dataesr/scientific-paragraphs-categorization huggingface dataset:
We also note that approximately one third of bacterial colonies grown in isolation failed to yield appreciable concentrations of DNA (> 0. 1 ng/uL), a failure rate that could likely be reduced by screening for isolate growth prior to extraction. Similarly, the rate of contamination (or sequence libraries containing DNA from multiple bacterial types) could be reduced by adding a secondary re-streaked plate culture step rather than picking directly into liquid culture; in this experiment, we chose to pick directly to maximize throughput. Second, although the protocols we provide can in principle be run with very little specific prior training or programming experience, some working knowledge of Python programming in general, and the Opentrons Python API in particular, is helpful. And third, the logistical challenges of moving from hundreds to potentially tens of thousands of samples-including storage, labeling, and in particular sample provenance validation and metadata tracking-are largely unaddressed here.
We will be continuing to address each of these issues in future development of these protocols. To ensure the greatest utility of our workflow for the research community, all protocols and hardware schematics are freely available for public use at https:// github. com/ tanaes/ Moell er_ Opent rons_ proto col_ libra ry  [30] , https:// github. com/ tanaes/ opent rons_ funct ions  [31] , and https:// github. com/ CUMoe llerL ab/ Labwa re  [32]. These repositories will be maintained and updated as we make further additions and improvements to the protocols in the future. The isolates sequenced in this study represent, to our knowledge, the first large-scale compendium of cultured bacterial genomes from wild chimpanzee and bonobo gut microbiomes. The samples used to demonstrate the methods in this paper were far from ideal for purposes of generic cultivation: preserved in RNALater, and stored for many years at -80 °C, a substantial portion of the diversity in the original samples was most likely no longer viable, resulting in a dearth of some common gut bacterial lineages in our dataset (Fig.
2 ). Although the entire process of cultivation, from initial inoculation of plates through colony isolation and regrowth in liquid media took place within an anaerobic chamber, we did not recover many expected anaerobes, leading to uncertainty of whether the preservation or growth conditions may have been responsible for their absence. Accordingly, only 3% of the metagenomic diversity detected by sequencing of the fecal samples used for cultivation was present in our dataset of isolate genomes. Fresh samples (or those collected into cryoprotectants specifically for purposes of later cultivation) will no doubt yield a greater diversity of original gut cultivars. However, the genome resources generated from this wild hominid gut bacterial isolate collection complement and enable comparative analyses with existing gut bacterial genome databases for a subset of chimpanzee and bonobo gut bacterial taxa.
Methods To accomplish our goals of maximum isolate genome throughput with minimal capital and labor costs, we developed a workflow based around the OpenTrons OT-2 robotic liquid handling platform (Fig. 1 ). This instrument allows for repeatable automation of many protocols, while costing less than $10,000 as configured. Where possible, we took advantage of previously-published low-cost molecular biology protocols, adapting them for automation on the OpenTrons platform. All the protocols described here are available at https:// github. com/ tanaes/ Moell er_ Opent rons_ proto col_ libra ry  [30]. In addition, we wrote extensions to the OpenTrons Protocol API to improve certain aspects of instrument behavior, especially relating to use with magnetic bead protocols. An installable library of these extensions is available at https:// github.
com/ tanaes/ opent rons_ funct ions  [31]. For some protocols, we found that there were key steps that would require laboratory apparatus that were either not available for commercial purchase, uncommon in a typical molecular biology lab, or would require substantial investment. For these steps, we designed our own versions suitable for rapid manufacture with 3D printers and/or laser cutters and using inexpensive commodity electronic components. These apparatus were designed using Fusion360 CAD software (Autodesk, Inc. ). Full source files and component lists can be found at https:// github. com/ CUMoe llerL ab/ Labwa re  [32]. All data in this paper were generated using versions of the 3D-printed apparatus described below, rather than commercially purchased alternatives. Commercial alternatives to all labbuilt equipment are listed in Additional File 2: Table  S2.
Sequence analysis Isolate genomes derived from fecal samples preserved in RNAlater, which selects for a subset of the bacteria in the Pan microbiota  [33] , enabled analyses of intraspecific bacterial genomic diversity within and among Pan individuals and populations. To demonstrate the utility of isolate genomes for strain-level analyses of microbiota, we focused on two sets of analyses of genomic diversity that remain difficult or not possible with shotgun metagenomic data alone. First, we characterized intraspecific bacterial genomic diversity both within and between individual Pan hosts. Isolate sequences were processed using the Bactopia pipeline  [42]. This pipeline does sequence trimming and QC with FastQC (https:// www. bioin forma tics. babra ham. ac. uk/ proje cts/ fastqc/), assembled sequences with Shovill (https:// github.
com/ tseem ann/ shovi ll) and SKESA  [43] , and performs assembly quality checking with CheckM  [18]. Gene prediction and annotation was performed with Prokka  [44]. Taxonomy of each isolate sequence library was estimated from unassembled reads using Sourmash with the GTDB R06-RS202 LCA-formatted database and k = 31  [45]. To create a phylogeny of isolates, assembled genomes predicted to be less than 5% contaminated with CheckM were processed using PhyloPhlAn2  [46]  using the Amphora2 marker set  [47]  and the "Fast / High Diversity" default settings. To estimate the relative abundances of isolates in original samples, we used CoverM (https:// github. com/ wwood/ CoverM) to calculate coverage for each isolate genome in each of the available chimpanzee metagenomes. 16S rRNA gene sequences were recovered from isolate genome assemblies using PhyloFlash  [48]  as implemented in Bactopia.
To assess novelty relative to current known plasmid sequences, putative plasmid sequences were searched against the latest version of the PLSDB plasmid database (v. 2021_06_23_v2,  [22] ) using BLASTn  [50] , and considered as previously observed if they matched at least one database sequence across at least 90% of the query length at ≥ 60% sequence identity. Finally, putative plasmid similarity networks and gene alignment visualizations were generated using MobMess  [21].
Availability of data and materials All raw sequence data from this publication are available in the Qiita data repository, study number 14410 (https:// qiita. ucsd. edu/ study/ descr iption/ 14410), as well as at the EBI ENA repository with accession number ERP136830  [51]. Opentrons protocols are available at https:// github. com/ CUMoe llerL ab/ Moell er_ Opent rons_ proto col_ libra ry  [30]  and custom function library at https:// github. com/ CUMoe llerL ab/ opent rons_ funct ions  [31]. Printable labware files and assembly instructions are available at https:// github. com/ CUMoe llerL ab/ Labwa re  [32]. Isolates are available upon request.
The resulting models computed the relationship between pressure and flow rate in the microfluidic circuit but also takes the concentration gradients that can appear in the direction perpendicular to the channel into account. The model is compared with the finite element simulation performed with COMSOL Multiphysics in several study cases. We estimate that the global error introduced by our model compared to the finite element simulation is less than 5% in every use case. In counterparts, the cost in terms of computational resources is drastically reduced. The analytical model can be implemented in a large range of modelling and simulation languages, including SPICE and hardware description language such as Verilog-AMS. This feature is very interesting in the context of the in silico prototyping of large-scale microfluidic devices or multi-physics devices involving microfluidic circuits, e.
On the one hand, fluid behavior and molecular diffusion inside microfluidic channels are respectively governed by the Navier-Stokes equations  [15]  and the reaction-advection-diffusion equation  [16]. Both of them are partial differential equations (PDE) that must be solved by finite element simulators  [17]. Commercial simulators (e. g. COMSOL Multiphysics  [18]  and more specifically its Microfluidic Module  [19] ), or academic open-source simulators (e. g. FeelPP  [20] ) natively implemented such features. This approach provides very accurate and faithful results towards actual behavior of the fluids. The downside is the heaviness of models from a computational perspective. Consequently, such tools are very suitable for design and the study of a microfluidic device alone, but can quickly become limiting for the simulation of this device as a part of a complete microfluidic system.
Validation of the sigmoid model for the initial profile First, COMSOL Multiphysics simulation of the Y-shaped junction described on Figure  1  is performed for different combinations of inlets flow rate and the cross-section concentration profile for  = 0 are exported. Then, the Curve Fitting toolbox of MATLAB with the Levenberg-Marquart algorithm is used to extract the optimal parameters of the sigmoid (d is assumed to be 0). Extracted parameters are then compared to the expected one. Figure  2  shows the results for  1 =  2 = 0. 5 nL • s -1 and  1 = 1 mM. The fitting of the concentration profile by the sigmoidal function is very good. The RMSE is less than 1% of the average concentration. The values of  and  are consistent with the expected ones:  =  1 = 1 mM and  = 5 µm for a 10-µm width as  1 =  2.
COMSOL provides specific modules for that purpose. The last point that deserves to be discussed is the assumption of the sigmoid profile of the concentration, which is the most common profile encountered in the case of a two-fluid mixture. However, other profiles might be observed, for instance if one considers a three-inlet mixer. In such cases, more complex, deserve deeper investigation. In such cases, several avenues can already be put forward. One of them consists of adapting the profile function and, thus, changing the set of parameters that characterize the profile. Equation  (15)  suggests that the concentration profile is conserved along the channel. Thus, as soon as we have a mathematical equation for the profile at the input of the channel, the associated Fourier coefficients can be calculated and altered by equation  (15) , and then used to compute the output concentration profile.
2005 Klimenko et al. , 2006)) , and a ringdown templated search  (Aasi et al. 2014). Improvement in the detector sensitivity at low frequencies in the advanced era made IMBH binaries a target for a matched filtering search that would probe the short inspiral phase. In  Abbott et al. (2017b) , we used a combined search with the matched filtering GstLAL  (Messick et al. 2017; Hanna et al. 2020; Sachdev et al. 2019 ) search and model independent cWB  (Klimenko et al. 2011 (Klimenko et al. , 2016)). This combined search was further extended with an additional matched filtering PyCBC search  (Usman et al. 2016; Allen 2005; Dal Canton et al. 2014; Nitz et al. 2017)  in  Abbott et al. (2019)  using the data from the first two observing runs. No significant IMBH binary event was found in these searches.
We provide the results from the combined search with the next most significant events and follow up investigations to assess their origin. The increased sensitivity of the O3 run allows us to set more stringent bounds on the binary merger rate density. The lack of a confirmed IMBH population as well as possible formation channels of IMBH distinct from those of stellar-mass BHs preclude us from using an overall mass model for the IMBH population. Thus, we confine all the upper limit studies to a suite of discrete points in the IMBH parameter space. We incorporate more detailed physics in selecting the suite of IMBH binary waveforms as compared to earlier upper limit studies. In  Abbott et al. (2017b)  we simulated a limited set of discrete mass and aligned-spin binary waveforms in the first advanced detector observation data to obtain upper limits on merger rate.
Search methods In this section, we describe the analysis methods algorithms (pipelines) used to search the LIGO-Virgo data from O3 for IMBH binary merger signals. Such signals have short durations in the detectors' sensitive frequency band, typically less than 1 s. Thus, methods for detection of generic short transient GW events (bursts) may be competitive compared to search methods which use parameterized models of the expected signals (templates) from binary coalescences (e. g. Chandra et al. 2020). As in the IMBH binary search of O1 and O2  (Abbott et al. 2017b (Abbott et al. , 2019)) , we employ both generic transient search methods and modelled template searches. We first describe the generic transient pipeline, cWB, in the configuration used here, and then the two templated pipelines, GstLAL and PyCBC, which have been adapted to maximize sensitivity to IMBH binary mergers.
We then summarize the method used to combine the search outputs into a single candidate list, and finally discuss selection criteria to distinguish IMBH binary candidates from the known heavy stellar-mass BBH population  (Abbott et al. 2020c,d). The output of a transient search algorithm or pipeline is a set of candidate events, each with an estimated time of peak strain at the participating detector(s). 1 Each event is also assigned a ranking statistic value, and its significance is quantified by estimating the corresponding false alarm rate (FAR), which is the expected number per time of events caused by detector noise that have an equal or higher ranking statistic value. The sensitivity of a search to a population of IMBH mergers can be evaluated by adding simulated signals (injections) to real GW detector strain data and analyzing the resulting data streams, to output the ranking statistic and estimated FAR that each simulated signal would be assigned if present in an actual search.
In the O3 search, we combine the two searches by applying a trials factor of 2 to the estimated FAR for events in the overlap region. This improves the overall search sensitivity to borderline IMBH events (?). The cWB search analyzes data from all three detectors in low latency. However, the follow up offline cWB analysis does not improve detection efficiency with the inclusion of Virgo. This is primarily due to the additional noise in the Virgo detector. Thus, at a given time, the cWB search uses the best available (most sensitive) two detector network configuration. This ensures that the cWB search does not analyze the same data with multiple detector configurations. In case, if any event shows high significance in low latency cWB analysis with the three-detector network and low significance in offline cWB analysis with the best two-detector configuration, we re-analyze that observing time with both the LLO-LHO-Virgo and LLO-LHO networks and apply a trials factor of 2 to the minimum FAR over the two networks for the final significance.
Candidate IMBH events The individual searches are applied on the full O3 data with the analysis time of 0. 734 yr, 0. 747 yr and 0. 874 yr for cWB, PyCBC-IMBH and GstLAL-IMBH search respectively. Table  1  summarises the results from the combined cWB-GstLAL-PyCBC IMBH search on full O3 data detailed in Sect. 3. These events have a combined p-value less than 0. 26 (a threshold determined by the loudest noise event in the combined search, 200214_224526 and satisfy the criteria for potential IMBH binary sources of Sect. 3. 4. For completeness, we have also listed marginal triggers found by our combined search in Appendix A. The top-ranked event is GW190521 and it has a highly significant combined p-value of 4. 5 × 10 -4. If this signal is from a quasi-circular merger, then the signal is found to be consistent with the merger of two black holes in a mildly precessing orbit, with component masses of 85 +21 -14 M and 66 +17 -18 M and a remnant black hole of 142 +28 -16 M falling in the mass range of intermediate-mass black holes.
15 s. A similar change in statistical significance is also observed for GW190602_175927 for the same reasons. However, the IMBH search results assign lower significance to GW190519_153544 and GW190706_222641 as compared to the GWTC-2 results. The GstLAL pipeline recovers the GW190521 event at a FAR of 1. 9×10 -3 yr -1 over all of O3 data. It was reported earlier  (Abbott et al. 2020b ,c) at a FAR of 1. 2 × 10 -3 yr -1 over O3a. As described in 3. 2. 1, the GstLAL pipeline has employed a dedicated search for IMBH binaries with better coverage for the heavier mass binaries than the catalog search. Also, the iDQ based data quality information used to inform the calculation of the ranking statistics, now incorporates multi-detector triggers, as against the only single detector triggers that were used before.
This material is based upon work supported by  NSF 's  LIGO Laboratory  which is a major facility fully funded by the  National Science Foundation. The authors also gratefully acknowledge the support of the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Netherlands Organization for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
The authors gratefully acknowledge the support of the  NSF ,  STFC ,  INFN  and  CNRS  for provision of computational resources. This work was supported by  MEXT ,  JSPS Leading-edge Research Infrastructure Program ,  JSPS   Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905 :  JP17H06358 ,  JP17H06361  and  JP17H06364 ,  JSPS Core-to-Core Program A. Advanced Research Networks, JSPS   Grant-in-Aid for Scientific Research (S)   17H06133 , the joint research program of the  Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF)  and  Computing Infrastructure Project  of  KISTI-GSDC in Korea, Academia Sinica (AS), AS Grid Center (ASGC)  and the  Ministry of Science and Technology (MoST) in Taiwan  under grants including  AS-CDA-105-M06 ,  Advanced Technology Center (ATC) of NAOJ, and Mechanical Engineering Center of KEK.
We propose a novel algorithm to estimate such Inverse Kinematics weights from observed kinematic data. These estimated weights serve as a quantification for spatial inter-joint coordination; i. e. , how costly a redundant joint is in its contribution to creating an end-effector velocity. We apply our estimation algorithm to datasets obtained from two different experiments. In the first experiment, the estimated Inverse Kinematics weights pinpoint how individuals change their Inverse Kinematics strategy when exposed to the viscous field wearing an exoskeleton. The second experiment shows how the resulting Inverse Kinematics weights can quantify a robotic prosthetic arm's contribution (or the level of assistance).
Experimental and numerical validation To validate our proposed algorithm, first, we consider two low-dimensional simulations. The main purpose of these simulations is to illustrate the overall behavior of the proposed algorithm in terms of convergence to the nominal IK weights. The further details and the results of these simulations are reported in Subsection 4. 1. For the experimental validation, we consider kinematic data recorded in two different scenarios in the context of robotic-assisted rehabilitation and upper-limb prosthetic robotics, respectively. These studies were approved by the ethics committee of the Paris Descartes University (CERES-IRB number 20162000001072) and the Sorbonne University (SU CER-2021-111), respectively. All participants gave written and informed consent before participation.
For example, in this simulation, we tested γ 0 = 0. 6 with γ t+ 1 = γ t + 0. 6(1-γ t ) in order to benefit from the fast convergence of γ = 6 at the beginning, and the optimality of a high γ toward the end. Generally, there is no procedure for choosing an optimal γ since the results depend on the dataset, as we see from the two last simulations. A helpful rule of thumb is to start with γ = 0. 9; i. e. , sacrificing convergence speed for optimality.
Experiment 1-Inter-joint coordination in a viscous field We collected kinematic data from 17 asymptomatic participants (11 males and 6 females), reaching three different targets. Each target was reached in 20 trials (some trials had to be removed due to recording issues) under two conditions: "Natural" and "Viscous". As shown in Fig  4 , the participants were wearing a shoulder-elbow exoskeleton; i. e. , 4-DOF ABLE upperlimb exoskeleton  [87]. This device allows us to impose a viscous force field (as presented in our previous work  [70] ) to modify the subjects' inter-joint coordination. The ABLE exoskeleton offers quasi-static gravity and friction compensation with highly reversible mechanical transmission. However, due to a lack of dynamic compensation, the device exhibits a certain level of undesired resistance which is characterized in our previous work  [88].
Tropical cyclones (TCs) are one of the most devastating natural disasters, which justifies monitoring and prediction on short and long timescales in the context of a changing climate. In this study, we have adapted and tested a convolutional neural network (CNN) for the classification of reanalysis outputs according to the presence or absence of TCs. This study compares the performance and sensitivity of a CNN to the learning dataset. For this purpose, we chose two meteorological reanalysis, ERA5 and MERRA-2, and used a number of meteorological variables from them to form TCcontaining and background images. The presence of TCs is labeled from the HURDAT2 dataset. Special attention was paid to the design of the background image set to make sure it samples similar locations and times to the TC-containing images.
Most approaches for TC detection use supervised methods which require a training dataset. While such techniques are now mainstream, they are not always well documented and their description may lack sufficient details which are often key in ML, e. g. , the data engineering involved in the preparation of the training dataset, the hyperparameters of the CNN, and the evaluation meth-ods of the metrics used to measure the performance of the models. Studies evaluating the performance and sensitivity of TC detection algorithms to the input and training datasets are also relatively scarce. It should be noted that labeled TC datasets exist for the past observed climate record (satellite data, reanalysis), but it may not be practical to generate such datasets in climate model outputs for every new simulation that is made and to which the detection algorithm is to be applied.