dataesr/scientific-paragraphs-categorization huggingface dataset:
65 m and the latter of 2. 53 m. There is a slight improvement by changing from the raw features to the preprocessed one. In terms of maximum distance error, both approaches suffer from large errors that exceed 10 m. Compared with the published dataset of IPIN 2016  [1] , the performance of the Random Forest model with the preprocessing features is comparable. To reduce the error further, it would require a larger amount of training data. mean error of 3. 61 m. The former has a standard deviation of 2. 65 m and the latter of 2. 53 m. There is a slight improvement by changing from the raw features to the preprocessed one. In terms of maximum distance error, both approaches suffer from large errors that exceed 10 m. Compared with the published dataset of IPIN 2016  [1] , the performance of the Random Forest model with the preprocessing features is comparable.
Evaluation Setup Three approaches are used to localize the users: Wi-Fi only, Non-temporal and Temporal. In the Wi-Fi only approach, the output of the Wi-Fi fingerprinting method is provided as the reference tracking results to evaluate the performance of the two other approaches. First, the data is collected along the designed walking path. The baseline Random Forest (RF) regressor model is used without utilizing the Bluetooth data. In the testing phase, for each incoming Wi-Fi scan from the test devices, the RF model is used to estimate the user position. In the Non-temporal approach, the output positions from the pre-trained RF regressor model on Wi-Fi are calculated for each device. If there is any Bluetooth data available, the Bluetooth scanned information is used for adjusting the positions of two involved devices.
Results and Discussion Figure  12  shows the localization output results along with the users' walking ground truth from the three approaches for different scenarios. Visually, it can be observed that in all the experiments, Wi-Fi only performs very poorly with highly disjointed spots as multiple data scans result in fixed pre-trained locations due to the nature of the fingerprinting method in use. When fusion is applied, the Wi-Fi based results are combined with Bluetooth-based ones and spread along the real path. However, for the Non-temporal approach, because of the grouping of data using time windows, the resulting locations are much more disjointed in comparison with those from the Temporal approach. It is clear in the figure that the Temporal results track the real path more continuously.
A summary of the tracking results using the three mentioned approaches is presented in Table  4. In the configurations with only one user per group (i. e. , number of users equals number of groups), the users are asked to move separately. In other configurations, users are asked to move with distance to each other kept under 10 m. The last scenario is with four users divided into two groups of two, which lets the system use Bluetooth data to identify both close and distant devices. The results are reported as the mean average distance errors across all the testing devices in the specific situation. The Wi-Fi only approach reaches a stable performance of under 4. 0 m in mean distance error. Both Non-temporal and Temporal approaches have better results than the Wi-Fi only one. However, the Non-temporal approach's results are not as stable as the Temporal one.
When the information from the Bluetooth scanning process is added, the distance error is reduced in both Non-temporal and Temporal approaches. The Non-temporal has a clear improvement in the one-group scenario. However, the impact of Bluetooth data when discarding the temporal relationship is insignificant in the two-group scenario. With the Temporal approach, we have stable improvements across the two scenarios. For experiments with three users, Devices 1, 3 and 4 are used, and the results are summarized in Table  6. With the Wi-Fi only approach, the RF regressor model results in similar distance errors for Devices 1 and 3. The positioning results for Device 4 are not as good as the other two devices because the data does not contain training samples for Device 4. Compared to the Wi-Fi only approach, there is a slight improvement by using the Non-temporal one.
Across all the devices and scenarios, the average improvement is around 0. 5 m in mean distance error. The appearance of Device 4 with high distance error clearly affects the performance of the other two devices. The Temporal approach provides more stable improvement compared to the Wi-Fi only by outperforming the two other approaches in all scenarios. The highest improvement is with Device 1 in one-group and Device 3 in three-group settings. With the contribution of the motion model and the Bluetooth-based distance, the Temporal approach could make the effect of non-training data on Device 4 minimal. Results for four users are shown in Table  7. Compared to the previous one-group scenarios with two or three users, it is difficult to keep a close relative distance in this step. The group is supposed to move within a circle of 2 m in radius.
In the second scenario with two groups, the first group uses Devices 1 and 2 whereas the second uses Devices 3 and 4. The distance between the two groups is kept in a range of 5-10 m. Similar results for the three-user case can be seen for the Wi-Fi only approach. Both new Devices 2 and 4 have larger distance errors than the two others. Devices 1 and 3 have a similar performance to the five-fold cross validation testing. It is noticeable that the RF fingerprinting model has trouble in tracking Devices 2 and 4, which are not present in the training data. High errors from RF fingerprinting model add more noise into the later fusion step with the Bluetooth information. The Non-temporal approach can improve the results in most cases. However, its impact is quite low. There are several exceptions where using the Bluetooth data makes the results worse.
For example, the results from Device 1 increase from 3. 05 m to 3. 46 m in the one-group setting. The Temporal approach results in lower errors than the two others. In general, good results are obtained with Devices 1 and 3, whose RSS data are present in the training phase. There is an exception for the results of Device 2 in the one-group scenario, that is the Temporal mean error is slightly higher than that of the Non-temporal. Nevertheless, it is able to reduce to errors compared with the Wi-Fi only approach. Figure  13  illustrates the distance error for three approaches over all the scenarios together. The Wi-Fi only and Non-temporal approaches perform similarly. For 75% of the time, the distance errors of two approaches are around 5 m. The Bluetooth-based relative distance is employed more efficiently in the Temporal approach.
It has a significant improvement from the Wi-Fi-based tracking. For 75% and 90% of the time, the errors of the Temporal and Non-temporal approaches remain under 3. 0 m and 5. 0 m, respectively. Besides the Bluetooth information, employing map information and moving model constraints also help to reduce noisy output from the standard Wi-Fi fingerprinting model. Individual error distribution for each test device is given in Figure  14. Both smartphones, Samsung Galaxy Note 4 (Device 1) and HTC One ME (Device 2) have a similar distribution. The Non-temporal approach gains a slight improvement from using only Wi-Fi data, and the Temporal approach can reduce the error significantly for the range under 7. 5 m. However, the additional Bluetooth data does not help when the tracking error exceed 8 m, and the error of all three models are distributed similarly in the range above 10 m.
Individual error distribution for each test device is given in Figure  14. Both smartphones, Samsung Galaxy Note 4 (Device 1) and HTC One ME (Device 2) have a similar distribution. The Nontemporal approach gains a slight improvement from using only Wi-Fi data, and the Temporal approach can reduce the error significantly for the range under 7. 5 m. However, the additional Bluetooth data does not help when the tracking error exceed 8 m, and the error of all three models are distributed similarly in the range above 10 m. It even adds more noise to the tracking results of Device 2. In the case of Device 3 and Device 4, both Non-temporal and Wi-Fi only have nearly identical distributions while the Temporal one outperforms the two others. The Temporal has the highest improvement with Device 4, which can overcome the issue of non-training data.
temporal approach gains a slight improvement from using only Wi-Fi data, and the Temporal approach reduce the error significantly for the range under 7. 5 m. However, the additional Bluetooth data does not help when the tracking error exceed 8 m, and the error of all three models are distributed similarly in the range above 10 m. It even adds more noise to the tracking results of Device 2. In the case of Device 3 and Device 4, both Non-temporal and Wi-Fi only have nearly identical distributions while the Temporal one outperforms the two others. The Temporal has the highest improvement with Device 4, which can overcome the issue of non-training data.
Conclusions In this work, a collaborative tracking framework based on the smartphone's Wi-Fi and Bluetooth scanning data is introduced. The Wi-Fi data is used as a raw positioning output, which is then improved using the relative distance from Bluetooth inquiry RSS values. Two combination approaches are introduced, namely Non-temporal and Temporal approaches. The Non-temporal approach attempts to simplify the information fusion task by ignoring the time relationship between different Wi-Fi and Bluetooth scans. The Temporal approach takes a more direct way to establish the conditions between the two types of data. Both approaches have been tested and compared with a standard Wi-Fi fingerprinting model. From the testing results, while the Non-temporal is only applicable in some specific scenarios, the Temporal approach outperforms the Wi-Fi fingerprinting models significantly.
Results show that the collaborative positioning based on the Wi-Fi and Bluetooth data would be applicable in a multi-user context. Combining two types of wireless data can reduce the noise from the Wi-Fi fingerprinting model significantly. The testing scenario is still dealing with simple contexts of multiple users. There are also some remaining issues on the technical aspects, such as the communication between the users and the server, energy impacts on the smartphones, and signal inference between multiple devices. For the Temporal, we are currently using a simplistic particle filter based on distributions with constant parameters (e. g. , moving speed, heading direction), which can be targeted for further improvement. Moreover, for demonstration of the effect gained by adding Bluetooth information, a simple Wi-Fi model is used in the experiments, but can be replaced by more advanced models in reality.
Genome Aggregation Database (GnomAD) metrics (misZ: 4. 17; pLI: 0. 99) predict the NR2F1 gene to be highly intolerant to missense mutations, in agreement with the pathogenic phenotype caused by loss-of-function (LOF) variants  (Bosch et al, 2014; Chen et al, 2016). We identified six novel mutations in the NR2F1 gene  (Table 1; Fig 1G) , absent from the GnomAD database. In P1, a missense variant (c. 425G>A; p. Arg142His) was detected in the functional C4-type zinc-finger domain of the DBD. Several pathogenic variants have been previously described in this domain  (Chen et al, 2016). Differently, P2 showed a stop-gain variant in exon 2 (c. 729_730delinsCT; p. Gln244*) predicted to lead to nonsensemediated mRNA decay, since located at 50-55 nucleotides upstream of the last intron-exon junction  (Lykke-Andersen & Jensen, 2015).
Moreover, advances in DNA sequencing technologies and concurrent improved resolution of neuroimaging techniques have allowed the identification of more genes and signaling pathways underlying different types of MCDs  (Juric-Sekhar & Hevner, 2019). In this study, we aimed to investigate the cellular and molecular mechanisms underlying brain malformations of an emerging neurodevelopmental disease, called BBSOA syndrome, in which patients (mainly children) show clinical Data information: Nuclei (blue) were stained with DAPI. In (I, J), the pixel intensity or the number of positive cells was quantified in 100 lm-width boxes, randomly placed across the LP. Data are represented as means AE SEM. Two-way ANOVA (A, C, E, I, J, K; *P < 0. 05, **P < 0. 01, ***P < 0. 001). For statistical analysis of (E, K), see Appendix Table  S5.
Immunofluorescence Mouse embryonic brains/whole heads were processed for immunostaining, as previously described  (Armentano et al, 2006 (Armentano et al, , 2007;; Terrigno et al, 2018). Primary and secondary antibodies used are listed in detailed Materials and Methods (see Appendix File). Images were acquired at an Apotome Zeiss, using the AxioVision software.
Acknowledgements We are grateful to  E. Setti  for technical help and  F. Calegari  for insightful comments. We thank  E. Marti  and  G. Le Dréau  for the Sox2-GFP and Tis21-RFP plasmids,  A. Mansouri  for the Pax6 null line, and  Salsabiel El Nagar  for cell cycle primers. We also thank the iBV animal facility and especially  A. Martres  and  K. Moneret  for animal handling and care, the  PRISM Microscopy facility  for their regular support, and  V. Virolle  at the molecular biology facility for the generation of the  PX458 aNr2f1 CRISPR/Cas9  plasmid. Cryostat sections of human samples were kindly provided by  C. Allet  and  P. Giacobini  ( Lille, France ). This work was supported by an  ERA-NET Neuron   II grant  ( ImprovVision )  ANR-15-NEUR-0002-04 , by the  Jerome Lejeune Foundation  (grant No.
199162 ), and by  "Investments for the Future" LabEx SIGNALIFE  (grant  ANR-11-LABX-0028-01 ) to MS and by a postdoctoral fellowship from the  city of Nice, France  (" Aides Individuelles aux Jeunes Chercheurs "), to MB. The visit of MB in the Lab of SC to perform human organoid experiments was supported by an  EMBO  short-term fellowship (STF# 8035 ).
(2015)  published a detailed database for the Amazon watershed, gathering 39 sample sites, from the Andes to the outlet in the Atlantic Ocean. Measured Li isotopic composition of river waters range from 2‰ to 33‰ and can be explained by considering three types of waters contributing to the Amazon main flow: the white, clear, and black waters. "Black" waters are characterized by high organic matter content and low δ 7 Li (2‰ to 10‰), close to the bedrock values. Dellinger et al. (2015)  suggest that the high organic acid content of the black waters promotes a complete dissolution of the secondary phases. This weathering congruency drives the δ 7 Li of the black waters close to the continental bedrock values, explaining the low riverine δ 7 Li. This process was also evidenced for some rivers draining the Congo basin  (Henchiri et al.
Coupling regolith and floodplain components The cells of the model spatial grid are sorted from upstream to downstream. Then the integration of the two model components is performed cell by cell according to the following steps: 1) Running the regolith model component (Eq. The required climatic fields, including temperature (N) and runoff (B), the erosion field ( ), the water routing and the distribution of flooded areas are taken from global databases, reconstructions or remote sensing cartography. Water routing has been taken from the Global Runoff Data Center at its original resolution 0. 5°x0. 5°. The erosion field is taken from  Ludwig and Probst (1998) , still at its original resolution 0. 5°x0. 5°, and the climatic fields have been taken from the Climate Research Unit database and interpolated on the 0.
Hillslopes We first test the efficiency of the regolith model component, in simulating the effect of batch fractionation on the lithium isotopic composition of the rivers. Accordingly, the regolith component has been run alone without using the floodplain component. We compare our model results to a selection of data points documented by  Dellinger et al. (2015)  as the result of batch fractionation only. As erosion decreases from the Andes to the Amazonian plain, the regolithic cover becomes thicker (Eq. 3), the residence time of water becomes longer and consequently river δ 7 Li should increase as more lithium is retained in secondary phase precipitation (Eq. 2 and 5). Such trends have been observed in the field by  Dellinger et al. (2015)  for 8 points of their database. Being the sole free parameter of our batch fractionation model, the half-retention residence timeH has been tuned so that two conditions are verified: ( ‚ 56 g = • ! -: ' Ž•• '.
i • Ž•• '. i M = ; S g • Ž•• '. i M (11) This is illustrated in Fig. 7  for parameterization case #1. The same result is also observed for the other parameterization (Eq. 9) (not shown). The grid cell where the "regolith" component of the numerical model is dominant plots along the batch fractionation line (black line in Fig. 7 ), with a fractionation factor  Only the case #1 is considered here: Eq. 8 for : Li with parameters optimised over the whole dataset (see section 4. 6). The highest R ; S T Li are calculated for rivers flowing through inundated areas (see for instance Fig. 9 , best-fit parameters of case #1). This result does not depend on the chosen parameterization for : Li. The calculated isotopic composition of the dissolved Li in the Madeira river increases progressively during its journey through a large floodplain (Fig.
5 ). The highest R ; S T Li are found downstream of the inundated area between 12°S and 15°S, and the lower R ; S T Li are found when the Béni river leaves the Andes. In the northern region of the Amazon watershed, the Solimões river seems to display an opposite trend: the highest measured R ; S T Li are found at the entrance of the inundated area (25‰ and more), very close to the Andes (Huallaga, Pastaza, Morona, Marñón at Borja), and R ; S T Li is lower downstream (Tamshiyacu, Iquitos) or for longer river path (Ucayali). Conversely, the model predicts values of 15‰, with a maximum of 20‰ at the feet of the Andes, before entering the floodplain. The optimization of the model parameters has been conducted using all the river samples available in  Dellinger et al. (2015). Doing so, the model fails to explain the whole dataset at the scale of the Amazon basin.
Li (mol/m 2 /yr) is the difference between the specific flux of Li released by primary mineral dissolution and the specific flux of Li retained by secondary phases. ( Li and h, for each grid cell of the Amazon watershed. Practically, the model is fed with the output of two published models giving the spatial distribution of the weathering flux Fdiss (main cations) and of the physical erosion E (respectively West, 2012; Ludwig and Probst, 1998). To limit the propagation of the uncertainties brought by these models into the lithium model, we applied corrective factors to the calculation of Fdiss and E. Over each monitored sub-basin of the Amazon, we use the HYBAM database to correct the calculated fields of erosion and cation release by silicate dissolution. The weathering and erosion are multiplied by a corrective factor, so that they fit the measured values at the outlet of the sub-basin, once integrated over the sub-basin.
P<87( Li (mol/m 2 /yr) is the difference between the specific flux of Li released by primary mineral dissolution and the specific flux of Li retained by secondary phases. ( Li and h, for each grid cell of the Amazon watershed. Practically, the model is fed with the output of two published models giving the spatial distribution of the weathering flux Fdiss (main cations) and of the physical erosion E (respectively West, 2012; Ludwig and Probst, 1998). To limit the propagation of the uncertainties brought by these models into the lithium model, we applied corrective factors to the calculation of Fdiss and E. Over each monitored sub-basin of the Amazon, we use the HYBAM database to correct the calculated fields of erosion and cation release by silicate dissolution. The weathering and erosion are multiplied by a corrective factor, so that they fit the measured values at the outlet of the sub-basin, once integrated over the sub-basin.
The dataset collected to study those dynamics consists of water column physical parameters (temperature, salinity, currents and water level) and atmospheric forcing (wind speed and direction, atmospheric pressure, air temperature and solar radiation) taken at several points around the fjord using oceanographic moorings and land-based stations. The program lasted 2 full years and achieved a good data return of 90%, providing a comprehensive dataset not only for Fortune Bay studies but also for the field of broad fjord studies.
The experiment was, however, unsuccessful with the loss of both surface buoys after about 5 months deployment due to wave action wearing the mooring lines. Of those surface measurements, only one CTD dataset was partially recovered (RBR#60134 from F3B01, found on the shore with its spar buoy). Two main types of mooring were used during year 1 (Figure  2 ), an "ADCP" type having a set of two upward-looking ADCP separated by a string of thermistors and a "CTD" type consisting of two CTDs separated by a string of thermistors. The CTD type was declined in two version for leg 1: surface (F3B01&02) and sub-surface (F3B03&04). For leg 2 (November 2015 -May 2016), only the subsurface version was retained; adding a 9 m rope on the top part of F3B01&02. In year 2 (leg 3 and leg 4), the "CTD" mooring design of year 1 was used as a base and equipped with an ADCP on the bottom part (around 80 m) to combine water stratification with ocean currents measures for most of the sites  (F3B01-08).
Outside the failure of F3B01&02 top mooring part (10 units lost at once), UTBI were typically lost during grappling operations when releases could not be triggered. There were also one failed deployment attempt at F3B08 during Leg 3 (acoustic release failure in May 2016) which was successfully re-deployed in June 2016 but resulted in about 30 days of observation time lost (from early May to early June). Percent coverages were calculated based on the good data recovery of current speed, current direction, current vertical speed and water column backscatter volume for the ADCPs; temperature, salinity and depth for the CTDs; temperature, dissolved oxygen and depth for the RBR TD-DO sensors; temperature and depth (or atmospheric pressure when used as a weather station sensor) for the U20s; temperature for the UTBIs; depth and temperature for the SUTRON tide gauge; wind speed and direction for the SUTRON weather station and wind speed, wind direction, atmospheric pressure, air temperature, PAR and solar radiation for the U30 weather station.
g. pressure measurements). The main biases found were with the pressure sensors of the moored SeaBird Electronic model 19 (SBE19) instruments, which could be as large as 6 dbar (~6 m of water depth). A combination of tank test results and in-situ check using the ADCP (and other instruments when available) and mooring line length were used to determine these pressure biases. Both pressure sensor data and corrected backscatter data (i. e. converted to volumetric backscatter values Sv in dB) were used to determine the insitu depth of the ADCP (i. e. the distance to water surface when using backscatter values) which was then used to crosscheck the depth of the CTDs along the line. Biases that could not be determined with reasonable certainty resulted in discarding the data. Except for leg 1, each moored CTD was sent to the manufacturer for calibration prior to deployment.