the extra-embryonic epigenome reflects ongoing and dynamic de novo methyltransferase recruitment, which is continuously antagonized by Polycomb to maintain intermediate, locally disordered methylation. Despite its disorganized molecular appearance, our data point to a highly controlled equilibrium between counteracting repressors within extra-embryonic cells, one that can seemingly persist indefinitely without bistable features typically seen for embryonic forms of epigenetic regulation. Weigert et al. show that an antagonistic relationship between DNA methyltransferase and Polycomb activity is globally responsible for the maintenance of intermediate methylation levels observed in trophoblast stem cells.

Digital reflections of physical energy plants can help support and optimize energy technologies within their lifecycle. So far, no framework for the evolution of virtual representations throughout the process development lifecycle exists. Based on various concepts of virtual representations in different industries, this review paper focuses on developing a novel virtual representation framework for the process development environment within the energy sector. The proposed methodology enables the continuous evolution of virtual representations along the process development lifecycle. A novel definition for virtual representations in the process development environment is developed. Additionally, the most important virtual representation challenges, properties, and applications for developing a widely applicable framework are summarized. The essential sustainability indicators for the energy sector are listed to standardize the process evaluation throughout the process development lifecycle. The virtual representation and physical facility development can be synchronized by introducing a novel model readiness level. All these thoughts are covered through the novel virtual representation framework. Finally, the digital twin of a Bio-SNG production route is presented, to show the benefits of the methodology through a use case. This methodology helps to accelerate and monitor energy technology developments through the early implementation of virtual representations.

Reduced respiratory sinus arrhythmia, quantified as expiration-triggered sinus arrhythmia (ETA) from simultaneous electrocardiogram and respiration recordings, is a strong long-term mortality predictor in myocardial infarction survivors. Here, we investigated whether ETA also predicts mortality risk in the general elderly population. ETA was quantified from 30-min electrocardiogram and respiration recordings in 1788 general population subjects aged ≥60 years, who were then followed for a median of 4.0 years (median age 72 years, 58% female). Four-year all-cause mortality was 4.6%. Abnormal ETA using a predefined cutoff (≤0.19 ms) was associated with a 4-year mortality of 6.9%, compared to 3.7% in the remaining participants (p = 0.0022). ETA remained a significant mortality predictor in multivariable Cox analysis, also considering a modified Framingham score incorporating sex, age, smoking, cholesterol, blood pressure, antihypertensive medication, family history, diabetes and clinical atherosclerosis (multivariable hazard ratio 1.81; 95% confidence interval 1.17–2.81; p = 0.008). Combined risk prediction by ETA (using an optimized cutoff of ≤0.86 ms) and the Framingham score stratified patients into a low-risk (both parameters normal), an intermediate-risk (one parameter abnormal) and a high-risk group (both parameters abnormal), with 4-year mortality rates of 1.9%, 4.4% and 10.1%, respectively. We conclude that in elderly general population subjects, ETA is a mortality risk predictor that complements classical clinical risk stratification.

Anthropogenic emissions are dominant contributors to air pollution. Consequently, mitigation policies have been attempted since the 1990s in Europe to reduce pollution by anthropogenic emissions. To evaluate the effectiveness of these mitigation policies, the German Ultrafine Aerosol Network (GUAN) was established in 2008, focusing on black carbon (BC) and sub-micrometre aerosol particles. In this study, long-term trends of atmospheric particle number concentrations (PNCs) and equivalent BC (eBC) mass concentration over a 10-year period (2009–2018) were determined for 16 GUAN sites ranging from roadside to high Alpine environments. Overall, statistically significant decreasing trends are found for most of these parameters and environments in Germany. The annual relative slope of eBC mass concentration varies between −13.1 % and −1.7 % per year. The slopes of the PNCs vary from −17.2 % to −1.7 %, −7.8 % to −1.1 %, and −11.1 % to −1.2 % per year for 10–30, 30–200, and 200–800 nm size ranges, respectively. The reductions in various anthropogenic emissions are found to be the dominant factors responsible for the decreasing trends of eBC mass concentration and PNCs. The diurnal and seasonal variations in the trends clearly show the effects of the mitigation policies for road transport and residential emissions. The influences of other factors such as air masses, precipitation, and temperature were also examined and found to be less important or negligible. This study proves that a combination of emission mitigation policies can effectively improve the air quality on large spatial scales. It also suggests that a long-term aerosol measurement network at multi-type sites is an efficient and necessary tool for evaluating emission mitigation policies.

Background: Psychiatric and psychosomatic consultation-liaison services (CL) are important providers of diagnosis and treatment for hospital patients with mental comorbidities and psychological burdens. Objective: To perform a systematic review and meta-analysis investigating the effects of CL on depression and anxiety. Methods: Following PRISMA guidelines, a systematic literature search was conducted until 2017. Included were published randomized controlled trials using CL interventions with adults in general hospitals, treatment as usual as control groups, and depression and/or anxiety as outcomes. Risk of bias was assessed using the Cochrane Risk of Bias Tool. Level of integration was assessed using the Standard Framework for Levels of Integrated Healthcare. Meta-analyses were performed using random effects models and meta-regression for moderator effects. Results: We included 38 studies (9,994 patients). Risk of bias was high in 17, unclear in 15, and low in 6 studies. Studies were grouped by type of intervention: brief interventions tailored to the patients (8), interventions based on specific treatment manuals (19), and integrated, collaborative care (11). Studies showed small to medium effects on depression and anxiety. Meta-analyses for depression yielded a small effect (d = –0.19, 95% CI: –0.30 to –0.09) in manual studies and a small effect (d = –0.33, 95% CI: –0.53 to –0.13) in integrated, collaborative care studies, the latter using mostly active control groups with the possibility of traditional consultation. Conclusions: CL can provide a helpful first treatment for symptoms of depression and anxiety. Given that especially depressive symptoms in medically ill patients are long-lasting, the results underline the benefit of integrative approaches that respect the complexity of the illness.