dataesr/scientific-paragraphs-categorization huggingface dataset:
We then compute (ρ i 0 , a i ) of the excluded variants using the estimated α, β. For this subset, we use the variants that satisfy the following two criterions: (i) their counts are larger than 0 more than twice in the selection rounds and (ii) whose total NGS count (as summed over all the rounds) is above a threshold. We set this threshold to 100 for all the datasets except for Data-C -D, where 10000 is used. This is because the noise in these experiments is larger than the others. Results are stable by changing the threshold value (Fig. S2F ).
The obtained quantity is log C for the next iteration. For the external loop, to obtain (α, β), we first infer the dispersion parameter r for a list of different values of λ. To do so, for a fixed value of λ, we select a set of index i and the time t by the condition λ < ρ i t Nt < λ + (with a small parameter ). Only using these i and t, we then maximize the likelihood function (2) and determine the value of r(λ). After obtaining the function r(λ) for several values of λ, we fit a linear function r(λ) = αλ β to it and determine α and β. (B,C,D) Ground truth comparisons for the synthetic data-rich dataset (Fig. 1E ) after 30th iterations demonstrates that the algorithm can recover the generating parameters. In the panel B, we plot the estimated parameters (a i ) M i=1 (left) and (log ρ i 0 ) M i=1 (right) against their ground truths.
For the representative variants, we select the variants whose total counts are larger than a threshold value m b. In (F), by using the synthetic data, we show that the inference results (α and β) are robust against the change of this parameter m b. Here we use synthetic datasets, ranging from data-poor to data-rich regimes, to show that the empirical quantity RR correlates with our ability to recover the true values of scores a i and initial frequencies ρ i 0. For this, we generate synthetic datasets with different values of total NGS reads N and number of variants M : (N, M ) = (10 7 , 5 × 10 5 ); (8 × 10 6 , 5 × 10 5 ); (6 × 10 6 , 5 × 10 5 ); (4 × 10 6 , 5 × 10 5 ); (2 × 10 6 , 5 × 10 5 ); (10 6 , 5 × 10 5 ); (10 7 , 10 6 ); (8 × 10 6 , 10 6 ); (4 × 10 6 , 10 6 ); (2 × 10 6 , 10 6 ); (10 6 , 10 6 ).
In these datasets, the parameters (α, β) are the same as those used in Fig. 1E, F  A) For all the synthetic datasets introduced in Fig. S3. hit rates of the confidence interval of ranking graphs are plotted against their RR, where the hit rate is defined as the number of true ranking (red crosses in Fig. 1E , F for example) that are within the 95%confidence intervals (green lines in Fig. 1E , F), divided by 50. The hit rates fluctuate around 0. 95 as expected, demonstrating the quality of our estimation of ranking CI. B) Rank robustness (RR) estimated from inferred parameters is plotted against the true value (ground truth) for the synthetic datasets. The obtained high coefficient of determination shows that ACIDES can estimate RR also in the data-poor regime. For this, we first estimate the parameters of ACIDES from the dataset.
We then generate synthetic NGS data using the likelihood function (2) with smaller numbers of the total NGS reads. For example, if we downsample the data to 40%, we set Nt to be 0. 4Nt. We then estimate RR using ACIDES for this downsampled dataset without reinferring α, β. (B) We show the validity of this down sampling method on the synthetic data. The parameters for the synthetic data are (Nt, M ) = (10 7 , 5×10 5 ), (α, β) = (0. 69, 0. 8) and (a i , log ρ i 0 ) M i=1 generated from the normal distribution with the expected values (-1,1) and the standard deviation (0. 25, 1). We plot RRs obtained from this downsampling method (blue circles) and from a standard sampling method (orange crosses) as a function of the total number of NGS (where 100% means the original data). Here the standard sampling method means using ACIDES directly on the dataset with the total number of NGS 0.
experiments. It is possible to apply ACIDES 74 either a posteriori over data collected previously, or along 75 the course of the experiment as soon as the NGS data 76 become available. The latter strategy allows for moni-77 toring the selection convergence on the fly, and to un-78 derstand when the experiment can be ended. In this 79 way, ACIDES can be integrated into protein engineering 80 pipelines as well as studies of protein function using mu-81 tagenesis. The tutorial for using ACIDES, along with an 82 executable code in Python, will be available in GitHub 83 upon publication of this manuscript. 84 RESULTS 85 The first step of ACIDES estimates the selectivity of 86 each individual variant present in the dataset (Fig. 1B).
1D). Specifically, RR is the 114 ratio at which the top-50 variants in the naive rank are 115 correctly identified (Materials and Methods). RR ranges 116 from 0 to 1: a low value points out that the variants have 117 not been selected enough, and therefore calls for the ne-118 cessity to perform more rounds, deeper NGS sampling 119 or possibly more replicates. Conversely, a large value 120 confirms that the selection has properly converged, and 121 suggests that the experiment can be ended without per-122 forming additional experimental steps. 123 Before focusing on experimental data, we apply 124 ACIDES to two synthetic datasets (Materials and Meth-125 ods) describing two opposite scenarios (See Fig. s S3 and 126 S4for more systematic comparison): data-rich case (more 127 NGS reads with fewer unique variants) and data-poor 128 case (less NGS reads with more considered variants).
550 Pre-processing of Data-C and Data-D 551 In their original datasets, Data-C and -D contain a 552 large number of variants whose total counts are very low 553 (but not zero). In order to speed up the analysis and 554 make the analysis more robust we removed the variants 555 whose total counts are smaller than 1000 (Data-C) and 556 than 100 (Data-D). The NGS depth and the number of 557 unique variants shown in.
2. Rank graph for various experimental datasets. The panel labels A-G correspond to the experiments listed in table I. (H) Rank robustness (RR) for each experiment. When technical replicates are available (Data-A and -B), the mean and standard deviation are shown. 186 A and Data-B, which would normally help these datasets 187 with having higher RR, given the same NGS depth. As ). In these cases, the obtained naive rank is robust, as indicated by the value of RR (RR > 0. 8). In the intermediate group, the value of RR ranges between 0. 6 and 0. 8. The experimental techniques used in these datasets are similar to those in the high 188 189 190 191 192 this is not the case, we see that some experiments are in-trinsically more difficult than the others, i. e. , in-vivo DE (Data-C and Data-D) and RNA based screening (Data-E) will result in lower RRs than the other experiments if the NGS depth and number of variants are similar.
group, but the NGS depths (or the numbers of unique 193 In datasets with low RRs, some variants seem to per- variants) are smaller (or larger), which could be the rea- 194 form better than the others, but the difference between son why they result in lower RRs. The low group suffers 195 their scores is marginal compared with their statistical from the noise in the data. In Data-C and Data-D, the 196 errors. This means that we cannot distinguish if the ob- numbers of unique variants are lower than those of Data- 197 tained variants are selected because of their ability to per-.
For this reason, this strategy is 316 333 suboptimal and may lead to noisy score estimations. A 317 plied ACIDES to several DE and DMS datasets where a 334 more sophisticated approach that uses all the data con- 318 number of different target proteins and one set of target 335 sists in inferring the slope of a linear line fitted to the log- 319 RNA molecules have been screened using different exper- 336 frequencies of variants over all the screening rounds/time 320 imental protocols. The heterogeneity of these datasets points [23, 24, 28, 30]. This method gives the same im- 321 shows that ACIDES is a method of general use, applica- 322 ble to many different experiments. 323 The first step of ACIDES estimates the score (selectiv- ity) of each observed variant. This is a necessary step, 329 306 DISCUSSION 307 In this work we have presented ACIDES, a method to 308 quantify DE and DMS selectivities (fitness), rank vari-309 ants with accurate credibility scores and measure the de-310 gree of experimental convergence.
Enzyme Promiscu- ity: A Mechanistic and Evolutionary Perspective. Annual 558 559 cessing. 560 DATA AVAILABILTIY 561 All data analyzed in this article (Table I) are publicly 562 available except for the random-peptide inserted library 563 used for Fig. S1. This library will be deposited in a public 564 database upon publication of this article. 565 CODE AVAILABILITY 566 A Python implementation of ACIDES will be available 567 on GitHub upon publication of this article. 568 ACKNOWLEDGMENTS 569 The authors would like to thank A. Rubin and D. 570 Fowler for kindly providing them with datasets (Data-A 571 and Data-B) and a working code for enrich2. The authors 572 also would like to thank L. C. Byrne and T. Mora for 573 useful comments and discussions, M. Desrosiers and C. 574 Robert for their technical assistance with the production 575 of plasmids and viral vectors, and O.
Marre for facilitat- 576 ing and initiating the collaboration and helpful discus- 577 sions. This work was supported by ERC Starting Grant 578 (REGENETHER 639888), European Research Council 579 (ERC) Horizon 2020 Framework Programme Project: 580 863214 -NEUROPA, UNADEV, the Institut National 581 de la Santé et de la Recherche Médicale (INSERM), Sor- 582 bonne Université, The Foundation Fighting Blindness, 583 Agence National de Recherche (ANR) RHU Light4Deaf, 584 LabEx LIFESENSES (ANR-10-LABX-65), IHU FORe- 585 SIGHT (ANR-18-IAHU-01), and JSPS KAKENHI Grant 586 Number 22K17994. [1] Frances H Arnold. Design by directed evolution. Ac- 589 [2] Philip A Romero and Frances H Arnold. Exploring pro- counts of chemical research 31, 125 (1998). tein fitness landscapes by directed evolution. Nature re- 597 mamide.
LMO radiopurity for the U and Th chains of less than 10 and 3 µBq/kg respectively are needed to meet the CUPID goals  [17] , this can be validated on a mid-scale with CUPID-Mo. In this paper we present the background model which describes the background sources in the CUPID-Mo experiment. This model is based on fitting the CUPID-Mo data to detailed Monte Carlo simulations. We show that the residual α background contribution and the radiopurity of the LMO crystals are sufficient to meet the CUPID background goal. CUPID-Mo was also an important experiment in its own right. In particular, it has set the world-leading limits on the half-life of 0νββ decay of 100 Mo to both ground and excited states  [6, 11, 18]. The detailed study of the experimental backgrounds in the CUPID-Mo experiment enables a high precision measurement of the 2νββ decay rate and allows to disentangle between the Single State Dominance (SSD) or High State Dominance (HSD) mechanisms  [19, 20]  of the 2νββ decay process in 100 Mo.
Data taking In this paper, we use the same dataset as in  [6]  with an exposure of 2. 71 kg × year of LMO corresponding to Inside the five towers are placed the LMO crystals, the light detectors, the clamps and the reflective foils (not seen). The readout cables and the structure supporting the towers are indicated Fig. 3  GEANT4 rendering of the CUPID-Mo Monte Carlo simulation geometry, showing the cryogenic set-up 1. 47 kg × year of 100 Mo. Our data is acquired as a continuous time-stream and digitized at 500 Hz by the EDEL-Fig. 4  Visualisation of the EDELWEISS cryostat and shielding as implemented in our MC simulations, we show the cryostat surrounded by the lead shield, the external polyethylene shielding and the muon veto panels. The muon panels are free to move to give a full geometric coverage WEISS DAQ  [36]  and stored at both CC-IN2P3 (France) and NERSC (USA) for offline analysis.
We acquire runs, periods of around 10-100 h of stable data taking, of both physics and calibration data, where a calibration source was placed in the vicinity of the experiment. We use regular calibrations with a 232 Th/ 238 U source to calibrate the LMO detectors and a high activity 60 Co source, which generates 100 Mo X-rays in the detectors, to calibrate the LDs. We divide the data into twelve periods of ∼ 1 month of stable data taking, called datasets. We discard three short periods of data (≈ 1 week each) due to the low statistics causing an inability to accurately calibrate this data.
Data processing We process our data using the C++ softwares Apollo and Diana  [37, 38] , first developed for the CUORE experiment and also used by CUORICINO, CUPID-0 and CUPID  [39]. A complete description of the data processing can be found in  [6]. We identify physics events using an optimal trigger, also used for previous CUPID-Mo analysis. This triggering is used for both the LDs and the LMO bolometers. We then store a 3 s waveform for both LDs and the LMO channels for each triggered events. For each LMO we associate (up to) two light detectors, called side-channels, which correspond to the LDs facing this LMO detector. These are numbered S1/S2 where S1 is the LD with the better detector performance (lower noise and higher detector light yield). We estimate the amplitude of peaks using an optimal filter, which maximises the signal to noise ratio based on inputs of the known signal shape and spectral noise power density.
Particle identification Since CUPID-Mo is a dual readout experiment we can discriminate α from β/γ particles. The use of light detectors also allows us to remove background events in which a particle deposits energy on our LDs. We select β/γ candidate events using the LD signal as following. We normalise the measured LD signals by defining the variable n, as the difference between the measured LD energy, E L D , and the mean expected β/γ LD energy L, normalized by the light band width (σ ). We compute n for M 1 events. As each LD has different characteristics, the calculation is done for each channel (crystal) c, each dataset d and for both side channels s, i. e. : n c,s,d = E L D -L c,d,s (E) σ c,d,s (E) , (1) with E the measured LMO energy. The parameter n c,s,d has a distribution expected to be centered at zero for β/γ 's and at a value different from zero for α particles.
Data spectra Based on these cuts we construct the three data spectra used in our analysis: -M 1,β/γ : Events in one detector identified as β/γ , -M 2 : Events in coincidence between 2 crystals, the two energies deposited in each crystal are summed, -M 1,α : Events in one detector with alpha energy scale (> 3 MeV). Because of the relatively fast half-life of 2νββ in 100 Mo (∼ 7×10 18 year) and extremely low levels of contamination, relatively few peaks are observed in the M 1,β/γ spectrum, where the spectrum of 2νββ decays of 100 Mo is the dominant feature. The secondary datasets, M 2 and M 1,α however contain a lower fraction of 2νββ events and therefore provide useful information to determine the location of radioactive contaminations. The experimental spectra after all cuts are shown in Fig.
Energy scale and resolution We use the observed γ peaks in both background and calibration data to predict the energy linearity and resolution. Each LMO detector in each dataset has a distinct energy resolution. As in  [6, 11]  we perform a fit of the 2615 keV peak in calibration data to extract the resolution of each detector-dataset pair. We use these resolutions to build a function including a common scale factor R(E) which will be determined for the peaks in background data. For our Monte Carlo simulations for each event we sample from a Gaussian with mean E and width R ×σ c,d , where σ c,d is the energy resolution in channel c and dataset d. This energy calibration is discussed in detail in  [6]. PSD (M 1,β/γ ) M 1,β/γ γ -peaks 95. 2 ± 0. 5 PSD (M 2 ) M 2 γ -peaks 96. 9 ± 0. 5 Light distance (M 1,β/γ ) M 1,β/γ γ -peaks 99.
Features of data spectra We observe in Fig. 5  that the spectrum of 2νββ decays of 100 Mo dominates the M 1,β/γ data, whereas the M 2 spectrum has significant contributions from natural radioactivity, shown by prominent γ peaks. These consist predominantly of decays from the 238 U and 232 Th decay chains, however we also observe contributions from 40 K and cosmogenic activation products 60 Co and 57 Co. We also observe a short lived peak of 99 Mo, present for ∼ 1 dataset, from neutron activation after a calibration with an AmBe neutron source. The spectrum M 1,α is dominated by α decays from components very close to the detectors. As shown in Fig. 5 , in our data we observe a large contribution of 210 Po, E α = 5303 keV, with both a large Q-value and α-energy peak and peaks from several other nuclides in the U/Th chains.
Taking into account the surface of the CUPID-Mo detectors, we get that the neutron flux expected is less than 1 neutron/year. Thus, ambient neutrons are not taken into account among our background sources. The gamma flux at LSM has been measured with a portable Ge detector at several locations in the laboratory. At the place where the EDELWEISS set-up is installed, the flux of 2. 6 MeV photons was measured as 5. 1±0. 2 (stat. )×10 -  γ /s/cm 2  [46]. Considering that 20 cm of lead reduce the flux by about a factor 10 4 , then the contribution of environmental gammas may not be negligible. We expect about 6 photons of 2. 6 MeV on the detectors surface during the course of all data taking. We take them into account by generating decays at the level of the outermost cryogenic thermal shield, as the spectral shapes measured in the detector from a source generated outside the external lead and outside the outermost cryogenic thermal differ slightly only below 500 keV.
Detector response model To compare the simulated spectra to the measured data we need to account for the finite energy resolution and response of the detectors. The following features are accounted for through a post processing of the MC simulation spectra: -Energy resolution; -Energy threshold of 40 keV; -Event multiplicity; -Scintillation light and LD resolution; -Cut efficiencies; -Inactive periods of detectors; -Pile-up and delayed coincidences in decay chains. We compute the energy resolution per detector-dataset pair as explained in Sect. 3. 10. In particular, for a pulse with energy E MC in channel c and dataset d we sample from a Gaussian with mean E MC and standard deviation R × σ c,d. As is done in experimental data we discard pulses below the energy threshold, < 40 keV, and compute the multiplicity as the number of detectors with E > 40 keV for each simulated event.
Acknowledgements This work has been performed in the framework of the  CUPID-1  ( ANR-21-CE31-0014 ) and LUMINEU programs, funded by the  Agence Nationale de la Recherche  ( ANR, France ). We acknowledge also the support of the  P2IO LabEx  ( ANR-10-LABX0038 ) in the framework " Investissements d'Avenir"  ( ANR-11-IDEX-0003-01  Project " BSM-nu ") managed by  ANR, France. The help of the technical staff of the  Laboratoire Souterrain de Modane  and of the other participant laboratories is gratefully acknowledged. We thank the mechanical workshops of  LAL (now IJCLab ) for the detector holders fabrication and  CEA/SPEC  for their valuable contribution in the detector conception. F. A. Danevich ,  V. V. Kobychev ,  V. I. Tretyak  and  M. M. Zarytskyy  were supported in part by the  National Research Foundation of Ukraine  Grant No.
2020. 02/0011. A. S. Barabash ,  S. I. Konovalov ,  I. M. Makarov ,  V. N. Shlegel  and  V. I. Umatov  were supported by the  Russian Science Foundation  under Grant No. 18-12-00003. J. Kotila  is supported by  Academy of Finland  (Grant Nos. 314733 ,  320062 ,  345869 ). Additionally the work is supported by the  Istituto Nazionale di Fisica Nucleare (INFN)  and by the  EU   Horizon2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No. 754496. This work is also based on support by the  US Department of Energy (DOE) Office of Science  under Contract Nos. DE-AC02-05CH11231 , and by the  DOE Office of Science, Office of Nuclear Physics  under Contract Nos. DE-FG02-08ER41551 ,  DE-SC0011091. This research used resources of the  National Energy Research Scientific Computing Center (NERSC)  and the  IN2P3 Computing Centre.
The results show that (i) the lift to drag ratios of the optimized airfoils are significantly improved compared to the baseline S809 airfoil, (ii) this improvement is sensitive to the Reynolds number, and (iii) the   /  ratios are also improved for another angle of attack values. Thereafter, the optimized airfoils are used for the design of the NREL Phase VI blade and the aerodynamic performances of this new wind turbine are assessed using the open-source code QBlade. These latter results indicate that when the blades are designed with the optimized airfoils, the wind turbine aerodynamic performances increase significantly. Indeed, at a wind speed of 10 m/s, the power output of the wind turbine is improved by about 38% compared to that of the original turbine.
Li et al. (2010)  suggested an optimization technique that combines the response surface method and uniform experimental design to optimize the profile of a wind turbine blade. The profile was fitted using a B-spline curve. Their numerical simulations were performed using the commercial software Fine/Turbo. Ribeiro et al. (2012)  coupled computational fluid dynamics (CFD) simulations with optimization algorithms to design new airfoils for wind turbines. Their simulations were performed using a steady-state incompressible RANS model and a one equation turbulence model. They applied single and multiobjective genetic algorithms to optimize the airfoil, with artificial neural networks serving as surrogate models. Their results showed that using artificial neural networks reduces CPU time by about 50%.
Grasso (2012)  developed an optimization strategy that combines the genetic algorithm with the gradient-based method to design thick airfoils dedicated to the root area of wind turbine blades. The results showed that the applied hybrid technique has improved the accuracy and robustness of the design and reduced the computational cost. He and Agarwal (2014)  employed a multi-objective genetic algorithm to improve the shape and aerodynamic characteristics of the S809 profile. Bezier curves were used to parameterize the profiles, and numerical simulations were performed using the Fluent software. Their results showed a significant enhancement in lift coefficient and lift to drag ratio compared to the baseline S809 profile. Chen et al. (2016)  used Taylor high order polynomial series to develop a new parametric representation function for profiles.
These computations were applied to NACA 0012 profile at a fixed angle of attack of 2° and 12°, and to DU 93-W-210 profile at an angle of attack of 2°. A Reynolds number of 2 10 6 was used in all simulations. Their results were compared with the frozen turbulence assumption. Li et al. (2018)  adopted the sequential quadratic programming (SQP) methodology to design new airfoils for vertical axis wind turbines. Their main objectives were to increase the maximum lift coefficient and the lift to drag ratio, and to achieve gentle stall characteristics. Akram and Kim (2021)  coupled the parametric section (PARSEC) and the class shape transformation (CST) with a genetic algorithm (GA) to optimize the S809 profile. An inhouse MATLAB code coupled with XFOIL was used to determine the aerodynamic characteristics of the profile.