Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence-guided approach to portfolio-guided teaching and assessing communications, ethics and professionalism for medical students and physicians: a systematic scoping review

Objectives Guiding the development of longitudinal competencies in communication, ethics and professionalism underlines the role of portfolios to capture and evaluate the multiple multisource appraisals and direct personalised support to clinicians. However, a common approach to these combined portfolios continues to elude medical practice. A systematic scoping review is proposed to map portfolio use in training and assessments of ethics, communication and professionalism competencies particularly in its inculcation of new values, beliefs and principles changes attitudes, thinking and practice while nurturing professional identity formation. It is posited that effective structuring of portfolios can promote self-directed learning, personalised assessment and appropriate support of professional identity formation. Design Krishna’s Systematic Evidence-Based Approach (SEBA) is employed to guide this systematic scoping review of portfolio use in communication, ethics and professionalism training and assessment. Data sources PubMed, Embase, PsycINFO, ERIC, Scopus and Google Scholar databases. Eligibility criteria Articles published between 1 January 2000 and 31 December 2020 were included. Data extraction and synthesis The included articles are concurrently content and thematically analysed using the split approach. Overlapping categories and themes identified are combined using the jigsaw perspective. The themes/categories are compared with the summaries of the included articles in the funnelling process to ensure their accuracy. The domains identified form the framework for the discussion. Results 12 300 abstracts were reviewed, 946 full-text articles were evaluated and 82 articles were analysed, and the four domains identified were indications, content, design, and strengths and limitations. Conclusions This review reveals that when using a consistent framework, accepted endpoints and outcome measures, longitudinal multisource, multimodal assessment data fashions professional and personal development and enhances identity construction. Future studies into effective assessment tools and support mechanisms are required if portfolio use is to be maximised.
Multiple disturbances classifier for electric signals using adaptive structuring neural networks

This work proposes a novel classifier to recognize multiple disturbances for electric signals of power systems. The proposed classifier consists of a series of pipeline-based processing components, including amplitude estimator, transient disturbance detector, transient impulsive detector, wavelet transform and a brand-new neural network for recognizing multiple disturbances in a power quality (PQ) event. Most of the previously proposed methods usually treated a PQ event as a single disturbance at a time. In practice, however, a PQ event often consists of various types of disturbances at the same time. Therefore, the performances of those methods might be limited in real power systems. This work considers the PQ event as a combination of several disturbances, including steady-state and transient disturbances, which is more analogous to the real status of a power system. Six types of commonly encountered power quality disturbances are considered for training and testing the proposed classifier. The proposed classifier has been tested on electric signals that contain single disturbance or several disturbances at a time. Experimental results indicate that the proposed PQ disturbance classification algorithm can achieve a high accuracy of more than 97% in various complex testing cases.
A Struggled Rupture Initiation of the Mw 6.1 2009 L’Aquila Earthquake

Understanding under which physical conditions large earthquakes begin, is a key question in Earth science. Laboratory experiments and numerical models have shown that earthquake nucleation has distinct phases: a quasi‐static and an acceleration stage, followed by dynamic propagation. However, obtaining observations of such or similar processes in nature is complex. Here, we report on the rupture initiation of the Mw 6.1 2009 L'Aquila earthquake. From the detailed analysis of seismic waves recorded at several stations, we identify an ∼0.6‐s signal preceding the large dynamic rupture. From the geometrical characterization and rupture parameters of this initial phase, we infer that the rupture struggled to initiate exhibiting a slow rupture velocity ( Vr=0.9±0.2 $Vr=0.9\pm 0.2$ km/s) and low seismic efficiency ( η=0.24 $\eta =0.24$ ) due to a complex environment in the region where the rupture starts. We also show that the parameters of the rupture initiation are representative of scale‐dependent quantities for slip‐dependent nucleation models.
Electron Heating in Magnetosheath Turbulence: Dominant Role of the Parallel Electric Field Within Coherent Structures

How particles are being energized by turbulent electromagnetic fields is an outstanding question in plasma physics and astrophysics. This paper investigates the electron acceleration mechanism in strong turbulence (δB/B0 ∼ 1) in the Earth's magnetosheath based on the novel observations of the Magnetospheric Multiscale mission. We find that electrons are magnetized in turbulent fields for the majority of the time. By directly calculating the electron acceleration rate from Fermi, betatron mechanism, and parallel electric field, it is found that electrons are primarily accelerated by the parallel electric field within coherent structures. Moreover, the acceleration rate by parallel electric fields increases as the spatial scale reduces, with the most intense acceleration occurring over about one ion inertial length. This study is an important step toward fully understanding the turbulent energy dissipation in weakly collisional plasmas.
Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies

The study of the effect of a treatment may involve the evaluation of a variable at a number of moments. When assuming a smooth curve for the mean response along time, estimation can be afforded by spline regression, in the context of generalized additive models. The novelty of our work lies in the construction of hypothesis tests to compare two curves of treatments in any interval of time for several types of response variables. The within-subject correlation is not modeled but is considered to obtain valid inferences by the use of bootstrap. We propose both semiparametric and nonparametric bootstrap approaches, based on resampling vectors of residuals or responses, respectively. Simulation studies revealed a good performance of the tests, considering, for the outcome, different distribution functions in the exponential family and varying the correlation between observations along time. We show that the sizes of bootstrap tests are close to the nominal value, with tests based on a standardized statistic having slightly better size properties. The power increases as the distance between curves increases and decreases when correlation gets higher. The usefulness of these statistical tools was confirmed using real data, thus allowing to detect changes in fish behavior when exposed to the toxin microcystin-RR.
Typology and dynamics of actors` roles and positions within entrepreneurial ecosystems: an exploratory study

ABSTRACT This study explores how health-tech small and mid-size enterprises (SMEs) could better utilize the entrepreneurial ecosystems (EEs) around them in developing their business within this fast-growing yet under-researched industry. Based on the qualitative empirical study, we examine actor roles and related dynamics in the health-tech ecosystem to understand how firms could benefit from the ecosystem's resources. The study contributes to EE research by providing an empirically grounded typology of ten actor roles and examining how an individual company could change and develop its role in the network to grow and succeed. Moreover, it extends the current research on role typologies by explaining the various roles in EEs and underlines the importance of ecosystem dynamics. Managerially, the study highlights the importance of recognizing the company’s role(s) and the roles of other ecosystem members, which further aids in their strategic decision-making and future planning.
Experimental heatwaves and warming cause distinctive community responses through their interactions with a novel species

As mean temperatures increase and heatwaves become more frequent, species are expanding their distributions to colonise new habitats. The resulting novel species interactions will simultaneously shape the temperature-driven reorganization of resident communities. The interactive effects of climate change and climate change-facilitated invasion have rarely been studied in multi-trophic communities, and are likely to differ depending on the nature of the climatic driver (i.e. climate extremes or constant warming). We recreated under laboratory conditions a host-parasitoid community typical of high-elevation rainforest sites in Queensland, Australia, comprising four Drosophila species and two associated parasitoid species. We subjected these communities to climate change in the form of either heatwaves or constant warming, in combination with an invasion treatment involving a novel host species from lower-elevation habitats. The two parasitoid species were sensitive to both warming and heatwaves, while the demographic responses of Drosophila species were highly idiosyncratic, reflecting the combined effects of thermal tolerance, parasitism, competition, and facilitation. After multiple generations, heatwaves (but not constant warming) promoted the establishment of low-elevation species in upland communities. The introduction of this invading species correlated negatively with the abundance of one of the parasitoid species, leading to cascading effects on its hosts and their competitors. Our study, therefore, reveals differing, sometimes contrasting, impacts of extreme temperatures and constant warming on community composition. It also highlights how the scale and direction of climate impacts could be further modified by range-expanding species within a bi-trophic community network.
Learning the functional landscape of microbial communities

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines emergent function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes – analogs to fitness landscapes – that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically-inferred landscapes quantitatively predict community functions from knowledge of strain presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species, and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes are typically not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show this observation is generic across many ecological models, suggesting community-function landscapes can be applied broadly across many contexts. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.
Inducible protein degradation as a strategy to identify Phosphoprotein Phosphatase 6 substrates in RAS-mutant colorectal cancer cells

Protein phosphorylation is an essential regulatory mechanism that controls most cellular processes, including cell cycle progression, cell division, and response to extracellular stimuli, among many others, and is deregulated in many diseases. Protein phosphorylation is coordinated by the opposing activities of protein kinases and protein phosphatases. In eukaryotic cells, most serine/threonine phosphorylation sites are dephosphorylated by members of the Phosphoprotein Phosphatase (PPP) family. However, we only know for a few phosphorylation sites which specific PPP dephosphorylates them. Although natural compounds such as calyculin A and okadaic acid inhibit PPPs at low nanomolar concentrations, no selective chemical PPP inhibitors exist. Here, we demonstrate the utility of endogenous tagging of genomic loci with an auxin-inducible degron (AID) as a strategy to investigate specific PPP signaling. Using Protein Phosphatase 6 (PP6) as an example, we demonstrate how rapidly inducible protein degradation can be employed to identify dephosphorylation SITES and elucidate PP6 biology. Using genome editing, we introduce AID-tags into each allele of the PP6 catalytic subunit (PP6c) in DLD-1 cells expressing the auxin receptor Tir1. Upon rapid auxin-induced degradation of PP6c, we perform quantitative mass spectrometry-based proteomics and phosphoproteomics to identify PP6 substrates in mitosis. PP6 is an essential enzyme with conserved roles in mitosis and growth signaling. Consistently, we identify candidate PP6c-dependent phosphorylation sites on proteins implicated in coordinating the mitotic cell cycle, cytoskeleton, gene expression, and mitogen-activated protein kinase (MAPK) and Hippo signaling. Finally, we demonstrate that PP6c opposes the activation of large tumor suppressor 1 (LATS1) by dephosphorylating Threonine 35 (T35) on Mps One Binder (MOB1), thereby blocking the interaction of MOB1 and LATS1. Our analyses highlight the utility of combining genome engineering, inducible degradation, and multiplexed phosphoproteomics to investigate signaling by individual PPPs on a global level, which is currently limited by the lack of tools for specific interrogation.
Elastomeric Pillar Cages Modulate Actomyosin Contractility of Epithelial Microtissues by Substrate Stiffness and Topography

Cell contractility regulates epithelial tissue geometry development and homeostasis. The underlying mechanobiological regulation circuits are poorly understood and experimentally challenging. We developed an elastomeric pillar cage (EPC) array to quantify cell contractility as a mechanoresponse of epithelial microtissues to substrate stiffness and topography. The spatially confined EPC geometry consisted of 24 circularly arranged slender pillars (1.2 MPa, height: 50 μm, diameter: 10 μm, distance: 5 μm). These high-aspect-ratio pillars were confined at both ends by planar substrates with different stiffness (0.15 – 1.2 MPa). Analytical modeling and finite elements simulation retrieved cell forces from pillar displacements. For evaluation, highly contractile myofibroblasts and cardiomyocytes were assessed to demonstrate that the EPC device can resolve static and dynamic cellular force modes. Human breast (MCF10A) and skin (HaCaT) cells grew as adherence junction-stabilized 3D microtissues within the EPC geometry. Planar substrate areas triggered the spread of monolayered clusters with substrate stiffness-dependent actin stress fiber (SF)-formation and substantial single-cell actomyosin contractility (150 - 200 nN). Within same continuous microtissues, the pillar-ring topography induced bilayered cell tube growth. Here, low effective pillar stiffness overwrote local substrate stiffness sensing and induced SF-lacking roundish cell shapes with extremely low cortical actin tension (11 - 15 nN). This work introduced a versatile biophysical tool to explore mechanobiological regulation circuits driving low- and high-tensional states in developing and homeostatic microtissues. EPC arrays facilitate simultaneously analyzing the impact of planar substrate stiffness and topography on microtissue contractility hence microtissue geometry and function.
MUTATIONEXPLORER- A WEBSERVER FOR MUTATION OF PROTEINS AND 3D VISUALIZATION OF ENERGETIC IMPACTS

The possible effects of mutations on stability and function of a protein can only be understood in the context of protein 3D structure. The MutationExplorer webserver maps sequence changes onto protein structures and allows users to study variation by inputting sequence changes. As the user enters variants, the 3D model evolves, and estimated changes in energy are highlighted. In addition to a basic per-residue input format, MutationExplorer can also upload an entire replacement sequence. Previously the purview of desktop applications, such an upload can back-mutate PDB structures to wildtype sequence in a single step. Another supported variation source is human single nucelotide polymorphisms (SNPs), genomic coordinates input in VCF format. Structures are flexibly colorable, not only by energetic differences, but also by hydrophobicity, sequence conservation, or other biochemical profiling. Coloring by interface score reveals mutation impacts on binding surfaces. MutationExplorer strives for efficiency in user experience. For example, we have prepared 45,000 PDB depositions for instant retrieval and initial display. All modeling steps are performed by Rosetta. Visualizations leverage MDsrv/Mol*. MutationExplorer is available at: http://proteinformatics.org/mutation_explorer/
Identifying environmental factors affecting the production of pea aphid dispersal morphs in field populations

Many species of insects show phenotypically plastic dispersal traits in response to suboptimal environmental conditions. These polyphenetic traits can have large impacts on the population dynamics of the dispersing insects. Therefore, it is important to understand what environmental factors affect the development of dispersal traits and how these factors affect population dynamics via immigration/emigration. Pea aphids (Acyrthosiphon pisum) exhibit a polyphenetic wing dimorphism in which individuals are either winged (alate) or not (apterous). The production of alate offspring is determined by environmental cues experienced by the mother. Environmental factors associated with these cues have been intensively studied, with crowding, host plant quality, natural enemies, and fungal infection being shown to affect the production of pea aphid alates under controlled laboratory conditions. Nonetheless, environmental factors affecting alate production have rarely been studied in field populations of pea aphids. Using data from a three-year study of alate production in 5-9 alfalfa (lucerne) fields, we examined the effects of (i) pea aphid abundance, (ii) host plant maturity, (iii) temperature, (iv) predator and herbivore abundance, (v) parasitism, and (vi) entomopathic fungal incidence. In twice-weekly samples taken over the growing season, alate production ranged from 0 to 83% of the nymphal population. Pea aphid abundance, temperature, and alfalfa maturity together explained 67% of the variation in alate production. The other factors we investigated explained little variation. These results suggest there is a limited number of key environmental factors that consistently predict changes in alate production in field populations, while many factors identified in lab studies may be unimportant. Our results highlight the value of investigating factors affecting the expression of plastic traits in insect species at a broad spatiotemporal scale and under natural conditions.
Intronic TNR-Retained Isopropylmalate Isomerase Large Subunit1 transcripts impair leaf development in Arabidopsis

Intronic trinucleotide repeat (TNR) is widely distributed in plant genomes. In Arabidopsis accession Bur-0, abnormally expanded TTC repeat in intron-3 of the ISOPROPYLMALATE ISOMERASE LARGE SUBUNIT1 (IIL1) gene causes growth defects called the irregularly impaired leaves (iil) phenotype, triggered by DNA methylation-mediated IIL1 gene silencing at elevated temperature. However, little is known about how the reduced expression of IIL1 causes the iil phenotype. We demonstrated that the iil phenotype was resulted from the relative increase of intron-3-retained IIL1 transcripts through the experiments where the iil phenotype was reproduced by introducing the IIL1 gene harboring 100 copies of TTC repeat into Col-0. The iil phenotype appeared when the total amount of the IIL1 transcripts was decreased by co-suppression and the percentage of intron-3-retained IIL1 transcripts was increased. The IIL1 gene encodes an isopropylmalate isomerase large subunit, forming heterodimers with small subunits (AtLeuD1, AtLeuD2, or AtLeuD3). In the myb28 myb29 mutant lacking AtLeuD1 and AtLeuD2, the iil phenotype was almost completely suppressed regardless of higher percentage of intron-3-retained IIL1 transcripts. The results indicated that the iil phenotype was associated with interaction with AtLeuDs, suggesting that intronic TNR-containing transcripts were translated into abnormal proteins and perturbed the metabolic pathway supporting the leaf development.
Production of extracellular polymeric substances in granular sludge under selection for Accumulibacter and Competibacter

Granular sludge intensifies the removal of nutrients from wastewater. Granules structured by extracellular polymeric substances (EPS) can be recovered as biomaterial. Links between microbial selection and EPS formation during granulation need to get uncovered. We inoculated anaerobic-aerobic sequencing batch reactors with either flocs or granules to study the relationships between microbial selection, bioaggregation, exopolymer formation, and EPS composition. Selection for slow-growing organisms like the model polyphosphate- accumulating organism “Candidatus Accumulibacter” (max. 83% vs. amplicon sequencing read counts) and glycogen-accumulating organism “Ca. Competibacter” (max. 45%) sustained granulation. Gel-forming exopolymers were produced as high as above 40% of the volatile solids of the biomass by stepwise increase of the organic loading rate (0.3 to 2.0 g CODAc d-1 LR-1). Confocal laser scanning microscopy, FT-IR spectroscopy, and HPAE-PAD chromatography revealed the complex and dynamic chemical compositions of the structural EPS in relation to microbial population shifts along reactor regimes. The analysis of 20 representative genomes of “Ca. Accumulibacter” and “Ca. Competibacter” recovered from public databases revealed their functional potential to produce EPS among other representative wastewater microorganisms. The more than 40 functional gene categories annotated highlight the complexity of EPS metabolic networks from monomers processing to assembly, export, and epimerizations. The combination of ecological engineering principles and systems microbiology will help unravel and direct the production of EPS from wastewater, valorizing residual granular sludge into beneficial biomaterials for the circular economy. Highlights Selection for slow-growing organisms like PAOs and GAOs fostered a robust granulation. Structural EPS were produced above 40% of biomass volatile content under high loading. Chemical composition of EPS evolved together with the microbial community composition. Genomic insights highlighted the genetic potential of PAOs and GAOs for EPS formation. Microbial communities are complex; further are their EPS compositions and metabolisms. Graphical abstract