Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Approaches to Unobtrusive Sensing of Humans

The increasing amount of human-related and/or human-originated data in current systems is both an opportunity and a challenge. Nevertheless, despite relying on the processing of large amounts of data, most of the so-called smart systems that we have nowadays merely consider humans as sources of data, not as system beneficiaries or even active “components. ” For truly smart systems, we need to create systems that are able to understand human actions and emotions, and take them into account when deciding on the system behavior. Naturally, in order to achieve this, we first have to empower systems with human sensing capabilities, possibly in ways that are as inconspicuous as possible. In this context, in this article we survey existing approaches to unobtrusive monitorization of human beings, namely, of their activity, vital signs, and emotional states. After setting a taxonomy for human sensing, we proceed to present and analyze existing solutions for unobtrusive sensing. Subsequently, we identify and discuss open issues and challenges in this area. Although there are surveys that address some of the concerned fields of research, such as healthcare, human monitorization, or even the use-specific techniques like channel state information or image recognition, as far as we know this is the first comprehensive survey on unobtrusive sensing of human beings.
Trustworthy Artificial Intelligence: A Review

Artificial intelligence (AI) and algorithmic decision making are having a profound impact on our daily lives. These systems are vastly used in different high-stakes applications like healthcare, business, government, education, and justice, moving us toward a more algorithmic society. However, despite so many advantages of these systems, they sometimes directly or indirectly cause harm to the users and society. Therefore, it has become essential to make these systems safe, reliable, and trustworthy. Several requirements, such as fairness, explainability, accountability, reliability, and acceptance, have been proposed in this direction to make these systems trustworthy. This survey analyzes all of these different requirements through the lens of the literature. It provides an overview of different approaches that can help mitigate AI risks and increase trust and acceptance of the systems by utilizing the users and society. It also discusses existing strategies for validating and verifying these systems and the current standardization efforts for trustworthy AI. Finally, we present a holistic view of the recent advancements in trustworthy AI to help the interested researchers grasp the crucial facets of the topic efficiently and offer possible future research directions.
Postdiapause reproduction of spotted-wing drosophila (Diptera: Drosophilidae) in realistically simulated cold climatic springtime conditions of Québec, Canada

Abstract Knowing the biology and ecology of Drosophila suzukii (Diptera: Drosophilidae) is important to predict its potential for permanent establishment in cold climatic regions. We studied the reproductive potential of postdiapause male and female flies collected in lowbush blueberry fields in late season over two years. After being submitted to a six-month cold-winter regime, survivors were exposed to a simulated springtime warming regime to monitor reproductive potential and expected population growth. Overwintered fly survivors of both sexes that were aged up to nine months after field capture reproduced successfully in spring and early summer when mated to nondiapause flies experimentally made available for mating as potential migrant colonisers from lower latitudes. Relative to control nondiapause flies, the lifetime fertility of postdiapause flies was reduced by 84% in females and 70% in males, revealing large fitness costs of reproductive diapause under cold stress. Modelling potential reproduction of overwintered flies in warming conditions using models parametrised for age-dependent fertility rate and diapause status showed that nondiapause migrant flies would be strongly advantaged over locally overwintered postdiapause flies due to precocious reproductive maturity, broader pattern of age-specific fertility, and higher lifetime fertility.
Utilizing social and behavioral change methods in clinical pharmacy initiatives

Pharmacists are often involved in creating, implementing, and evaluating initiatives that aim to improve medication use. Pharmacy initiatives can occur in various settings, can involve interactions with patients or prescribers to optimize medication use, and incorporate information from multiple sources. Similar to pharmacy initiatives, public health initiatives are heterogeneous, complex, and work with the public to improve health behaviors in a variety of settings. Various program planning and health behavior change techniques are utilized in the planning of public health programs. Community‐based public health programs designed using social and behavioral theories have shown improved outcomes when compared with programs not based on theory. Given the overlapping objectives for planning community‐based public health programs and pharmacy initiatives, the use of social and behavioral theories may provide pharmacists with an additional tool to assist in creation of multi‐layered, effective pharmacy initiatives. Data supporting two health behavior theories focused on individuals (Transtheoretical Model and Health Belief Model), and one ecological model (Social Ecological Model) are reviewed and applied to three different pharmacy initiative scenarios. Greater incorporation of behavioral change theories and models may be helpful in creating effective, comprehensive pharmacy initiatives.
Active anemosensing hypothesis: how flying insects could estimate ambient wind direction through sensory integration and active movement

Estimating the direction of ambient fluid flow is a crucial step during chemical plume tracking for flying and swimming animals. How animals accomplish this remains an open area of investigation. Recent calcium imaging with tethered flying Drosophila has shown that flies encode the angular direction of multiple sensory modalities in their central complex: orientation, apparent wind (or airspeed) direction, and direction of motion. Here we describe a general framework for how these three sensory modalities can be integrated over time to provide a continuous estimate of ambient wind direction. After validating our framework using a flying drone, we use simulations to show that ambient wind direction can be most accurately estimated with trajectories characterized by frequent, large magnitude turns. Furthermore, sensory measurements and estimates of their derivatives must be integrated over a period of time that incorporates at least one of these turns. Finally, we discuss approaches that insects might use to simplify the required computations, and present a list of testable predictions. Together, our results suggest that ambient flow estimation may be an important driver underlying the zigzagging maneuvers characteristic of plume tracking animals’ trajectories.
Predicting brain functions from structural connectome using graph neural network

The relationship between brain structure and function remains elusive, amidst the tremendous advances in brain mapping techniques. In this work, we attempt to partially disentangle this relationship by connecting task–evoked functional MRI (fMRI) responses with the underlying structural connectome using graph neural network (GNN). MRI data (n = 1,063) were collected from the Human Connectome Project. We demonstrate that our GNN–based model predicts task–evoked fMRI responses with high fidelity. Using a graph attention mechanism, it is possible to infer the subsets of neighboring cortical regions whose structural connections are important for the prediction of the functional responses of individual cortical regions. Notably, for each cortical region, such subset of neighboring cortical regions is predominantly localized to the ipsilateral hemisphere and much smaller than that with direct structural connections. We found that the higher cognitive functions subserved by the cingulo–opercular, dorsal attention, frontoparietal and default mode clusters may depend on neighboring cortical regions across a wide range of functional brain clusters in the ipsilateral hemisphere, whilst the sensory functions subserved by the visual1 and auditory clusters on neighboring cortical regions across much fewer functional brain clusters.
A recurrent network architecture explains tectal activity dynamics and experience-dependent behaviour

The ongoing activity of neuronal populations represents an internal brain state that influences how sensory information is processed to control behaviour. Conversely, external sensory inputs perturb network dynamics, resulting in lasting effects that persist beyond the duration of the stimulus. However, the relationship between these dynamics and circuit architecture and their impact on sensory processing, cognition and behaviour are poorly understood. By combining cellular-resolution calcium imaging with mechanistic network modelling, we aimed to infer the spatial and temporal network interactions in the zebrafish optic tectum that shape its ongoing activity and state-dependent responses to visual input. We showed that a simple recurrent network architecture, wherein tectal dynamics are dominated by fast, short range, excitation countered by long-lasting, activity-dependent suppression, was sufficient to explain multiple facets of population activity including intermittent bursting, trial-to-trial sensory response variability and spatially-selective response adaptation. Moreover, these dynamics also predicted behavioural trends such as selective habituation of visually evoked prey-catching responses. Overall, we demonstrate that a mechanistic circuit model, built upon a uniform recurrent connectivity motif, can estimate the incidental state of a dynamic neural network and account for experience-dependent effects on sensory encoding and visually guided behaviour.
PIEZO1-dependent erythrocyte dehydration as the mechanism for selection of an allele protecting from severe malaria

PIEZO1 is a cation specific mechanoreceptor channel implicated in red blood cell (RBC) volume homeostasis. Several PIEZO1 gain of function (GoF) variants demonstrate delayed channel inactivation and can cause hereditary xerocytosis (HX), a disease characterized by hemolytic anemia, RBC dehydration, and shape distortion. The milder PIEZO1E756del GoF variant, prevalent in populations of African descent, protects carriers from severe malaria caused by Plasmodium falciparum and ameliorate disease in a rodent malaria model. To explore the mechanism of this malaria protection, P. falciparum infection of human PIEZO1E756del RBC was analyzed in shear-stressed and static cultures with and without Yoda1, a PIEZO1 agonist. RBC dehydration was a common pathophysiological factor affecting parasite replication in both culture conditions. PIEZO1 channel opening by either Yoda1 or shear stress produced dehydration-dependent cell hemolysis, inhibiting P. falciparum infection. Since the physiological activator of PIEZO1 in circulating RBC is shear stress, we propose that shear stress-induced dehydration, disproportionally affecting RBC of GoF PIEZO1 E756del carriers, makes erythrocytes less habitable for P. falciparum to the point of hemolysis, and thus ameliorates malaria in GoF PIEZO1E756del carriers. More generally, RBC dehydration processes may be a pathway for protection from the severe form of malaria common to several hematological disorders, including sickle cell trait. Key points PIEZO1E756del activation in African American donor RBC provokes dehydration-dependent cell hemolysis, impairing P. falciparum replication. RBC dehydration could be a malaria ameliorating factor in several known RBC hematological disorders, including sickle cell trait.
Climate adaptation and vulnerability of foundation species in a global change hotspot

Climate change is altering species ranges, and abundances within ranges, as populations become differentially adapted and vulnerable to the climates they face. Hence, characterising current ranges, whether species harbour and exchange adaptive genetic variants, and how variants are distributed across landscapes undergoing rapid change, is crucial to predicting responses to future climates and informing conservation strategies. Such insights are nonetheless lacking for most species of conservation concern. We characterise genomic patterns of neutral variation, climate adaptation, and climate vulnerability (the amount of genomic change needed to track climate change by adaptation) in sister foundation species, the endemic marine tubeworms Galeolaria caespitosa and Galeolaria gemineoa, across a sentinel region for climate change impacts. First, species are shown to be partly sympatric despite previous support for non-overlapping ranges, and genetically isolated despite known capacity for hybrid crosses to yield viable early offspring. Second, species show signals of polygenic adaptation, but to differing components of temperature and involving mostly different loci. Last, species are predicted to be differentially vulnerable to climate change, with G. gemineoa — the less genetically diverse species — needing double the adaptation to track projected changes in temperature compared to its sister species. Together, our findings provide new insights into climate adaptation and its potential disruption by climate change for foundation species that enhance local biodiversity, with implications for evolutionarily-enlightened management of coastal ecosystems.
The impact of sparsity in low-rank recurrent neural networks

Neural population dynamics are often highly coordinated, allowing task-related computations to be understood as neural trajectories through low-dimensional subspaces. How the network connectivity and input structure give rise to such activity can be investigated with the aid of low-rank recurrent neural networks, a recently-developed class of computational models which offer a rich theoretical framework linking the underlying connectivity structure to emergent low-dimensional dynamics. This framework has so far relied on the assumption of all-to-all connectivity, yet cortical networks are known to be highly sparse. Here we investigate the dynamics of low-rank recurrent networks in which the connections are randomly sparsified, which makes the network connectivity formally full-rank. We first analyse the impact of sparsity on the eigenvalue spectrum of low-rank connectivity matrices, and use this to examine the implications for the dynamics. We find that in the presence of sparsity, the eigenspectra in the complex plane consist of a continuous bulk and isolated outliers, a form analogous to the eigenspectra of connectivity matrices composed of a low-rank and a full-rank random component. This analogy allows us to characterise distinct dynamical regimes of the sparsified low-rank network as a function of key network parameters. Altogether, we find that the low-dimensional dynamics induced by low-rank connectivity structure are preserved even at high levels of sparsity, and can therefore support rich and robust computations even in networks sparsified to a biologically-realistic extent. Author summary In large networks of neurons, the activity displayed by the population depends on the strength of the connections between each neuron. In cortical regions engaged in cognitive tasks, this population activity is often seen to be highly coordinated and low-dimensional. A recent line of theoretical work explores how such coordinated activity can arise in a network of neurons in which the matrix defining the connections is constrained to be mathematically low-rank. Until now, this connectivity structure has only been explored in fully-connected networks, in which every neuron is connected to every other. However, in the brain, network connections are often highly sparse, in the sense that most neurons do not share direct connections. Here, we test the robustness of the theoretical framework of low-rank networks to the reality of sparsity present in biological networks. By mathematically analysing the impact of removing connections, we find that the low-dimensional dynamics previously found in dense low-rank networks can in fact persist even at very high levels of sparsity. This has promising implications for the proposal that complex cortical computations which appear to rely on low-dimensional dynamics may be underpinned by a network which has a fundamentally low-rank structure, albeit with only a small fraction of possible connections present.
dartR v2: an accessible genetic analysis platform for conservation, ecology, and agriculture

Innumerable approaches to analyse genetic data are now available to guide conservation, ecological and agricultural projects. However, streamlined and accessible tools are needed to bring these approaches within reach of a broader user base. dartR was released in 2018 to lessen the intrinsic complexity of analysing single nucleotide polymorphisms (SNPs) and dominant markers (presence/absence of amplified sequence tags) by providing user-friendly data quality control and marker selection functions. dartR users have grown steadily since its release and provided valuable feedback on their interaction with the package allowing us to enhance dartR capabilities. Here, we present Version 2 of dartR. In this iteration, we substantially increased the number of available functions from 45 to 144. In addition to improved functionality, we focused on enhancing the user experience by extending plot customisation, function standardisation, increasing user support and function speed. dartR provides functions for various stages in analysing genetic data, from data manipulation to reporting. dartR provides many functions for importing, exporting and linking to other packages, to provide an easy-to-navigate conduit between data generation and analysis options already available via other packages. We also implemented simulation functions whose results can be analysed seamlessly with several other dartR functions. As more methods and approaches mature to inform conservation, we envision that accessible platforms to analyse genetic data will play a crucial role in translating science into practice.
A 3’ UTR-derived small RNA connecting nitrogen and carbon metabolism in enteric bacteria

Increasing numbers of small, regulatory RNAs (sRNAs) corresponding to 3’ untranslated regions (UTR) are being discovered in bacteria. One such sRNA, denoted GlnZ, corresponds to the 3’ UTR of the Escherichia coli glnA mRNA encoding glutamine synthetase. Several forms of GlnZ, processed from the glnA mRNA, are detected in cells growing with limiting ammonium. GlnZ levels are regulated transcriptionally by the NtrC transcription factor and post-transcriptionally by RNase III. Consistent with the expression, E. coli cells lacking glnZ show delayed outgrowth from nitrogen starvation compared to wild type cells. Transcriptome-wide RNA-RNA interactome datasets indicated that GlnZ binds to multiple target RNAs. Immunoblot and assays of fusions confirmed GlnZ-mediated repression of glnP and sucA, encoding proteins that contribute to glutamine transport and the citric acid cycle, respectively. Although the overall sequences of GlnZ from E. coli K-12, Enterohemorrhagic E. coli and Salmonella enterica have significant differences due to various sequence insertions, all forms of the sRNA were able to regulate the two targets characterized. Together our data show that GlnZ promotes survival of E. coli under low nitrogen conditions by modulating genes that affect carbon and nitrogen flux.
History of Previous Midlife Estradiol Treatment Permanently Alters Interactions of Brain Insulin-like Growth Factor-1 Signaling and Hippocampal Estrogen Synthesis to Enhance Cognitive Aging in a Rat Model of Menopause

Across species, including humans, elevated levels of brain estrogen receptor (ER) α are associated with enhanced cognitive aging, even in the absence of circulating estrogens. In rodents, short-term estrogen treatment, such as that commonly used in the menopausal transition, results in long-term increases in ERα levels in the hippocampus, leading to enhanced memory long after termination of estrogen treatment. However, mechanisms by which increased levels of brain ERα enhances cognitive aging remain unclear. Here we demonstrate in aging female rats that insulin-like growth factor-1 (IGF-1), which can activate ER via ligand-independent mechanisms, requires concomitant synthesis of brain-derived neuroestrogens to phosphorylate ERα via MAPK signaling, ultimately resulting in enhanced memory. In a rat model of menopause involving long-term ovarian hormone deprivation, hippocampal neuroestrogen activity decreases, altering IGF-1 activity and resulting in impaired memory. However, this process is reversed by short-term estradiol treatment. Forty days of estradiol exposure following ovariectomy results in maintenance of neuroestrogen levels that persist beyond the period of hormone treatment, allowing for continued interactions between IGF-1 and neuroestrogen signaling, elevated levels of hippocampal ERα, and ultimately enhanced memory. Collectively, results demonstrate that short-term estradiol use following loss of ovarian function has long-lasting effects on hippocampal function and memory by dynamically regulating cellular mechanisms that promote activity of ERα in the absence of circulating estrogens. Translational impacts of these findings suggest lasting cognitive benefits of short-term estrogen use near menopause and highlight the importance of hippocampal ERα, independent from the role of circulating estrogens, in regulating memory in aging females. SIGNIFICANCE STATEMENT Declines in ovarian hormones following menopause coincide with increased risk of cognitive decline. Because of potential health risks, current recommendations are that menopausal estrogen therapy be limited to a few years. Long-term consequences for the brain and memory of this short-term midlife estrogen therapy are unclear. Here, in a rodent model of menopause, we determined mechanisms by which short-term midlife estrogen exposure can enhance hippocampal function and memory with cognitive benefits and molecular changes enduring long after termination of estrogen exposure. Our model indicates long-lasting benefits of maintaining hippocampal estrogen receptor function in the absence of ongoing estrogen exposure and suggests potential strategies for combating age-related cognitive decline.