Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-validation of cut-points in preschool children using different accelerometer placements and data axes

ABSTRACT The present study cross-validated various cut-points to assess physical activity and sedentary behaviour in preschoolers, using hip- and wrist-worn accelerometers and both vertical axis and vector magnitude data. Secondly, we examined the influence of epoch length on time estimates of physical activity and sedentary behaviour. Sixty-four preschoolers (34 girls) wore two accelerometers, on their right hip and dominant wrist, during 1 hour of free play. Preschoolers’ activities were observed by two trained researchers. Area under the curve (AUC) was calculated for the receiving operating characteristic (ROC) curves as a measure of precision. AUC ranges were 0.603–0.723 for sedentary behaviour, 0.472–0.545 for light physical activity and 0.503–0.661 for moderate-to-vigorous physical activity (MVPA), indicating poor to fair precision. Percentage of time classified as sedentary behaviour, light or MVPA according to observation and accelerometer data varied largely between cut-points, accelerometer placements and axes. The influence of epoch length on time estimates was minimal across cut-points, except for one hip-based vector magnitude cut-point. Across all accelerometer placements and data axes, no set of cut-points demonstrated adequate precision for sedentary behaviour, light physical activity and MVPA. The highly variable and omnidirectional activity pattern of preschoolers may explain the lack of adequate cut-points.
Reconciling romanticization and vilification: constituting post-pandemic communication pedagogy

Whether online teaching is a challenge or an opportunity is the wrong question for us to ask and answer. Online teaching in myriad forms has been essential to teaching and learning--though to different degrees depending on the discipline, institution, student, and educator--for decades and will remain so. Though the authors of this article have some concerns about what it means to maximize variables and to engage in course design in an opportunistic way, they agree not only that communication scholars are well poised to lead other disciplines in defining the post-pandemic higher education landscape, but also that they have a responsibility to do so. However, to succeed, this endeavor must be fundamentally intersectional--of identities, methodologies, and paradigms--and communication scholars must re-engage their most fundamental assumptions about communication itself and the purpose of their work as teacher-scholars.
Toward Quality of Information Aware Distributed Machine Learning

In the era of big data, data are usually distributed across numerous connected computing and storage units (i.e., nodes or workers). Under such an environment, many machine learning problems can be reformulated as a consensus optimization problem, which consists of one objective and constraint terms splitting into N parts (each corresponds to a node). Such a problem can be solved efficiently in a distributed manner via Alternating Direction Method of Multipliers (ADMM). However, existing consensus optimization frameworks assume that every node has the same quality of information (QoI), i.e., the data from all the nodes are equally informative for the estimation of global model parameters. As a consequence, they may lead to inaccurate estimates in the presence of nodes with low QoI. To overcome this challenge, in this article, we propose a novel consensus optimization framework for distributed machine-learning that incorporates the crucial metric, QoI. Theoretically, we prove that the convergence rate of the proposed framework is linear to the number of iterations, but has a tighter upper bound compared with ADMM. Experimentally, we show that the proposed framework is more efficient and effective than existing ADMM-based solutions on both synthetic and real-world datasets due to its faster convergence rate and higher accuracy.
AIP: A Named Entity Recognition Method Combining Glyphs and Sounds

In recent years, a large number of Chinese electronic texts have been produced in the process of information construction in various fields. Identifying specific entities in these electronic texts has become a major research focus. Most existing research methods use radicals to extract the glyph features of Chinese characters but have seen its limitation. This paper extracts the features of Chinese characters from three aspects: glyph features, phonetic features, and character features, and improves conventional feature extraction methods for each kind of feature. A new named entity recognition method (AIP) is proposed by transforming Chinese characters into corresponding images for glyph feature extraction, dividing pinyin into initials, vowels, and tones for phonetic feature extraction, and fine-tuning the A Lite Bert model for character feature extraction to improve the performance of the model. This paper compares the performance of the AIP model and mainstream neural network models on Chinese named entity recognition tasks on commonly used data sets and the data sets in specific domain. The results showed that AIP achieved better results than the related arts. The F1 values on the two data sets are 94.4% and 80.5%, respectively, which validates the model's versatility.
Static and Dynamic Progressive Geospatial Interlinking

Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its sources are insufficiently interlinked with topological relations in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap through space tiling techniques, which significantly restrict the search space. Yet, the state-of-the-art techniques operate exclusively in a batch manner that produces results only after processing all their geometries. In this work, we address this issue by defining the task of Progressive Geospatial Interlinking, which produces results in a pay-as-you-go manner when the available computational or temporal resources are limited. We propose a static progressive algorithm, which employs a fixed processing order, and a dynamic one, whose processing order is updated whenever new topological relations are discovered. We equip both algorithms with a series of weighting schemes and explain how they can be adapted to massive parallelization with Apache Spark. We conduct a thorough experimental study over six large, real datasets, demonstrating the superiority of our techniques over the current state-of-the-art. Special care is also taken to analyze the performance of the various weighting schemes.
Systematic dissection of phosphorylation-dependent YAP1 complex formation elucidates a key role for PTPN14 in Hippo signal integration

Cellular signaling relies on the temporal and spatial control of the formation of transient protein complexes by post-translational modifications, most notably by phosphorylation. While several computational methods have been developed to predict the functional relevance of phosphorylation sites, assessing experimentally the interdependency between protein phosphorylation and protein-protein interactions (PPIs) remains a major challenge. Here, we describe an experimental strategy to establish interdependencies between specific phosphorylation events and complex formation. This strategy is based on three main steps: (i) systematically charting the phosphorylation landscape of a target protein; (ii) assigning distinct proteoforms of the target protein to different protein complexes by electrophoretic separation of native complexes (BNPAGE) and protein/phopho correlation profiling; and (iii) genetically deleting known regulators of the target protein to identify which ones are required for given proteoforms and complexes. We applied this strategy to study phosphorylation- dependent modulation of complexes containing the transcriptional co-regulator YAP1. YAP1 is highly phosphorylated and among the most extensively connected proteins in the human interactome. It functions as the main signal integrator and effector protein of the Hippo pathway which controls organ size and tissue homeostasis. Using our workflow, we could identify several distinct YAP1 proteoforms specifically associated with physically distinct complexes and infer how their formation is affected by known Hippo pathway members. Importantly, our findings suggest that the tyrosine phosphatase PTPN14 controls the co-transcriptional activity of YAP1 by regulating its interaction with the LATS1/2 kinases. In summary, we present a powerful strategy to establish interdependencies between specific phosphorylation events and complex formation, thus contributing to the “functionalization” of phosphorylation events and by this means provide new insights into Hippo signaling.
Regulation of Cardiomyocyte Adhesion and Mechanosignalling Through Distinct Nanoscale Behaviour of Integrin Ligands Mimicking Healthy or Fibrotic ECM

The stiffness of the cardiovascular environment changes during ageing and in disease and contributes to disease incidence and progression. Changing collagen expression and crosslinking regulate the rigidity of the cardiac ECM. Additionally, basal lamina glycoproteins, especially laminin and fibronectin regulate cardiomyocyte adhesion formation, mechanics and mechano-signalling. Laminin is abundant in the healthy heart, but fibronectin is increasingly expressed in the fibrotic heart. ECM receptors are co-regulated with the changing ECM. Due to differences in integrin dynamics, clustering, and downstream adhesion formation this is expected to ultimately influence cardiomyocyte mechanosignalling; however details remain elusive. Here we sought to investigate how different cardiomyocyte ligand/integrin combinations are affecting adhesion formation, traction forces and mechanosignalling, using a combination of uniformly coated surfaces with defined stiffness, PDMS nanopillars, micropatterning and specifically designed bionanoarrays for precise ligand presentation. Thereby we find that neonatal rat cardiomyocytes (which express both laminin and fibronectin binding integrins) adhesion nanoscale organisation, signalling and traction force generation are strongly dependent on the integrin/ligand combination. Together our data indicates that the presence of fibronectin in combination with the enhanced stiffness in fibrotic areas will strongly impact on the cardiomyocyte behaviour and influence disease progression.
CATHe: detection of remote homologues for CATH superfamilies using embeddings from protein language models

CATH is a protein domain classification resource that combines an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues that might be missed by state-of-the-art HMM-based approaches. The proposed algorithm for this task (CATHe) combines a neural network with sequence representations obtained from protein language models. The employed dataset consisted of remote homologues that had less than 20% sequence identity. The CATHe models trained on 1773 largest, and 50 largest CATH superfamilies had an accuracy of 85.6+−0.4, and 98.15+−0.30 respectively. To examine whether CATHe was able to detect more remote homologues than HMM-based approaches, we employed a dataset consisting of protein regions that had annotations in Pfam, but not in CATH. For this experiment, we used highly reliable CATHe predictions (expected error rate <0.5%), which provided CATH annotations for 4.62 million Pfam domains. For a subset of these domains from homo sapiens, we structurally validated 90.86% of the predictions by comparing their corresponding AlphaFold structures with experimental structures from the CATHe predicted superfamilies.
A genomic analysis of parasite-mediated population differentiation in a metapopulation

Understanding the genetics of host adaptation is a powerful approach to study host – parasite interactions. Hosts are often assumed to have a simple genetic architecture underlying resistance. However, in natural populations the genetics are rarely known and the link between host adaptation and evolutionary models cannot be easily established. To shed light on the genetic basis of host evolution in the presence and absence of parasites we studied a highly dynamic rockpool metapopulation of the planktonic crustacean Daphnia magna and its microsporidian parasite Hamiltosporidium tvaerminnensis. We examined genome-wide allele frequencies estimated from pooled Illumina sequencing (Pool-seq) of 12 subpopulations of a metapopulation. Subpopulations that had evolved for several years with the parasite were contrasted to uninfected subpopulations, with the aim to find genomic sites of diversifying selection. Consistent with earlier attempts to find resistance genes in this system, we observe many minor-effect outliers, suggesting that the response of the host to this parasite is based on a quantitative-trait architecture. We found 34 outliers across 11 genomic regions that indicate increased differentiation between population groups as well as signs of positive selection. Some of these regions contain immune-related genes, of which four are likely involved in immune downregulation. Our findings show that in the presence of the microsporidium parasite, hosts evolve in a complex polygenic way, driving population differentiation in the metapopulation under study. Such evolutionary differentiation is a powerful mechanism to maintain genetic diversity in spatially structured populations.
High nutrient loads hinder successful restoration of natural habitats in freshwater wetlands

Restoration of natural processes in ecosystems is key to halt the biodiversity crisis. Here, we evaluate 20 different stream-valley wetland restoration projects – mainly rewetting – in a large region in Denmark in terms of successful restoration of natural wetland habitats. We used quadratic discriminant analysis and generalized linear models to compare the projects’ 80 vegetation plots with >50.000 natural wetland-habitat reference plots and modelled the influence of time, grazing, rewetting and nutrient availability on the study plots’ probabilities of belonging to such natural habitats and their richness of high-quality habitat indicator species. In our study, the probability of a restored wetland being a natural wetland habitat – almost always an alkaline fen – was generally below 10 %. Also, we only found half as many indicator species in restored wetlands than in reference wetlands and we demonstrated that the number of characteristic alkaline fen species did not deviate from what could be expected under the prevailing nutrient conditions. We found a negative effect of nutrient availability on the number of high-quality habitat indicator species and the lowest probability of plots being natural wetlands in the most nutrient rich plots. The effect of grazing was only positive in the first years after restoration and only in the most nutrient rich plots, while the effect of rewetting sites to their historical hydrological conditions was generally negative. Our findings reveal that unnaturally high nutrient availability is probably the core limiting factor for successful restoration of natural wetlands and their associated plant diversity. Implications for practice - To successfully restore natural and characteristic freshwater wetland habitats focus on recreating natural processes and conditions is needed - Restoring natural hydrology and grazing is not enough, the soil and water must be naturally nutrient poor for successful restoration of these habitats - Restoration of stream-valley wetlands such as alkaline springs and fens is more likely to be successful in spring-dominated landscapes where clean groundwater diffusely exfiltrates the soil
Evaluation of Satellite-Derived Bathymetry from High and Medium-Resolution Sensors Using Empirical Methods

: This study evaluates the accuracy of bathymetric maps generated from multispectral satellite datasets acquired from different multispectral sensors, namely the Worldview 2, PlanetScope, and the Sentinel 2, in the bay of Elounda in Crete. Image pre-processing steps were implemented before the use of the three empirical methods for estimating bathymetry. A dedicated correction and median ﬁlter have been applied to minimize noise from the sun glint and the sea waves. Due to the spectral complexity of the selected study area, statistical correlation with different numbers of bands was applied. The analysis indicated that blue and green bands obtained the best results with higher accuracy. Then, three empirical models, namely the Single Band Linear Algorithm, the Multiband Linear Algorithm, and the Ratio Transform Algorithm, were applied to the three multispectral images. Bathymetric and error distribution maps were created and used for the error assessment of results. The accuracy of the bathymetric maps estimated from different empirical models is compared with on-site Single beam Echo Sounder measurements. The most accurate bathymetric maps were obtained using the WorldView 2 and the empirical model of the Ratio Transform algorithm, with the RMSE reaching 1.01 m.
Benchmarking Neural Networks on Heterogeneous Hardware Resources

In recent years, artificial intelligence (AI) became a key enabling technology for many domains. To achieve best performance, modern AI methods have high resource demands, e.g., GPU servers for the training of neural networks. With the advent of further processor technologies, such as tensor processors or re-wirable processors, AI methods may be executed in shorter time while even saving energy. For many application domains such as autonomous driving or unmanned aerial vehicles, real-time constraints mandate low end-to-end latencies in AI processing. In this paper, we present a combined micro- and macro-benchmarking approach to analyze the performance as well as the power demands of modern processor architectures using convolutional neural networks as workload. We discuss tradeoffs among the different processor types and indicate issues and challenges that arise when performing such benchmarks on heterogeneous hardware resources. We show that FPGAs allow for an increase of 7x up to 45x in performance over high-end GPUs while using only 10% of the power. In the consumer space, novel architectures such as the Apple M1 are able to offer 3-5x better performance at 10-20% the power draw of current x86 CPU or GPU hardware.
MiSim - A Lightweight and Extensible Simulator for a Scenario-Based Resilience Evaluation of Microservice Architectures (Poster)

With the growing popularity of microservice-based architectures, the need for an effective resilience assessment of these systems occurred. A resilience assessment is often done in production using so-called chaos experiments. While producing representative results, chaos experiments often (1) require a significant investment of time, (2) impact customer experiences as they stress the system under test, and (3) do not lean towards parallel running because of interference. Simulating chaos experiments is an alternative approach to running experiments in a production environment. Today, there are many simulators available for distributed and service-oriented architectures. Popular examples are SimuLizar [1], DraCeo [2], BigHouse [3], μqsim [4], PacketStorm [5], iFogSim [6] or GreenCloud [7]. Most of these focus on either performance or efficiency by simulating or solving queuing models. However, none of them satisfies the following requirements: (1) supporting many resilience patterns, (2) simulating multiple typical chaos injections such as killing a service instance, and (3) being a lightweight simulator that has low overhead in modeling and simulation.
Insight Into Regulatory T Cells in Sepsis-Associated Encephalopathy

Sepsis-associated encephalopathy (SAE) is a diffuse central nervous system (CNS) dysfunction during sepsis, and is associated with increased mortality and poor outcomes in septic patients. Despite the high incidence and clinical relevance, the exact mechanisms driving SAE pathogenesis are not yet fully understood, and no specific therapeutic strategies are available. Regulatory T cells (Tregs) have a role in SAE pathogenesis, thought to be related with alleviation of sepsis-induced hyper-inflammation and immune responses, promotion of T helper (Th) 2 cells functional shift, neuroinflammation resolution, improvement of the blood-brain barrier (BBB) function, among others. Moreover, in a clinical point of view, these cells have the potential value of improving neurological and psychiatric/mental symptoms in SAE patients. This review aims to provide a general overview of SAE from its initial clinical presentation to long-term cognitive impairment and summarizes the main features of its pathogenesis. Additionally, a detailed overview on the main mechanisms by which Tregs may impact SAE pathogenesis is given. Finally, and considering that Tregs may be a novel target for immunomodulatory intervention in SAE, different therapeutic options, aiming to boost peripheral and brain infiltration of Tregs, are discussed.
The Influence of Teacher–Student Interaction on the Effects of Online Learning: Based on a Serial Mediating Model

During the COVID-19 pandemic, online education has become an important approach to learning in the information era and an important research topic in the field of educational technology as well as that of education in general. Teacher–student interaction in online education is an important factor affecting students’ learning performance. This study employed a questionnaire survey to explore the influence of teacher–student interaction on learning effects in online education as well as the mediating role of psychological atmosphere and learning engagement. The study involved 398 college students studying at Chinese universities as the research object. Participants filled out a self-report questionnaire. The study found that (1) the level of teacher–student interaction positively affected students’ learning effects (r = 0.649, p < 0.01). (2) The psychological atmosphere mediated the positive effect of the level of teacher–student interaction on learning effects with mediating effect value of 0.1248. (3) Learning engagement mediated the positive effect of teacher–student interaction on learning effects with a mediating effect value of 0.1539. (4) The psychological atmosphere and learning engagement play a chain-mediating role in the mechanism of teacher–student interaction affecting students’ learning effects; that is, teacher–student interaction promotes students’ learning engagement by creating a good psychological atmosphere, which, in turn, influences learning effects. The mediating effect value was 0.0403. The results indicate that teacher–student interaction not only directly affects students’ learning effects but also influences students’ learning effects through the mediating effect of the psychological atmosphere and learning engagement.