Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-Scale Plant Richness Mapping of the Andean Páramo According to Macroclimate

How the macroclimate conditions spatial patterns of plant diversity has been extensively studied in many systems and usually proven a strong relationship, however tropical mountains remain understudied. Because understanding primary drivers of diversity patterns in the Andean paramo is still in its infancy, we focused on evaluating the role of the current macroclimate in form of three complementary hypotheses, energy, seasonality and harshness, in explaining local variation of plant species richness. We relied on 1559 vegetation plots that offered a fine-scale outlook on real species assemblages due to community rules and species’ interactions with their surrounding environment, including climate. Generalized Least Squares (GLS) regression models provided insight on the significance of the different hypotheses in explaining local plant richness, but only the energy and seasonality hypotheses received partial support. The best model was then combined with spatial interpolation Kriging modelling techniques to project species richness for a standardized 25m2 plot throughout the entire paramo biogeographic province. We highlighted a North-South increase in richness with several species-rich areas, potential local biodiversity hotspots, independent of the general gradient: the Amotape-Huancabamba zone, Sangay and Cotacachi areas, and eastern Venezuelan Andes. Our endeavor to finely map local richness is the first effort predicting macroecological patterns in the emblematic Andean paramo and contributes novel biogeographic knowledge useful to further support in-depth research and conservation focus in the northern Andes.
Marijuana-Derived Cannabinoids Trigger a CB2/PI3K Axis of Suppression of the Innate Response to Oral Pathogens

Cannabis use is an emergent risk factor for periodontitis, a chronic bacterial-induced disease of the supporting structures of the teeth. However, the mechanisms by which marijuana exposure predisposes to periodontal tissue destruction have yet to be elucidated. Therefore, we examined the influence of physiologically relevant doses of major marijuana-derived phytocannabinoid subtypes (cannabidiol [CBD]; cannabinol [CBN]; and tetrahydrocannabinol [THC], 1.0 μg/ml) on the interactions of three ultrastructurally variant oral pathogens, Porphyromonas gingivalis, Filifactor alocis, and Treponema denticola with the immune system. CBD, CBN, and THC each suppressed P. gingivalis-induced IL-12 p40, IL-6, IL-8, and TNF release while enhancing the anti-inflammatory cytokine, IL-10, from human innate cells. Similar phenomena were observed in F. alocis- and T. denticola-exposed human monocytes and human gingival keratinocytes. Higher phytocannabinoid doses (≥5.0 μg/ml) compromised innate cell viability and inhibited the growth of P. gingivalis and F. alocis, relative to unexposed bacteria. T. denticola, however, was resistant to all cannabinoid doses tested (up to 10.0 μg/ml). Pharmaceutical inhibition and efficient gene silencing indicated that a common CB2/PI3K axis of immune suppression is triggered by phytocannabinoids in vitro. This pathway does not appear to perpetuate through the canonical GSK3β-dependent cholinergic anti-inflammatory pathway, the predominant endogenous inflammatory control system. In a repetitive, transient oral infection model, CBD also suppressed P. gingivalis-induced innate immune markers in wild-type mice, but not in CB2−/− mice. If such phenomena occur in humans in situ, environmental cannabinoids may enhance periodontitis via direct toxic effects on specific oral bacteria; by compromising innate cell vitality; and/or through a suppressed innate response to periodontal pathogens involving a CB2/PI3K signaling lineage.
Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task

Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants’ endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item’s chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities.
The Perception of Adolescents’ Encounter With a Flipped Learning Intervention in Norwegian Physical Education

Objective: The discrepancy between observed and suggested levels of physical activity among adolescents urges society to obtain more knowledge and insight into how physical education (PE) in school can be beneficial for all individuals. The purpose of the present study was to examine the perceptions of adolescents of a flipped learning (FL) framework, applied to enhance student motivation and learning in PE. Methods: Ten students, from 13 to 17 years of age, were recruited from one secondary school and one upper secondary school from two different counties in Norway during the spring of 2016. The data were generated using semi-structured interviews, reduced, analysed and interpreted by constructivist grounded theory and stepwise-deductive induction traditions through constructivist and situated learning lenses. Results: It was demonstrated that the video format is preferred as preparation material over text material, that instructions on class activities are preferably given in the preparation material and not in class, and that the students did not report any negative aspects of FL. The students showed a positive perception of preparing for PE classes if the preparation material is in the form of a video, and if it has a clear connection to the upcoming class content and outcome aim(s). FL has a positive effect on student understanding and learning in PE, and FL produces a positive change in how students value PE. Conclusion: A clearly positive perception of FL in PE was demonstrated. FL seems to facilitate deep learning in PE, facilitated by motivation, knowledge, and the nature of the learning framework.
Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach

Image segmentation among overlapping land cover areas in satellite images is a very crucial task. Detection of belongingness is the important problem for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of Fuzzy C-Means and Cellular automata methods. This new unsupervised method is able to detect clusters using 2-Dimensional Cellular Automata model based on fuzzy segmentations. This approach detects the overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase of our method, we utilize a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. We experiment our method on Indian Ajoy river watershed area. The clustered regions are compared with well-known FCM and K-Means methods and also with the ground truth knowledge. The results show the superiority of our new method.
Self-supervised Difference Detection for Refinement CRF and Seed Interpolation

To minimize annotation costs associated with training of semantic segmentation models, weakly-supervised segmentation approaches have been extensively studied. In this paper, we propose a novel method: Self-Supervised Difference Detection (SSDD) module which evaluates confidence of each of the pixels of segmentation masks and integrate highly confident pixels of two candidate masks.
Multiagent and Bargaining-Game-Based Real-Time Scheduling for Internet of Things-Enabled Flexible Job Shop

With the rapid advancement and widespread applications of information technology in the manufacturing shop floor, a huge amount of real-time data is generated, providing a good opportunity to effectively respond to unpredictable exceptions so that the productivity can be improved. Thus, how to schedule the manufacturing shop floor for achieving such a goal is very challenging. This paper addresses this issue and a new multiagent-based real-time scheduling architecture is proposed for an Internet of Things-enabled flexible job shop. Differing from traditional dynamic scheduling strategies, the proposed strategy optimally assigns tasks to machines according to their real-time status. A bargaining-game-based negotiation mechanism is developed to coordinate the agents so that the problem can be efficiently solved. To demonstrate the feasibility and effectiveness of the proposed architecture and scheduling method, a proof-of-concept prototype system is implemented with Java agent development framework platform. A case study is used to test the performance and effectiveness of the proposed method. Through simulation and comparison, it is shown that the proposed method outperforms the traditional dynamic scheduling strategies in terms of makespan, critical machine workload, and total energy consumption.
Using Machine Learning to Detect ICT in Criminal Court Cases

ICT involvement in criminal court cases is not always explicitly mentioned, which results in less attention being paid to crimes involving ICT. This research aims to detect ICT involvement in criminal court cases and classify these cases based on certain features. Naive Bayes is used as a machine learning algorithm to automatically classify the criminal court cases. As a result, this paper delivers a model that can detect ICT involvement in criminal court cases, the features which it is based on and the accuracy of this classification. A statistical analysis of the dataset is provided to show trends in ICT involved crimes.
TITLE: Epidermal Growth Factor Receptor Mutation Analysis in Advanced Non-Small Cell Lung Cancer: Review of Economic Evaluations and Framework for Economic Analyses

This document is prepared by the Health Technology Inquiry Service (HTIS), an information service of the Canadian Agency for Drugs and Technologies in Health. The service is provided to those involved in planning and providing health care in Canada. HTIS responses are based on a comprehensive and systematic search of literature available to CADTH at the time of preparation. The intent is to provide a summary and critical appraisal of the best evidence on the topic that CADTH could identify using all reasonable efforts within the time allowed. This response has been peer reviewed by clinical experts. The information in this document is intended to help Canadian health care decision-makers make well-informed decisions and thereby improve the quality of health care services. HTIS responses should be considered along with other types of information and health care considerations. It should not be used as a substitute for the application of clinical judgment in respect of the care of a particular patient or other professional judgment in any decision making process, or as a substitute for professional medical advice. Readers are also cautioned that a lack of good quality evidence does not necessarily mean a lack of effectiveness particularly in the case of new and emerging health technologies, for which little information can be found, but which may in future prove to be effective. While CADTH has taken care in the preparation of the document to ensure that its contents are accurate, complete and up to date as of the date of publication, CADTH does not make any guarantee to that effect. CADTH does not guarantee and is not responsible for the quality, currency, propriety, accuracy or reasonableness of any statements, information, or conclusions contained in the source documentation. CADTH is not responsible for any errors or omissions or injury, loss or damage arising from or relating to the use (or misuse) of any information, statements or conclusions contained in or implied by the information in this document or in any of the source documentation.
Research Based Practices for Improving the Effectiveness of Asynchronous Online Discussion Boards

Asynchronous online discussion boards are an effective tool for developing and enhancing critical thinking skills and writing in online as well as in-person courses. In this teacher-ready research review, we examine the literature on the benefits of implementing asynchronous online discussion boards as a way of fostering critical thinking and writing skills in psychology courses. We subsequently discuss some of the challenges associated with online discussion boards and offer solutions to address them. One of the primary challenges is the lack of participation or high-level participation of students. To address this challenge, we outline strategies for communicating the purpose and value of the discussion, setting clear expectations for responses, and designing a structure for the discussion. We also review best practices for designing effective question prompts, innovative approaches to discussion questions, and strategies to engage students in the discussion. Sample question prompts for psychology courses as well as a grading rubric for the discussions are provided. Finally, the role of the instructor in facilitating the discussion and techniques for doing so effectively are discussed.
gcKrig: An R Package for the Analysis of Geostatistical Count Data Using Gaussian Copulas

This work describes the R package gcKrig for the analysis of geostatistical count data using Gaussian copulas. The package performs likelihood-based inference and spatial prediction using Gaussian copula models with discrete marginals. Two different classes of methods are implemented to evaluate/approximate the likelihood and the predictive distribution. The package implements the computationally intensive tasks in C++ using an R/C++ interface, and has parallel computing capabilities to predict the response at multiple locations simultaneously. In addition, gcKrig also provides functions to simulate and visualize geostatistical count data, and to compute the correlation function of the counts. It is designed to allow a flexible specification of both the marginals and the spatial correlation function. The principal features of the package are illustrated by three data examples from ecology, agronomy and petrology, and a comparison between gcKrig and two other R packages.
New curve sprint test for soccer players: Reliability and relationship with linear sprint

ABSTRACT The speed performance is involved not only in linear sprints, but also in a wide spectrum of multi-directional movements, such as curve sprinting. Curved sprint can be defined as sprint with gradual and continuous change of direction (COD). Although ~85% of the actions performed at maximum velocity in a professional soccer league are curvilinear sprints, there is not any specific test to assess this ability. This study aimed to analyse the reliability of a new curve sprint test, and compare its results with those obtained by soccer players in linear sprint. Forty experienced soccer players performed 3 attempts of curve sprint (using the penalty arc) to right and left side (17 m), and 3 linear sprints (17 m) in two different days. The ICCs (inter-session reliability) were 0.93 for sprint curve right side (CSRS) and 0.89 for sprint curve left side (CSLS), considered “acceptable”. The CVs (intra-session reliability) were 0.87% in CSRS and 1.15% in CSLS. The coefficient of determination (R2) between linear and curve sprinting was ~35%. The association between curve sides was “very large” (r = 0.878; p < 0.01). In summary, we showed that “curve sprint test” is highly reliable, and that curvilinear and linear sprints are different and independent actions.
Long noncoding RNA LINC00319 regulates ROMO1 expression and promotes bladder cancer progression via miR‐4492/ROMO1 axis

Growing reports indicate that long noncoding RNA (lncRNA) are involved in the regulation of various biological processes of cancer cells. LINC00319 is an ill investigated lncRNA and has been shown to regulate lung cancer, nasopharyngeal carcinoma and ovarian cancer. Nevertheless, its roles in bladder cancer (BCa) remain unclear. In our research, LINC00319 was shown to be an upregulated lncRNA in BCa tissues. LINC00319 expression is negatively correlated with the patient's prognosis. Silencing of LINC00319 suppressed BCa proliferation and invasiveness. In addition, the data indicated LINC00319 was a sponge for miR‐4492 and miR‐4492 suppressed ROMO1 expression in BCa. Furthermore, our results illustrated miR‐4492/ROMO1 axis regulates proliferation, migration, and invasion and LINC00319 exerts oncogenic roles through modulating miR‐4492/ROMO1 axis. In sum, this study suggested that LINC00319 acts as oncogenic roles in BCa progression.
Clinical Utility of a Fully Automated Microsatellite Instability Test with Minimal Hands-on Time

Background Microsatellite instability (MSI) analysis is becoming increasingly important in many types of tumor including colorectal cancer (CRC). The commonly used MSI tests are either time-consuming or labor-intensive. A fully automated MSI test, the Idylla MSI assay, has recently been introduced. However, its diagnostic performance has not been extensively validated in clinical CRC samples. Methods We evaluated 133 samples whose MSI status had been rigorously validated by standard polymerase chain reaction (PCR), clinical next-generation sequencing (NGS) cancer panel test, or both. We evaluated the diagnostic performance of the Idylla MSI assay in terms of sensitivity, specificity, and positive and negative predictive values, as well as various sample requirements, such as minimum tumor purity and the quality of paraffin blocks. Results Compared with the gold standard results confirmed through both PCR MSI test and NGS, the Idylla MSI assay showed 99.05% accuracy (104/105), 100% sensitivity (11/11), 98.94% specificity (93/94), 91.67% positive predictive value (11/12), and 100% negative predictive value (93/93). In addition, the Idylla MSI assay did not require macro-dissection in most samples and reliably detected MSI-high in samples with approximately 10% tumor purity. The total turnaround time was about 150 minutes and the hands-on time was less than 2 minutes. Conclusions The Idylla MSI assay shows good diagnostic performance that is sufficient for its implementation in the clinic to determine the MSI status of at least the CRC samples. In addition, the fully automated procedure requires only a few slices of formalin-fixed paraffin-embedded tissue and might greatly save time and labor.
Fibrofog in Daily Life: An Examination of Ambulatory Subjective and Objective Cognitive Function in Fibromyalgia

Perceived cognitive dysfunction in fibromyalgia (FM), “fibrofog,” is common. Prior laboratory‐based studies have limited our understanding of cognitive function in FM in daily life. The objective of this study was to explore levels of subjective and objective cognitive functioning and the association between subjective and objective aspects of cognition in people with and without FM in the lived environment.
Complicated trafficking behaviors involved in paradoxical regulation of sortilin in lipid metabolism

This review aims to summarize and discuss the most recent advances in our understanding of the underlying mechanisms of the paradoxical effects of sortilin on lipid metabolism. The vacuolar protein sorting 10 protein (Vps10p) domain in the sortilin protein is responsible for substrate binding. Its cytoplasmic tail interacts with adaptor molecules, and modifications can determine whether sortilin trafficking occurs via the anterograde or retrograde pathway. The complicated trafficking behaviors likely contribute to the paradoxical roles of sortilin in lipid metabolism. The anterograde pathway of sortilin trafficking in hepatocytes, enterocytes, and peripheral cells likely causes an increase in plasma lipid levels, while the retrograde pathway leads to the opposite effect. Hepatocyte sortilin functions via the anterograde or retrograde pathway in a complicated and paradoxical manner to regulate apoB‐containing lipoprotein metabolism. Clarifying the regulatory mechanisms underlying the trafficking behaviors of sortilin is necessary and may lead to artificial sortilin intervention as a potential therapeutic strategy for lipid disorder diseases. Conclusively, the paradoxical regulation of sortilin in lipid metabolism is likely due to its complicated trafficking behaviors.
Quadriceps and Gastrocnemii Anatomical Cross-Sectional Area and Vastus Lateralis Fascicle Length Predict Peak-Power and Time-To-Peak-Power

ABSTRACT Purpose: The current study investigated the role of quadriceps and gastrocnemii size and vastus lateralis and gastrocnemius medialis muscle architecture in peak-power and time-to-peak-power exerted in an all-out Wingate test. Twenty-one amateur cyclists were recruited. Methods: Quadriceps and gastrocnemii anatomical cross-sectional area (ACSA), and vastus lateralis and gastrocnemius medialis pennation angle and fascicle length were measured using ultrasound. Relative peak-power (normalized per body mass) and time-to-peak-power were measured during a 30s all-out test. Results: Relative peak-power was correlated with quadriceps ACSA (r = 0.896, p < .001), gastrocnemii ACSA (r = 0.811, p < .001), vastus lateralis (r = 0.787, p < .001) and gastrocnemius medialis pennation angle (r = 0.638, p < .003). Multiple regression revealed that quadriceps and gastrocnemii ACSA accounted for 85% (R2= 0.85) of peak-power variance. Time-to-peak-power showed very large (r = −0.868, p < .001) and large correlation (r = −0.680, p = .001) with VL and GM fascicle length, respectively. Multiple regression analysis revealed that VL fascicle length explained 75% (R2= 0.75) of the time-to-peak-power variance. Conclusions: Quadriceps and gastrocnemii ACSA largely explained relative peak-power in an all-out Wingate test. Vastus lateralis fascicle length was the main predictor of the time-to-peak-power. Muscle architecture characteristics seem to be involved in the power generating capacity.