Miracle-dz/UniFAR-FTUs huggingface dataset:
The Development of Mindful-Based Dance Movement Therapy Intervention for Chronic Pain: A Pilot Study With Chronic Headache Patients

Chronic pain is of significant global concern. There is growing evidence that body–mind therapies and psychological approaches can contribute toward changing chronic pain perceptions. This is the first model described in the literature that combines a mindfulness-based approach with dance movement therapy and explores the potential psychological and pain-related changes for this client population. In this paper, the results from the pilot study are presented involving patients with chronic headache recruited in an outpatient rehabilitation setting. Methods: In this pilot study, 29 patients (n = 29) with chronic headache were randomized to either the Mindful-Based Dance Movement Therapy (MBDMT) group or the waiting list control group (treatment as usual, TAU). The MBDMT group was offered 10 sessions in a clinical outpatient rehabilitation setting for 5 weeks. Data were collected pre- and post-intervention and 16 weeks after the intervention was finished. The Hospital Anxiety and Depression Scale (HADS), Patient Health Questionnaire−9 (PHQ-9), Five Facet Mindfulness Questionnaire (FFMQ), and Numeric Rating Scale (NRS) were used as outcome measures. Results: The working model of MBDMT identifies nine therapeutic mechanisms (safe therapeutic environment, mindfulness skills, body awareness, relaxation/releasing, distancing and staying with discomfort, meaning making, self-regulation, acceptance and integration, creative process). Per-protocol analysis reveals statistically significant reduction of pain intensity and depression scores in favor of the MBDMT group, and these improvements were maintained in the follow-up assessment. Conclusions: The results suggest that MBDMT is a feasible and promising therapy approach for chronic pain patients. The pilot study offered sufficient information and preliminary results in the desirable direction to enable the researchers to move to a randomized controlled trial (RCT) stage in order to establish the efficacy of the intervention. Clinical Trial Registration: The study was registered in the www.researchregistry. com, registry (5483).
Species-specific partial gene duplication in Arabidopsis thaliana evolved novel phenotypic effects on morphological traits under strong positive selection

Abstract Gene duplication is increasingly recognized as an important mechanism for the origination of new genes, as revealed by comparative genomic analysis. However, how new duplicate genes contribute to phenotypic evolution remains largely unknown, especially in plants. Here, we identified the new gene EXOV, derived from a partial gene duplication of its parental gene EXOVL in Arabidopsis thaliana. EXOV is a species-specific gene that originated within the last 3.5 million years and shows strong signals of positive selection. Unexpectedly, RNA-sequencing analyses revealed that, despite its young age, EXOV has acquired many novel direct and indirect interactions in which the parental gene does not engage. This observation is consistent with the high, selection-driven substitution rate of its encoded protein, in contrast to the slowly evolving EXOVL, suggesting an important role for EXOV in phenotypic evolution. We observed significant differentiation of morphological changes for all phenotypes assessed in genome-edited and T-DNA insertional single mutants and in double T-DNA insertion mutants in EXOV and EXOVL. We discovered a substantial divergence of phenotypic effects by principal component analyses, suggesting neofunctionalization of the new gene. These results reveal a young gene that plays critical roles in biological processes that underlie morphological evolution in A. thaliana.
The Environment at Lake El’gygytgyn Area (Northeastern Russian Arctic) Prior to and After the Meteorite Impact at 3.58 Ma

Upper Pliocene sediments from a number of fluvial outcrops in central Chukotka, northeastern Russian Arctic, along the Enmyvaam, Mechekrynnetveem, and Chanuvenvaam Rivers, have been newly studied for pollen, non-pollen-palynomorphs and, for the first time for Pliocene sediments in Eurasia, charcoals. The sediments have survived the El’gygytgyn meteorite impact event at ∼3.58 Ma. The stratigraphy of the studied outcrops suggests that the lowermost sediments were accumulated shortly before the impact event, between ∼3.60 and 3.58 Ma. At that time, coniferous forests with spruces, pines, firs, birches, larches, and alders dominated in the area. Some relatively thermophilic broad-leaved taxa (Corylus, Carpinus, Ulmus, and Myrica) might also have grown in local forests. Summer temperatures were at least 10°C warmer than today. Charcoal concentrations and composition suggest the presence of high intensity fires. Periods of rather wet climate and soil conditions are marked by common shrubby and boggy habitats with ericaceous plants and Sphagnum, and are associated with less, and probably low-intensity surface fires with less charcoal. The impact event caused widespread fires reflected by up to 4 times higher charcoal concentrations in the sediments. The sediments found above the so-called “chaotic horizon” (sediments accumulated synchronously or very shortly after the impact event) contain late Pliocene pollen assemblages comparable to those in Lake El’gygytgyn, reflecting that pine-spruce forests with some firs, birches, larches, and alder dominated in the study area. Some thermophilic taxa might also still have grown in the area. However, the age control for the sediments above the so-called chaotic horizon is poor. The uppermost sediments from the studied sections can be attributed with certainty to the Late Pleistocene and Holocene according to their stratigraphic positions and pollen assemblages. The combined pollen and charcoal analysis allowed correlating hardly datable fluvial sediments and points to varying fire regimes in warmer-than-present climates, when forest extended further north compared to today.
Chronic Thromboembolic Pulmonary Hypertension – What Have We Learned From Large Animal Models

Chronic thrombo-embolic pulmonary hypertension (CTEPH) develops in a subset of patients after acute pulmonary embolism. In CTEPH, pulmonary vascular resistance, which is initially elevated due to the obstructions in the larger pulmonary arteries, is further increased by pulmonary microvascular remodeling. The increased afterload of the right ventricle (RV) leads to RV dilation and hypertrophy. This RV remodeling predisposes to arrhythmogenesis and RV failure. Yet, mechanisms involved in pulmonary microvascular remodeling, processes underlying the RV structural and functional adaptability in CTEPH as well as determinants of the susceptibility to arrhythmias such as atrial fibrillation in the context of CTEPH remain incompletely understood. Several large animal models with critical clinical features of human CTEPH and subsequent RV remodeling have relatively recently been developed in swine, sheep, and dogs. In this review we will discuss the current knowledge on the processes underlying development and progression of CTEPH, and on how animal models can help enlarge understanding of these processes.
Motion Smoothness Metrics for Cannulation Skill Assessment: What Factors Matter?

Medical training simulators have the potential to provide remote and automated assessment of skill vital for medical training. Consequently, there is a need to develop “smart” training devices with robust metrics that can quantify clinical skills for effective training and self-assessment. Recently, metrics that quantify motion smoothness such as log dimensionless jerk (LDLJ) and spectral arc length (SPARC) are increasingly being applied in medical simulators. However, two key questions remain about the efficacy of such metrics: how do these metrics relate to clinical skill, and how to best compute these metrics from sensor data and relate them with similar metrics? This study addresses these questions in the context of hemodialysis cannulation by enrolling 52 clinicians who performed cannulation in a simulated arteriovenous (AV) fistula. For clinical skill, results demonstrate that the objective outcome metric flash ratio (FR), developed to measure the quality of task completion, outperformed traditional skill indicator metrics (years of experience and global rating sheet scores). For computing motion smoothness metrics for skill assessment, we observed that the lowest amount of smoothing could result in unreliable metrics. Furthermore, the relative efficacy of motion smoothness metrics when compared with other process metrics in correlating with skill was similar for FR, the most accurate measure of skill. These results provide guidance for the computation and use of motion-based metrics for clinical skill assessment, including utilizing objective outcome metrics as ideal measures for quantifying skill.
UnrealHaptics: Plugins for Advanced VR Interactions in Modern Game Engines

We present UnrealHaptics, a plugin-architecture that enables advanced virtual reality (VR) interactions, such as haptics or grasping in modern game engines. The core is a combination of a state-of-the-art collision detection library with support for very fast and stable force and torque computations and a general device plugin for communication with different input/output hardware devices, such as haptic devices or Cybergloves. Our modular and lightweight architecture makes it easy for other researchers to adapt our plugins to their requirements. We prove the versatility of our plugin architecture by providing two use cases implemented in the Unreal Engine 4 (UE4). In the first use case, we have tested our plugin with a haptic device in different test scenes. For the second use case, we show a virtual hand grasping an object with precise collision detection and handling multiple contacts. We have evaluated the performance in our use cases. The results show that our plugin easily meets the requirements of stable force rendering at 1 kHz for haptic rendering even in highly non-convex scenes, and it can handle the complex contact scenarios of virtual grasping.
Fast and adaptive protein structure representations for machine learning

The growing prevalence and popularity of protein structure data, both experimental and computationally modelled, necessitates fast tools and algorithms to enable exploratory and interpretable structure-based machine learning. Alignment-free approaches have been developed for divergent proteins, but proteins sharing func-tional and structural similarity are often better understood via structural alignment, which has typically been too computationally expensive for larger datasets. Here, we introduce the concept of rotation-invariant shape-mers to multiple structure alignment, creating a structure aligner that scales well with the number of proteins and allows for aligning over a thousand structures in 20 minutes. We demonstrate how alignment-free shape-mer counts and aligned structural features, when used in machine learning tasks, can adapt to different levels of functional hierarchy in protein kinases, pinpointing residues and structural fragments that play a role in catalytic activity.
Generalizing Longitudinal Age Effects on Brain Structure – A Two-Study Comparison Approach

Cross-sectional studies indicate that normal aging is accompanied by decreases in brain structure. Longitudinal studies, however, are relatively rare and inconsistent regarding their outcomes. Particularly the heterogeneity of methods, sample characteristics and the high inter-individual variability in older adults prevent the deduction of general trends. Therefore, the current study aimed to compare longitudinal age-related changes in brain structure (measured through cortical thickness) in two large independent samples of healthy older adults (n = 161 each); the Longitudinal Healthy Aging Brain (LHAB) database project at the University of Zurich, Switzerland, and 1000BRAINS at the Research Center Juelich, Germany. Annual percentage changes in the two samples revealed stable to slight decreases in cortical thickness over time. After correction for major covariates, i.e., baseline age, sex, education, and image quality, sample differences were only marginally present. Results suggest that general trends across time might be generalizable over independent samples, assuming the same methodology is used, and similar sample characteristics are present.
Xiaoyaosan Improves Antibiotic-Induced Depressive-Like and Anxiety-Like Behavior in Mice Through Modulating the Gut Microbiota and Regulating the NLRP3 Inflammasome in the Colon

Disturbance of the gut microbiota plays an essential role in mental disorders such as depression and anxiety. Xiaoyaosan, a traditional Chinese medicine formula, has a wide therapeutic spectrum and is used especially in the management of depression and anxiety. In this study, we used an antibiotic-induced microbiome-depleted (AIMD) mouse model to determine the possible relationship between imbalance of the intestinal flora and behavioral abnormalities in rodents. We explored the regulatory effect of Xiaoyaosan on the intestinal flora and attempted to elucidate the potential mechanism of behavioral improvement. We screened NLRP3, ASC, and CASPASE-1 as target genes based on the changes in gut microbiota and explored the effect of Xiaoyaosan on the colonic NLRP3 pathway. After Xiaoyaosan intervention, AIMD mice showed a change in body weight and an improvement in depressive and anxious behaviors. Moreover, the gut flora diversity was significantly improved. Xiaoyaosan increased the abundance of Lachnospiraceae in AIMD mice and decreased that of Bacteroidaceae, the main lipopolysaccharide (LPS)-producing bacteria, resulting in decreased levels of LPS in feces, blood, and colon tissue. Moreover, serum levels of the inflammatory factor, IL-1β, and the levels of NLRP3, ASC, and CASPASE-1 mRNA and DNA in the colon were significantly reduced. Therefore, Xiaoyaosan may alleviate anxiety and depression by modulating the gut microbiota, correcting excessive LPS release, and inhibiting the immoderate activation of the NLRP3 inflammasome in the colon.
Improving Source Apportionment of Urban Aerosol Using Multi-Isotopic Fingerprints (MIF) and Positive Matrix Factorization (PMF): Cross-Validation and New Insights

Urban air pollution is a matter of concern due to its health hazards and the continuous population growth exposed to it at different urban areas worldwide. Nowadays, more than 55% of the world population live in urban areas. One of the main challenges to guide pollution control policies is related to pollutant source assessment. In this line, U.S. Environmental Protection Agency's Positive Matrix Factorization (EPA-PMF) has been extensively employed worldwide as a reference model for quantification of source contributions. However, EPA-PMF presents issues associated to source identification and discrimination due to the collinearities among the source tracers. Multi-Isotopic Fingerprints (MIF) have demonstrated good resolution for source discrimination, since urban sources are characterized by specific isotopic signatures. Source quantification based on total aerosol mass is the main limitation of MIF. This study reports strategies for PMF and MIF combination to improve source identification/discrimination and its quantification in urban areas. We have three main findings: (1) cross-validation of PMF source identification based on Pb and Zn isotopic fingerprints, (2) source apportionment in the MIF model for total PM mass, and (3) new insights into potential Zn isotopic signatures of biomass burning and secondary aerosol. We support future studies on the improvement of isotopic fingerprints database of sources based on diverse elements or compounds to boost advances of MIF model applications in atmospheric sciences.
Opportunities and Challenges in Democratizing Immunology Datasets

The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.
An Acceptance Analysis of Subsoil Amelioration Amongst Agricultural Actors in Two Regions in Germany

The subsoil, commonly defined as horizons below the working depth of 30 cm, often receives little attention in farming practice. Yet plants extract between 10 and 80% of their nutrient and water requirements from the subsoil. Recent research indicates that subsoil amelioration measures, which enhance water storage capacity, root penetration and microbial activity, could contribute to stabilizing yields in times of drought. Therefore, we investigated farmers' and other soil experts' perceptions of subsoil amelioration as an approach to adapt to climate change as well as the factors that influence their willingness to adopt specific measures to improve the subsoil. We applied the Q-method combined with focus groups in two case study regions in Germany. Two subsoil amelioration techniques were considered: (1) Deep loosening combined with the incorporation of compost into deep soil layers (30–60 cm) and (2) the cultivation of alfalfa as deep-rooting pre-crop. Our results show three distinct views on subsoil amelioration, which we termed as the “pioneers,” the “skeptics,” and the “ecologists. ” While the pioneers were open toward applying deep loosening combined with incorporation of compost into the subsoil, the skeptics had concerns about the method and perceived it as hardly feasible in practice, and the ecologists clearly preferred biological approaches such as alfalfa cultivation. Despite the different views, all three perspectives view subsoil amelioration as a useful approach to adapt to changing climate conditions. In conclusion, we identified a number of factors that influence the willingness to implement specific techniques to improve the subsoil: economic and farm-level considerations, awareness of subsoil functions, environmental awareness, individual norms and beliefs as well as risk perception. We recommend considering these factors in the design of a policy framework that promotes subsoil amelioration in Germany. Our findings could be of relevance for agricultural systems around the world, which are prone to drought risk.
Physical Demands of Women's Soccer Matches: A Perspective Across the Developmental Spectrum

Female soccer players are exposed to specific physical demands during matches, which vary according to the standard of play. Existing studies have largely focused on quantifying the distances covered for professional and international level players. This approach is limited in scope regarding the broader aspects around physical demands and is detached from development pathway models. An understanding of the demands across all standards will provide valuable insights about appropriate player development and help ensure physical readiness for the demands of the sport. The aim of this perspective paper is to describe the physical demands experienced during women's soccer matches across the developmental spectrum. A combination of evidence from the literature and data from the author's research (JDV) is presented. Specifically highlighted are the trends for locomotor distances, acceleration and deceleration frequency, and metabolic power metrics for youth (≤U17), college (NCAA/U20), professional (domestic) and international standards of women's soccer. In addition, the changes in match demands between levels of play are used to help illustrate gaps that must be overcome in order to successfully achieve physical readiness to compete at higher levels. The evidence demonstrates the importance of training appropriate attributes to prepare female soccer players who are striving to play at progressively higher standards.
Vacuole phase-partitioning boosts mitochondria activity and cell lifespan through an inter-organelle lipid pipeline

Functional linkage between mitochondria and lysosomes is crucial for survival under starvation and lifespan extension. Despite such co-dependency, the supportive pathways connecting mitochondria and lysosomes remain unclear. Here, we identify an inter-organelle lipid trafficking pathway linking yeast vacuole and mitochondria that results in increased mitochondria growth and respiratory activity under glucose starvation. The pathway depends on vacuolar phase-separated, lipid domains, which provide zones for: activation of the vacuolar proton pump; lipid droplet (LD) docking and internalization; and, lipid transfer from vacuole-to-ER-to-mitochondria. Partitioned vacuolar domains form through a specialized type of macro-autophagy, triggered only under acute glucose starvation, that delivers sterol-rich, endosomal-derived lipids to the vacuole. To balance this lipid influx, the vacuole reroutes lipids back to the ER to support both LD biogenesis and mitochondria growth and activity. Energy produced by enhanced mitochondrial activity then feeds back to support the inter-organelle lipid trafficking pathways to ensure survival under nutrient stress.
Single-cell transcriptome profiling of the human developing spinal cord reveals a conserved genetic programme with human-specific features

The spinal cord receives input from peripheral sensory neurons and controls motor output by regulating muscle innervating motor neurons. These functions are carried out by neural circuits comprising molecularly and physiologically distinct neuronal subtypes that are generated in a characteristic spatial-temporal arrangement from progenitors in the embryonic neural tube. The systematic mapping of gene expression in mouse embryos has provided insight into the diversity and complexity of cells in the neural tube. For human embryos, however, less information has been available. To address this, we used single cell mRNA sequencing to profile cervical and thoracic regions in four human embryos of Carnegie Stages (CS) CS12, CS14, CS17 and CS19 from Gestational Weeks (W) 4-7. In total we recovered the transcriptomes of 71,219 cells. Analysis of progenitor and neuronal populations from the neural tube, as well as cells of the peripheral nervous system, in dorsal root ganglia adjacent to the neural tube, identified dozens of distinct cell types and facilitated the reconstruction of the differentiation pathways of specific neuronal subtypes. Comparison with existing mouse datasets revealed the overall similarity of mouse and human neural tube development while highlighting specific features that differed between species. These data provide a catalogue of gene expression and cell type identity in the developing neural tube that will support future studies of sensory and motor control systems and can be explored at https://shiny.crick. ac.uk/scviewer/neuraltube/.