Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence for grid-cell-related activity in the time domain

The relation between the processing of space and time in the brain has been an enduring cross-disciplinary question. Grid cells have been recognized as a hallmark of the mammalian navigation system, with recent studies attesting to their involvement in organization of conceptual knowledge in humans. To determine whether grid-cell-like representations support temporal processing, we asked subjects to mentally simulate changes in age and time-of-day, each constituting “trajectory” in an age-day space, while undergoing fMRI. We found that grid-cell-like representations supported trajecting across this age-day space. Furthermore, brain regions concurrently coding past-to-future orientation positively modulated the magnitude of grid-cell-like representation in the left entorhinal cortex. Our findings suggest that temporal processing may be supported by spatially modulated systems, and that innate regularities of abstract domains may interface and alter grid-cell-like representations, similarly to spatial geometry.
Functional Divergence in a Multi-gene Family Is a Key Evolutionary Innovation for Anaerobic Growth in Saccharomyces cerevisiae

The amplification and diversification of genes into large multi-gene families often marks key evolutionary innovations, but this process often creates genetic redundancy that hinders functional investigations. When the model budding yeast Saccharomyces cerevisiae transitions from aerobic to anaerobic growth conditions, the cell massively induces the expression of seven cell wall mannoproteins (anCWMPs): TIP1, TIR1, TIR2, TIR3, TIR4, DAN1, and DAN4. Here we show that these genes likely derive evolutionarily from a single ancestral anCWMP locus, which was duplicated and translocated to new genomic contexts several times both prior to and following the budding yeast whole genome duplication (WGD) event. Based on synteny and their phylogeny, we separate the anCWMPs into four gene subfamilies. To resolve prior inconclusive genetic investigations of these genes, we constructed a set of combinatorial deletion mutants to determine their contributions toward anaerobic growth in S. cerevisiae. We found that two genes, TIR1 and TIR3, were together necessary and sufficient for the anCWMP contribution to anaerobic growth. Overexpressing either gene alone was insufficient for anaerobic growth, implying that they encode non-overlapping functional roles in the cell during anaerobic growth. We infer from the phylogeny of the anCWMP genes that these two important genes derive from an ancient duplication that predates the WGD event, whereas the TIR1 subfamily experienced gene family amplification after the WGD event. Taken together, the genetic and molecular evidence suggest that one key anCWMP gene duplication event, several auxiliary gene duplication events, and functional divergence underpin the evolution of anaerobic growth in budding yeasts.
Therapeutic activation of endothelial sphingosine 1-phosphate receptor-1 by chaperone-bound S1P suppresses proliferative retinal vasculopathy

Sphingosine-1-phosphate (S1P), a bioactive lipid mediator that signals via G protein-coupled S1P receptors (S1PR), is required for normal vascular development. The role of this signaling axis in vascular retinopathies is largely unexplored. Here we show in a mouse model of oxygen-induced retinopathy (OIR) that endothelial overexpression of S1pr1 suppresses while endothelial knockout (KO) of S1pr1 worsens neovascular tuft formation. Furthermore, neovascular tufts are increased in Apom KO mice which lack HDL-bound S1P while they are suppressed in Apom TG mice which has more circulating HDL-S1P. These results suggest that circulating HDL-S1P activation of endothelial S1PR1 specifically suppresses proliferative retinal vasculopathy. Moreover, systemic administration of ApoM-Fc-bound S1P or a small molecule Gi-biased S1PR1 agonist suppressed neovascular tuft formation. Circulating HDL-S1P activation of endothelial S1PR1 may be a key protective mechanism to guard against neovascular retinopathies that occur not only in premature infants but also in diabetes and aging.
Probabilistic Tsunami Loss Estimation Methodology: Stochastic Earthquake Scenario Approach

This study develops a probabilistic tsunami loss estimation methodology for enhancing community resilience against tsunami disasters. The method is based on novel stochastic earthquake source modeling and state-of-the-art tsunami fragility modeling. It facilitates the quantitative evaluation of tsunami loss for coastal community by accounting for uncertainties of earthquake occurrence and rupture characteristics. A case study is set up to illustrate an application of the developed method to the Sendai Plain area by focusing on possible tsunami events in the Tohoku region of Japan. The quantitative tsunami hazard as well as risk assessment results serve as effective means to make decisions regarding tsunami disaster risk reduction.
Characterizing the polarization continuum of macrophage subtypes M1, M2a and M2c

Macrophages are vital components of the inflammatory response and exhibit phenotypical plasticity through active conversion between pro- and anti-inflammatory cell subtypes, a feature which can be reproduced in ex vivo culture. We employed a multifaceted approach utilizing proteomics, flow cytometry, activity assays and live-cell microscopy imaging to characterize four cultured macrophage subtypes: unstimulated MØ, classically activated M1, alternatively activated M2a, and deactivated M2c macrophages. Whole cell proteomics identified a total of 5435 proteins, with >50% of these proteins exhibiting significant alterations in abundance between the different subtypes. This confirms that four distinct macrophage subtypes are induced from the same originating donor material through stimulation with specific cytokines. Additional surfaceome analysis revealed that M2c macrophages significantly upregulate pro-inflammatory markers compared to the MØ baseline and thus appear to be activated or primed to activate, similar to M1. Surface protein expression provided further subtype characterization, in particular distinguishing between the M2a and M2c macrophages. We next explored the re-polarization capabilities of macrophages using dexamethasone, an anti-inflammatory glucocorticoid known to induce macrophage polarization towards the M2c de-activated phenotype. We show that activated M1 macrophages treated with dexamethasone for 48-hours upregulate the levels of CD163 and CD206, markers synonymous with a phenotypical shift from M1 to M2c yet retain key surface markers and display the functional phenotype of M1 macrophages. The observed repolarization of M1 pro-inflammatory macrophages provides a potential mechanism through which dexamethasone treatment improves COVID-19 prognosis and constitutes evidence of partial repolarization along the macrophage continuum. These proteomic and functional ex vivo macrophage datasets provide a valuable resource for studying macrophage polarity and the impact of dexamethasone on macrophage phenotype and function.
DriverSEAT: A spatially-explicit stochastic modelling framework for the evaluation of gene drives in novel target species

Gene drives represent a potentially ground breaking technology for the control of undesirable species or the introduction of desirable traits in wild population, and there is strong interest in applying these technologies to a wide range of species across many domains including agriculture, health, conservation and biosecurity. There remains however considerable uncertainty regarding the feasibility and efficacy of gene drives in various species, based in particular on biological and ecological specificities of each target. In this paper we introduce DriverSEAT, a new spatial, modular modelling framework designed to assess the outcome of gene drives in a range of target species based on their specific ecological dynamics and genetics. In addition to the main structure and characteristics of the model, we present an example of its application on scenarios of genetic control of weeds, a potential candidate for gene drive control that presents significant challenges associated with plant population dynamics. We illustrate here how the results from DriverSEAT can inform on the potential value of gene drives in this specific context, and generally provide ecologically informed guidance for the development and feasibility of gene drives as a control method in new target species.
REMOTE SENSING IN EUROPE: STATUS ANALYSIS AND TRENDS FOCUSING ON ENVIRONMENT AND AGRICULTURE

Target of the European policy is to safeguard and improve, wherever possible, through directives and regulations the quality of life of the citizens. High priority is set to the protection of the environment through the rational usage of the resources, the food security, and the reduction of risks and threats. Tackling the challenge, Information Services, based on the combination, analysis and modeling of data received from Earth Observation satellites as well as ground-based networks, operate or are planned to operate in an integrated manner to provide wide-area and cross border harmonized geo-information products. To this end, Remote Sensing is a key element for registering the surface status near real time, monitoring changes, and supporting and testing scenariaby projecting the validated trends in the present and past to delineate possible situations in the future. This article attempts to provide a brief overview of European initiatives, potentials and limitations of existing approaches, existing service specifications and operation requirements, and an update regarding on-going projects, fostering Remote Sensing incorporation in the Decision Support and Policy Implementation chain.
Correlations between behavior and neuronal responses in the cochlear nucleus ( CN ) and inferior colliculus ( IC ) of behaving monkeys

Acknowledgements: This research was funded by a grant from the National Institutes of Health, RO1 DC 11092. CAM was supported by the Ruth Kirchstein F31 fellowship from NIDCD (F31 DC019823-02). The authors would like to thank Ms. Mary Feurtado and Dr. William Vaughan for help during surgery, Bruce and Roger Williams for hardware, Drs. Emilio Salinas, Jeff Schall, Richard Heitz, and Mark Wallace for helpful comments on an earlier version of the manuscript, Dr. Steve Lisberger and Mr. Scott Ruffner for the use of the FigureComposer software, and Ms. Laura Trice, and Drs. Troy Hackett and Jon Kaas for help with histology.
Dermal nerve growth factor is increased in prurigo nodularis compared to atopic dermatitis

Background Prurigo nodularis (PN) is a chronic, pruritic, inflammatory skin disease characterized by hyperkeratotic nodules on the trunk and extremities. While there is growing research on the immunological basis of PN, the neuropathic and structural components of PN lesions are unknown. Objective To determine the inflammatory, neuropathic, and structural pathways in PN compared to atopic dermatitis (AD). Methods Lesional and non-lesional skin biopsies were collected from 13 PN and 6 AD patients. mRNA and protein expression in biopsies was determined using RNA-Sequencing and immunohistochemistry (IHC), respectively. Differentially expressed genes (DEGs) were identified using the DESeq2 R package and pathway level enrichment was determined using Gene Set Enrichment Analysis. IHC expression was quantified with QuPath followed by statistical comparison with the Student’s t-test and Mann-Whitney U. Results Compared to lesional AD, lesional PN had greater mRNA expression of MMPs, OSM, NGF, IL1β, CXCL2, CXCL5, CXCL8, and insulin-like growth factors, and lower expression of CCL13, CCL26, EPHB1, and collagens. Compared to non-lesional AD, non-lesional PN showed upregulation of keratin-family genes. GSEA revealed that lesional PN had greater keratinization, cornified envelope, myelin sheath, TGF-beta signaling, extracellular matrix disassembly, metalloendopeptidase activity, and neutrotrophin-TRK receptor signaling, while non-lesional PN had higher keratin filament, extracellular structure organization, extracellular matrix disassembly, and angiogenesis. IHC showed increased dermal nerve growth factor (NGF) expression in lesional PN compared to lesional AD (p=0.038), and greater epidermal NGF compared to dermal NGF in non-lesional PN (p=0.014). Limitations Single, tertiary care center. Conclusions PN demonstrated increased neurotrophic and extracellular matrix (ECM) remodeling signatures compared to AD, possibly explaining the morphological differences in their lesions. These signatures may therefore be important components of the PN pathogenesis and may serve as therapeutic targets.
Mesoscopic Field-Effect-Induced Devices in Depleted Two-Dimensional Electron Systems

Nanoelectronic devices embedded in the two-dimensional electron system (2DES) of a GaAs/(Al, Ga) As heterostructure enable a large variety of applications ranging from fundamental research to high-speed transistors. Electrical circuits are thereby commonly defined by creating barriers for carriers by the selective depletion of a preexisting 2DES. We explore an alternative approach: we deplete the 2DES globally by applying a negative voltage to a global top gate and screen the electric field of the top gate only locally using nanoscale gates placed on the wafer surface between the plane of the 2DES and the top gate. Free carriers are located beneath the screen gates, and their properties can be controlled by means of geometry and applied voltages. This method promises considerable advantages for the definition of complex circuits by the electric-field effect, as it allows us to reduce the number of gates and simplify gate geometries. Examples are carrier systems with ring topology or large arrays of quantum dots. We present a first exploration of this method pursuing field effect, Hall effect, and Aharonov-Bohm measurements to study electrostatic, dynamic, and coherent properties.
Child–Robot Interaction in Education

Advances in the field of robotics in recent years have enabled the deployment of robots in a multitude of settings, and it is predicted that this will continue to increase, leading to a profound impact on society in the future. This thesis takes its starting point in educational robots; specifically the kind of robots that are designed to interact socially with children. Such robots are often modeled on humans, and made to express and/or perceive emotions, for the purpose of creating some social or emotional attachment in children. This thesis presents a research effort in which an empathic robotic tutor was developed and studied in a school setting, focusing on children’s interactions with the robot over time and across different educational scenarios. With support from the Responsible Research and Innovation Framework, this thesis furthermore sheds light on ethical dilemmas and the social desirability of implementing robots in future classrooms, seen from the eyes of teachers and students. The thesis concludes that children willingly follow instructions from a robotic tutor, and they may also develop a sense of connection with robots, treating them as social actors. However, children’s interactions with robots often break down in unconstrained classroom settings when expectations go unmet, making the potential gain of robots in education questionable. From an ethical perspective, there are many open questions regarding stakeholders’ concerns on matters of privacy, roles and responsibility, as well as unintended consequences. These issues need to be dealt with when attempting to implement autonomous robots in education on a larger scale.
OPTIMAL CLUSTERING TECHNIQUES FOR THE SEGMENTATION OF TOURIST SPENDING. ANALYSIS OF TOURIST SURVEYS IN THE VALENCIAN COMMUNITY (SPAIN): A CASE STUDY

The Valencian Community (South-East Spain) is one of the most important tourist destinations in Europe. The Valencian Government has been carrying out surveys about the types of travel, the type of transport, the type of accommodation, the duration of the trip and the number of travellers, as well as other issues. The aim is to discover the different spending typologies incurred by foreign visitors. In their task of drawing up more attractive tourist strategies, the following questions may become particularly relevant to the Valencian Public Services: what type of traveller spends more on transportation in their own country, or pays for it in the Valencian Community; visitors’ nationalities and their higher or lower propensity to spend money on leisure; or the number of overnight stays in low-end destinations. But the surveys gathering all this information consist of multiple and nested responses, distributed in thematic blocks that overlap, and whose translation to flat file systems (susceptible to being analysed with acceptable counting times) is a complex problem. This paper presents a treatment process of the surveys, especially oriented towards having a suitable dataset to generate models of optimal segmentation of the different types of expenditure. Likewise, some results of such segmentation are shown, which are proving to be of great value to public managers in their challenge to offer suitable tourist alternatives to each type of traveller. The paper includes an example of how open data sources can be incorporated into the original dataset in order to obtain better segmentation. A variation to the classical segmentation methods (algorithms of the K means family) is also provided, which leads to the establishment of the optimal number of groups for each computational experiment.
Predictive Control under Uncertainty for Safe Autonomous Driving: Integrating Data-Driven Forecasts with Control Design

Self-driving vehicles have attracted a lot of interest due to their potential to significantly reduce traffic fatalities and transform people's lives. The reducing costs of advanced sensing technologies and the increasing capabilities of embedded computing hardware have enabled the commercialization of highly automated driving features. However, the reliable operation of autonomous vehicles is still a challenge and a major barrier in the large scale acceptance and deployment of the technology.This dissertation focuses on the challenges of designing safe control strategies for self-driving vehicles due to the presence of uncertainty arising from the non-deterministic forecasts of the driving scene. The overall goal is to unify elements from the fields of vehicle dynamics modeling, machine learning, real-time optimization and control design under uncertainty to enable the safe operation of self-driving vehicles. We propose a systematic framework based on Model Predictive Control (MPC) for the controller design, the effectiveness of which is demonstrated via applications such as lateral stability control, autonomous cruise control and autonomous overtaking on highways. Data collected from our experimental vehicles is used to build predictive models of the vehicle and the environment, and characterize the uncertainty therein. Several approaches for the control design are presented based on a worst-case or probabilistic view of the uncertain forecasts, depending on the application. The proposed control methodologies are validated by experiments performed on prototype passenger vehicles and are executed in real-time on embedded hardware with limited computational power. The experiments show the ability of the proposed framework to handle a variety of driving scenarios including aggressive maneuvers on low-friction surfaces such as snow and navigation in the presence of multiple vehicles.
Religiosity, Emotions and Health: The Role of Trust/Mistrust in God in People Affected by Cancer

Trust in God implies the conviction that God looks after a person’s own interests. The first evidence of a relationship between this construct and people’s psychological and emotional health dates back several centuries. However, the literature on this is limited, especially for people with physical health conditions, such as cancer. Therefore, the purpose of this study is to test the relationships between trust/mistrust in God, social support and emotions in people affected by cancer. The sample consisted of 177 women and men in Spain diagnosed with cancer. The instruments used were the Trust/Mistrust in God Scale, the Positive and Negative Affect Schedule and the Multidimensional Scale of Perceived Social Support. Correlation analysis and hierarchical regression analysis were performed to compare several explanatory models for the dependent variables: positive and negative emotions. The results show significant relationships between all variables. It was observed that, when trust/mistrust in God is included in the model, only mistrust in God predicts both types of emotions. In addition, both social support and some sociodemographic variables help to predict the dependent variables. This study shows that valuing the religiosity and spirituality of oncology patients in healthcare settings can have a significant positive impact on the health of these individuals. Moreover, it represents an important approach to the study of trust/mistrust in God in the context of a traditionally Catholic country.
Management of material flows within a hospital: a two interconnected vehicle routing problem

The Hospital Centre of Tours (France) is reorganizing the logistic services between its hospital units. We focus on the vehicles routing problem (VRP) between these units and on the definition of a warehousemen team inside a particularly large hospital. The design of this team (composition, planning, .) is strongly related to the routes of vehicles between hospital units. The whole problem can be seen as a two-level pickup and delivery problem with time windows, where two VRP interact
A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

In recent years, understanding human behavior has become one of the most important topics in the field of computer vision research. The reason for this growing attention is the wide range of applications that can benefit from the results of this field of research. The human behavior analysis (HBA) includes a wide range of research areas from the detection of human motion and action. Datasets created through actions and detection of human activities make it possible to compare different detection methods with the same input data. Data mining and big data approaches are very popular for analyzing data related to human behavior and can be used to address the challenges of fast processing. This article provides a systematic survey of data mining and big data in the HBA. We focus on current datasets and models related to the detection of human behavior patterns in the literature. The purpose of this survey is to assist researchers in select appropriate datasets and models to evaluate algorithms, as well as to identify research gaps for future work. To achieve this goal, articles published between 2010 and 2021 have been reviewed. These articles fall into five general categories in terms of dataset focus: object detection, motion, action, activity, and behavior. This article provides a summary of data mining and big data models in the HBA, as well as related datasets based on these categories, to highlight promising research avenues for future work.