Miracle-dz/UniFAR-FTUs huggingface dataset:
Storage facilities reclamation using dredged sediments from waterways: Growing media formulation for plants according to the EU Ecolabel requirements

Dredged sediments display a great potential for growing media applications; however, there are few studies about their beneficial reuse for the waste storage reclamation. This research study aims at checking the agronomic values and environmental impacts of three growing media based on waterways sediments (WSs) and green waste (GW) according ecolabel requirements. For this purpose, three growing media named GW0, GW25, and GW50 were prepared at field pilot scale by co-composting WS and GWs during 12 months. Samples were submitted to ecolabel analyses package. Following to the ecolabel requirements, the growing media comply with criteria like pH, electrical conductivity, trace elements and polycyclic aromatic hydrocarbon contents, chlorides, and pathogens, whereas they are not in compliance with the EU Ecolabel guidelines for propagules, organic matter, and zinc contents. Results of laboratory leaching tests performed according to NF EN 12457-2 for GW0, GW25, and GW50 have shown that sulfates, soluble fraction exceed limit for inert waste storage. Lysimeter tests at pilot scale were performed during 6 months to check the leaching potential of pollutants from growing media under real field conditions, including a European ecolabel product. Results demonstrate that Ba, Mo, Sb, Zn, Se, and Sb are higher in GW0, GW25, and GW50 than in the European ecolabel. As, Cd, Cr, Ni, and F− are more soluble in the commercial product compared to other growing media. This study allowed to demonstrate that main characteristics are fulfilling for reusing these growing media in the specific field of waste storage reclamation.
Decreased wheat production in the USA from climate change driven by yield losses rather than crop abandonment

An increase in global average surface temperature over the 21st century will affect food production. There is still uncertainty if the source of the production losses caused by climate change could be driven either by lower yield or reduced area harvested. We use county-level production data on winter wheat coupled with fine-scale weather outcomes between 1981-2007 to examine the impact of climate change on winter wheat production in Kansas. We decompose the total impact of weather variables through both the yield and harvested acreage channels. We find that an insignificant portion—both in terms of magnitude and statistical significance—of the production losses are due to reduced harvested acres (i.e., crop abandonment). The proportion harvested only account for 14.88% and 21.71% of the total damages under RCPs 4.5 and 8.5 and neither effect is statistically significant. An implication of this result implies that studies that only examine climate impacts on harvested yields are not significantly underestimating the climate change impacts on production.
Oral prosthetic microbiology: aspects related to the oral microbiome, surface properties, and strategies for controlling biofilms

Abstract The oral cavity is an environment that allows for the development of complex ecosystems; the placement of prosthetic devices as a consequence of partial or total tooth loss may alter the diversity of microbial communities. Biofilms on the surface of materials used in dental prostheses can promote important changes in the mechanic and aesthetic properties of the material itself and may cause local and systemic diseases for the prosthetic wearer. This review presents the main features of the oral microbiome associated with complete or partial dentures and dental implants. The main diseases associated with microbial colonization of prosthetic surfaces, factors that may affect biofilm formation on prosthetic materials, as well as novel alternative therapies aiming to reduce biofilm formation and/or to eradicate biofilms formed on these materials are also explored.
Heterozygous OAS1 gain-of-function variants cause an autoinflammatory immunodeficiency

OAS1-GOF/RNase L–mediated RNA cleavage caused an interferon-induced hyperinflammatory monocyte and B cell immunodeficiency. Mutated OAS1 leads to immunodeficiency Inborn errors of immunity can result in autoinflammatory immunodeficiencies. Type I interferon–inducible oligoadenylate synthetase 1 (OAS1) can initiate antiviral immune responses and is linked to pulmonary alveolar proteinosis with hypogammaglobulinemia. Here, Magg et al. identified four heterozygous OAS1 gain-of-function mutations that cause a polymorphic immunodeficiency in six patients. The OAS1 mutants induced functional and transcriptomic alterations in monocytes, B cells, and T cells, which related to RNase L–dependent cellular dysfunction, apoptosis, protein translation arrest, and cellular RNA degradation. These deficiencies correlated to the altered structure of the OAS1 mutants. Some of these OAS1 gain-of-function patients were cured by hematopoietic stem cell transplantation, suggesting that these immune defects directly contributed to the disease phenotypes. These data give further insight into a rare autoinflammatory immunodeficiency. Analysis of autoinflammatory and immunodeficiency disorders elucidates human immunity and fosters the development of targeted therapies. Oligoadenylate synthetase 1 is a type I interferon–induced, intracellular double-stranded RNA (dsRNA) sensor that generates 2′-5′-oligoadenylate to activate ribonuclease L (RNase L) as a means of antiviral defense. We identified four de novo heterozygous OAS1 gain-of-function variants in six patients with a polymorphic autoinflammatory immunodeficiency characterized by recurrent fever, dermatitis, inflammatory bowel disease, pulmonary alveolar proteinosis, and hypogammaglobulinemia. To establish causality, we applied genetic, molecular dynamics simulation, biochemical, and cellular functional analyses in heterologous, autologous, and inducible pluripotent stem cell–derived macrophages and/or monocytes and B cells. We found that upon interferon-induced expression, OAS1 variant proteins displayed dsRNA-independent activity, which resulted in RNase L–mediated RNA cleavage, transcriptomic alteration, translational arrest, and dysfunction and apoptosis of monocytes, macrophages, and B cells. RNase L inhibition with curcumin modulated and allogeneic hematopoietic cell transplantation cured the disorder. Together, these data suggest that human OAS1 is a regulator of interferon-induced hyperinflammatory monocyte, macrophage, and B cell pathophysiology.
Appreciating News Algorithms: Examining Audiences’ Perceptions to Different News Selection Mechanisms

Abstract As news selection is increasingly controlled by algorithms, a growing number of scholars are exploring how news recommenders can serve public services. Despite aspirations towards public service algorithms, little is known about which type of news recommender people prefer, let alone about a news recommender that aims to promote societal values. This study aims to give insights into audiences’ perceptions to news recommenders and their underlying news selection mechanisms. To do so, we distinguish between three news selection mechanisms, namely between content-based similarity, collaborative similarity and content-based diversity. The first two strive for similarity, respectively between news content and news users, while the third one aims for diversity in the news content consumed. Results of a large-scale survey (n = 943) show that people prefer content-based similarity over collaborative similarity and content-based diversity. Audience characteristics, such as news information overload and concerns towards missing challenging viewpoints, explain how audiences evaluate the different news selection mechanisms. We discuss how these results align with concerns about selectivity and how news algorithms can be used to tackle these concerns. We therefore introduce the concept ‘personalized diversity’ and promote the idea of news recommenders as an individual filter for the growing abundance of online information.
Transnational unities, challenges and opportunities for sport volunteering: lessons from the European PlayGreen project

Abstract Within global sport, volunteering has been identified as a fundamental resource to the effective operation and continuity of operations. However, investments in, and the success of, sport volunteering is contingent on amiable socio-cultural, political, and economic conditions. In Europe, the context of this paper, the vibrancy of sport volunteering remains a concern since the onset of the COVID-19 pandemic. Notwithstanding the pandemic, some transnational sport networks and regional governmental partnerships have been making more dedicated investments to fortify volunteer cultures in the region. In addition, collaborative ventures are finding ways to connect sport volunteering with wider international issues to support its growth across all sectors. Here, sustainability and environmental change have provided a key issue platform for sport volunteer leverage. As the pandemic continues to confront the existence and practices of sport organisations, and specifically their ability to engage volunteers, these issues have become salient. We present a commentary of the Erasmus + Sport-funded, pan-European, sport volunteering and sustainability focused, PlayGreen consortia and its organisation members responses to the unfolding pandemic across Europe. Our commentary is guided by spatial theory and internal administration insights. The pandemic has brought new ideas and cohesion into sport volunteer communities, challenged, and changed modes of production, and led to new forms of social transformation and action vis-à-vis environmental and sustainability issues. As sport organisations continue to find viable ways of existing, the experiences within the PlayGreen consortia evidence creative potential for future volunteer engagements.
Review of Prediction Analytics Studies on Readmission for the Chronic Conditions of CHF and COPD: Utilizing the PRISMA Method

ABSTRACT Hospital readmission for chronic illness is a ubiquitous phenomenon that is a major contributor to the growing costs of the healthcare sector. Here, PRISMA was used to identify studies dealing with predicting readmissions for CHF and COPD patients that implemented machine learning techniques. The PRISMA output yielded 21 articles that met the inclusion criteria. It is recommended to include previous visit data, and track the same patients over multiple visits when predicting these readmissions.
Pensioner Employment, Well-Being, and Gender: Lessons from Russia1

Encouraging pensioner employment is one answer to the challenge of aging societies. Employment positively influences the subjective well-being (SWB) of working-age populations, but the implications for pensioners, including variance by gender and occupational class, are unclear. We examine this variance using mixed methods on data from Russia, where pensioner employment is comparatively high. Utilizing data on 5,703 individuals ages 45–70 from 12 waves of the Russian Longitudinal Monitoring Survey (2003–15), we estimate individual fixed-effects models for life satisfaction, exploring mechanisms using longitudinal qualitative data. We find pensioner employment positively influences SWB of both genders across the occupational hierarchy. We attribute the muting of occupational variance to the decommodifying action of pensions. We find gender differences in mechanisms: pensioner employment gives women a noneconomic SWB boost, but additional income explains men’s SWB improvements. We theorize this finding using our qualitative data, showing how gendered age schemas shape pensioner well-being.
Rodents (Mammalia) from the Troublesome Formation, Late Oligocene to Miocene (Middle Arikareean–Early Clarendonian) of Colorado

ABSTRACT The Troublesome Formation of northcentral Colorado ranges in age from the middle Arikareean North American Land Mammal Age (NALMA: latest Oligocene) to the early Clarendonian NALMA (early–late Miocene), a range of 14.5 million years. The rodent fauna from this formation varies greatly through the section, which is dominated by entoptychine geomyids in the Arikareean, mylagaulids in the Hemingfordian, and heteormyids in the Barstovian. The Clarendonian is the least-well represented horizon from which only four species are recognized. Of the nearly forty species of rodents identified, there is only one new genus, Argaleogaulus a primitive promylagauline mylagaulid from the Arikareean. There are 12 new species recognized: the mylagaulid Argaleogaulus primoticus; a sciurid, Protospermophilus parvus; six heteromyids, Harrymys cyanothos, Harrymys taussigi, Schizodontomys bareia, Balantiomys coloradensis, Oregonomys perilaccos, and Cupidinimus robinsoni; and four entoptychine geomyids, Pleurolicus compressus, Pleurolicus mensae, Gregorymys montanus, and Entoptychus rensbergeri. The genera from the Troublesome Formation are similar to those of equivalent faunas throughout North America. The presence of the unique species of otherwise common North American genera suggests some minor degree of isolation of the basin during the time of formation.
The Boltysh impact structure: An early Danian impact event during recovery from the K-Pg mass extinction

The Boltysh impact structure formed 0.65 Ma after the K-Pg boundary, coincident with the Lower C29n hyperthermal. Both the Chicxulub and Boltysh impact events are associated with the K-Pg boundary. While Chicxulub is firmly linked to the end-Cretaceous mass extinction, the temporal relationship of the ~24-km-diameter Boltysh impact to these events is uncertain, although it is thought to have occurred 2 to 5 ka before the mass extinction. Here, we conduct the first direct geochronological comparison of Boltysh to the K-Pg boundary. Our 40Ar/39Ar age of 65.39 ± 0.14/0.16 Ma shows that the impact occurred ~0.65 Ma after the mass extinction. At that time, the climate was recovering from the effects of the Chicxulub impact and Deccan trap flood volcanism. This age shows that Boltysh has a close temporal association with the Lower C29n hyperthermal recorded by global sediment archives and in the Boltysh crater lake sediments. The temporal coincidence raises the possibility that even a small impact event could disrupt recovery of the Earth system from catastrophic events.
Disrupting the Traffic Stop–to-Deportation Pipeline: The New York State Greenlight Law’s Intent and Implementation

This article analyzes the traffic stop–to-deportation pipeline in New York State, how it harms children of immigrants, and how New York’s Greenlight Law seeks to disrupt it but has been hobbled by an implementation gap. It first establishes the phenomenon of the traffic stop–to-deportation pipeline by documenting how traffic stops are a key cause of deportations in New York State. Second, it analyzes how the pipeline harms (mostly US citizen) children of undocumented immigrants in New York State, who are more than 7 percent (more than 300,000) of New York State’s children. The pipeline makes these children fear and mistrust the police; harms their educational, social, and brain development; and consumes family income with the Mexican driver tax (costs incurred because parents could not get a driver’s license). Third, the article analyzes how the Greenlight Law should help remedy these harms, and how an implementation gap leaves many parents and children vulnerable to the pipeline. The implementation gap is partly due to the pandemic, but also driven by political and other factors that could be addressed by policy. Finally, the article analyzes how variation in implementing the Greenlight Law could leave the pipeline undisrupted and lead to unequal protection of the law by place in New York State. The article makes policy recommendations for stronger enactment to reduce the pipeline’s harms.
Iconicity ratings really do measure iconicity, and they open a new window onto the nature of language

Abstract This paper reviews recent research using participant ratings to measure the iconicity (form-meaning resemblance) of words and signs. This method, by enabling wide coverage of lexical items and cross-linguistic comparison, has revealed systematic patterns in how iconicity is distributed across the vocabularies of different languages. These findings are consistent with established linguistic and psychological theory on iconicity, and they connect iconicity to factors like learning and acquisition, semantics, pragmatic aspects of language like playfulness, and to the semantic neighborhood density of words and signs. After taking stock of this research, we look critically at the construct validity of iconicity ratings, considering an alternative account of iconicity ratings recently put forward by Thompson, Arthur Lewis, Kimi Akita & Youngah Do. 2020a. Iconicity ratings across the Japanese lexicon: A comparative study with English. Linguistics Vanguard 6. 20190088. They propose that, for most vocabulary, participants might rate the iconicity of different words based on their meaning alone – specifically the degree to which it relates to the senses – independently of actual form-meaning resemblance. We argue that their hypothesis cannot account for many of the various, theory-driven results from this line of research, which strongly support the conclusion that the ratings really do measure iconicity.
An analysis of certification processes for Good Clinical Practice and project management competencies

Abstract Aim/purpose – The purpose of this paper is to clarify the certification process of the Good Clinical Practice (GCP) competencies based on a comparison with the project management (PM) certification process. This aim was accomplished by model development. Design/methodology/approach – The study was divided into three main phases: 1st phase – the identification of key characteristics of PM and GCP certification processes, 2nd phase – the development of certification models for PM and GCP, 3rd phase – the conclusions from in-depth interviews. Findings – As a result of the research, key characteristics of PM and GCP certification processes were identified, certification models for PM and GCP were developed. In addition, based on conclusions from in-depth interviews, solutions for organizing the way of confirming knowledge of GCP guidelines were proposed. Research implications/limitations – The proposed rules may be too complex and may exceed the needs and expectations of the clinical trial environment. The models focus on stakeholder relations, without the rules of certification granting procedures, to enable broader contextualization of the issues discussed in the paper. The analysis might be fragmented as it regards the sole certification process. The research refers to Poland only, hence it will be valuable to identify whether foreign authorities have the same attitude to the GCP competency certification model. It is recommended to increase the number of respondents’ interviews to obtain results of higher validity and reliability. Originality/value/contribution – The paper raises research topics at the crossroads of project management, clinical trials, and GCP, topics that have been underdeveloped so far. The results might be significant for all organizations involved in conducting clinical research projects. The findings contribute to the quality of clinical trials and provide public assurance that the rights, safety, and well-being of trial subjects are protected and the clinical trial data are credible.
Leveraging Approximate Constraints for Localized Data Error Detection

Error detection is key for data quality management. AI techniques can leverage user domain knowledge to identifying sets of erroneous records that conflict with domain knowledge. To represent a wide range of user domain knowledge, several recent papers have developed and utilized soft approximate constraints (ACs) that a data relation is expected to satisfy only to a certain degree, rather than completely. We introduce error localization, a new AI-based technique for enhancing error detection with ACs. Our starting observation is that approximate constraints are context-sensitive: the degree to which they are satisfied depends on the sub-population being considered. An error region is a subset of the data that violates an AC to a higher degree than the data as a whole, and is therefore more likely to contain erroneous records. For example, an error region may contain the set of records from before a certain year, or from a certain location. We describe an efficient optimization algorithm for error localization: identifying distinct error regions that violate a given AC the most, based on a recursive tree partitioning scheme. The tree representation describes different error regions in terms of data attributes that are easily interpreted by users (e.g., all records before 2003). This helps to explain to the user why some records were identified as likely errors. After identifying error regions, we apply error detection methods to each error region separately, rather than to the dataset as a whole. Our empirical evaluation, based on four datasets containing both real world and synthetic errors, shows that error localization increases both accuracy and speed of error detection based on ACs.
Expand your Training Limits! Generating Training Data for ML-based Data Management

Machine Learning (ML) is quickly becoming a prominent method in many data management components, including query optimizers which have recently shown very promising results. However, the low availability of training data (i.e., large query workloads with execution time or output cardinality as labels) widely limits further advancement in research and compromises the technology transfer from research to industry. Collecting a labeled query workload has a very high cost in terms of time and money due to the development and execution of thousands of realistic queries/jobs. In this work, we face the problem of generating training data for data management components tailored to users' needs. We present DataFarm, an innovative framework for efficiently generating and labeling large query workloads. We follow a data-driven white-box approach to learn from pre-existing small workload patterns, input data, and computational resources. Our framework allows users to produce a large heterogeneous set of realistic jobs with their labels, which can be used by any ML-based data management component. We show that our framework outperforms the current state-of-the-art both in query generation and label estimation using synthetic and real datasets. It has up to 9x better labeling performance, in terms of R2 score. More importantly, it allows users to reduce the cost of getting labeled query workloads by 54x (and up to an estimated factor of 104x) compared to standard approaches.