Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Artifacts in Ambulatory Electrodermal Activity Data

Recent wearable devices enable continuous and unobtrusive monitoring of human's physiological parameters, like e.g., electrodermal activity and heart rate, over long periods of time in everyday life settings. Continuous monitoring of these parameters enables the creation of systems able to predict affective states and stress with the goal of providing feedback to improve them. Deployment of such systems in everyday life settings is still complex and prone to errors due to the low quality of the collected data impacted by the presence of artifacts. In this paper we present an automatic approach to detect artifacts in electrodermal activity (EDA) signals collected in-the-wild over long periods of time. To this end we first perform a systematic literature review and compile a set of guidelines for human annotators to label artifacts manually and we use these labels as ground-truth to test our automatic approach. To evaluate our approach, we collect physiological data from 13 participants in-the-wild and two human annotators label 107.56 hours of this data set. We make the data set publicly available to other researchers upon request. Our model achieves a recall of 98% for clean and shape artifacts classification on data collected in-the-wild using leave-one-subject-out cross-validation, which is 42 percentage points higher than the baseline. We show that state of the art approaches do not generalize well when tested with completely in-the-wild data and identify only 17% of the artifacts present in our data set, even after manual adaption. We further test the robustness of our approach over time using leave-one-day-out and achieve very similar performance. We then introduce a new metric to evaluate the quality of EDA segments that considers the impact of not only artifacts in the shape of EDA but also artifacts generated by environmental temperature changes or user's high intensity movement. Our results imply that we can eliminate the need for human annotators or significantly reduce the time they need to label data. Also, our approach can be used in an online manner to automatically detect artifacts in EDA signals.
Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-scale Nutrition Study

Mobile food logging is important but people find it tedious and difficult to do. Our work tackles the challenging aspect of searching a large food database on a small mobile screen. We describe the design of the EaT (Eat and Track) app with its Search-Accelerator to support searches on >6,000 foods. We designed a study to harness data from a large nutrition study to provide insights about the use and user experience of EaT. We report the results of our evaluation: a 12-participant lab study and a public health research field study where 1,027-participants entered their nutrition intake for 3 days, logging 30,715 food items. We also analysed 1,163 user-created food entries from 670 participants to gain insights about the causes of failures in the food search. Our core contributions are: 1) the design and evaluation of EaT's support for accurate and detailed food logging; 2) our study design that harnesses a nutrition research study to provide insights about timeliness of logging and the strengths and weaknesses of the search; 3) new performance benchmarks for mobile food logging.
Exploring an augmented reality social learning game for elementary school students

Social learning games can enhance students' learning engagement in the field of STEM. However, students do not always have the chance to experience social interactions during their learning activities. Augmented reality (AR) games have the ability to enable social interactions among students and allow them to interact with the virtual content while engaging in natural communication in the real world. However, little is still known on how to design social AR games for learning. In this paper, we presented a textbook-based AR social learning game for elementary school students to practice math together. We designed and developed the game concepts based on previous studies and co-design sessions and conducted a user study to explore how students would behave and interact with each other in the game. Our findings extend the understanding of students' social patterns in both collaboration and competition conditions under AR settings. Based on the findings, we propose design implications for designing AR social learning games in the future.
Investigating children's spontaneous gestures when programming using TUIs and GUIs

Spontaneous gestures produced during mathematics learning have been widely studied, however, research on the role of gesture in computing education is limited. This paper presents an investigation into children's use of spontaneous gestures when learning programming using either a tangible user interface (TUI) or a graphical user interface (GUI). The study explored the relationship between spontaneous gestures, interface type and learning outcomes in a programming lesson for primary school students aged 6-7. In the study, 34 participants engaged in a learning activity lasting approximately 37 minutes, using a TUI or a GUI. The study used a between-subjects design, and mixed methods. Pre-test and post-test data were collected, and sessions were video recorded and subsequently coded and analysed. A video analysis scheme, adapted from mathematics education research, was used to code the spontaneous gestures produced during the learning session. We found a statistically significant difference between the mean learning gains of high-frequency gesturers and low-frequency gesturers, with the top quartile showing significantly greater learning gains. There was no significant difference in the frequency of gestures between interface types. A qualitative analysis of representational gestures showed that some children use spontaneous hand gestures to demonstrate abstract computational concepts, providing evidence for the embodiment of children's offline thinking in the computing domain.
Teachers' perspectives on social robots in education: an exploratory case study

Research has shown that social robots carry potential to be used in an educational setting. The possibility to have multiple roles carried out by one tool does not only instigate curiosity but also raises concerns. Whereas practical challenges get tackled by rapid technological advances, the moral challenges often get overlooked. In this study, we examined the moral values related to educational robots from a teachers' perspective, by first identifying concerns and opportunities, and subsequently linking them to (moral) values. We conducted focus group sessions with teachers to explore their perceptions regarding concerns and opportunities related to educational robots. Teachers voiced several considerations ranging from having concerns towards privacy to seeing opportunities in adding friendship and attachment a robot could emanate.
Using tabletop robots to promote inclusive classroom experiences

Geometry and handwriting rely heavily on the visual representation of basic shapes. It can become challenging for students with visual impairments to perceive these shapes and understand complex spatial constructs. For instance, knowing how to draw is highly dependent on spatial and temporal components, which are often inaccessible to children with visual impairments. Hand-held robots, such as the Cellulo robots, open unique opportunities to teach drawing and writing through haptic feedback. In this paper, we investigate how these tangible robots could support inclusive, collaborative learning activities, particularly for children with visual impairments. We conducted a user study with 20 pupils with and without visual impairments, where they engaged in multiple drawing activities with tangible robots. We contribute novel insights on the design of children-robot interaction, learning shapes and letters, children engagement, and responses in a collaborative scenario that address the challenges of inclusive learning.
A Novel ERF Transcription Factor, ZmERF105, Positively Regulates Maize Resistance to Exserohilum turcicum

The ethylene response factor (ERF) plays a crucial role in plant innate immunity. However, the molecular function of ERF in response to Exserohilum turcicum (E. turcicum) remains unknown in maize. In this study, a novel ERF gene, designated as ZmERF105, was firstly isolated and characterized. The ZmERF105 protein contains an APETALA2/ETHYLENE RESPONSIVE FACTOR (AP2/ERF) domain and a conserved LSPLSPHP motif in its C-terminal region. ZmERF105 protein was exclusively localized to the nucleus. ZmERF105 expression responded to E. turcicum treatment. Yeast one-hybrid and transcription activity assays revealed that ZmERF105 is an activator of transcription and binds to GCC-box elements. Over-expression of ZmERF105 was shown to increase maize resistance against E. turcicum, and erf105 mutant lines displayed opposite phenotype. Moreover, the activities of superoxide dismutase (SOD) and peroxidase (POD) in the ZmERF105 over-expression lines were markedly higher than in the wild-type maize lines (WT) after infection with E. turcicum, and were compromised in the erf105 mutant lines. Simultaneously, ZmERF105 over-expression lines enhanced the expression of several pathogenesis-related (PR) genes, including ZmPR1a, ZmPR2, ZmPR5, ZmPR10.1, and ZmPR10.2 after infection with E. turcicum. In contrast, the expression of PR genes was reduced in erf105 mutant lines. Our work reveals that ZmERF105 as a novel player of the ERF network and positively regulates the maize resistance response to E. turcicum.
Gravitational Waves From Binary Black Hole Mergers: Modeling and Observations

Only a few years after the first detection of gravitational waves from coalescing stellar-mass black holes, the field of gravitational-wave astronomy is now firmly established after the detection of several more. These discoveries have opened up a new window onto the universe, which allows us to probe gravity and astrophysics in some of the most extreme environments in the universe. The detection and, in particular, the subsequent inference of the binaries' properties rely heavily on theoretical models of the signals. In this review, we first discuss the techniques used to model the gravitational-wave signals from coalescing black holes, before we summarize the observations made to date. We conclude with a brief outlook onto the prospects for binary black hole observations in the future.
COVID-19: An Appeal for an Intersectoral Approach to Tackle With the Emergency

The knowledge of disease determinants is a pre-requisite for disease prevention. Infectious diseases determinants can be classified in three ways, as: primary or secondary; intrinsic or extrinsic; and associated with host, agent, or environment. In the specific case of COVID-19 several of these determinants are currently unknown leading to difficulties in public health approach to this disease. In this paper, we attempt to address several of the current gaps on COVID-19 using a systematic analysis on recent findings and some preliminary knowledge on animal coronaviruses. A discussion on the impact of COVID-19 determinants in disease prevention and control will be based on the Environmental Change and Infectious Disease (EnVID) systemic framework to address several challenges that may affect the control of the SARS- CoV-2 pandemic spread both in industrialized and in developing Countries.
Modeling δD-δ18O Steady-State of Well-Sealed Perennially Ice-Covered Lakes and Their Recharge Source: Examples From Lake Untersee and Lake Vostok, Antarctica

Perennially ice-covered lakes that are tightly sealed from the atmosphere represent a unique group of polar lakes. In these lakes, the δD-δ18O evolution of the water column and steady-state conditions are controlled by rates of recharge and freezing at the bottom of the ice cover. We developed a recursive model (FREEZCH9) that takes into account the changing salinity in the water column as a result of freezing and mixes the recharge water to the residual water in well-sealed perennially ice-covered lakes. Our model is tested against datasets from Lake Vostok and is used to assess the δD-δ18O mass balance of Lake Untersee and evaluate if the lake is in isotopic steady-state. Our FREEZCH9 simulations fit well with the predicted δD-δ18O values of Lake Vostok’s upper water column and the overlying accreted ice. Simulations with FREEZCH9 also suggests that Lake Untersee is in isotopic steady-state and that its two input sources (i.e., subaqueous terminus melting of the Anuchin Glacier and subglacial meltwater) have similar δD-δ18O composition. Our modeling demonstrates that Lake Untersee most likely did not receive additional input from surface streams during the last 300–500 years. FREEZCH9 may be also used to determine if any groundwater systems of the McMurdo Dry Valleys are fully or partially recharged by subglacial lakes.
Composite Hydrogels in Three-Dimensional in vitro Models

3-dimensional (3D) in vitro models were developed in order to mimic the complexity of real organ/tissue in a dish. They offer new possibilities to model biological processes in more physiologically relevant ways which can be applied to a myriad of applications including drug development, toxicity screening and regenerative medicine. Hydrogels are the most relevant tissue-like matrices to support the development of 3D in vitro models since they are in many ways akin to the native extracellular matrix (ECM). For the purpose of further improving matrix relevance or to impart specific functionalities, composite hydrogels have attracted increasing attention. These could incorporate drugs to control cell fates, additional ECM elements to improve mechanical properties, biomolecules to improve biological activities or any combinations of the above. In this Review, recent developments in using composite hydrogels laden with cells as biomimetic tissue- or organ-like constructs, and as matrices for multi-cell type organoid cultures are highlighted. The latest composite hydrogel systems that contain nanomaterials, biological factors, and combinations of biopolymers (e.g., proteins and polysaccharide), such as Interpenetrating Networks (IPNs) and Soft Network Composites (SNCs) are also presented. While promising, challenges remain. These will be discussed in light of future perspectives toward encompassing diverse composite hydrogel platforms for an improved organ environment in vitro.
Amygdala Nuclei Volumes Are Selectively Associated With Social Network Size in Homeless and Precariously Housed Persons

Objective: The amygdala is a brain region comprised of a group of functionally distinct nuclei that play a central role in social behavior. In homeless and precariously housed individuals, high rates of multimorbidity, and structural aspects of the environment may dysregulate social functioning. This study examined the neurobiological substrates of social connection in homeless and precariously housed persons by examining associations between amygdala nuclei volumes and social network size. Methods: Sixty participants (mean age 43.6 years; 73.3% male) were enrolled from an ongoing study of homeless and precariously housed adults in Vancouver, Canada. Social network size was assessed using the Arizona Social Support Interview Schedule. Amygdala nuclei volumes were extracted from anatomic T1-weighted MRI data. The central and basolateral amygdala nuclei were selected as they are implicated in anxiety-related and social behaviors. The hippocampus was included as a control brain region. Multivariable regression analysis investigated the relationship between amygdala nuclei volumes and social network size. Results: After controlling for age, sex, and total brain volume, individuals with the larger amygdala and central nucleus volumes had a larger network size. This association was not observed for the basolateral amygdala complex, though subsequent analysis found the basal and accessory basal nuclei of the basolateral amygdala were significantly associated with social network size. No association was found for the lateral amygdala nucleus or hippocampus. Conclusions: These findings suggest that select amygdala nuclei may be differentially involved in the social connections of persons with multimorbid illness and social marginalization.
Immunosuppressive IDO in Cancer: Mechanisms of Action, Animal Models, and Targeting Strategies

Indoleamine 2, 3-dioxygenase 1 (IDO; IDO1; INDO) is a rate-limiting enzyme that metabolizes the essential amino acid, tryptophan, into downstream kynurenines. Canonically, the metabolic depletion of tryptophan and/or the accumulation of kynurenine is the mechanism that defines how immunosuppressive IDO inhibits immune cell effector functions and/or facilitates T cell death. Non-canonically, IDO also suppresses immunity through non-enzymic effects. Since IDO targeting compounds predominantly aim to inhibit metabolic activity as evidenced across the numerous clinical trials currently evaluating safety/efficacy in patients with cancer, in addition to the recent disappointment of IDO enzyme inhibitor therapy during the phase III ECHO-301 trial, the issue of IDO non-enzyme effects have come to the forefront of mechanistic and therapeutic consideration(s). Here, we review enzyme-dependent and -independent IDO-mediated immunosuppression as it primarily relates to glioblastoma (GBM); the most common and aggressive primary brain tumor in adults. Our group's recent discovery that IDO levels increase in the brain parenchyma during advanced age and regardless of whether GBM is present, highlights an immunosuppressive synergy between aging-increased IDO activity in cells of the central nervous system that reside outside of the brain tumor but collaborate with GBM cell IDO activity inside of the tumor. Because of their potential value for the in vivo study of IDO, we also review current transgenic animal modeling systems while highlighting three new constructs recently created by our group. This work converges on the central premise that maximal immunotherapeutic efficacy in subjects with advanced cancer requires both IDO enzyme- and non-enzyme-neutralization, which is not adequately addressed by available IDO-targeting pharmacologic approaches at this time.
Plasma Metabolic Profiling of Human Thyroid Nodules by Gas Chromatography-Mass Spectrometry (GC-MS)-Based Untargeted Metabolomics

One of the challenges in the area of diagnostics of human thyroid cancer is a preoperative diagnosis of thyroid nodules with indeterminate cytology. Herein, we report an untargeted metabolomics analysis to identify circulating thyroid nodule metabolic signatures, to find new novel metabolic biomarkers. Untargeted gas chromatography-quadrupole-mass spectrometry was used to ascertain the specific plasma metabolic changes of thyroid nodule patients, which consisted of papillary thyroid carcinoma (PTC; n = 19), and multinodular goiter (MNG; n = 16), as compared to healthy subjects (n = 20). Diagnostic models were constructed using multivariate analyses such as principal component analysis, orthogonal partial least squares-discriminant analysis, and univariate analysis including One-way ANOVA and volcano plot by MetaboAnalyst and SIMCA software. Because of the multiple-testing issue, false discovery rate p-values were also computed for these functions. A total of 60 structurally annotated metabolites were subjected to statistical analysis. A combination of univariate and multivariate statistical analyses revealed a panel of metabolites responsible for the discrimination between thyroid nodules and healthy subjects, with variable importance in the projection (VIP) value greater than 0.8 and p-value less than 0.05. Significantly altered metabolites between thyroid nodules versus healthy persons are those associated with amino acids metabolism, the tricarboxylic acid cycle, fatty acids, and purine and pyrimidine metabolism, including cysteine, cystine, glutamic acid, α-ketoglutarate, 3-hydroxybutyric acid, adenosine-5-monophosphate, and uracil, respectively. Further, sucrose metabolism differed profoundly between thyroid nodule patients and healthy subjects. Moreover, according to the receiver operating characteristic (ROC) curve analysis, sucrose could discriminate PTC from MNG (area under ROC curve value = 0.92). This study enhanced our understanding of the distinct metabolic pathways associated with thyroid nodules, which enabled us to distinguish between patients and healthy subjects. In addition, our study showed extensive sucrose metabolism in the plasma of thyroid nodule patients, which provides a new metabolic signature of the thyroid nodule’s tumorigenesis. Accordingly, it suggests that sucrose can be considered as a circulating biomarker for differential diagnosis between malignancy and benignity in indeterminate thyroid nodules.
Structural and Functional Characteristics of Two Molecular Variants of the Nitrogen Sensor PII in Maritime Pine

High levels of nitrogen are stored as arginine during the last stages of seed formation in maritime pine (Pinus pinaster Aiton). The protein sensor PII regulates the feedback inhibition of arginine biosynthesis through interaction with the key enzyme N-acetylglutamate kinase (NAGK). In this study, the structural and functional characteristics of PII have been investigated in maritime pine to get insights into the regulation of arginine metabolism. Two different forms of PII have been identified, PpPIIa and PpPIIb, which differ in their amino acid sequence and most likely correspond to splicing variants of a single gene in the pine genome. Two PII variants are also present in other pine species but not in other conifers such as spruces. PpPIIa and PpPIIb are trimeric proteins for which structural modeling predicts similar tridimensional protein core structures. Both are located in the chloroplast, where the PII-target enzyme PpNAGK is also found. PpPIIa, PpPIIb, and PpNAGK have been recombinantly produced to investigate the formation of NAGK-PII complexes. The interaction of PpPIIa/PpPIIb and PpNAGK may be enhanced by glutamine and contribute to relieve the feedback inhibition of PpNAGK by arginine. Expression analysis of PpPII genes revealed that PpIIa transcripts were predominant during embryogenesis and germination. The potential roles of PpPIIa and PpPIIb in the regulation of arginine metabolism of maritime pine are discussed.
Conserving African Ungulates Under Climate Change: Do Communal and Private Conservancies Fill Gaps in the Protected Area Network Effectively?

Throughout Africa, conservancies under community and private management are becoming increasingly important as a complement to the protection offered by the established core network of protected areas (PAs), which are largely under national management. However, opportunities for creating new conservation areas are restricted by increasing demand on land use by growing human populations, and it is unclear how effectively the current protected area network captures spatial priorities for conservation. Taking into account climate-induced range-shifts, we first identified spatial priorities for antelope conservation in Africa by gap analysis of the network of PAs listed with an IUCN category in the World Database for Protected Areas. For three countries from which information were available, we then assessed to what extent the gaps identified were covered by PAs not listed with an IUCN category, for the latter making a distinction between whether management was referred to as community-based or not. The results showed limited overlap, suggesting that the success of community-based and privately managed PAs in covering spatial priorities from a continent-wide perspective could be increased by more strategic land use planning at the national level.