dataesr/scientific-paragraphs-categorization huggingface dataset:
To determine the number of combinations of metals (i. e. different permutations of As, Sb, Ba, Cd, Cr, Cu, Fe, Pb, Mn, Ni, Se, and U combinations, from a mixture containing all 12 down to that of three metals remaining in each combination) here n = 12 and r is all integers between and including 11 and 3, resulting in a total of 4016 combinations without repetition. (Although Be and Tl levels were determined in the metal analysis, they were excluded in subsequent mixture analyses, since they were undetected in the majority of well samples tested). PC1 and PC2 for all possible 4016 combinations were generated. (1) � n!/r!(nr)! PC1 and PC2 for each of the 92 samples were then plotted for each of the 4016 combinations. Each of the 92 samples within a PCA plot was then overlayed with behavioral data (i.
Discussion In this study, we present a zebrafish-based toxicity analysis in determining behavioral effects of exposure to metal mixtures in the drinking water. The study was significantly enhanced by utilizing a citizen science approach that enabled collection of a large set of well water samples from families in Maine and New Hampshire. Teachers and students involved in this study gained knowledge in the following areas: health risks due to metal exposure, methods for sampling well water, submitting metadata associated with the well water sample, analyzing the data using a data literacy software called Tuva, and public outreach about the well water results. In this way, we are sharing knowledge and preparing future citizens so that they may generate community awareness around water quality and demand better water quality monitoring standards.
Acknowledgements We thank the  University of Maine zebrafish facility  and  Mark Nilan  for supplying zebrafish embryos for this project. We are incredibly grateful to the following schools and teachers,  Jon Ramgren  and  Justin Giroux  of  Waterville HS ME ,  Lynn Hanna  of  Conners Emerson ME ,  Hanna Mondrach  of  MDI HS ME ,  Jacob Cockrell   of Kents Hill HS ME ,  Janet Holden   of Pelham HS NH ,  Mary Wright  of  Kearsarge HS NH ,  Nick Baer   of Colby-Sawyer College NH , and  Sarah Hall  of  College of the Atlantic ME  for their time and facilitation of water sample collection used in this project. We thank the students of these schools for their participation in water sample collection. We also thank  NIH/NIGMS Science Education Partnership Awards (SEPA )  IR25GM129796-01  and  3R25GM129796-02S1  for funding this research.
Route planning represents a major challenge with a substantial impact on safety, economy, and even climate. An ever-growing urban population caused a significant increase in commuting times, therefore, stressing the prominence of efficient realtime route planning. In essence, the goal is to compute the fastest route to reach the target location in a realistic environment where traffic conditions are time-evolving. Consequently, a large volume of traffic data is potentially required and the route continuously updated. We thereby address the re-routing problem to answer questions such as when, how often, and where is re-routing worthwhile. We base our study on a real dataset, comprising the travel times of the road segments of New York, London, and Chicago, collected over three months. By exploiting this dataset, we implement an optimal algorithm, able to mimic ideal predictions of road segment speeds in the network.
In particular, it becomes challenging to exploit route planning algorithms that rely on pre-processed data because of the continuous traffic changes measured on the network. In this paper, we quantify the benefit of using dynamic shortest path algorithms that re-route vehicles when congestion increases. Indeed, alternative paths may provide a faster route, but can only be identified when using real-time information. For this purpose, we exploit a real dataset comprising the travel times of the road segments of several cities (i. e. , New-York, London, Chicago, and Cologne). To compare simulations with these real datasets, we also used the TAPAS (simulated) dataset  (Uppoor et al. , 2014). The contributions of this paper are as follows: (1) we quantify the travel-time gain when using real-time data to re-route vehicles, compared to a no re-routing approach that prohibits diverting from the path evaluated at the departure.
Our dataset highlights that re-routing is very seldom required real conditions, even during rush hours; (2) we provide an optimal algorithm to compute a lower bound for the travel time between any pair of locations. The algorithm replays the real dataset to mimic ideal predictions. The gain of using ideal predictions is below 10% compared with a continuous re-routing solution, even for the worst route; (3) we compare the impact of re-routing based on two types of datasets (a real dataset vs. the simulated TAPAS dataset). Surprisingly, and contrary to current belief, we obtain very different behaviors when studying re-routing strategies. Simulations tend to exacerbate the randomness of travel time. Thus, simulations seem to not accurately capture the complexity of urban road network dynamics, proving the relevance of exploiting real datasets; The remainder of this paper is organized as follows.
Real-time data sources Collecting travel-time measurements of each road segment in real-time is a challenging objective. We may use the GPS trajectories of a collection of vehicles to deduce the specific travel time of each road segment  (Duan et al. , 2018; Sanaullah et al. , 2016). For this purpose, each trajectory has to be mapped to a set of road segments while minimizing the mismatch ratio  (Falek et al. , 2018). Ladino et al. (2016)  propose to merge heterogeneous data sources (e. g. , cameras, induction loops), to create a combined, more accurate dataset. Imputation techniques  (Chen et al. , 2018)  help to reconstruct missing data (e. g. , a road segment is not crossed for short time-period). Recent approaches propose to adopt a synthetic model. Typically, the SUMO simulator  (Behrisch et al.
, 2011)  is used to generate the mobility pattern of a large number of devices, using realistic urban points of interest. Travel and Activity PAtterns Simulation (TAPAS) was used to generate the well-known TAPAS-Cologne dataset  (Uppoor et al. , 2014). However, to the best of our knowledge, no study has been conducted to verify the accuracy of these simulations. In  (Kamga et al. , 2011) , the authors, use simulated data from VISTA  (Ziliaskopoulos et al. , 1999)  to determine the impact of incidents on travel times. Other traffic simulation software such as Dynameq (Dynameq, 2020) and TRANSIMS  (Barrett et al. , 2002)  provide simulated datasets of the travel times in a road network. In this paper, we highlight in Sections 4 and 4. 4 the differences obtained between the different approaches when using real (measurement-based) datasets vs.
Since existing prediction techniques are partly inaccurate and computationally intensive, we aim in this paper to quantify its gain: if individual travel times for each road segment can be ideally predicted, is the end-to-end travel time gain significant? 2. 4. Evaluating routing performance on dynamic road networks  Smith et al. (2014)  evaluate the impact of accidents on traffic congestion using a vehicular simulation and highlight the need for using actual traffic conditions to predict the travel time. Wang et al. (2013)  provide a performance analysis of different route planning algorithms (i. e. , Dijkstra, static A * , dynamic Dijkstra, and dynamic A * ) in smart cities. Their experiments use the TAPAS-Cologne dataset  (Uppoor et al. , 2014) , which was built by generating traffic demand using TAPAS and Gawron's algorithm for traffic assignment.
They consider a rush hour during a weekday to evaluate the impact of traffic jams. McArdle et al. (2012)  attempt to simulate traffic in the Greater Dublin region. Typically, each vehicle selects its destination according to a radiation model, to model probabilities of interactions between different regions. Unfortunately, all these performance comparisons use simulations with synthetic models. The objective of our paper is instead to use real datasets, with the actual travel time for each segment, at any instant, during very long periods (i. e. , a few months).
For the sake of clarification, we use the term dynamic to refer to a graph whose structure remains the same (no edges are deleted or inserted), but edge weights change over the period T. We consider road networks with traffic congestion, but we neglect the new roads that may appear. We exploit a dataset where the speed of each road segment is monitored periodically and synchronously. Thus, we denote by timeslot the discretized time, during which the speeds for all the road segments remain unchanged. An edge weight w ij (t) = d ij /s ij (t) represents the travel time in seconds required to join the vertices i and j, where d ij and s ij (t) represent the length of the edge e ij and its corresponding speed at time t respectively. A road segment r i = {u, v, w,. , z} ∈ V consists of an ordered list of vertices in the graph.
Instead, we consider each query as representing a probe vehicle, to measure the impact of the measured traffic (from our datasets) on its route (and not the inverse). We apply here the following workflow to quantify the interest in exploiting real-time data (Figure  1 ): (1) we use real vs. simulated (TAPAS) datasets, and emulate different sampling rates (from 1 to 30 min) by subsampling the datasets; (2) we randomly select 1000 pairs of source and destination vertices in each road network; (3) for every (source, destination) pair, we generate a broad set of queries at different timestamps (every 1 min for the simulation and every 10 min on the real dataset); (4) for each query, we execute each route planning strategy to extract the route to follow; (5) we then emulate a vehicle moving along the route returned by the algorithm in the previous step.
Datasets To evaluate the impact of the different route planning strategies, we use real travel times for a broad set of road segments. Additionally, we also use a simulated dataset for comparison purposes. We rely upon: s1 d1 s2 d2 s3 d3 0/1 1/1 2/3 1/2 1/1 2/3 0/1 1/1 1/1 1/1 1/2 0/1 1/1 Diverging vertex Route using ideal prediction 0/1 0/1 0/1 0/1 0/1 1/1 1/1 1/1 1/1 0/1 1/1 1/1 1/1 1/1 1/1 0/1 0/1 div. routes total routes = 2/3 Route using continuous re-routing Diverging route segments (1) the simulated TAPAS dataset:  (Uppoor et al. , 2014)  focuses on a small German City (Cologne), during a weekday (24 hours). It relies on the simulator SUMO to simulate the traffic from a large set of emulated vehicles, pseudorandomly selecting pairs of sources and destinations (e. g. , home, office).
We run the simulation using SUMO to generate a dump file specifying the speed along every road segment at 1-sec intervals. We subsample the dataset to get the same sampling rate used in the real dataset. (2) a real dataset: we use the dataset of three major cities (New York, London, and Chicago) for which HERE (HERE Technologies, 2018) provides a fine-grain estimation of the speeds for the most important road segments. We collected three months (i. e. , from September 21 st to December 18 th 2017) worth of data at a sampling rate of 1 min, which allows an accurate estimation of the actual travel time experienced by users. Additionally, we also use the city of Cologne, for the same geographical area and during the same time window as the TAPAS dataset for a fair comparison.
Identification of congestion To identify the rush hour period, we compute the Congestion Factor (CF ) of each query as the ratio of the optimal travel time (using the ideal routing strategy) to the free-flow travel time. In the TAPAS dataset, the free-flow speed of a road segment corresponds to its speed limit. In the real dataset, the free-flow speed was provided as the average measured speed during low-volume periods and depends on the road characteristics such as lane width. Hence, given a query R = q(s, d, t s ), the congestion factor of the end-to-end route R is defined as: CF [R] = T T ime[R, ideal] T T ime[R, f reef low] (1) To analyze more clearly the behavior of each routing strategy, we need to classify the routes according to their congestion level. For this purpose, we define for each route its traffic flow (TF) metric, which represents the congestion of its most congested road segment.
Identification of divergences We focus on the continuous re-routing strategy to precisely determine where a vehicle is practically re-routed because the congestion has changed. Practically, we identify the divergence vertices, i. e. , geographical locations where the next edge is different with or without rerouting. Formally, given two routes R 1 and R 2 that share at least the same first and last vertices, we define their diverging vertices as the set {j ∈ V | ∃i, k, w ∈ V | (e ij , e jk ) ∈ R 1 ∧ (e ij , e jw ) ∈ R 2 ∧ k = w}. We construct a grid overlay where each cell is a 10 × 10 square meters. We consider a large number of route queries (pairs of sources/destinations). We execute the route computation for each query at different instants covering to cover the whole dataset. For each cell cell i , we consider all the routes that cross this cell.
We compute its divergence ratio (0 ≤ div[cell i ] ≤ 1) as the ratio of the number of diverging routes to the number of total routes. A route is diverging if the routes with and without the re-routing strategy are different for at least one instant of the dataset. Hence, a cell with a high divergence ratio means that more vehicles are re-routed when crossing this cell. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22   Some cells may be traversed by only a few routes, leading to statistically meaningless results. Thus, we only consider cells traversed by a significant number of routes (300 in this case).
Results In a road network with no congestion, all routing strategies should return optimal routes. Hence, our first goal is to distinguish the critical parts of the dataset by identifying the rush hours in each city. The remainder of the paper will solely focus on evaluating the presented routing strategies during rush hours only. We also provide a visual, interactive interface to showcase a sample of the NYC dataset and obtained results at http://its-icube. com/. Each query is represented individually, to visually represent the temporal characteristics of each route all along the week. In particular, we can identify the diverging vertices, as well as the evolution of the congestion.
Absolute Travel Time and Rush Hours Figure  3  illustrates the CF in the Cologne TAPAS dataset. As expected, we identify the rush hours in the morning (6:00-9:00) and afternoon (15:00-19:00) of the working day. The optimal travel time is almost six times longer than the free-flow travel time during these two periods. Figure  4  illustrates the congestion factor for the experimental datasets. We focused uniquely on Thursday, Friday, Saturday, and Sunday to provide readable charts (the remaining weekdays display similar characteristics to Thursday). We can derive the following observations: (1) some queries benefit from travel times smaller than the free-flow. They correspond to very short distances (a few hundred meters) when streets are empty; (2) we observe the rise in congestion starting at 6:00 and intensifying in the afternoon with a peak at 17:00.
Travel Time Stretch and Gain Factors We now compare the achieved travel time for each routing strategy. In particular, Fig. 5  illustrates the stretch factor for the TAPAS dataset (left) vs. the real datasets (right). The stretch factor denotes the travel time increase compared with the shortest path, with an ideal dataset (i. e. , with ideal predictions). We clipped the plot at SF = 3, as it reaches ≈ 6 in the simulation. The static routing strategy provides, as expected, the longest travel time. However, the distribution is significantly different between the simulation and the real dataset. With TAPAS, 28% of the queries have an SF greater than 1. 5. In contrast, we only report 1. 6% of the queries in Cologne and approximately 4% in New York, London, and Chicago. In Cologne, the SF using either the no re-routing or continuous re-routing strategies yields optimal travel times most of the time.
6 ). We normalize the travel time difference of using continuous re-routing (and ideal routing for reference) over the no re-routing strategy. As expected, re-routing is relevant only when congestion occurs. The gain is, however, often negligible when the actual speed is close to 50% the speed limit. For very congested routes (T F ≤ 10%), we reduce the travel time significantly by re-routing the vehicle. Exploiting ideal predictions allows the gain to be even higher, to choose a better route: the best route is selected, before the congestion's increase. Conclusion: continuous re-routing significantly reduces travel time for most queries. In New York City, we can reduce by 15% (1st quartile), which seems to justify the usage of a smart route planning strategy. However, predictions only marginally decrease the travel time for all the measured datasets.
Route Divergence Patterns Figure  8  illustrates the distribution of the diverging vertices in each road network, as defined in section 3. 5. 3. A divergence vertex corresponds to a crossroad where at least one vehicle has been re-routed to reduce travel time (i. e. , traffic congestion has changed since its departure). The divergence ratio counts the ratio of routes (source/destination) for which the path diverges when crossing the cell, with and without the re-routing strategy. The road networks in the real dataset exhibit a small set of diverging vertices with The yellow dots depict diverging vertices with a divergence ratio ≥ 0. 5. a high divergence ratio, typically almost all diverging vertices have a divergence ratio ≤ 0. 5. This means that only 50% of the routes that cross these cells are re-routed at least once during the whole duration of the dataset.
Inversely, the simulated dataset (with TAPAS) exhibits a significant number of diverging vertices. TAPAS generates pseudorandomly the traffic and estimates the level of congestion, and the speed for each road segment. It seems that TAPAS exhibits a very different pattern compared with the real datasets. Figure  9  summarizes the obtained divergence patterns for both the TAPAS and real datasets. We only represent here New-York City since other real datasets behave similarly. Each graph corresponds to a heat map (a red cell corresponds to a cell with a high divergence ratio). We also highlighted the divergence vertices with a divergence ratio ≥ 0. 5 (yellow dots). Remark: while we identify many cells with a high divergence ratio for all the graphs, we can, however, still make a distinction between the TAPAS and the real datasets.
In the TAPAS dataset of Cologne, the divergence seems present everywhere. We assume that this behavior comes from the fact that the source/destination of each vehicle is picked pseudorandomly, and the shortest route is used. Individual routing strategies seem more complex, and the trips seem to follow a uniform distribution. Conclusion: many cells exhibit a large divergence ratio, and we cannot directly use this metric to trigger the route re-computation efficiently. However, we identified only a small number of diverging vertices. Thus, we would be able to execute the computation only when the route crosses these specific points, making the computation much more efficient. We would reduce the processing load without increasing the end-to-end travel time.
Conclusion & Perspectives Modern intelligent systems guide vehicles through the fastest routes to avoid congested areas. However, they need to exploit real-time data, where the speed of each road segment has to be known precisely. Even worse, this real-time feature has a cost, in bandwidth (data collection), and computation (re-computation of the route to the destination). Interestingly, the no re-routing strategy provides close to ideal travel times most of the time. Using the continuous re-routing strategy can further improve travel time by avoiding very congested areas when they appear. We also used a simulated dataset (TAPAS) that leads to very different results concerning the travel time and the re-rerouting gain. TAPAS seems not able to capture the road network characteristics accurately, and particularly its dynamics.
This observation speaks in favor of working with real datasets to model realistic environments. In future work, we plan to enhance the use of real-time data in our route planning strategies. We considered here that each vehicle triggers a route computation after reaching each crossroad (if the speed has changed). We may improve the re-routing strategy by triggering a computation only when the routes may diverge. We identified specific crossroads where vehicles have a higher probability of being re-routed. We only considered selfish decisions in this study, i. e. , each vehicle decides by itself when it should re-compute its route to the destination. To reduce the congestion, we may consider a more collective objective, redirecting some of the vehicles. Thus, we expect to propose a strategy that could also consider the gain in travel time, depending on the characteristics of the route and of the local area.
A subset of only four of them, SOX2, OLIG2, POU3F2, and SALL2, was sufficient to completely reprogram differentiated cells into GSC. Homeobox genes, encoding transcription factors, have been described as major player of stem property regulation during embryonic development and in cancers  [9]. Among homeobox genes involved in cancer, MEOX2 has recently been described as a possible actor of carcinogenesis in Wilms tumor, lung cancer, and in laryngeal cancers  [10] [11] [12]. In non-small cell lung cancer, Ávila-Moreno et al. showed that the overexpression of MEOX2 was correlated with poor patient survival. Moreover, its overexpression was responsible for chemoresistance of A-427 and INER-37 cell lines to cisplatinium. In gliomas, Bao et al. analyzed TCGA dataset for 117 glioblastomas (GBM) with mesenchymal subtype and identified a molecular signature of 17 transcription factors, including MEOX2, which correlated with overall survival  [13].
Patient Cohort Formalin-fixed, paraffin-embedded tissues from 37 surgical resections or biopsies of gliomas were obtained from enrolled patients operated at the University Hospital of Poitiers. The use of human tissue was granted by the secretary of state for education and research, directorate-general for research and innovation, bioethics unit (DC-2008-565), in accordance with the Declaration of Helsinki. Median age at diagnosis was 50 years. Histopathology and molecular characterization were retrieved from the clinical report database (Table  S2 ).
1% Tween 20 (Sigma-Aldrich, Saint-Quentin Fallavier, France) and 5% skim milk. Primary antibodies were incubated overnight at 4 • C and secondary antibodies were incubated for 1 h 30 min at room temperature. Immunoblotting signal detection was carried out by a chemiluminescence method (Clarity ™ Western ECL Substrate, BioRad) by the Luminescent Image Analyzer LAS-3000 (Fujifilm, Minato, Tokyo, Japan). Signal quantification was performed using ImageJ software (https://imagej. nih. gov/ij/, accessed on 1 October 2021, La Jolla, CA, USA) and normalized with β-actin. LAMIN A/C and GAPDH were used as control to identify nuclear and cytosolic fractions, respectively. The list of antibodies used is indicated in Table  S3.