Miracle-dz/UniFAR-FTUs huggingface dataset:
Low incidence of plastic ingestion among three fish species significant for human consumption on the island of Newfoundland, Canada

This study reports the first baselines of plastic ingestion for three fish species that are common food fish in Newfoundland, Canada. Species collections occurred between 2015-2016 for Atlantic cod (Gadus morhua), Atlantic salmon (Salmo salar), and capelin (Mallotus villosus). The frequency of occurrence (%FO) of plastic ingestion for both spawning Atlantic salmon (n=69) and capelin (n=350) was 0%. Of the 1,010 Atlantic cod collected over two years, 17 individuals had ingested plastics, a %FO of 1.68%. This is the only multi-year investigation of plastic ingestion in Atlantic cod for the Northwest Atlantic, and the first for capelin and salmon in the region. Considering the ecological, economic, and cultural importance of these fish species, this study is the beginning of a longitudinal study of plastic ingestion to detect future changes in contamination levels. Highlights Ingestion rate (%FO) of plastics in Atlantic cod is 1.68% Ingestion rate (%FO) of plastics in Atlantic salmon and capelin is 0% First study of plastic ingestion rates in Atlantic salmon and capelin Multi-year baseline of plastic ingestion in Atlantic cod in the Northwest Atlantic Plastic ingestion rates for three food fish species in Newfoundland, Canada, are low Terms Frequency of occurrence (%FO): the number of individuals in a population or group that have ingested plastics (not indicative of the number of particles ingested per individual)
The complete mitochondrial genome of wild honeybee Apis florea (Hymenoptera: Apidae) in south-western China

Abstract We sequenced almost complete mitochondrial genome of Apis florea (Insecta: Hymenoptera: Apocrita: Apidae) with length of 15,933 bp. The genome has similar codon usage and gene organization to those of mitogenome reported for other Hymenoptera. It includes 13 protein-coding genes, two ribosomal RNA genes, 22 transfer RNA genes and a noncoding region with very high AT-base content. Phylogenetic analyses showed that, Apis florea and (Apis cerana+ Apis mellifera) are the sister taxa, and more original, which is widely accepted view.
Large-scale RNA editing profiling in different adult chicken tissues

RNA editing is a post-transcription maturation process that diversifies genomically encoded information and can lead to diversity and complexity of transcriptome, especially in the brain. Thanks to next-generation sequencing technologies, a large number of editing sites have been identified in different species, especially in human, mouse and rat. While this mechanism is well described in mammals, only a few studies have been performed in the chicken. Here, we developed a rigorous computational strategy to identify RNA editing sites in eight different tissues of the chicken (brain, spleen, colon, lung, kidney, heart, testes and liver), based on RNA sequencing data alone. We identified 68 A-to-G editing sites in 46 genes. Only two of these were previously reported in chicken. We found no C-to-U sites, attesting the lack of this type of editing mechanism in the chicken. Similar to mammals, the editing sites were enriched in non-coding regions, rarely resulted in change of amino acids, showed a critical role in nervous system and had a low guanosine level upstream of the editing site and some enrichment downstream from the site. Moreover, in contrast to mammals, editing sites were weakly enriched in interspersed repeats and the frequency and editing ratio of non-synonymous sites were higher than those of synonymous sites. Interestingly, we found several tissue-specific edited genes including GABRA3, SORL1 and HTR1D in brain and RYR2 and FHOD3 in heart that were associated with functional processes relevant to the corresponding tissue. This finding highlighted the importance of the RNA editing in several chicken tissues, especially the brain. This study extends our understanding of RNA editing in chicken tissues and establish a foundation for further exploration of this process.
Local Forward Model Learning for GVGAI Games

In this paper, we are going to explain the design process for our GVGAI game-learning agent, which is going to be submitted to the GVGAI competition’s learning track 2020. The agent relies on a local forward modeling approach, which uses predictions of future game-states to allow the application of simulation-based search algorithms. We first explain our process in identifying repeating tiles throughout a pixel-based state observation. Using the tile information, a local forward model is trained to predict the future state of each tile based on its current state and its surrounding tiles. We accompany this approach with a simple reward model, which determines the expected reward of a predicted state transition. The proposed approach has been tested using multiple games of the GVGAI framework. Results show that the approach seems to be especially feasible for learning how to play deterministic games. Except for one non-deterministic game, the agent performance is very similar to agents using the true forward model. Nevertheless, the prediction accuracy needs to be further improved to facilitate a better game-playing performance.
The population dynamics of rare annual plants on the Lizard Peninsula, Cornwall, UK, 2009–2016

The Lizard Peninsula, Cornwall, UK, is one of the richest botanical sites in Britain, in terms of rare plants at least, many of which have more southerly distributions in Europe reaching their northern limits in southwest England. Though much of the coastal habitat has escaped destruction, and most of the sites of the rare plants survive, very little is known of the details and vitality of the individual populations, and the extent to which species are increasing or declining due to changes in management and climate. Annuals are the easiest to monitor, despite their fluctuating yearly populations, if only because of their exclusivity to a highly localised but easily identified habitat and their relatively low numbers of sites as well as smaller populations compared with the perennials. An attempt was made over an 8-year period (2009–2016) to visit and monitor populations of five of the rarest species, all annuals (three Trifolium species and two Juncus species), whose sole or principal sites in Britain and Ireland are on the Lizard, and to count all plants at each site in each year. Apart from one survey for one species there is no evidence that this has ever been done before, and the rationale was partly to test whether current population figures bore any relation to those estimated in the past, and through that to see how they are faring in relation to climate and management and to set a reliable benchmark for future monitoring. Most of the historical sites for these annuals still seem to be in a suitable condition for their re-appearance, but the results showed a marked decline in the number of populations at those sites and a probable even larger decline in the numbers of individual plants. These declines probably arise from gradual cessation of traditional management over the last century, together with the possibility that drier springs are affecting germination, and wetter autumns and winters are encouraging the growth of competing perennials.
Invasive alien species and disease risk: An open challenge in public and animal health

The anthropogenic movement of pathogens into new geographic locations or host species, socalled “pathogen pollution” [1], is one of the main threats to human and animal health in a globalized world. Since the majority of zoonotic emerging diseases originate from wildlife [2], as recent outbreaks like Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Nipah, or Chikungunya point out, particular attention should be paid to wild animals’ translocations, which represent a potential driver of change in pathogen ecology and distribution [1]. Invasive alien species (IAS) are species of animals, plants, fungi, or microorganisms translocated by humans into environments outside their natural range, in which they establish and spread, negatively affecting the dynamics of local ecosystems. They are characterized by rapid reproduction and growth, high dispersal ability, and high adaptability to new conditions, thus often outcompeting native organisms in their introduced range [3], and have been recognized as one of the main causes for biodiversity loss globally [4]. Some well-known examples of IAS include the south-American coypu Myocastor coypus, invasive in North America, Europe, and Asia, where it causes both environmental and economic impacts consuming aquatic vegetation and undermining riverbanks [5], and the eastern-Asiatic brown marmorated stink bug Halyomorpha halys, a successful global invader causing severe economic damages to agricultural crops [6]. Besides affecting biodiversity conservation and economy, IAS, as translocated species, may promote pathogen pollution in the invaded area leading to the emergence of diseases [1,7–9]. It would thus be fair to expect animal IAS to be the focus of intense study by epidemiologists with regard to their disease risk toward native animals (both wild and domestic) and humans, as most of them thrive in anthropogenic environments, potentially increasing the risk for zoonotic pathogen emergence [9]. Within the field of invasion ecology, there has been a wide interest in exploring the relationships between invasions and infections during the last decades. Researchers focused in particular in understanding how parasites (or the lack of them) may facilitate or hamper the invasion process [10–13], how co-introduced parasites may themselves succeed in becoming invasive [13–15], and explored the effects that IAS may have on native parasites dynamics [13,14,16– 18]. However, outside the invasion ecology field, IAS have yet to gain attention among people working in the fields of animal and public health, and the concepts explored in the ecological context cannot always find application in the development of health initiatives aimed at PLOS PATHOGENS
Clinical characteristics of COVID-19 with cardiac injury: a systematic review and meta-analysis

Abstract Objectives Cardiac injury is associated with poor prognosis of 2019 novel coronavirus disease 2019 (COVID-19), but the risk factors for cardiac injury have not been fully studied. In this study, we carried out a systematic analysis of clinical characteristics in COVID-19 patients to determine potential risk factors for cardiac injury complicated COVID-19 virus infection. Methods We systematically searched relevant literature published in Pubmed, Embase, Europe PMC, CNKI and other databases. All statistical analyses were performed using STATA 16.0. Results We analysed 5726 confirmed cases from 17 studies. The results indicated that compared with non-cardiac-injured patients, patients with cardiac injury are older, with a greater proportion of male patients, with higher possibilities of existing comorbidities, with higher risks of clinical complications, need for mechanical ventilation, ICU transfer and mortality. Moreover, C-reactive protein, procalcitonin, D-dimer, NT-proBNP and blood creatinine in patients with cardiac injury are also higher while lymphocyte counts and platelet counts decreased. However, we fortuitously found that patients with cardiac injury did not present higher clinical specificity for chest distress (P = 0.304), chest pain (P = 0.334), palpitations (P = 0.793) and smoking (P = 0.234). Similarly, the risk of concomitant arrhythmia (P = 0.103) did not increase observably either. Conclusion Age, male gender and comorbidities are risk factors for cardiac injury complicated COVID-19 infection. Such patients are susceptible to complications and usually have abnormal results of laboratory tests, leading to poor outcomes. Contrary to common cardiac diseases, cardiac injury complicated COVID-19 infection did not significantly induce chest distress, chest pain, palpitations or arrhythmias. Our study indicates that early prevention should be applied to COVID-19 patients with cardiac injury to reduce adverse outcomes.
Local perspectives on Ebola during its tenth outbreak in DR Congo: A nationwide qualitative study

Background The Democratic Republic of Congo (DR Congo) struggled to end the tenth outbreak of Ebola virus disease (Ebola), which appeared in North Kivu in 2018. It was reported that rumors were hampering the response effort. We sought to identify any rumors that could have influenced outbreak containment and affected prevention in unaffected areas of DR Congo. Methods We conducted a qualitative study in DR Congo over a period of 2 months (from August 1 to September 30, 2019) using in-depth interviews (IDIs) and focus group discussions (FGDs). The participants were recruited from five regional blocks using purposeful sampling. Both areas currently undergoing outbreaks and presently unaffected areas were included. We collected participants’ opinions, views, and beliefs about the Ebola virus. The IDIs (n = 60) were performed with key influencers (schoolteachers, religious and political leaders/analysts, and Ebola-frontline workers), following a semi-structured interview guide. FGDs (n = 10) were conducted with community members. Interviews were recorded with a digital voice recorder and simultaneous note-taking. Participant responses were categorized in terms of their themes and subthemes. Results We identified 3 high-level themes and 15 subthemes (given here in parentheses): (1) inadequate knowledge of the origin or cause of Ebola (belief in a metaphysical origin, insufficient awareness of Ebola transmission via an infected corpse, interpretation of disease as God’s punishment, belief in nosocomial Ebola, poor hygiene, and bathing in the Congo River). Ebola was interpreted as (2) a plot by multinational corporations (fears of genocide, Ebola understood as a biological weapon, concerns over organ trafficking, and Ebola was taken to be the result of business actions). Finally Ebola was rumored to be subject to (3) politicization (political authorities seen as ambivalent, exclusion of some community leaders from response efforts, distrust of political authorities, and distrust in the healthcare system). Conclusions Due to the skepticism against Ebola countermeasures, it is critical to understand widespread beliefs about the disease to implement actions that will be effective, including integrating response with the unmet needs of the population.
A New Take on Prion Protein Dynamics in Cellular Trafficking

The mobility of cellular prion protein (PrPC) in specific cell membrane domains and among distinct cell compartments dictates its molecular interactions and directs its cell function. PrPC works in concert with several partners to organize signaling platforms implicated in various cellular processes. The scaffold property of PrPC is able to gather a molecular repertoire to create heterogeneous membrane domains that favor endocytic events. Dynamic trafficking of PrPC through multiple pathways, in a well-orchestrated mechanism of intra and extracellular vesicular transport, defines its functional plasticity, and also assists the conversion and spreading of its infectious isoform associated with neurodegenerative diseases. In this review, we highlight how PrPC traffics across intra- and extracellular compartments and the consequences of this dynamic transport in governing cell functions and contributing to prion disease pathogenesis.
Application of deep learning to predict advanced neoplasia using big clinical data in colorectal cancer screening of asymptomatic adults

Background/Aims We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal neoplasia (ACRN) in asymptomatic adults, based on which colorectal cancer screening could be customized. Methods We collected data on 26 clinical and laboratory parameters, including age, sex, smoking status, body mass index, complete blood count, blood chemistry, and tumor marker, from 70,336 first-time colonoscopy screening recipients. For reference, we used a logistic regression (LR) model with nine variables manually selected from the 26 variables. A deep neural network (DNN) model was developed using all 26 variables. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the models were compared in a randomly split validation group. Results In comparison with the LR model (AUC, 0.724; 95% confidence interval [CI], 0.684 to 0.765), the DNN model (AUC, 0.760; 95% CI, 0.724 to 0.795) demonstrated significantly improved performance with respect to the prediction of ACRN (p < 0.001). At a sensitivity of 90%, the specificity significantly increased with the application of the DNN model (41.0%) in comparison with the LR model (26.5%) (p < 0.001), indicating that the colonoscopy workload required to detect the same number of ACRNs could be reduced by 20%. Conclusions The application of DNN to big clinical data could significantly improve the prediction of ACRNs in comparison with the LR model, potentially realizing further customization by utilizing large quantities and various types of biomedical information.
Protocols for Monitoring Harmful Algal Blooms for Sustainable Aquaculture and Coastal Fisheries in Chile

Harmful algae blooms (HABs) cause acute effects on marine ecosystems due to their production of endogenous toxins or their enormous biomass, leading to significant impacts on local economies and public health. Although HAB monitoring has been intensively performed at spatiotemporal scales in coastal areas of the world over the last decades, procedures have not yet been standardized. HAB monitoring procedures are complicated and consist of many methodologies, including physical, chemical, and biological water sample measurements. Each monitoring program currently uses different combinations of methodologies depending on site specific purposes, and many prior programs refer to the procedures in quotations. HAB monitoring programs in Chile have adopted the traditional microscopic and toxin analyses but not molecular biology and bacterial assemblage approaches. Here we select and optimize the HAB monitoring methodologies suitable for Chilean geography, emphasizing on metabarcoding analyses accompanied by the classical tools with considerations including cost, materials and instrument availability, and easiness and efficiency of performance. We present results from a pilot study using the standardized stepwise protocols, demonstrating feasibility and plausibility for sampling and analysis for the HAB monitoring. Such specific instructions in the standardized protocol are critical obtaining quality data under various research environments involving multiple stations, different analysts, various time-points, and long HAB monitoring duration.
Forecasting of Wastewater Treatment Plant Key Features Using Deep Learning-Based Models: A Case Study

The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend and predict the plant behavior to support process design and controls, improve system reliability, reduce operational costs, and endorse optimization of overall performances. Deep learning technologies as proven data-driven soft-sensors should be developed for WWTP applications to tackle the process of non-linearity and the dynamic nature of environmental data. This study adopts deep learning-based models as soft-sensors to forecast WWTP key features, such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption. We constructed six deep learning models derived from long short-term memory (LSTM) and gated recurrent unit (GRU), namely traditional LSTM and GRU, the exponentially smoothed LSTM, and the adaptive version of LSTM and smoothed LSTM. The employment of a smoothed LSTM technique is expected to reduce the outlier effect and to improve forecasting accuracy. Meanwhile, the usage of adaptive deep models will enhance the capabilities of the LSTM to quickly and accurately follow the trend of future data. We compared the performance of these models with Bi-directional LSTM (BiLSTM) and the seasonal decomposition using local regression. The historical records from a coastal municipal WWTP in Saudi Arabia are used to verify the investigated models’ effectiveness. The proposed models provide promising forecasting results but require no assumptions on the data distributions. In terms of efficiency, GRU based models converge faster than LSTM based models. In terms of accuracy, the LSTM soft-sensor shows overall the optimal result for all key features followed by the exponentially-smoothed GRU and LSTM. By contrast, the adaptive models achieved the lowest forecasting performance compared to the other models. These findings will benefit practitioners to achieve data-driven WWTP management.
Revisiting The Impact of Project-Based Learning on Online Learning In Vocational Education: Analysis of Learning in Pandemic Covid-19

Online learning is a learning system that uses internet media as a learning model. Implementation of online learning activities using various media such as Webex, Google Zoom, Google Meet. During COVID 19, online learning was needed for the continuity of the learning process. Online learning with Project-Based Learning (PBL) is a step to solve the problem of practical knowledge. The method in this research uses research and development (R&D). The research sample consisted of the control class and the experimental class. The division of the groups was carried out: (1) vocational education in Indonesia (2) Posttest was carried out to see the abilities in the experimental class and the control class (3) a basic test of students' initial knowledge. The population in this study were vocational students consisting of the practical course and the control class. The results showed that there were differences and improvements in student learning outcomes during the COVID 19 period.
Feature Projection-Based Unsupervised Domain Adaptation for Acoustic Scene Classification

The mismatch between the data distributions of training and test data acquired under different recording conditions and using different devices is known to severely impair the performance of acoustic scene classification (ASC) systems. To address this issue, we propose an unsupervised domain adaptation method for ASC based on the projection of spectro-temporal features extracted from both the source and target domain onto the principal subspace spanned by the eigenvectors of the sample covariance matrix of source-domain training data. Using the TUT Urban Acoustic Scenes 2018 Mobile Development dataset we show that the proposed method outperforms state-of-the-art unsupervised domain adaptation techniques when applied jointly with a convolutional ASC model and can also be practically employed as a feature extraction procedure for shallower artificial neural networks.