Miracle-dz/UniFAR-FTUs huggingface dataset:
Allyship as activism: advancing social change in global sport through transformational allyship

Abstract As the world is racing to find a cure to save lives and economies amidst a global pandemic, COVID-19 has inadvertently caused a shift of focus towards the inequities and injustices plaguing society economically, politically, and socially. In response, athletes, coaches, managers, and other change agents with institutional power in sport organisations across the globe have begun to utilise their platforms to champion social justice generally, and racial justice in particular, in solidarity. Historically, in concert with trends in neoliberal and neocolonial hegemonic societies, sport has reproduced various oppressive ideologies, discourses, and systems grounded in racism, sexism, heterosexism, ableism, homophobia, xenophobia and related systemically oppressive forces. Given the visibility of sport in the global marketplace, racialized groups have used sport as a site of resistance and activism for social justice. Since the athlete activist has re-emerged as an important change agent in the public consciousness, an increasing number of supporters holding privileged identities, within and beyond sport, have backed social justice movements through their allyship. However, scholarship has focussed largely on the allyship of athletes. Perhaps more problematically, the ambiguity of allyship has resulted in instances of decentring the core targets or victims of oppressive systems, who are best positioned to lead social movements. In an effort to cultivate a deeper understanding of allyship and how allies can optimise social movements, the purpose of this conceptual paper is to call for more intentional, critically reflexive, and culturally conscious allyship that is reflective of a transformation of allyship from reactive advocacy to proactive activism for long-term substantive gains. The authors posit that transformational allyship is allyship as activism on both individual and institutional levels. Allyship as activism should consist of intentionality to centre the core targets and victims of oppression, critical reflexivity of the relationship between ally and core activist, the presence of a clear opposition, a challenge or disruption to hegemonic systems, and a connection to a broader social movement. Implications for coaches, administrators, sport managers, and athletes are discussed.
Sound atmospheres in architecture: a case study in the South of Italy

ABSTRACT This essay explores sound atmospheres on the ground of the city of Matera, Italy, through the authors’ academic practices that work across disciplinary borders, i.e. architectural design and sensory experiences, storytelling/poetry and architecture. In particular, the authors carry out a research project composed of sound recordings, perception workshops and soundscape installations for students of architecture. A designer or architectural student’s curriculum should include the acoustic properties of architectural space that may shape a user’s esthetic judgment. Matera exhibits a long history of contradictions. Seventy years ago the city bore the label, “the shame of Italy” for the precarious living conditions of the inhabitants, but today the city is listed as a World Heritage Site, and European Capital of Culture 2019. The polarities of Matera present a basis to study the characteristic soundscapes of a modern city still engraved by the past. The research represents a first attempt at methodical sonic experiments on the vast scale of the entire city of Matera with the involvement of university students, researchers, teachers, and citizens that culminated in the production of five urban soundscape installations.
Visibility, Connectivity, Agency: Journalism’s Prospects in an Age of Automated Social News Sharing

Abstract The visibility of news on social media platforms is now as much mediated by opaque algorithmic power as quantifiable social news sharing. As public interest journalism has been transformed and even held to ransom by platformization, and its metrification of news value, there are ways for the news media to reassert their significance and independence, while building the post-scale engagement necessary to sustain its move from advertising to audience-based revenues. Regulatory activism, engagement re-orientation, and deeper research into commendary cultures offer avenues for journalism to reconfigure its relationship to platform power in an age of social news sharing.
SynTracker: a synteny based tool for tracking microbial strains

In the human gut microbiome, specific strains emerge due to within-host evolution and can occasionally be transferred to or from other hosts. Phenotypic variance among such strains can have implications for strain transmission and interaction with the host. Surveilling strains of the same species, within and between individuals, can further our knowledge about the way in which microbial diversity is generated and maintained in host populations. Existing methods to estimate the biological relatedness of similar strains usually rely on either detection of single nucleotide polymorphisms (SNP), which may include sequencing errors, or on the analysis of pangenomes, which can be limited by the requirement for extensive gene databases. To complement existing methods, we developed SynTracker. This strain-comparison tool is based on synteny comparisons between strains, or the comparison of the arrangement of sequence blocks in two homologous genomic regions in pairs of metagenomic assemblies or genomes. Our method is executed in a species-specific manner, has a low sensitivity to SNPs, does not require a pre-existing database, and can correctly resolve strains using complete or draft genomes and metagenomic samples using <5% of the genome length. When applied to metagenomic datasets, we detected person-specific strains with an average sensitivity of 97% and specificity of 99%, and strain-sharing events in mother-infant pairs. SynTracker can be used to study the population structure of specific microbial species between and within environments, to identify evolutionary trajectories in longitudinal datasets, and to further understanding of strain sharing networks.
Cholesterol dynamics are essential for the growth and development of Plasmodium falciparum within the erythrocyte

Plasmodium spp. lack de novo cholesterol synthetic pathways and can only scavenge it from their host erythrocyte. Here we report that depletion of cholesterol from the erythrocyte plasma membrane by methyl-β-cyclodextrin (MBCD) has dramatic consequences. The removal of cholesterol results in invasion defects as well as inhibition of parasite development through the intra-erythrocytic cycle. These defects could be rescued by reconstitution with cholesterol and desmosterol but not with epicholesterol. By using live microscopy of fluorescently tagged trophozoite stage parasites, we detected rapid expulsion of the parasites from erythrocyte when exposed to MBCD for just 30 mins. Strikingly, the parasites transition from being intra-erythrocytic to extracellular within 10 seconds and do so without rupturing the erythrocyte membrane. These extruded parasites were still surrounded by the parasitophorous vacuolar membrane (PVM) and remained tethered to the erythrocyte. Electron microscopy revealed that although extracellular parasites retained their PVM, it was heavily compromised. Treatment with antimalarials that disrupt cholesterol homeostasis prior to MBCD exposure prevented the extrusion of trophozoites. These results reveal importance of cholesterol during the intra-erythrocytic development of P. falciparum and the dramatic consequences resulting from tampering with cholesterol content in the infected erythrocyte. These findings suggest dynamic nature of cholesterol within the infected erythrocyte that is critical for parasite survival.
Optimizing the Live Attenuated Influenza A Vaccine Backbone for High-Risk Patient Groups

Live attenuated influenza vaccine (LAIV) is a needle-free, mucosal vaccine approved for healthy 2- to 49-year old individuals. Its replicative nature and higher rate of adverse events excludes at-risk populations. ABSTRACT Together with inactivated influenza vaccines (IIV), live attenuated influenza vaccines (LAIV) are an important tool to prevent influenza A virus (IAV) illnesses in patients. LAIVs present the advantages to have a needle-free administration and to trigger a mucosal immune response. LAIV is approved for healthy 2- to 49-year old individuals. However, due to its replicative nature and higher rate of adverse events at-risk populations are excluded from the benefits of this vaccine. Using targeted mutagenesis, we modified the nonstructural protein 1 of the currently licensed LAIV in order to impair its ability to bind the host cellular protein CPSF30 and thus its ability to inhibit host mRNA poly-adenylation. We characterized our optimized LAIV (optiLAIV) in three different mouse models mimicking healthy and high-risk patients. Using a neonatal mouse model, we show faster clearance of our optimized vaccine compared to the licensed LAIV. Despite lower replication, optiLAIV equally protected mice against homosubtypic and hetesubtypic influenza strain challenges. We confirmed the safer profile of optiLAIV in Stat1−/− mice (highly susceptible to viral infections) by showing no signs of morbidity compared to a 50% mortality rate observed following LAIV inoculation. Using a human nasal 3D tissue model, we showed an increased induction of ER stress-related genes following immunization with optiLAIV. Induction of ER stress was previously shown to improve antigen-specific immune responses and is proposed as the mechanism of action of the licensed adjuvant AS03. This study characterizes a safer LAIV candidate in two mouse models mimicking infants and severely immunocompromised patients and proposes a simple attenuation strategy that could broaden LAIV application and reduce influenza burden in high-risk populations. IMPORTANCE Live attenuated influenza vaccine (LAIV) is a needle-free, mucosal vaccine approved for healthy 2- to 49-year old individuals. Its replicative nature and higher rate of adverse events excludes at-risk populations. We propose a strategy to improve LAIV safety and explore the possibility to expand its applications in children under 2-year old and immunocompromised patients. Using a neonatal mouse model, we show faster clearance of our optimized vaccine (optiLAIV) compared to the licensed LAIV. Despite lower replication, optiLAIV equally protected mice against influenza virus challenges. We confirmed the safer profile of optiLAIV in Stat1−/− mice (highly susceptible to viral infections) by showing no signs of morbidity compared to a 50% mortality rate from LAIV. OptiLAIV could expand the applications of the current LAIV and help mitigate the burden of IAV in susceptible populations.
A genetic screen identifies a protective type III interferon response to Cryptosporidium that requires TLR3 dependent recognition

Cryptosporidium is a leading cause of severe diarrhea and diarrheal-related death in children worldwide. As an obligate intracellular parasite, Cryptosporidium relies on intestinal epithelial cells to provide a niche for its growth and survival, but little is known about the contributions that the infected cell makes to this relationship. Here we conducted a genome wide CRISPR/Cas9 knockout screen to discover host genes required for Cryptosporidium parvum infection and/or host cell survival. Gene enrichment analysis indicated that the host interferon response, glycosaminoglycan (GAG) and glycosylphosphatidylinositol (GPI) anchor biosynthesis are important determinants of susceptibility to C. parvum infection. Several of these pathways are linked to parasite attachment and invasion and C-type lectins on the surface of the parasite. Evaluation of transcript and protein induction of innate interferons revealed a pronounced type III interferon response to Cryptosporidium in human cells as well as in mice. Treatment of mice with IFNλ reduced infection burden and protected immunocompromised mice from severe outcomes including death, with effects that required STAT1 signaling in the enterocyte. Initiation of this type III interferon response was dependent on sustained intracellular growth and mediated by the pattern recognition receptor TLR3. We conclude that host cell intrinsic recognition of Cryptosporidium results in IFNλ production critical to early protection against this infection. Author Summary Cryptosporidium infection is an important contributor to global childhood mortality. There are currently no vaccines available, and the only drug has limited efficacy in immunocompromised individuals and malnourished children who need it most. To discover which host proteins are essential for Cryptosporidium infection, we conducted a genome wide knockout screen in human host cells. Our results confirm the importance of glycosaminoglycans on the surface of epithelial cells for attachment and invasion of the parasite. We also found that host GPI anchor biosynthesis and interferon signaling pathways were enriched by our screen. Examining the role of interferon signaling further we found a type III interferon response, IFNλ, was generated in response to infection and shown to be initiated in the infected cell. Utilizing mouse models of infection, we found that the type III interferon response was important early during infection with its induction likely preceding IFNγ, a key cytokine for the control of this infection. We also determined that TLR3 was the pattern recognition receptor responsible for IFNλ production during Cryptosporidium infection. Our work shows that IFNλ acts directly on the enterocyte and its use in treating immunocompromised mice produced striking reductions in infection.
Plant-pollinator specialization: Origin and measurement of curvature

A feature of biodiversity is the abundance of curves displayed by organs and organisms. Curvature is a widespread, convergent trait that has important ecological and evolutionary implications. In pollination ecology, the curvature of flowers and pollinator mouthparts (e.g. hummingbird bills) along the dorsiventral plane has been associated with specialization, competition, and species co-existence. Six methods have historically been used to measure curvature in pollination systems; we provide a solution to this inconsistency by defining curvature using well-established concepts from differential geometry. Intuitively, curvature is the degree to which a line is not straight, but more formally, it is the rate at which the tangent of a curve changes direction with respect to arc length. Here, we establish a protocol wherein a line is fitted against landmarks placed on an image of a curved organ or organism, then curvature is computed at many points along the fitted line and the sum taken. The protocol is demonstrated by studying the development of nectar spur curvature in the flowering plant genus Epimedium (Berberidaceae). By clarifying the definition of curvature, our aim is to make the language of comparative morphology more precise and broadly applicable to capture other curved structures in nature.
CSF-1 maintains pathogenic but not homeostatic myeloid cells in the central nervous system during autoimmune neuroinflammation

Significance Multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE), are autoimmune diseases characterized by accumulation of myeloid cells in the central nervous system (CNS). Both harmful and beneficial myeloid cells are present in EAE/MS, and a goal of MS therapy is to preferentially remove harmful myeloid cells. The receptor for CSF-1 (CSF-1R) is found on myeloid cells and is important for their survival. CSF-1R can bind two ligands, CSF-1 and IL-34, but it is not known whether their functions in EAE/MS differ. We found that blocking CSF-1 depleted only harmful myeloid cells in the CNS and suppressed EAE, whereas blocking IL-34 had no effect. Thus, we propose that blocking CSF-1 could be a therapy for MS.
Environmental factors, but not abundance and diversity of nitrifying microorganisms, explain sediment nitrification rates in Yangtze lakes

Sediment nitrification plays a vital role in nitrogen (N) biogeochemical cycling and ecological function of an aquatic ecosystem. The relative importance of environmental factors and nitrifying microbial communities in regulating sediment nitrification process has received less attention, especially in aquatic habitats where high N loads are frequently detected. Here, we report the potential nitrification rates of 35 sediment samples from 10 shallow lakes in the Yangtze River basin. The diversity and abundance of ammonia-oxidizing archaea (AOA) and bacteria (AOB) were quantified using archaeal and bacterial amoA genes. The results showed that there was no significant difference in sediment nitrification rates among sites of different trophic state. The nitrification rates were positively related to water chlorophyll-a, sediment N and carbon levels, but not significantly associated with diversity and abundance of ammonia-oxidizing microorganisms and submerged plants. Interestingly, the abundance and diversity of sediment AOB but not AOA communities were significantly influenced by trophic state. In addition, AOB communities were more sensitive to changes in local environments and catchment land uses than the AOA communities. Using path analysis, we found that 55–60% of the indirect effect of catchment land uses on nitrification rates was mediated via sediment N content. Our findings suggest that, although nitrification is a microbial process, variation in sediment nitrification rates in Yangtze lakes is mainly explained by abiotic factors but not by microbial abundance and diversity.
Asymptotic Analysis for Cell-Free Massive MIMO With MMSE Combining and Low-Resolution ADCs

This letter analyses the asymptotic performance of cell-free massive multiple-input and multiple-output (MIMO) with low-resolution analog-to-digital converters (ADCs) over spatially correlated channels. Specifically, we first introduce a quantization-aware technique for channel estimation based on linear minimum mean-square error (LMMSE) theory. On the basis of imperfect channel estimation, we then propose a quantization-aware MMSE combining and derive the asymptotic expression of achievable uplink rate over spatially correlated Rayleigh fading channels. Finally, simulation results validate our theoretical analysis and corroborate that the quantization-aware MMSE combiner can improve performance to a certain extent.
Explainable Artificial Intelligence for Tabular Data: A Survey

Machine learning techniques are increasingly gaining attention due to their widespread use in various disciplines across academia and industry. Despite their tremendous success, many such techniques suffer from the “black-box” problem, which refers to situations where the data analyst is unable to explain why such techniques arrive at certain decisions. This problem has fuelled interest in Explainable Artificial Intelligence (XAI), which refers to techniques that can easily be interpreted by humans. Unfortunately, many of these techniques are not suitable for tabular data, which is surprising given the importance and widespread use of tabular data in critical applications such as finance, healthcare, and criminal justice. Also surprising is the fact that, despite the vast literature on XAI, there are still no survey articles to date that focus on tabular data. Consequently, despite the existing survey articles that cover a wide range of XAI techniques, it remains challenging for researchers working on tabular data to go through all of these surveys and extract the techniques that are suitable for their analysis. Our article fills this gap by providing a comprehensive and up-to-date survey of the XAI techniques that are relevant to tabular data. Furthermore, we categorize the references covered in our survey, indicating the type of the model being explained, the approach being used to provide the explanation, and the XAI problem being addressed. Our article is the first to provide researchers with a map that helps them navigate the XAI literature in the context of tabular data.
Interoperability in Open IoT Platforms: WoT-FIWARE Comparison and Integration

The rapid and exponential growth of the Internet of Things (IoT) has been generating a new breed of technologies that introduce several different protocols and interfaces. The Web of Things (WoT) architecture stands out as an emerging and potential solution to improve interoperability across IoT platforms by describing well-defined software interfaces. However, few studies analyze and compare WoT to other interoperability solutions proposed in the IoT literature. In this paper, we attempt to bridge the gap by three main contributions. First, we qualitative compare the WoT approach with the well-known FIWARE-based interoperability solution.Second, based on the previous analysis, we design and implement a connector to bridge the WoT architecture to the FIWARE ecosystem. Third, we conduct a performance analysis emulating a real IoT-based environment to understand scalability, response time, and computer resource usage of the two interoperability solutions. The results reveal that conceptual design choices impact the applications’ performance: the WoT architecture effectively enables interoperability across IoT Platforms, though it incorporates several characteristics that hinder the implementation of applications. On the other hand, the FIWARE IoT Agent solution is platform-specific. Hence new implementations are needed for each different IoT data model.
Reinforcement Learning Algorithm and FDTD-Based Simulation Applied to Schroeder Diffuser Design Optimization

This paper aims to propose a novel approach to the algorithmic design of Schroeder acoustic diffusers by employing a deep learning optimization algorithm and a fitness function, which are based on a computer simulation of the propagation of acoustic waves. The deep learning method employed for the research consists of a deep policy gradient algorithm. It is used as a tool for carrying out a sequential optimization process, which seeks to maximize the fitness function based on parameters characterizing the autocorrelation diffusion coefficient of the designed acoustic diffuser. As the autocorrelation acoustic diffusion coefficients are calculated based on the polar response of a diffuser, the finite-difference time-domain (FDTD) simulation method is used to obtain a set of impulse responses, which are necessary to calculate the polar responses of the optimized Schroeder diffusers. The results obtained from the optimization derived from the deep learning algorithm were compared with the outcomes of a similar algorithm by employing a genetic algorithm and based on random selection of the Schroeder diffuser well-depth pattern. We found that the best result was achieved by the deep policy gradient, as it produced outcomes that, in terms of the provided autocorrelation diffusion coefficient, were statistically better than the properties of the designs supplied by two other baseline approaches.