dataesr/scientific-paragraphs-categorization huggingface dataset:
Adhesion Under Shear Stress Satellite cells were plated 24 h before the experiment in a fibronectin-coated channel of an Ibitreat micro-slide VI 0. 4 (Ibidi). DMEM/F12 + Glutamax (Life Technologies) supplemented with 4 mM EDTA (Sigma-Aldrich) was injected with a pump syringe (Standard Infusion Only PHD ULTRA ™ Syringe Pumps, Harvard Apparatus, France) in the chamber slide at 218 ml/h as previously described  (Modulevsky et al. , 2012). Images of adhesive cells were taken every 15 s for 24 min with an Olympus IX83 microscope. Cells at each time point were counted with Fiji Cell Counter software with at least 80 cells per condition.
Cellular Migration Satellite cells were plated on fibronectin or laminin-coated 96well plates 24 h before the experiment and medium was renewed 2 h before the start. Cells were placed at 37 °C under 5% CO 2 in a microscope enclosure. Bright field images were taken every 7 min for each position. Cell migration was followed with the Fiji Manual Tracking plugin. Cell persistence quantifies the ability of a cell to maintain its direction of motion. It is the ratio between its distance to origin at the end of the trajectory over the total distance travelled. Mean speed and persistence for each cell were calculated with Excel software. For each condition, at least 250 cells were counted.
Statistical Analysis For microarray analysis, to identify differentially expressed genes with a fold change ≥2, one-way ANOVA was performed using a threshold p-value ≤ 0. 01. For other statistical analyses, nonparametric statistical tests were carried out using GraphPadPrism software or R free software. A non-parametric ad hoc "nparcomp" test was performed for FA area comparison. Histograms were generated using GraphPadPrism software and a Mann-Whitney test was used for immunodetection results. A Kruskall-Wallis test followed by a Dunnet multiple comparisons post-test was used for the migration experiment. A Friedman test followed by a Dunnet multiple comparisons post-test was applied to the percentages of detachment from the shear stress experiment. Differences were considered significant at p < 0.
DesKO and DesKI Satellite Cells Exhibit Decreased Adhesive Properties on Fibronectin Substrate Under Hydrodynamic Flow As FAs are involved in the strength of adhesive bonds between cells and the ECM, we decided to measure the strength of cell adhesion under shear stress, elicited by a flow with a fixed and constant speed. We have previously seen that desmin p. R406W presents a loss of function compared to WT desmin. Our results show that satellite cells lacking desmin or expressing p. R405W desmin detach more rapidly than WT cells. This suggests that these cells adhere less to the substrate, i. e. , fibronectin, compared to control cells. These results are consistent with the TIRFM data obtained on C2C12 myoblasts, suggesting a loss of function of the R406W mutated desmin with respect to AF regulation.
ACKNOWLEDGMENTS We thank  Z. Li  for the DesKO mice. We acknowledge the  "plateforme d'Hébergement et d'expérimentation animale Buffon " for the DesKO-and DesKI-R405W mice. Confocal images were acquired at the imaging facility of the BFA unit. We are grateful to  C. Tourrel-Cuzin  of the " Plateau imagerie cellulaire et cytométrie PIC2 " for technical help and access to TIRFM. We thank the  ImagoSeine core facility  of the  Institut Jacques Monod , member of  IBiSA and France-BioImaging  ( ANR-10-INBS-04 ) infrastructures for videomicroscopy experiments.
03) derived from this search, which will eventually be extended to all KMTNet fields and at least the 2016-2019 seasons. The present paper is based on a search of the KMT database that is restricted to 2018-2019 and to the three pairs of overlapping KMT fields BLG01/BLG41, BLG02/BLG42, and BLG03/BLG43. Each of these fields has a nominal cadence Γ = 2 hr -1 , so the combined cadence is usually Γ = 4 hr -1. The first restriction is determined by the current state of the photometry files (see above). The second is based on maintaining a stable work flow in what will be a very big project. In particular, each event that is selected for publication must be subjected to detailed analysis, so their publication in groups does not substantially reduce either the amount of work required or the length of the descriptions.
For completeness, we report the static solutions for the OGLEonly data set: (χ 2 , t 0 , u 0 , t E , I S ) = (1653. 7, 8590. 5587 ± 0. 0134, 0. 0558 ± 0. 0018, 56. 35 ± 1. 53, 19. 771 ± 0. 036) and (1652. 7, 8590. 5686 ± 0. 0144, 0. 0576 ± 0. 0018, 54. 91 ± 1. 46, 19. 735 ± 0. 035) for the inner and outer solutions, respectively. Because there are of order 9 times fewer OGLE points compared to OGLE +KMT, we expect that the errors will be of order 9 3 = times Figure  1. Light curve and models of KMT-2019-BLG-0253. The main evidence for the "inner" and "outer" planetary model (which are virtually identical) is the "dip" in KMTC data from Chile, near 8588. 6. This dip is supported by contemporaneous OGLE data, also from Chile, and also by two points from KMTS in South Africa. The dip is the signature of a minor-image perturbation, passing either "inside" or "outside" the two triangular caustics associated with the minor image.
See Figure  2. (The data used to create this figure are available. ) larger than those of the static solutions from the full data set, and this expectation is confirmed by comparison to Table  3. Within these errors, the static and parallax solutions are consistent for these parameters. Comparing the OGLE-only static and parallax fits, we see that the errors in the latter are of order two times larger for (u 0 , t E , I S ). This is partially explained by correlations between each of these three parameters and π E,⊥. That is, to leading order, π E,∥ and π E,⊥ give rise to light-curve distortions that are, respectively, antisymmetric and symmetric in (tt 0 )  (Smith et al. 2003; Gould 2004 ). Because the  Paczyński (1986)  formula is even in (tt 0 ) for (u 0 , t E , I S ), these three parameters are correlated with π E,⊥ , which has a large uncertainty.
OGLE-2018-BLG-0506 Figure  3  shows a clear ΔI ∼ 0. 06 mag dip in the KMTC data at Δt anom ∼ 0. 54 days after the peak, with a duration Δt dip = 0. 4 days. The depression defined by these 26 data points, which are taken in good seeing (1 2-1 6) and low background, is confirmed by the two contemporaneous OGLE points. Hence, the anomaly is secure.
Parallax Analysis The timescale of this event is relatively short, and the source is faint. Therefore, we do not expect to be able to measure the microlens parallax π E. Nevertheless, as a matter of due diligence, we explore this possibility. We first find, after including both π E and the lens orbital parameters γ, that the latter are not meaningfully constrained (relative to the physical condition β < 0. 8). Hence, we suppress these 2 dof. As in the case of KMT-2019-BLG-0253, correlated errors in the KMT baseline data induced spurious parallax signals, but in contrast to that case, there was no evidence of spurious signals in the body of this event. Hence, most KMT baseline data were removed, i. e. , roughly 70% of the full-year KMT data set. For consistency, the same cuts were applied for the static fits.
Parallax Analysis The overall situation here is similar to that of OGLE-2018-BLG-0506 (Section 3. 3. 3). However, in contrast to that case, there were no spurious signals from the KMT baseline data, so the full data set was included in the fit. Again, we test for and then suppress the orbital motion parameters γ. In this case, we find that π E,∥ is consistent with zero at the ;1. 5σ level, so the χ 2 improvement is ;2 for 2 dof. Again, we find little difference for the other parameters between the static and parallax analyses. And again, we adopt the parallax solution for our final result, noting that the π E,∥ constraint can play some role in the Bayesian analysis of Section 5.
Parallax Analysis The situation here is similar to that of OGLE-2018-BLG-0506 (Section 3. 4. 3). Spurious parallax signals from the KMT baseline force us to remove most of these data points, corresponding to about half of the full-year KMT data set. Again, we test for and then suppress the orbital motion parameters γ. Again, we find that π E is only weakly constrained (and only in the π E,∥ direction). Again, we find little difference for the other parameters between the static and parallax analyses. And again, we adopt the parallax solution for our final result.
OGLE-2018-BLG-0977 The KMTS data show a strong and sudden dip ending in a flat minimum, which is characteristic of the source passing into the trough between the two triangular minor-image caustics, with the entrance close to one of these caustics. One therefore expects the source to pass over the other caustic, an expectation that is confirmed by the rapid drop of the KMTA data during the first three points of the night, which span 69 minutes. Thus, KMTS and KMTA give independent and completely consistent evidence for a minor-image caustic crossing. The center of the dip occurs Δt anom = + 4. 0 days after peak. Because the source crosses the two triangular caustics, the Δt dip = 0. 5-day depression is a much more clearly defined structure than in the four previous cases.
OGLE-2018-BLG-0506 We note that the pyDIA reductions of the KMTC data are only approximately calibrated. However, this has no practical importance because the filters are near standard and the only quantity entering the calculations that follow is the offset between the source and the clump. Moreover, this offset is small. See Figure  8. For reference, we note that in the other four events the KMT → OGLE-III correction was found to be [(V -I), I] s,kmt -[(V -I), I] s,ogle-III = [0. 06, 0. 22], [0. 17, 0. 12], [0. 14, 0. 01], and [0. 03 ,0. 22], for  KMT-2019 -BLG-0253, OGLE-2018 -BLG-0516, OGLE-2019 -BLG-1492 , and OGLE-2018 -BLG-0977, respectively. We find a best estimate for the blended light I b = 21. 08 on the OGLE-IV system and I b = 21. 11 on the KMTC41 pyDIA system. Even ignoring the uncertainties in this estimate due to the mottled background light, which can be considerable (e.
OGLE-2018-BLG-0516 The KMTC01 pyDIA analysis formally yields a negative blended flux that, after conversion to the OGLE-III system, , which is not much larger than the individual error bars, but because the event lies in a small region with very high cadence, there are >100 data points during this 12 hr interval. Eight OGLE points near the trough confirm the dip. The dip indicates that the source passed near, but not over, the minor image due to the host. See Figure  2. (The data used to create this figure are available. ) corresponds to an I b = 21. 1 "anti-star. " This level of negative blending would be easily explained by errors in the PSF-fitting photometry of faint stars in the baseline images, even without taking account of the unresolved mottled background. Hence, it is of no concern in itself and can be regarded as consistent with zero.
KMT-2019-BLG-0953 There was significant difficulty measuring the source color of KMT-2019-BLG-0953. As with the other four events that lie in the OGLE-III catalog  (Szymański et al. 2011 ) footprint, our approach is to first measure the source color in the KMT system by regression and then to transform this color to the OGLE-III system by comparison of field stars. The results for all events are shown as blue points in Figure  8. However, for KMT-2019-BLG-0953, we applied this procedure five times, using the independent data sets from KMTC02, KMTC42, KMTC03, KMTC43, and KMTS02. Strikingly, these five determinations are composed of two groups: (KMTC02, KMTC43) were closely grouped near (V -I) s,OGLE-III ; 1. 94, while (KMTC03, KMTC42, KMTS02) were closely grouped near (V -I) s,OGLE-III ; 2.
Methods. Once a minimum initial number of radial velocity datapoints is reached in such a way that a periodicity starts to emerge according to generalized Lomb-Scargle periodograms, that period is targeted with the proposed algorithm, named KOBEsim. The algorithm selects the next observing date that maximizes the Bayesian evidence for this periodicity in comparison with a model with no Keplerian orbits. Results. By means of simulated data, we proved that the algorithm accelerates the exoplanet detection, needing 29-33% fewer observations and a 41-47% smaller time span of the full dataset for low-mass planets (m p < 10 M ⊕ ) in comparison with a conventional monotonic cadence strategy. For 20 M ⊕ planets we found a 16% enhancement in the number of datapoints. We also tested KOBEsim with real data for a particular KOBE target and for the confirmed planet HD 102365 b.
Some of these approaches have been presented, such as for ALMA  (Espada et al. 2014) , JWST  (Giuliano et al. 2007) , and the CARMENES Guaranteed Time Observation (GTO) survey  (Garcia-Piquer et al. 2017 ). In the context of RV searches,  Cabona et al. (2021)  proposed that uniformly distributed data along the phase-folded RV diagram favors efficiency. As a consequence, observing in an already explored orbital phase (ϕ) does not significantly increase the information in hand. In this paper we present KOBEsim 2  , a Bayesian algorithm to improve the efficiency of planet detections in RV-blind searches through optimizing the scheduling of observations. It is an open source code written in Python language available to the community. Bayesian adaptative scheduling algorithms have been already demonstrated to be powerful in previous works (e.
2022). KOBE is a legacy program of the Calar Alto Observatory (CAHA; Almería, Spain), making use of the Calar Alto high-Resolution search for M dwarfs with Exoearths with Near-infrared and optical Echelle Spectrographs (CARMENES), a fiber-fed échelle spectrograph  (Quirrenbach et al. 2020 ) at the 3. 5 m telescope. KOBE observations began in January 2021 and will be monitoring 50 late K-dwarf stars over five consecutive semesters. The main goal of this experiment is to bridge the gap between G and M dwarfs in the search for planets within the habitable zone (HZ), a parameter space that has been barely explored (see Fig. 1  in  Lillo-Box et al. 2022). This paper is organized as follows. In Sect. 2 we describe the methodology, and provide a description of the architecture of the KOBEsim code.
(4) Meanwhile, K(t j ) is zero for the null hypothesis H 0 , thus the parameters are Θ 0 = (V sys , S j ). (5) We estimate the posterior probability of each hypothesis using the Bayes relation P(H i |D) = P(H i )Z i P(H i )Z i + P(H j )Z j , (6) where Z i is the evidence (i. e. , marginal likelihood) under the hypothesis i, and D is an array of datapoints. Thus, the ratio for the competing models (the posterior odds) is P(H 1 |D) P(H 0 |D) = P(H 1 ) P(H 0 ) B 10 , (7) where B 10 is the evidence (or marginal likelihood) ratio, called the Bayes factor. Assuming the same prior probability of a star hosting one or no planets (i. e. , P(H 1 )/P(H 0 ) = 1), we can use B 10 as a metric to analyze how significant one model is compared to another for a given dataset. An intrinsic feature of this mathematical construction is Occam's razor (e.
Estimating the evidence of the models Once we have a predominant signal (P peak ) we are ready to run KOBEsim. First, it derives the set of parameters from the data for both the null (H 0 ) and planet (H 1 ) hypotheses (corresponding to Fig. 1 panel a ). We explore the parameter space and sample the posterior distribution by using the Markov chain Monte Carlo (MCMC) affine invariant ensemble sampler emcee (Foreman-Mackey et al. 2013). To compute the Keplerian in the H 1 model, Eq. (3), we use the python module RadVel  (Fulton et al. 2018). Considering normally distributed data around the theoretical -2 ln L(D|θ) = Q + n j=1 ln σ 2 j + S 2 j + n j=1 V sys + K(t j ) -v j 2 σ 2 j + S 2 j , (8) where D is our RV measurement and its associated uncertainties (D = {(v j , σ j )} n j=1 ), and Q is a constant.
Forecasting the optimum observing date In the second step of the code we use the existing data to select the future date that most (or more optimally) increases the evidence of the planet model at the targeted periodicity. For this purpose, we predict and compare the expected increase in the ln(B 10 ) for each candidate date in our schedule. The Bayes factor is our metric for measuring the gain, which in decision theory is called the utility function. This quantity is commonly used in RV datasets to test different hypotheses, and thus can be used to claim a planet candidate (e. g. ,  Lillo-Box et al. 2020; Mortier et al. 2020; Faria et al. 2022). Previous works on Bayesian adaptative schedulers adopt other utility functions based on their goals. For instance,  Loredo et al. (2011)  opt for the Shannon entropy as they aim to improve the efficiency in constraining the parameter inference, and this utility function is used to reduce the posterior uncertainties.
Running again emcee and bayev over each of the datasets (including one additional datapoint corresponding to each predicted RV at a proposed date), we end up with an estimation of ∆ ln(B 10 ) for each of the proposed future dates. KOBEsim sorts the tested dates according to the utility function, giving the maximum priority to the highest ∆ ln(B 10 ) (stage illustrated in Fig. 1  panel c ). For long targeted periodicities, the largest ln(B 10 ) increase may occur at a very distant date, which is against the efficiency of the observations. Consequently, the detection using KOBEsim could require a long time span despite needing a lower number of measurements. To prevent this situation, we introduce a weight to the utility function with the shape of a density function of a beta distribution, such that ∆ ln (B 10 ) = β(∆t, a, b) ln (B 10,n+1 ) -ln (B 10,n ) , ( 9 ) where ∆t is the difference in days between the new proposed observation and the current date (normalized on all candidate dates), and n denotes the number of gathered datapoints at the moment of running KOBEsim.
Results In this section, we show different analyses to test the algorithm against simulated (Sect. 3. 1) and real (Sect. 3. 2) datasets. Parameter Value for the simulation Units V sys U (-10, 10) m s -1 P U (50, 120) d t 0 U (t 1 , t 1 + P) d M ⋆ 0. 5 M ⊙ m p (5, 10, 20, 60) M ⊕ i 90 deg e 0 - 3. 1. Testing KOBEsim on simulated datasets.
In this particular simulation, we imposed the constraint that if the next matching date is more than three months away from the current date, that sub-phase is discarded 6 : since the KOBE program monitors 50 targets and it was awarded with ∼55% of the nights, the chances of observing the target are high, and waiting that long would imply a waste of telescope time. Once all of the candidate dates are calculated, KOBEsim computes which is the best given the current data and assuming the input periodicity. The execution of the code takes ∼32 s/phase and is repeated N phases + 1 times, which leads to a total runtime of ∼15 min. In Fig. 3  we show the output plot returned by the algorithm. The difference in ln(B 10 ) of the one-planet model against the null hypothesis is shown for each orbital phase.
The figure shows that the algorithm generated fewer than 20 candidate 6 The maximum days apart parameter can be set by the user; see Table  A. 1. A18, page 5 of 14  A&A 669, A18 (2023)  points. This is caused by the impossibility of observing the target over the next three months, either because the corresponding dates are not assigned to the program or because the star has already set. As in this example, we use the beta distribution, meaning that the criterion for selecting the best observing date is not the one that maximizes the increase in ln(B 10 ). Instead, KOBEsim maximizes the weighted ∆ ln(B 10 ) given by Eq. (  9 ), thus finding a trade-off between efficiency and the time we have to wait until the next observation. In this example, we find out that this occurs for an orbital phase around ϕ = 0.
In Fig. 4  we show the results for planets of 5, 10, 20, and 60 M ⊕ using P = 59 d and P peak = 62. 6 d. In this section we do not update P peak ; we keep it fixed to run KOBEsim at each iteration. For all of these planetary masses except for 5 M ⊕ , we considered the visibility of our testing target. As the precision of CARMENES in its optical arm is around 1 m s -1 , a planet of 5 M ⊕ corresponds to the detectability limit case for the KOBE experiment (RV semi-amplitude within the HZ of late K dwarfs between 0. 72 and 1. 15 m s -1 ). Since such a detection is very demanding, we simulated these measurements considering a circumpolar star, and thus every night of the year can be used to collect data if the weather conditions are favorable. In addition, to achieve a detection of this kind in the time that the KOBE program lasts, it would be necessary to increase the exposure time (thereby increasing the signal-to-noise ratio to reduce the uncertainty).
In practice, this would only be feasible with the brightest targets as the maximum exposure time allowed by the CARMENES instrument is 1800 s. For this reason for 10, 20, and 60 M ⊕ (see Fig. 4 ), we considered a conservative uncertainty following a normal distribution with a mean of 3 m s -1 , whereas for 5 M ⊕ we reduced the mean to 1. 5 m s -1. We started with ten initial datapoints. In every planetary mass case, we see the gradual increase in ln (B 10 ) that each strategy follows as new observations are added. We checked that different initial sets of simulated data (i. e. , varying the white noise in the RV, the first observing date, and phase coverage, but keeping the same time span and number of measurements) did not significantly change the results. Particularly, in Fig. 4  the standard deviation in the y-axis (ln (B 10 )) is below 1 for every number of observations and the relative error in the corresponding slopes are 6%.
Throughout the manuscript we have mentioned some caveats to bear in mind when using KOBEsim. First of all, the user has to take care of the preprocessing of the data in order to mitigate any RV signal induced by activity. The RV time series should be corrected from activity before running KOBEsim, for instance subtracting linear and quadratic trends. The use of activity indicators such as line-bisector or the chromospheric contribution of the H and K Ca lines are also useful to disentangle the planetary and activity component in the signal (e. g. ,  Queloz et al. 2001). Second, as concluded in Sect. 3. 1. 4, the period to be targeted must always be updated after a new observation is added to the dataset. Third, the implementation in the code of a multiplanetary system model (hypothesis H n planets ) is straightforward.
Acknowledgements. We are grateful to  Dr. Rodrigo Díaz  for his useful suggestions as referee helping to improve the quality of the manuscript. O. B. -R. , J. L. -B. and A. C. -G. acknowledge financial support received from  "la Caixa" Foundation  ( ID 110000434 ) and from the  European Unions Horizon 2020  research and innovation programme under the  Marie Sklodowska-Curie  grant agreement No  847648 , with fellowship code  LCF/BQ/PI20/11760023. This research has also been partly funded by the  Spanish State Research Agency (AEI)  Projects No. PID2019-107061GB-C61  and No. MDM-2017-0737  Unidad de Excelencia "María de Maeztu"-Centro de Astrobiología (INTA-CSIC ). This work was supported by  Fundação para a Ciência e a Tecnologia (FCT)  and  Fundo Europeu de Desenvolvimento Regional (FEDER)  via  COMPETE2020 -Programa Operacional  Competitividade e Inter-nacionalização by these grants:  UIDB/04434/2020 ;  UIDP/04434/2020; PTDC/FIS-AST/32113/2017 & A18 , page 11 of  14 A&A 669 ,  A18 (2023 )  POCI-01-0145-FEDER-032113; PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953.
A. M. S. acknowledges support from  FCT  through the Fellowship 2020. 05387. BD. and  POCH/FSE (EC). O. D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by  FCT. J. P. F. is supported in the form of a work contract funded by national funds through FCT with reference  DL 57/2016/CP1364/CT0005. A. M. S. acknowledges financial support from the  French Programme National de Planétologie (PNP, INSU).