Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Effectiveness of Coastal Marine Protected Area Management: Four Learned Lessons for Science Uptake and Upscaling

For almost two decades, marine protected areas (MPAs) have been a central instrument of coastal conservation and management policies, but concerns about their abilities to meet conservation goals have grown as the number and sizes of MPAs have dramatically increased. This paper describes how a large (15 years) program of transdisciplinary research was used to successfully measure MPA management effectiveness (ME)—how well an MPA is managed, how well it is protecting values, and how well it is achieving the various goals and objectives for which it was created. This paper addresses the co-production and uptake of monitoring-based evidence for assessing ME in coastal MPAs by synthesizing the experiences of this program conducted with MPA managers. I present the main outcomes of the program, many were novel, and discuss four ingredients (learned lessons) that underpinned the successful uptake of science during and after the research program: (i) early and inclusive co-design of the project with MPA partners and scientists from all disciplines, (ii) co-construction of common references transcending the boundaries of disciplines, and standardized methodologies and tools, (iii) focus on outcomes that are management-oriented and understandable by end-users, and (iv) ensuring that capacity building and dissemination activities occurred during and persisted beyond the program. Standardized monitoring protocols and data management procedures, a user-friendly interface for indicator analysis, and dashboards of indicators related to biodiversity, uses, and governance, were the most valued practical outcomes. Seventy-five students were trained during the projects and most of the monitoring work was conducted with MPA rangers. Such outcomes were made possible by the extended timeline offered by the three successive projects. MPA managers’ and scientists a posteriori perceptions strongly supported the relevance of such collaboration. Local monitoring and assessment meets the needs of MPA managers, and forms the basis for large-scale assessments through upscaling. A long-term synergistic transdisciplinary collaboration between coastal MPA managers and research into social-ecological systems (SESs) would simultaneously (i) address the lack of long-term resources for coastal monitoring and SES-oriented research; (ii) increase science uptake by coastal managers, and (iii) benefit assessments at higher levels or at broader geographic scales.
Identification of Common Differentially Expressed Genes and Potential Therapeutic Targets in Ulcerative Colitis and Rheumatoid Arthritis

Ulcerative colitis (UC) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases (IMIDs) with similar symptoms and common genomics. However, the relationship between UC and RA has not been investigated thoroughly. Therefore, this study aimed to establish the differentially expressed genes (DEGs) and potential therapeutic targets in UC and RA. Three microarray datasets (GSE38713, GSE1919, and GSE12251) were selected from the Gene Expression Omnibus (GEO) database for analysis. We used R software to identify the DEGs and performed enrichment analyses. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape software were used to construct the protein-protein interaction (PPI) network and identify the hub genes. A regulatory network based on the constructed PPI was generated using StarBase and PROMO databases. We identified a total of 1542 and 261 DEGs in UC and RA. There were 169 common DEGs identified in both UC and RA, including 63 upregulated genes (DEGs1) and nine downregulated genes (DEGs2). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs1 and DEGs2 in the PPI network revealed that the genes enriched were involved in immunity. A total of 45 hub genes were selected based on high scores of correlation; three hub genes (SRGN, PLEK, and FCGR3B) were found to be upregulated in UC and RA, and downregulated in UC patients with response to infliximab treatment. The identification of novel DEGs and hub genes in the current study contributes to a novel perception for latent functional mechanisms and presents potential prognostic indicators and therapeutic targets in UC and RA.
Overexpression of an Antisense RNA of Maize Receptor-Like Kinase Gene ZmRLK7 Enlarges the Organ and Seed Size of Transgenic Arabidopsis Plants

Leucine-rich repeat (LRR)-receptor-like protein kinases (LRR-RLKs) play vital roles in plant growth, development, and responses to environmental stresses. In this study, a new LRR-RLK gene, ZmRLK7, was isolated from maize, and its function within plant development was investigated through ectopic expression in Arabidopsis. The spatial expression pattern analysis reveals that ZmRLK7 is highly expressed in embryos prior to programmed cell death (PCD) of starchy endosperm tissues, and its encoded protein has been localized to both plasm and nuclear membranes subcellularly. Overexpression of sense ZmRLK7 reduced the plant height, organ size (e.g., petals, silique, and seeds), and 1000-seed weight in transgenic lines, while the antisense transgene enlarged these traits. Cytological analysis suggested that ZmRLK7 negatively regulates petal size through restricting both cell expansion and proliferation. In addition, abnormal epidermal cell structure was observed, and the stomata number decreased obviously in sense ZmRLK7 transgenic lines with a lower stomatal index than that in the wild type. Quantitative RT-PCR analysis indicated that transcript levels of genes that are involved in the brassinosteroid and ERACTA signaling pathways were coordinately altered, which could partially explain the phenotypic variation. Moreover, overexpression of antisense ZmRLK7 substantially rescued the Arabidopsis bak1-3 mutant phenotype. All these results together suggest that ZmRLK7 can serve as an important regulator in regulating plant architecture and organ size formation. This work will provide insight into the function of ZmRLK7 in maize.
The University of Florence Technique for Robot-Assisted Kidney Transplantation: 3-Year Experience

Objective: To report the University of Florence technique for robot-assisted kidney transplantation (RAKT) from living donor (LD) and deceased donor (DD), highlighting the evolution of surgical indications and technical nuances in light of a single surgeon's learning curve. Materials and Methods: A dedicated program for RAKT from LDs was developed at our Institution in 2017 and implemented later with a specific framework for DDs. All RAKTs were performed by a single highly experienced surgeon. Data from patients undergoing RAKT between January 2017 and December 2019 were prospectively collected in a dedicated web-based data platform. In this report we provide a comprehensive step-by-step overview of our technique for RAKT, focusing on the potential differences in peri-operative and mid-term functional outcomes between LDs vs. DDs. Results: Overall, 160 KTs were performed in our center during the study period. Of these, 39 (24%) were performed with a robot-assisted laparoscopic technique, both from LDs (n = 18/39 [46%]) and from DDs (n = 21/39 [54%]). Eleven (11/39 [18%]), 13(13/39 [26%]), and 15 (15/39 [30%]) RAKTs were performed in 2017, 2018, and 2019, respectively, highlighting an increasing adoption of robotics for KT over time at our Institution. Median time for arterial (19 min for LD and 18 min for DD groups), venous (21 min for LD, 20 min for DD) and uretero–vesical (18 min for LD and 15 for DD) anastomosis were comparable between the two groups (all p > 0.05), as the median rewarming time (59 min vs. 56 min, p = 0.4). The rate of postoperative surgical complications according to Clavien–Dindo classification did not differ between the two study groups, except for Clavien–Dindo grade II complications (higher among patients undergoing RAKT from DDs, 76 vs. 44%, p = 0.042). Overall, 7/39 (18%) patients (all recipients from DDs) experienced DGF; two of them were on dialysis at last FU. Conclusions: Our experience confirms the feasibility, safety, and favorable mid-term outcomes of RAKT from both LDs and DDs in appropriately selected recipients, highlighting the opportunity to tailor the technique to specific recipient- and/or graft-characteristics. Further research is needed to refine the technique for RAKT and to evaluate the benefits and harms of robotics for kidney transplantation from DDs.
Hierarchical Sequencing and Feedforward and Feedback Control Mechanisms in Speech Production: A Preliminary Approach for Modeling Normal and Disordered Speech

Our understanding of the neurofunctional mechanisms of speech production and their pathologies is still incomplete. In this paper, a comprehensive model of speech production based on the Neural Engineering Framework (NEF) is presented. This model is able to activate sensorimotor plans based on cognitive-functional processes (i.e., generation of the intention of an utterance, selection of words and syntactic frames, generation of the phonological form and motor plan; feedforward mechanism). Since the generation of different states of the utterance are tied to different levels in the speech production hierarchy, it is shown that different forms of speech errors as well as speech disorders can arise at different levels in the production hierarchy or are linked to different levels and different modules in the speech production model. In addition, the influence of the inner feedback mechanisms on normal as well as on disordered speech is examined in terms of the model. The model uses a small number of core concepts provided by the NEF, and we show that these are sufficient to create this neurobiologically detailed model of the complex process of speech production in a manner that is, we believe, clear, efficient, and understandable.
History and Domestication of Saccharomyces cerevisiae in Bread Baking

The yeast Saccharomyces cerevisiae has been instrumental in the fermentation of foods and beverages for millennia. In addition to fermentations like wine, beer, cider, sake, and bread, S. cerevisiae has been isolated from environments ranging from soil and trees, to human clinical isolates. Each of these environments has unique selection pressures that S. cerevisiae must adapt to. Bread dough, for example, requires S. cerevisiae to efficiently utilize the complex sugar maltose; tolerate osmotic stress due to the semi-solid state of dough, high salt, and high sugar content of some doughs; withstand various processing conditions, including freezing and drying; and produce desirable aromas and flavors. In this review, we explore the history of bread that gave rise to modern commercial baking yeast, and the genetic and genomic changes that accompanied this. We illustrate the genetic and phenotypic variation that has been documented in baking strains and wild strains, and how this variation might be used for baking strain improvement. While we continue to improve our understanding of how baking strains have adapted to bread dough, we conclude by highlighting some of the remaining open questions in the field.
Comparative Study of Distance Measures for the Fuzzy C-means and K-means Non-Supervised Methods Applied to Image Segmentation

Recent studies have revealed that the performance of the FCM and K-means is completely related to the distance measures. However, the literature does not provide evidence that the distance used for data-clustering is useful for image segmentation. Therefore, a comparative study of the performance of different distance measures applied to image segmentation, using the mentioned clustering methods is proposed in this work. The selection of the distance measures was based on a literature study of their benefits. As a consequence, the selected distances to be tested are Euclidean, Manhattan, Canberra, and Spearman. Since our principal goal is to compare the effectiveness of the distance, the experiment had been evaluated according to two centroids selected by the user. According to primary results, the best-rated distance employed for image segmentation is the Canberra distance.
Can online assessment be trusted? a pilot study of computer-based - vs. 'paper and pencil' - version of the Adult Self-Report 18-59

In the last few years, the technology-based approach in psychology for clinical intervention, assessment, and scientific research has shown the several advantage. Researchers typically use the digital versions of existing pen and paper instruments Therefore, the study of psychometric properties of the measures appears necessary. The present study aimed to compare the density distribution and scale results of the Adult Self Report 18-59 self-report instrument. 40 Italian young adults were involved and randomly associated to one of two conditions. The first group ( n = 20) completed the ASR through paper-and-pencil protocol. The second group ( n = 20) completed a computer-based version of the questionnaire. Bayesian correlations and Bayesian independent sample t-tests were performed. The results show the possibility of using the ASR through computer-based assessment. The relevance of testing the psychometric properties of the self-report questionnaire before their online use is discussed.
KNN Algorithm with DTW Distance for Signature Classification of Wine Leaves

: The European Union has created a way of classifying wines to make life easier for consumers when choosing the product that most appeals to them, this clas-siﬁcation may require control that is hampered by the distancing of production. The automation of control processes is a good way out, but this comes up against the difﬁculty of computational methods for image interpretation. Thus, this paper presents a form of abstraction of image data into a series of values that can be more easily understood by the computer. A Machine Learning algo-rithm is also applied to create a baseline for the classiﬁcation of these entry images, obtaining up to 60% accuracy while classifying ﬁve classes of vine varieties.
Framing Algorithms for Approximate Multicriteria Shortest Paths

This paper deals with the computation of d -dimensional multicriteria shortest paths. In a weighted graph with arc weights represented by vectors, the cost of a path is the vector sum of the weights of its arcs. For a given pair consisting of a source s and a destination t , a path P dominates a path Q if and only if P ’s cost is component-wise smaller than or equal to Q ’s cost. The set of Pareto paths, or Pareto set, from s to t is the set of paths that are not dominated. The computation time of the Pareto paths can be prohibitive whenever the set of Pareto paths is large. We propose in this article new algorithms to compute approximated Pareto paths in any dimension. For d = 2, we exhibit the ﬁrst approximation algorithm, called Frame , whose output is guaranteed to be always a subset of the Pareto set. Finally, we provide a small experimental study in order to conﬁrm the relevance of our Frame algorithm.
An Immersive Experience in the Virtual 3D VirBELA Environment for Leadership Development in Undergraduate Students during the COVID-19 Quarantine

Leadership is an essential soft skill in any professional today However, its development can be a significant challenge in traditional classes focused on disciplinary competencies In this sense, the use of an immersive 3D virtual environment can be an advantage for leadership development, since it can facilitate communication and interaction between the participants;thanks to overcoming fear associated with face-to-face interaction In this regard, this work exposes a leadership activity developed in Vir- BELA, explaining their designing, developing, and evaluating processes The results obtained show that this activity under the participants' perception was facilitating the interaction of the participants among themselves and with the professor and improve the learning process in different ways This process was carried out during quarantine due to Covid19 © 2020 Copyright for this paper by its authors
Two Semi-supervised Approaches to Malware Detection with Neural Networks

: Semi-supervised learning is characterized by using the additional information from the unlabeled data. In this paper, we compare two semi-supervised algorithms for deep neural networks on a large real-world malware dataset. Speciﬁcally, we evaluate the performance of a rather straightforward method called Pseudo-labeling, which uses unlabeled samples, classiﬁed with high con-ﬁdence, as if they were the actual labels. The second approach is based on an idea to increase the consistency of the network’s prediction under altered circumstances. We implemented such an algorithm called Π -model, which compares outputs with different data augmentation and different dropout setting. As a baseline, we also provide results of the same deep network, trained in the fully supervised mode using only the labeled data. We analyze the prediction accuracy of the algorithms in relation to the size of the labeled part of the training dataset.
Polarity and Variable Selection Heuristics for SAT-Based Anytime MaxSAT

This paper is a system description of the anytime MaxSAT solver TT-Open-WBO-Inc, which won both of the weighted incomplete tracks of MaxSAT Evaluation 2019. We implemented the recently introduced polarity and variable selection heuristics, TORC and TSB, respectively, in the Open-WBO-Inc-BMO algorithm within the open-source anytime MaxSAT solver Open-WBO-Inc. As a result, the solver is substantially more efficient.
The Big Five game of personality: a digital role-playing game to assess and train students on Big Five Theory

The university institution has recently been making a great effort to affirm the value of an open education accessible to everyone. An example of this intention is the creation of Massive and Open Online Courses (MOOC) provided for a wide audience. In the learning process with MOOCs, learning becomes a dynamic process, within which it is possible to train both declarative and procedural components. This work is aimed to analyze the use of technology in the learning process. Within a syllabus dedicated to students containing a large number of theoretical lessons, a game concerning one of the lessons of the syllabus (the Big Five Model) was created to test what is learned through the courses. The Big Five Game of Personality has the structure of a role-playing game. The user has to help a recruiter to interview some applicants by asking question appropriately to the dimension of personality. Also, on the basis of the applicant’s answers, the user has to evaluate the level of each dimension. The game experience is accompanied by a Tutor who introduces the next steps. This article shows the specific structure of the game, opening up a space for the potential use of technology in the academic field.
CrowdCog

While crowd workers typically complete a variety of tasks in crowdsourcing platforms, there is no widely accepted method to successfully match workers to different types of tasks. Researchers have considered using worker demographics, behavioural traces, and prior task completion records to optimise task assignment. However, optimum task assignment remains a challenging research problem due to limitations of proposed approaches, which in turn can have a significant impact on the future of crowdsourcing. We present 'CrowdCog', an online dynamic system that performs both task assignment and task recommendations, by relying on fast-paced online cognitive tests to estimate worker performance across a variety of tasks. Our work extends prior work that highlights the effect of workers' cognitive ability on crowdsourcing task performance. Our study, deployed on Amazon Mechanical Turk, involved 574 workers and 983 HITs that span across four typical crowd tasks (Classification, Counting, Transcription, and Sentiment Analysis). Our results show that both our assignment method and recommendation method result in a significant performance increase (5% to 20%) as compared to a generic or random task assignment. Our findings pave the way for the use of quick cognitive tests to provide robust recommendations and assignments to crowd workers.
Data mining of deck archetypes in Hearthstone

Computer games have become a very interesting environment or testbed to develop new algorithms in many of the branches of Artiﬁcial Intelligence. In fact, collectible card games, such as Hearth-stone, have recently attracted the attention of researchers because of their characteristics: uncertainty, randomness, or the inﬁnite and unpredictable interactions that can occur in a game. In this game each player composes decks to face other players from a pool of more than 3,000 cards, each one with its own rules and statistics. This implies a great variability of decks and card combinations with rich eﬀects. This paper proposes the use of clustering techniques to extract information from data provided by Hearthstone players, i.e. a Game Mining approach. To do so, more than 500,000 decks created by game players (both experts and just enthusiasts) have been downloaded from Hearthpwn website. Thus, a descriptive analysis of this dataset, along with Data Mining techniques, have been carried out in order to understand which archetypes (or deck types) are the favourites among the community of players, and what relationships can be identiﬁed between them. The results show that it is possible to use clustering algorithms such as K-Means to automatically detect the archetypes used by the players.
Using Artificial Neural Networks in Reinforcement Learning Algorithms

: Reinforcement learning algorithms represent a speciﬁc category in the machine learning ﬁeld precisely because of their unique approach based on a trial-error basis. We introduce a new approach into the Q-algorithm, namely maximizing k -future rewards policy, which decreases learning time and increases maximal and average score value of an optimizing function signiﬁcantly. Our modiﬁed Deep NNQ-learning using feed-forward networks instead of originally proposed convolutional neural networks gives the best results in a tested problem. We implemented the developed algorithm for the Flappy Bird game where signiﬁcant improvements are achieved by appropriate setting of state space, act policy, and by pre-training neural networks.
Modular Surrogate-based Optimization Framework for Expensive Computational Simulations

: In practical applications, the use of computational modeling has been industry-wide adopted to speed up product development as well as reduce physical testing costs. Such models of complex or large systems are, however, often computationally expensive, hence solution times of hours or more are not uncommon. Additionally, as these models are typically evaluated using black-box solvers, the direct study of relations between design parameters renders demanding in terms of computational time and provides poor engineering insight and understanding. To address this, a modular framework integrating computation automation with the use of surrogate-based modeling, optimization and visualization techniques is presented. The framework is built in the Python programming language. Its use is illustrated on a study of the side impact response of a car body using an artiﬁcial neural network as a surrogate together with the NSGA-III genetic algorithm for optimization.