Miracle-dz/UniFAR-FTUs huggingface dataset:
Single cell visualisation of mir-9a and Senseless co-expression during Drosophila melanogaster embryonic and larval peripheral nervous system development

The Drosophila melanogaster peripheral nervous system (PNS) comprises the sensory organs that allow the fly to detect environmental factors such as temperature and pressure. PNS development is a highly specified process where each sensilla originates from a single sensory organ precursor (SOP) cell. One of the major genetic orchestrators of PNS development is Senseless, which encodes a zinc finger transcription factor (Sens). Sens is both necessary and sufficient for SOP differentiation. Senseless expression and SOP number are regulated by the microRNA miR-9a. However, the reciprocal dynamics of Senseless and miR-9a are still obscure. By coupling smFISH with immunofluorescence, we are able to visualize transcription of the mir-9a locus and expression of Sens simultaneously. During embryogenesis, we show that the expression of mir-9a in SOP cells is rapidly lost as Senseless expression increases. However, this mutually exclusive expression pattern is not observed in the third instar imaginal wing disc, where some Senseless-expressing cells show active sites of mir-9a transcription. These data challenge and extend previous models of Senseless regulation, and show complex co-expression dynamics between mir-9a and Senseless. The differences in this dynamic relationship between embryonic and larval PNS development suggest a possible switch in miR-9a function. Our work brings single-cell resolution to the understanding of dynamic regulation of PNS development by Senseless and miR-9a.
Survey of 8 UAV Set-Covering Algorithms for Terrain Photogrammetry

Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms α (iteration number) and β (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR.
CHK2 inhibition provides a strategy to suppress hematological toxicity from PARP inhibitors

Cancer patients treated with poly (ADP-ribose) polymerase inhibitors (PARPi) experience various side effects, with hematological toxicity being most common. Short term treatment of mice with olaparib resulted in depletion of reticulocytes, B cell progenitors and immature thymocytes, whereas longer treatment induced broader myelosuppression. We performed a CRISPR/Cas9 screen targeting DNA repair genes to identify strategies to suppress hematological toxicity. The screen revealed that sgRNAs targeting the serine/threonine kinase CHK2 were enriched following olaparib treatment. Genetic or pharmacological inhibition of CHK2 blunted PARPi response in lymphoid and myeloid cell lines, and in primary pre-B/pro-B cells. Using a Cas9 base editor, we found that blocking CHK2-mediated phosphorylation of p53 also impaired olaparib response. Our results identify the p53 pathway as a major determinant of the acute response to PARPi in normal blood cells and demonstrate that targeting CHK2 can short-circuit this response. Cotreatment with a CHK2 inhibitor did not antagonise olaparib response in ovarian cancer cells. Selective inhibition of CHK2 may spare blood cells from the toxic influence of PARPi and broaden the utility of these drugs.
Tet1 Isoforms Differentially Regulate Gene Expression, Synaptic Transmission, and Memory in the Mammalian Brain

The dynamic regulation of DNA methylation in postmitotic neurons is necessary for memory formation and other adaptive behaviors. Ten-eleven translocation 1 (TET1) plays a part in these processes by oxidizing 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), thereby initiating active DNA demethylation. However, attempts to pinpoint its exact role in the nervous system have been hindered by contradictory findings, perhaps due in part, to a recent discovery that two isoforms of the Tet1 gene are differentially expressed from early development into adulthood. Here, we demonstrate that both the shorter transcript (Tet1S) encoding an N-terminally truncated TET1 protein and a full-length Tet1 (Tet1FL) transcript encoding canonical TET1 are co-expressed in the adult mouse brain. We show that Tet1S is the predominantly expressed isoform and is highly enriched in neurons, whereas Tet1FL is generally expressed at lower levels and more abundant in glia, suggesting their roles are at least partially cell type-specific. Using viral-mediated, isoform and neuron-specific molecular tools, we find that the individual repression of each transcript leads to the dysregulation of unique gene ensembles and contrasting changes in basal synaptic transmission. In addition, Tet1S repression enhances, while Tet1FL impairs, hippocampal-dependent memory in male mice. Together, our findings demonstrate that each Tet1 isoform serves a distinct role in the mammalian brain. SIGNIFICANCE STATEMENT In the brain, activity-dependent changes in gene expression are required for the formation of long-term memories. DNA methylation plays an essential role in orchestrating these learning-induced transcriptional programs by influencing chromatin accessibility and transcription factor binding. Once thought of as a stable epigenetic mark, DNA methylation is now known to be impermanent and dynamically regulated, driving neuroplasticity in the brain. We found that Tet1, a member of the ten-eleven translocation (TET) family of enzymes that mediates removal of DNA methyl marks, is expressed as two separate isoforms in the adult mouse brain and that each differentially regulates gene expression, synaptic transmission and memory formation. Together, our findings demonstrate that each Tet1 isoform serves a distinct role in the CNS.
First evidence for an aposematic function of a very common color pattern in small insects

Many small parasitoid wasps have a black-orange-black (BOB) color pattern, which is usually present in both sexes. A likely function of this widespread pattern is aposematic (warning) coloration, but this has never been investigated. To test this hypothesis, we presented spider predators (Lyssomanes jemineus), both field-captured and lab-reared individuals, to a species with the BOB pattern and a congeneric all-black species in each of four scelionid genera (Baryconus, Chromoteleia, Macroteleia and Scelio). Each spider/wasp trial was filmed for 40 minutes under controlled conditions and three behavioral responses (detect, attack, avoid) were recorded in each of 136 trials, never using the same predator and prey more than once. In order to better understand the results obtained, two additional studies were performed. First, the reflection spectrum of the cuticle of the wasp and a theoretical visual sensibility model of the spider were used to calculate a parameter we called “absorption contrast” that allowed us to compare the perception contrast between black and orange in each wasp genus as viewed by the spider. Second, acute toxicity trials with the water flea, Daphnia magna, were performed to determine toxicity differences between BOB and non-BOB wasps. By combining the results from the three types of experiments, together with a statistical analysis, we confirmed that BOB color pattern plays an aposematic role.
The construction and deconstruction of sub-optimal preferences through range-adapting reinforcement learning

Converging evidence suggests that economic values are rescaled as a function of the range of the available options. Critically, although locally adaptive, range adaptation has been shown to lead to suboptimal choices. This is particularly striking in reinforcement learning (RL) situations when options are extrapolated from their original context. Range adaptation can be seen as the result of an adaptive coding process aiming at increasing the signal-to-noise ratio. However, this hypothesis leads to a counter-intuitive prediction: decreasing outcome uncertainty should increase range adaptation and, consequently, extrapolation errors. Here, we tested the paradoxical relation between range adaptation and performance in a large sample of subjects performing variants of a RL task, where we manipulated task difficulty. Results confirmed that range adaptation induces systematic extrapolation errors and is stronger when decreasing outcome uncertainty. Finally, we propose a range-adapting model and show that it is able to parsimoniously capture all the observed results.
Learning and Sleep Have Divergent Effects on Cytosolic and Membrane-Associated Ribosomal mRNA Profiles in Hippocampal Neurons

The hippocampus plays an essential role in consolidating transient experiences into long-lasting memories. Memory consolidation can be facilitated by post-learning sleep, although the underlying cellular mechanisms are undefined. Here, we addressed this question using a mouse model of hippocampally-mediated, sleep-dependent memory consolidation (contextual fear memory; CFM), which is known to be disrupted by post-learning sleep loss. We used translating ribosome affinity purification (TRAP) to quantify ribosome-associated RNAs in different subcellular compartments (cytosol and membrane) and in different hippocampal cell populations (either whole hippocampus, Camk2a+ excitatory neurons, or highly active neurons expressing phosphorylated ribosomal subunit S6 [pS6+]). Using RNA-seq, we examined how these transcript profiles change as a function of sleep vs. sleep deprivation (SD) and as a function of prior learning (contextual fear conditioning; CFC). Surprisingly, we found that while many mRNAs on cytosolic ribosomes were altered by sleep loss, almost none were altered by learning. Of the few changes in cytosolic ribosomal transcript abundance following CFC, almost all were occluded by subsequent SD. This effect was particularly pronounced in pS6+ neurons with the highest level of neuronal activity following CFC, suggesting SD-induced disruption of post-learning transcript changes in putative “engram” neurons. In striking contrast, far fewer transcripts on membranebound (MB) ribosomes were altered by SD, and many more mRNAs (and lncRNAs) were altered on MB ribosomes as a function of prior learning. For hippocampal neurons, cellular pathways most significantly affected by CFC were involved in structural remodeling. Comparisons of post-CFC transcript profiles between freely-sleeping and SD mice implicated changes in cellular metabolism in Camk2a+ neurons, and increased protein synthesis capacity in pS6+ neurons, as biological processes disrupted by post-learning sleep loss.
Expression of a Bet v 1 homologue gene encoding a PR 10 protein in birch roots: induction by auxin and localization of the transcripts by in situ hybridization

In birch roots (Betula pendula Roth), two members of the Bet v 1 gene family which encode PR 10 proteins have previously been characterized. One of these members, named Bet v 1-sc1, is significantly induced in response to biotic or abiotic factors. We have analysed the expression of Bet v 1-sc1 in birch roots treated either with 1 M indole-3-acetic acid (IAA) or 1 M kinetin using reverse transcription–polymerase chain reaction (RT–PCR), northern blotting and competitive PCR. High accumulation of the Bet v 1-sc1 transcripts was recorded only after auxin application, while kinetin had no effect. By in situ hybridization, we have investigated the localization of Bet v 1-sc1 mRNA in birch roots after induction of the gene by root treatment with 1 M IAA. Using root tip sections, we showed that Bet v 1-sc1 is significantly expressed in the apical meristem and the procambium. In sections taken in the zone producing lateral roots, the presence of Bet v 1-sc1 was found at sites of emerging secondary root primordia. This first report of localization of Bet v 1-sc1 expression suggests that this gene could be involved in the processes leading to lateral root initiation.
SST Comparison of AVHRR and MODIS Time Series in the Western Mediterranean Sea

Sea Surface Temperature (SST) is a key parameter for understanding atmospheric and oceanic processes. Since the late 1980s, infrared satellite images have been used to complement in situ records for studying the temporal and spatial variability of SST. The Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA)’s satellite was the first sensor successfully used to compute SST following the development and validation of the atmospheric correction algorithm known as “split-window”. More recently, the MODerate-resolution Imaging Spectroradiometer (MODIS) on board the National Aeronautics and Space Administration (NASA)’s Terra and Aqua satellites, launched in 1999 and 2002, respectively, also provides SST products which can be combined with AVHRR series to complete the analysis of time series. This paper presents a comparison of the monthly SST data derived from both sensors, AVHRR and MODIS, in a series of ten years (2000–2009) in the Western Mediterranean basins. The results showed a high correlation (R2 = 0.99) between the sensors when averaged values at the regional scale were compared. SST obtained from AVHRR were slightly higher (+0.18 °C ± 0.2 °C, on average) than SST from MODIS. The series were most similar during winter and spring (+0.09 °C ± 0.1 °C for January to May) with a greater difference from June to December (+0.24 °C ± 0.2 °C). The comparative analysis showed that the two sensors can be used jointly to estimate long-term trends at the regional scale.
Personality Traits, Gamification and Features to Develop an App to Reduce Physical Inactivity

Background: Health benefits from physical activity (PA) can be achieved by following the WHO recommendation for PA. To increase PA in inactive individuals, digital interventions can provide cost-effective and low-threshold access. Moreover, gamification elements can raise the motivation for PA. This study analyzed which factors (personality traits, app features, gamification) are relevant to increasing PA within this target group. Methods: N = 808 inactive participants (f = 480; m = 321; age = 48 ± 6) were integrated into the analysis of the desire for PA, the appearance of personality traits and resulting interest in app features and gamification. The statistical analysis included chi-squared tests, one-way ANOVA and regression analysis. Results: The main interests in PA were fitness (97%) and outdoor activities (75%). No significant interaction between personality traits, interest in PA goals, app features and gamification were found. The interest in gamification was determined by the PA goal. Participants’ requirements for features included feedback and suggestions for activities. Monetary incentives were reported as relevant gamification aspects. Conclusion: Inactive people can be reached by outdoor activities, interventions to increase an active lifestyle, fitness and health sports. The study highlighted the interest in specific app features and gamification to increase PA in inactive people through an app.
Global Wildfire Outlook Forecast with Neural Networks

Wildfire occurrence and spread are affected by atmospheric and land-cover conditions, and therefore meteorological and land-cover parameters can be used in area burned prediction. We apply three forecast methods, a generalized linear model, regression trees, and neural networks (Levenberg–Marquardt backpropagation) to produce monthly wildfire predictions 1 year in advance. The models are trained using the Global Fire Emissions Database version 4 with small fires (GFEDv4s). Continuous 1-year monthly fire predictions from 2011 to 2015 are evaluated with GFEDs data for 10 major fire regions around the globe. The predictions by the neural network method are superior. The 1-year moving predictions have good prediction skills over these regions, especially over the tropics and the southern hemisphere. The temporal refined index of agreement (IOA) between predictions and GFEDv4s regional burned areas are 0.82, 0.82, 0.8, 0.75, and 0.56 for northern and southern Africa, South America, equatorial Asia and Australia, respectively. The spatial refined IOA for 5-year averaged monthly burned area range from 0.69 in low-fire months to 0.86 in high-fire months over South America, 0.3–0.93 over northern Africa, 0.69–0.93 over southern Africa, 0.47–0.85 over equatorial Asia, and 0.53–0.8 over Australia. For fire regions in the northern temperate and boreal regions, the temporal and spatial IOA between predictions and GFEDv4s data in fire seasons are 0.7–0.79 and 0.24–0.83, respectively. The predictions in high-fire months are better than low-fire months. This study illustrates the feasibility of global fire activity outlook forecasts using a neural network model and the method can be applied to quickly assess the potential effects of climate change on wildfires.
Effect of Lockdown Measures on Atmospheric Nitrogen Dioxide during SARS-CoV-2 in Spain

The disease caused by SARS-CoV-2 has affected many countries and regions. In order to contain the spread of infection, many countries have adopted lockdown measures. As a result, SARS-CoV-2 has negatively influenced economies on a global scale and has caused a significant impact on the environment. In this study, changes in the concentration of the pollutant Nitrogen Dioxide (NO2) within the lockdown period were examined as well as how these changes relate to the Spanish population. NO2 is one of the reactive nitrogen oxides gases resulting from both anthropogenic and natural processes. One major source in urban areas is the combustion of fossil fuels from vehicles and industrial plants, both of which significantly contribute to air pollution. The long-term exposure to NO2 can also cause severe health problems. Remote sensing is a useful tool to analyze spatial variability of air quality. For this purpose, Sentinel-5P images registered from January to April of 2019 and 2020 were used to analyze spatial distribution of NO2 and its evolution under the lockdown measures in Spain. The results indicate a significant correlation between the population’s activity level and the reduction of NO2 values.
Recovery of Foredune and Blowout Habitats in a Freshwater Dune Following Removal of Invasive Austrian Pine (Pinus nigra)

Invasive species removal is an important first step toward restoring invaded ecosystems; however, restoration following removal may be hindered by (1) unintended consequences of management, such as habitat destabilization, and/or (2) legacy effects of the invader, such as persistent alterations of soil structure or plant community composition. During 1956–1972, approximately 26,000 individuals of the non‐native pine, Pinus nigra, were planted into multiple freshwater sand dune habitats as a stabilization measure on the eastern shore of Lake Michigan in Allegan County, MI, U.S.A. From 2004 to 2010, we evaluated the recovery of foredune and blowout habitats following P. nigra removal in 2003–2005. We compared sand movement and plant community structure, composition, and richness between removal and control sites over the 6 years following pine removal. In addition, we evaluated the impact of litter removal on recolonization of native graminoids in foredunes. Sand movement patterns never differed between removal and control sites in foredunes; however, accumulation was more common in removal sites in blowouts 1 and 6 years following pine removal. Vegetation cover in removal sites became indistinguishable from control sites in both foredunes and blowouts, but species richness for both forb and woody species was higher in removal sites in blowouts. Removal sites in both foredunes and blowouts had higher cover by forbs and lower cover by graminoids. Pine litter did not inhibit recolonization of foredunes by native graminoids. These results suggest that high disturbance habitats, such as sand dunes, have the potential to recover from invasion if the mechanism of disturbance is restored and pioneer species are present to recolonize the system.
Problem Solving: When Groups Perform Better Than Teammates

: People tend to form groups when they have to solve difficult problems because groups seem to have betterproblem-solvingcapabilitiesthanindividuals. Indeed, duringtheirevolution, humanbeingslearnedthat cooperation is frequently an optimal strategy to solve hard problems both quickly and accurately. The ability of a group to determine a solution to a given problem, once group members alone cannot, has been called “Collective Intelligence". Such emergent property of the group as a whole is the result of a complex interaction between many factors. Here, we propose a simple and analytically solvable model disentangling the direct link between collective intelligence and the average intelligence of group members. We found that there is a non-linear relation between the collective intelligence of a group and the average intelligence quotient of its members depending on task difficulty. We found three regimes as follows: for simple tasks, the level of collective intelligence of a group is a decreasing function of teammates’ intelligence quotient; when tasks have intermediate difficulties, the relation between collective intelligence and intelligence quotient shows a non-monotone behaviour; for complex tasks, the level of collective intelligence of a group monotonically increases withteammates’intelligencequotientwithphasetransitionsemergingwhenvaryingthelatter’slevel. Although simple and abstract, our model paves the way for future experimental explorations of the link between task complexity, individual intelligence and group performance.