Miracle-dz/UniFAR-FTUs huggingface dataset:
Roost sites of radio-marked Mexican Spotted Owls in Arizona and New Mexico: Sources of variability and descriptive characteristics

--To increase understanding of roosting habitat of Mexican Spotted Owls (Strix occidentalis lucida) and factors that influence use of roosting habitat, we sampled habitat characteristics at 1790 sites used for roosting by 28 radio-marked Mexican Spotted Owls in three study areas in Arizona and New Mexico. We explored potential patterns of variation in roost-site characteristics by estimating similarity among all possible pairs of roost sites and summarizing patterns in these similarity estimates using a linear model. Factors in the model included owl identity and season. We conducted these analyses within study areas, because habitat characteristics differed among study areas. We used a repeated-measures model which assumed that similarity estimates computed between roost sites of the same owl or pairs of owls were correlated. This model significantly improved model goodness-of-fit over a null model assuming no such correlation structure. Similarity estimates were relatively high (0.744-0.775) in all three study areas, suggesting consistent patterns of selection among owls within areas. Owl and season effects were relatively small but detectable in all study areas, with the relative magnitude of these effects differing among areas. The seasonal effect was greatest in the area dominated by pine-oak forest and relatively slight in two areas where owls roosted primarily in mixed-conifer forest. Relative to areas where owls roosted in mixed-conifer forest, roosts in pine-oak forest occurred on moderate slopes, on southwest to northwest aspects, and were less concentrated on lower portions of slopes. We suspected that much of this difference reflected differences in stand-development processes in different forest types. This suggested that land managers should incorporate knowledge of such patterns in different forest types and topographic locations in planning decisions involving management of Spotted Owl habitat.
Patient adherence to multivitamin supplementation after bariatric surgery: a narrative review

Morbid obesity is a growing problem worldwide and has subsequently resulted in a wide application of bariatric surgery to achieve long-term weight loss and improvement of obesity-related co-morbidities. In spite of these clinical benefits, vitamin deficiencies are common after bariatric surgery; therefore, lifelong multivitamin supplementation (MVS) is recommended. However, patient adherence to MVS intake is generally poor. The aim of this narrative review is to analyse which factors influence the adherence of MVS intake after bariatric surgery. To provide an extensive overview, we will discuss the different factors that influence MVS use in patients who underwent bariatric surgery, but also review the literature on MVS in other patient groups.
Does GDPR influence the hospital‐patient relationship? Trust and commitment between health data stakeholders

GDPR influences the way people define and use personal data. This study aims to discuss how the relationship between data subjects and data controllers in the healthcare context would be influenced under GDPR. We adopt the Key Mediating Variable, as a conceptual model, to reveal potential changes in the hospital‐patient relationship, specifically in terms of trust and commitment, as influenced by GDPR in the following five precursors: relationship termination costs, benefits, shared values of data, communication, and opportunistic behavior.
A Systematic Review of Creativity-Related Studies Applying the Remote Associates Test From 2000 to 2019

The study examines how the remote associates test (RAT) has been used to examine theories of creativity through a review of recent studies on creativity. Creativity-related studies published between 2000 and 2019 were retrieved from the SCOPUS database. A total of 172 papers were chosen for further analysis. Content analysis shows that research on creativity using RAT mainly concerns remote association, insight problem-solving, general creative process, test development, individual difference, effect of treatment, clinical case, social interaction effect, and predictor or criterion. The study constructs a theoretical framework based on the 4P (Product–Person–Process–Place) model and demonstrates how empirical studies using the RAT explore the individual differences, internal processes, and external influences of creative thinking. In addition, the most commonly used version of the RAT is the Compound Remote Associates Problems (Bowden and Jung-Beeman, 2003a). Current research shows a trend whereby the creative thinking process has been receiving greater attention. In particular, a growing number of studies in this field have been carried out using cognitive neuroscience technologies. These findings suggest that the RAT provides researchers with a way to deepen their understanding of different levels of creativity.
Bacteriophage Enumeration and Detection Methods

Application of phages as alternative antimicrobials to combat pathogenic bacteria and their association to a healthy gut microbiome has prompted a need for precise methods for detection and enumeration of phage particles. There are many applicable methods, but care should be taken considering the measured object (infectious phage, whole phage particle or nucleic acid and proteins) and the concept behind the technique to avoid misinterpretations. While molecular methods cannot discriminate between viable and non-infectious phages, the traditional techniques for counting infectious phages can be time consuming and poorly reproducible. Here, we describe the methods currently used for phage detection and enumeration and highlight their advantages as well as their limitations. Finally, we provide insight on how to deal with complex samples, as well as future prospects in the field of phage quantification.
The Risky Closed Economy: A Holistic, Longitudinal Approach to Studying Fear and Anxiety in Rodents

Basic research of fear and anxiety in rodents has historically utilized a limited set of behavioral paradigms, for example, Pavlovian (classical) fear conditioning, the elevated plus-maze, or inhibitory (passive) avoidance. These traditional paradigms measure a limited selection of variables over a short duration, providing only a “snapshot” of fear and anxiety-related behavior. Overreliance on these paradigms and such behavioral snapshots ultimately lead to a narrow understanding of these complex motivational states. Here, we elaborate on the closed economy; a seldom-used paradigm that has been modified to comprehensively study fear and anxiety-related behavior and neurocircuitry in rodents. In this modified “Risky Closed Economy (RCE)” paradigm, animals live nearly uninterrupted in behavioral chambers where the need to acquire food and water and avoid threat is integrated into the task. Briefly, animals are free to acquire all of their food and water in a designated foraging zone. An unsignaled, unpredictable threat (footshock) is introduced into the foraging zone after a baseline activity and consumption period to model the risk of predation, which is then removed for a final extinction assessment. This longitudinal design, wherein data from a multitude of variables are collected automatically and continuously for 23 h/day over several weeks to months, affords a more holistic understanding of the effects of fear and anxiety on day-to-day behavior. Also, we discuss its general benefits relevant to other topics in neuroscience research, its limitations, and present data demonstrating for the first time The Risky Closed Economy’s viability in mice.
Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance

Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.
AIR-BAGEL: An Interactive Root cause-Based Anomaly Generator for Event Logs

—We describe AIR-BAGEL, a tool 12 to generate pseudo-real trace-level anomalies in event logs. Anomalies to be injected are deﬁned by their root cause, i.e., resource behaviour or system malfunctioning. For each root cause, several anomaly types can be speciﬁed, e.g., deleting, replacing or moving events in a trace. Root causes and anomalies have been modelled based on existing literature on event log cleaning and data quality analysis. AIR-BAGEL addresses the issue of unavailability of labelled real world event logs for developing and evaluating event log cleaning and reconstruction techniques and it represents a step forward compared to current approaches in the literature that simply inject different types of anomalies randomly in event logs.
Adult Measures of General Health and Health‐Related Quality of Life

s was very high (intraclass correlation coefficient 0.996). Any disagreements were resolved through a discussion between all authors. Given the large number of potentially relevant measures identified, only those measures that were used at least 4 times in the screened abstracts were selected for this review. Consequently, this report provides reviews of 4 generic health profiles: the Medical Outcomes Study Short Form 36 and Short Form 12 Health Surveys, the Nottingham Health Profile, and the Sickness Impact Profile, and 4 health utility measures: the Medical Outcomes Study Short Form 6D, the Health Utilities Index Mark 3, Quality of Well-Being Scale (selfadministered), and the Assessment of Quality of Life Scale. Although the EuroQol 5-domain is also frequently used in rheumatology, the review of this measure is included in Measures of Disability article in this issue. MEDICAL OUTCOMES STUDY SHORT FORM 36-ITEM (SF-36) AND SHORT FORM 12-ITEM (SF-12) HEALTH SURVEYS
Using text classification to improve annotation quality by improving annotator consistency

This paper presents results of experiments in which annotators were asked to selectively reexamine their decisions when those decisions seemed inconsistent. The annotation task was binary topic classification. To operationalize the concept of annotation consistency, a text classifier was trained on all manual annotations made during a complete first pass and then used to automatically recode every document. Annotators were then asked to perform a second manual pass, limiting their attention to cases in which their first annotation disagreed with the text classifier. On average across three annotators, each working independently, 11% of first pass annotations were reconsidered, 46% of reconsidered annotations were changed in the second pass, and 71% of changed annotations agreed with decisions made independently by an experienced fourth annotator. The net result was that for an 11% average increase in annotation cost it was possible to increase overall chance corrected agreement with the annotation decisions of an experienced annotator (as measured by kappa) from 0.70 to 0.75.
From flat file to interface: Synthesis of personas and analytics for enhanced user understanding

The persona approach is a communicative design technique for understanding users. With increasing access to analytics data, personas are more and more generated from online user statistics using big data and algorithmic approaches. This integration offers new opportunities to shift personas from flat files of data presentation to that of interactive interfaces for analytics systems. We illustrate this transition towards the concept of “persona as interface” with a persona analytics system, Automatic Persona Generation (APG). In pushing advancements of both persona and analytics conceptualization, development, and use, the APG system presents a multi‐layered “full‐stack” integration. APG affords three levels of user data presentation, which are (a) the conceptual persona, (b) the analytical user metrics, and (c) the foundational user data. Moving to a “personas as interfaces” approach offers the benefits of both personas and analytics systems and addresses some of the shortcomings of each. We provide results from user experiments of and use cases for APG. The result is a better tool than either persona or analytics along for user understanding.
An exploratory study using mindfulness meditation apps to buffer workplace technostress and information overload

Information and Communication Technologies (ICTs) have revolutionized the effectiveness and productivity of business and employees. However, ICTs engender stress, including Technostress and Information Overload (IO), which has negative effects on employees' mental and physical well‐being. Companies increasingly are moving toward further integration of ICTs, expedited greatly by the global effects of the COVID‐19 pandemic. To buffer the effects of workplace stress, the authors propose to investigate how app‐based Mindfulness Meditation (MM) can effectively assist individuals dealing with ICTs' negative effects. A preliminary study, involving one IT employee and the popular app “Calm” was conducted to determine the design of a larger, more robust study.
Healthy diet recommendation via Food‐Nutrition‐Recipe Graph mining

Good nutrition and balance dietary pattern play vital roles of leading a healthy lifestyle. Prior studies showed that the healthy diet can successfully reduce the risk of chronic diseases (e.g., type 2 diabetes and cancer) and bring other well‐documented benefits. Existing food recommendation models, however, often solely rely on user's feedback (e.g., click and purchase data), which aims to optimize Click‐Through Rate (CTR) but ignores the importance of health needs of users. Intuitively, a healthy diet recommendation requires a comprehensive consideration of different kinds of information, such as nutrition, ingredients and cooking methods. In this study, by collecting the data from FoodData Central (FDC), recipe websites, and scientific literature, we construct a heterogeneous graph, Food‐Nutrition‐Recipe Graph (FNRG), by integrating information of nutrition, food (ingredients), and recipes. A random walk based graph mining approach is proposed to meet the health needs of users. Experiments results show that the proposed method can successfully address the health information needs for people who suffer from chronic diabetes.
Characterizing the binding and function of TARP γ8-selective AMPA receptor modulators

α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid(AMPA)-type glutamate receptors (AMPARs) are the predominant excitatory neurotransmitter receptors in the brain, where they mediate synaptic transmission and plasticity. Excessive AMPAR activation leads to diseases such as epilepsy. AMPAR properties are modulated by auxiliary proteins and foremost by the transmembrane AMPAR regulatory proteins (TARPs). These distribute in unique expression patterns across the brain, rendering AMPAR/TARP complexes promising targets for region-specific therapeutic intervention. TARP γ8 is predominantly expressed in the forebrain and is enriched in the hippocampus, a region associated with temporal lobe epilepsy. Recent high-throughput medicinal chemistry screens have identified multiple promising compounds that selectively target AMPARs associated with γ8 and hold promise for epilepsy treatment. However, how these modulators target the receptor complex is currently unknown. Here, we use a combination of ligand docking, molecular dynamics simulations, and electrophysiology to address this question. We identify a conserved oxindole isostere, shared between three structurally diverse modulators (LY-3130481, JNJ-55511118, and JNJ-61432059) as the major module engaging γ8 by an H-bond to Asn-172 (γ8). The remaining variable region of each molecule likely targets the receptor complex in ligand-selective modes. Functional data reveal parallels in the underlying modulatory action of two prominent compounds. This work will aid development of refined AMPAR epilepsy therapeutics and facilitate to uncover the mechanisms by which TARPs modulate the receptor.
Five Golden Rings to Measure Patient‐Centered Care in Rheumatology

Patientcentered care is a central pillar of highquality care and is crucial to ensure proper health care delivery (1). Patientcentered care is defined as “providing care that is respectful of and responsive to individual patient preferences, needs, and values and ensuring that patient values guide all clinical decisions” (2,3). This definition highlights that shared decision making (SDM) (ie, providing care based on best evidence and patient preferences), is also closely intertwined with patientcentered care (4–6). Implementing patientcentered care and SDM improves patients’ experiences with care, satisfaction, and potential health outcomes, as well as reduces costs (3). Patientcentered care and SDM are recommended in recent clinical practice guidelines in rheumatology (7,8). The American College of Rheumatology (ACR) and the European League Against Rheumatism advocate for SDM for managing rheumatoid arthritis, juvenile idiopathic arthritis, and psoriatic arthritis (7–9). Recent guidelines for treating pain in inflammatory arthritis and osteoarthritis propose that “the assessment and treatment process should be guided by a patientcentered framework” (10). The ACR/ Arthritis Foundation 2019 guide for managing osteoarthritis proposes that “clinicians and patients should engage in SDM that accounts for patients’ values, preferences, and comorbidities” (11). Quality improvement strategies are underway to foster patientcentered care and SDM (12). However, an important milestone in this endeavor will be to easily measure whether and how patientcentered care and SDM happen in daily clinical practice. This is a difficult task because these concepts are multidimensional in nature. In this article, we propose five tools to measure dimensions of patientcentered care in rheumatology. Patientcentered care is a multidimensional concept; a plethora of models have been designed over decades (1). Scholl et al (1) conducted a major initiative to systematically review all dimensions of patientcentered care. The authors characterized 15 dimensions and highlighted that patientcentered care extends beyond dimensions related solely to the clinicianpatient relationship and into health system dimensions, such as integration, access, and continuity of care. This result adds complexity to measuring patientcentered care, as reflected in the numerous possible tools to measure all 15 dimensions (13–17). Fortunately, Scholl and coworkers (18) went on to refine their integrative framework by asking experts to prioritize the most important dimensions. The prioritized domains were 1) patient as a unique person, 2) provision of information to patients, 3) patient involvement in care, 4) patient empowerment, and 5) clinicianpatient communication (18) (Table 1). Patients also considered these dimensions important (19). Accordingly, we propose that the first step in identifying key tools to measure patientcentered care is to focus on measuring clinicianpatient relationship and communication (18). For this review, we selected measures that had the following characteristics:
SVM-Based Multi-Dividing Ontology Learning Algorithm and Similarity Measuring on Topological Indices

Ontology is one of the oldest terminologies in physics and is used to describe the origin and most essential attributes of all things in the world. With the development of contemporary science, ontology was given a specific definition and then introduced into the computer science as a conceptual model to describe the relationship between objects. In the past decade, the algorithms and applications in the ontology-related field have attracted the attention of many scholars. In this work, a support vector machines based multi-dividing ontology learning algorithm is proposed. We pay attention to the similarity of topological indices in chemical graph theory, and apply SVM-based multi-dividing ontology learning algorithms to give some calculation results of similarity between topological indices.
Modeling Evaluations of Low-Level Sounds in Everyday Situations Using Linear Machine Learning for Variable Selection

Human sound evaluations not only depend on the characteristics of the sound but are also driven by factors related to the listener and the situation. Our research aimed to investigate crucial factors influencing the perception of low-level sounds as they—in addition to the often-researched loud-level sounds—might be decisive to people’s quality of life and health. We conducted an online study in which 1,301 participants reported on up to three everyday situations in which they perceived low-level sounds, resulting in a total of 2,800 listening situations. Participants rated the sounds’ perceived loudness, timbre, and tonality. Additionally, they described the listening situations employing situational eight dimensions and reported their affective states. All sounds were then assigned to the categories natural, human, and technical. Linear models suggest a significant difference of annoyance ratings across sound categories for binary loudness levels. The ability to mentally fade-out sound was the most crucial situational variable after valence, arousal, and the situation dimensions positivity and negativity. We ultimately selected the most important factors from a large number of independent variables by applying the percentile least absolute shrinkage and selection operator (Lasso) regularization method. The resulting linear regression showed that this novel machine-learning variable-selection technique is applicable in hypothesis testing of noise effects and soundscape research. The typical problems of overfitting and multicollinearity that occur when many situational and personal variables are involved were overcome. This study provides an extensive database of evaluated everyday sounds and listening situations, offering an enormous test power. Our machine learning approach, whose application leads to comprehensive models for the prediction of sound perception, is available for future study designs aiming to model sound perception and evaluation.