Miracle-dz/UniFAR-FTUs huggingface dataset:
The Dynamics of Functional Brain Networks Associated With Depressive Symptoms in a Nonclinical Sample

Brain function depends on the flexible and dynamic coordination of functional subsystems within distributed neural networks operating on multiple scales. Recent progress has been made in the characterization of functional connectivity (FC) at the whole-brain scale from a dynamic, rather than static, perspective, but its validity for cognitive sciences remains under debate. Here, we analyzed brain activity recorded with functional Magnetic Resonance Imaging from 71 healthy participants evaluated for depressive symptoms after a relationship breakup based on the conventional Major Depression Inventory (MDI). We compared both static and dynamic FC patterns between participants reporting high and low depressive symptoms. Between-group differences in static FC were estimated using a standard pipeline for network-based statistic (NBS). Additionally, FC was analyzed from a dynamic perspective by characterizing the occupancy, lifetime, and transition profiles of recurrent FC patterns. Recurrent FC patterns were defined by clustering the BOLD phase-locking patterns obtained using leading eigenvector dynamics analysis (LEiDA). NBS analysis revealed a brain subsystem exhibiting significantly lower within-subsystem correlation values in more depressed participants (high MDI). This subsystem predominantly comprised connections between regions of the default mode network (i.e., precuneus) and regions outside this network. On the other hand, LEiDA results showed that high MDI participants engaged more in a state connecting regions of the default mode, memory retrieval, and frontoparietal network (p-FDR = 0.012); and less in a state connecting mostly the visual and dorsal attention systems (p-FDR = 0.004). Although both our analyses on static and dynamic FC implicate the role of the precuneus in depressive symptoms, only including the temporal evolution of BOLD FC helped to disentangle over time the distinct configurations in which this region plays a role. This finding further indicates that a holistic understanding of brain function can only be gleaned if the temporal dynamics of FC is included.
Boardgames and Computational Thinking: how to identify games with potential to support CT in the classroom

Boardgames exist that explicitly address Computational Thinking (CT for short) concepts and practices. Some are actual games, while others are more akin to gamified learning activities. And since CT has been formalized only recently, many existing boardgames unknowingly might support aspects of CT. To help educators and game practitioners navigate this complex landscape, we analyze a selected sample of analog games, and propose to categorize their features with respect to CT concepts and practices. The main contribution of this paper is a novel way to identify potential CT-relevant games, that leverages on the authors’ experience with digital and analog games, playful and game-based learning. Although limited, this approach appears promising and practical for CT teachers and game designers interested in adapting existing games to the classroom or developing better CT-supporting boardgames.
Tracking the Human Papillomavirus Vaccine Risk Misinformation: An Explorative Study to Examine How the Misinformation Has Spread in User-Generated Content

The goal of the study is to identify and track the dissemination patterns of true and false information about Human Papillomavirus (HPV) vaccines on twitter from 2013 to 2017. We applied a patient-driven HPV-vaccine risk lexicon combining with natural language processing (NLP) models and network analyses to explore the spread of verified true and false HPV-vaccine information. The explorative analyses showed different dissemination patterns of the most popular verified true and false messages about HPV vaccines, which false messages went viral than the true messages. Implications on detecting false HPV-vaccine related information were also discussed.
Informing the Design of Mental Health Smartphone Apps for Adolescents

The purpose of this work is to obtain a set of best practices for the design of mental health smartphone applications (MHSAs) for an adolescent user base. The authors carried out a systematic literature review combined with a bibliographic coupling analysis to identify relevant literature, and then a thematic analysis of the literature to identify core principles. These efforts led to a framework of nine general principles for the creation and design of MHSAs. The nine core principles identified were: Using evidence-based treatments, encouraging intrinsic motivation in users, using extrinsic behavior triggers, providing professional support, enabling social support, engaging users during the development process, using user-centered design principles, including a parental mode, and protecting patients’ privacy.
Coordinated path-following control for networked unmanned surface vehicles

In this article, the coordinated path-following control problem for networked unmanned surface vehicles is investigated. The communication network brings time delays and packet dropouts to the fleet, which will have negative effects on the control performance of the fleet. To attenuate the negative effects, a novel networked predictive control scheme is proposed. By introducing the predictive error into the control scheme, the proposed control strategy admits some advantages compared with existing networked predictive control strategies, for example, a degree of robustness to disturbances, lower requirements for the computing capacity of the onboard processors, high flexibility in controller design, and so on. Conditions that guarantee the control performance of the overall system are derived in the theoretical analysis. At last, experiments on hovercraft test beds are implemented to verify the effectiveness of the proposed control scheme.
Ikaros: A framework for controlling robots with system-level brain models

Ikaros is an open framework for system-level brain modeling and real-time robot control. Version 2 of the system includes a range of computational components that implements various algorithms and methods ranging from models of neural circuits to control systems and hardware interfaces for robot. Ikaros supports the design and implementation of large-scale computation models using a flow programming paradigm. Version 2 includes a number of new features that support complex networks of hierarchically arranged components as well as a web-based interactive editor. More than 100 persons have contributed to the code base and over 100 scientific publications report on work that has used Ikaros for simulations or robot control.
Ghanaian science and mathematics teachers’ beliefs about gifted education strategies

Ghana is a rapidly developing West African country whose goal is to build a prosperous economy reliant on innovation and technology. Identifying and developing talent in the school system will play a role in achieving this goal; however, there is limited research on the status of gifted education and talent development in Ghana. This study investigated ten Junior High school mathematics and science teachers’ beliefs about giftedness and the strategies they proposed to develop gifted students’ potential into talent. The study drew on data from semi-structured interviews and lesson plans. The findings revealed that teachers had scant knowledge about giftedness and appropriate gifted education strategies. Respondents misconstrued differentiation and acceleration strategies and disregarded their use in developing the gifted. The study highlights the need for the development of a formal policy on gifted education, and the implementation of teacher education programmes that address teachers’ beliefs and knowledge about the gifted and gifted education strategies.
Cellular Therapy for Cardiovascular Disease Part 2—Delivery of Cells and Clinical Experience

Introduction Cell-based therapy is an exciting, emergent treatment frontier for cardiovascular repair that has seen a remarkable evolution over the last decade. Clinical studies have been published using different types of adult cells to treat the problems of acute and established myocardial infarction (MI), refractory coronary ischemia and both ischemic and non-ischemic cardiomyopathy. In this review, we will initially address the key issue of cell delivery to the myocardium, followed by an overview of past and present clinical experience in this fi eld.
Feeding of Low Birth Weight Neonates

Nutrition in this critical period is essential for immediate outcomes such as changes in anthropometry (weight, length, and head circumference) as well as adverse effects like necrotizing enterocolitis. Nutrition also affects long-term adverse outcomes such as developmental delay, diplegic cerebral palsy, and death during infancy and childhood. This review has looked for the effects of nutrition in these areas. Further long-term outcomes like risk of adult onset diseases like myocardial infarction and stroke may also be related to the nutrition of the LBW neonate but have not been included in this review. The guideline has been developed using standard methods adapted by National Neonatology Forum in accordance with the process described in the World Health Organization handbook for guideline development. The detailed methods are described elsewhere in this compilation of guidelines. Table 1 summarizes the recommendations for practice questions prioritized by the guideline development group in consultation with a wider group of National Neonatology Forum members. The recommendations made by this group are summarized in Table 1.
Designing a Cardiovascular Health App for Citizens Returning from Incarceration: Where You Start May Not Be Where You End Up

A mobile health (mHealth) smartphone app can help persons returning from incarceration better manage cardiovascular disease (CVD) and CVD risk factors during their re-entry period. A successful intervention to educate and increase self-efficacy in this population around cardiovascular health has potential to produce broad population health benefits. A participatory design (PD) approach was used to define the features and functionality of such an intervention in the form of a smart phone app. The PD process forced the research team to view CV health in the broader context of other challenges that RCs face day to day. It also led to a broadening of the process to include other stakeholders that work closely with RCs, and to explore how these challenges affect the re-entry process. Ultimately, PD led to an app design that emphasized a common problem-solving approach that can be applied to a broad range of problems. An interactive prototype of the app’s interface was assessed by a sample of RCs, users, with consensus that the problem-areas were appropriate and that the app was easy to use and to navigate. A quantitative assessment with the System Usability Survey instrument, yielded an average score of 81.5, placing it in the 90th percentile.
The European Trade Union Federations within the European polity: ETUFs and international trade union activity

This article examines the European Trade Union Federations’ (ETUFs) role within the European polity in representing the interests of their affiliates vis-à-vis decision-makers at European level. In order to influence processes at European level, however, ETUFs need to aggregate and coordinate the often heterogeneous interests of their affiliates. This dual focus of the ETUFs’ activities is captured in the article by using the concept of the ‘logic of membership’ and ‘logic of influence’ to investigate how changes in their internal and external environment have affected the ETUFs’ capacity to act within the institutional structures and decision-making processes that constitute the European polity. A key finding of the article is that the European Commission’s renewed focus on strengthening the social dimension in principle opens up new opportunities for ETUFs to increase their influence at European level. The analysis, however, also shows that this is only possible if the ETUFs manage to mobilise the support of their affiliates for joint European strategies. This in turn requires national trade unions to overcome their tendency to retreat to the national level to cope with transnational challenges.
Angiotensin II type 1 receptor variants alter endosomal receptor–β-arrestin complex stability and MAPK activation

The angiotensin II (AngII) type 1 receptor (AT1R), a member of the G protein–coupled receptor (GPCR) family, signals through G proteins and β-arrestins, which act as adaptors to regulate AT1R internalization and mitogen-activated protein kinase (MAPK) ERK1/2 activation. β-arrestin–dependent ERK1/2 regulation is the subject of important studies because its spatiotemporal control remains poorly understood for many GPCRs, including AT1R. To study the link between β-arrestin–dependent trafficking and ERK1/2 signaling, we investigated three naturally occurring AT1R variants that show distinct receptor–β-arrestin interactions: A163T, T282M, and C289W. Using bioluminescence resonance energy transfer (BRET)–based and conformational fluorescein arsenical hairpin–BRET sensors coupled with high-resolution fluorescence microscopy, we show that all AT1R variants form complexes with β-arrestin2 at the plasma membrane and efficiently internalize into endosomes upon AngII stimulation. However, mutant receptors imposed distinct conformations in β-arrestin2 and differentially impacted endosomal trafficking and MAPK signaling. Notably, T282M accumulated in endosomes, but its ability to form stable complexes following internalization was reduced, markedly impairing its ability to co-traffic with β-arrestin2. We also found that despite β-arrestin2 overexpression, T282M's and C289W's residency with β-arrestin2 in endosomes was greatly reduced, leading to decreased β-arrestin–dependent ERK1/2 activation, faster recycling of receptors to the plasma membrane, and impaired AngII-mediated proliferation. Our findings reveal that naturally occurring AT1R variants alter the patterns of receptor/β-arrestin2 trafficking and suggest conformationally dependent β-arrestin–mediated MAPK activation as well as endosomal receptor–β-arrestin complex stabilization in the mitogenic response of AT1R.
Artificial potential field-based swarm finding of the unmanned surface vehicles in the dynamic ocean environment

Using multiple unmanned surface vehicle swarms to implement tasks cooperatively is the most advanced technology in recent years. However, how to find which swarm the unmanned surface vehicle belongs to is a meaningful job. So, this article proposed an artificial potential field-based swarm finding algorithm, which applies the potential field force directly to unmanned surface vehicles and leads them to their belonging swarm quickly and accurately. Meanwhile, the proposed algorithm can also maintain the formation stable while following the desired path. Based on the swarm finding algorithm, the artificial potential field-based collision avoidance method and the International Regulations for Preventing Collisions at Sea-based dynamic collision avoidance strategy are applied to the swarm control of multi-unmanned surface vehicles to enhance the performance in the dynamic ocean environment. Methods in this article are verified through numerical simulations to illustrate the feasibility and effectiveness of proposed schemes.
Safety Requirements and Risk Analysis Related to Insulin Infusion Pumps: A Systematic Review

Diabetes mellitus is a metabolic disease characterized by high levels of blood glucose; in severe cases of diabetes, insulin infusion pumps are prescribed as treatment. Infusion pumps are automated devices with the purpose to simulate the functioning of the organism of a healthy person regarding the production and delivery of insulin, as this is a medical device, its operation must be reliable enough to avoid any risk to the patient’s health and well-being. The aim of this article is to conduct a systematic literature review to investigate and perceive what are the risks implicated for the user of insulin infusion pumps also understand how safety and functional requirements for these devices have been treated, developed and studied.
Understanding the focal points and sentiment of learners in MOOC reviews: A machine learning and SC-LIWC-based approach

Despite the popularity of massive open online courses (MOOCs), only a small portion of the course participants successfully complete the course. The low completion rate can be partially attributed to the mismatch between the participants' expectations and value delivered by the courses. Therefore, this study leverages MOOC reviews to investigate the focal point and sentiment of the learners by combining machine learning techniques and statistical analysis. Several text mining methods (ie, simplified Chinese-linguistic inquiry and word count dictionary, word embeddings, and bidirectional long short-term memory model) are combined to automatically extract the emotional and cognitive aspects, review focal point, and sentiment from the learner discourse. Multiple linear regression (MLR) analysis is performed to examine the relationships between the learner sentiment and the extracted content features. Using a set of real data from NetEase online open courses, our results reveal that the MOOC reviews mostly pertain to teaching and platform rather than the course content. Furthermore, the social process and personal concerns appear more frequently in the learner discourse. Overall, the learners exhibit positive attitudes towards teaching and platform and negative attitudes towards issues related to the course content. This study contributes to the literature regarding the MOOC research methodologies and provides a deeper understanding of the learner discourse behaviour in MOOCs.
Docking Studies of Pyrimidine Analogues on Geranyl Transferase

Introduction Rab proteins are small GTPases involved in the regulation of vesicular traffic [1]. To be functional, they have to undergo a post-translational modification in which one or two isoprenoid geranylgeranyl groups are attached to their C termini [2]. Rab geranylgeranyltransferase (RabGGTase or GGTase type II) catalyzes this reaction. RabGGTase belongs to a family of prenyltransferases that includes farnesyltransferase (FTase) and geranylgeranyltransferase type I. These enzymes are highly conserved in sequence and structure and possess a single active site [3]. Pyrimidines are an important class of heterocyclic compounds, which possess a wide range of biological activities such as anticancer [4,5], antibacterial [6], anti-inflammatory [7], antiviral [8], antitubercular [9], antihypertensive [10] and anticonvulsant [11] activities. The presence of 4-OCH3 and 2-NO2 and 3-NO2 substitutions on pyrimidines shows significant increase in antitubercular activity [12]. The bromo substituted, 2-hydroxy phenyl substituted pyrimidines also shows good antihistaminic activity [13]. To know the possibility of binding of RabGGTase protein to pyrimidine analogues is fascinating and molecular docking is frequently used to predict the binding orientation. Docking is used to bring out new ligands for biological targets with a known 3D structure [14]. Docking studies of the compounds were performed using GLIDE (Schrödinger, LLC, Portland) [15,16].
Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Most research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning-task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. Practitioner Notes What is already known about this topic Capturing and measuring learners? engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.
An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration

Research on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach. ,An enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from Twitter for a period of three months, resulting in 694,141 tweets. Of these, 2,500 tweets were segregated into two datasets for evaluation purposes after filtering and pre-processing. ,The enhanced approach outperformed ten online sentiment analysis tools with an overall accuracy of 81.4 and 82.3% for dataset 1 and 2, respectively as opposed to ten other sentiment analysis tools. ,The study is novel in the sense that data pertaining to a social issue were used instead of products and services, which require different mechanism due to the nature of the issue itself.
Different Lexicon-Based Approaches to Emotion Identification in Portuguese Tweets (Short Paper)

This paper presents the existing literature on the identiﬁcation of emotions and describes various lexica-based approaches and translation strategies to identify emotions in Portuguese tweets. A dataset of tweets was manually annotated to evaluate our classiﬁer and also to assess the diﬃculty of the task. A lexicon-based approach was used in order to classify the presence or absence of eight diﬀerent emotions in a tweet. Diﬀerent strategies have been applied to reﬁne and improve an existing and widely used lexicon, by means of automatic machine translation and aligned word embeddings. We tested six diﬀerent classiﬁcation approaches, exploring diﬀerent ways of directly applying resources available for English by means of diﬀerent translation strategies. The achieved results suggest that a better performance can be obtained both by improving a lexicon and by directly translating tweets into English and then applying an existing English lexicon