dataesr/scientific-paragraphs-categorization huggingface dataset:
Bow n sex determination. Fin clips were sampled from 30 males and 30 females caught near Thibodaux, Louisiana, after gonadal observation. Individual genomic DNAs were extracted (Qiagen DNeasy Blood & Tissue kit), quanti ed (NanoDrop spectrophotometer), and mixed in equimolar quantities to produce a male pool and a female pool. Pool-Sequencing (Pool-Seq) libraries were prepared with the Illumina TruSeq Nano DNA HT Library Prep Kit, quality assessed (Advanced Analytical Fragment Analyzer), and quanti ed by QPCR using (Kapa Library Quanti cation Kit). Pool-Seq libraries (mean insert size: 419 bp female pool, 412 bp male pool) were sequenced on an Illumina NovaSeq6000 S4 lane (paired-end read length of 2x150 bp), resulting in 283. 86 and 284. 28 million reads for the female and male pool. Pool-Seq analyses were carried out using a snakemake work ow [https://github.
com/SexGenomicsToolkit/PSASS-work ow] and gures generated with SexGenomicsToolkit R package, "SGTR" v1. 1. 1 [10. 5281/zenodo. 3773063]. Reads were aligned to the reference genome with BWA mem v0. 7. 17  122  , and PCR duplicates removed using samtools rmdup. A le containing nucleotide counts for each genomic position was generated with the PSASS pileup command v3. 0. 1b [47310. 5281/zenodo. 3702337] and used to compute FST between males and females, numbers of male-and female-speci c SNPs, and male and female read depth in a sliding window along the entire genome using the PSASS analyze command. Analyses were run using two sets of parameters: stringent (--window-size 50000, --output-resolution: 1000, --freq-het 0. 5, --range-het 0. 05, --freq-hom 1, --range-hom 0, and --group-snps set as true), and default (--window-size 50000, --output-resolution: 1000, --freq-het 0.
5, -range-het 0. 10, --freq-hom 1, --range-hom 0. 05, and --group-snps set as true). As a reference-free approach, we searched for sex-biased k-mers in the Pool-Seq data to overcome potential failure to identify sexspeci c regions due to a genome reference from the homogametic sex. First, 31-mers were identi ed and counted in the male and female Pool-Seq reads using the Jelly sh v2. 2. 10 123 ("count" command, option "-C" counting only canonical 31-mers and retaining only k-mers with occurrence 5-50,000). For each sex, 31-mer count tables were obtained with Jelly sh "dump". 786,071,887 and 786,272,592 31-mers were identi ed in the female and male reads, respectively. 31-mer count tables were merged and then ltered with Kpool [https://github. com/SexGenomicsToolkit/kpool]. Genome structure and gene order analyses.
Tissues were dissolved by incubation in RLT Plus. RNA was isolated from both whole animals (for cDNA cloning) and dissected n buds using the RNeasy Plus Micro Kit (Qiagen). For standard cloning, cDNA libraries were produced from RNA extracted from stage 23 and stage 25 single whole embryos and from stage 26 n buds using the SuperScript IV Reverse Transcriptase System (Invitrogen). Following poly-A selection, stranded mRNAseq libraries were produced using the PrepX RNA-Seq Library Kit for Illumina (IntegenX) from stage 23, 24, 25, and 26 n bud RNA. For each developmental stage, two replicate libraries were sequenced with 75 bp single-end reads, and a third was sequenced with 150 bp paired-end reads on an Illumina NextSeq machine. Reads were ltered using Trimmomatic v0. 38  107  , and ribosomal RNA reads were removed using SortMeRNA  136.
Filtered non-rRNA reads were aligned to the bow n nuclear transcriptome using CLC Genomics Workbench (Qiagen). The transcriptome toolkit in CLC Genomics Workbench was used to calculate RPKM values, fold-change, and analysis of differential gene expression. Heatmaps to visualize gene expression dynamics were created using the Pretty Heatmaps package in R [https://rdrr. io/cran/pheatmap/]. ATAC-Seq analysis. ATAC-Seq library preparation and ampli cation with barcoding was performed using a modi cation of published protocols  67, 137, 138. Samples were taken from the same clutches and stages as for the developmental transcriptome series (see above   132  were performed as described above for the developmental transcriptome RNA-Seq data. To identify Open Chromatin Regions (OCRs), ATAC-Seq peaks were called with MACS2 v2.
-23 and 24-25; one embryo/larvae for stages23-24, 26-27, 28-29, and 30-31. Libraries were generated with an Illumina TruSeq Stranded mRNA Library Preparation Kit with IDT for Illumina Unique Dual Index and sequenced on an Illumina NextSeq v2. 5 (Mid Output ow cell, 2x75bp paired end format, 150 cycle v2. 5 NextSeq500 reagent cartridge). After base calling with Illumina Real Time Analysis v2. 4. 11, sequences were demultiplexed and converted to fastq format with Illumina Bcl2fastq v2. 19. 1. Read pairs were trimmed for quality and adaptor sequences with Trimmomatic v0. 38 107. Remaining paired end reads were mapped to the repeat masked bow n genome via Bowtie2 130 ("very-sensitive", other parameters set to default). Reads mapping to the mitochondrial genome were removed with removeChrom. py [https://github.
Acknowledgments We are thankful for grant support from  NIH   R01OD011116  (I. B. ),  NSF   DDIG DEB-1600920  (M. B. H. ),  NSF   IOS-1755242  (A. D. ),  NSF   IOS-1755330  (J. A. Y. ). Parts of this work have been supported by the  Agence Nationale de la Recherche ,  France  ( ANR   GenoFish  project,  2016-2021 ,  ANR-16-CE12-0035 ) to H. R. C. , C. B. , E. P. , and Y. G. E. P. , C. B. , and H. R. C. received support under the program « Investissements d'Avenir » launched by the  French Government  and implemented by  ANR  with the references  ANR-10-IDEX-0001-02 PSL* Université Paris. We thank  Francis Feng  for access to Oneida Lake bow n spawning habitats and the  Bauer Core at Harvard University  for sequencing n transcriptomes. We are exceedingly grateful to the late  José Luis Gómez-Skarmeta  for his guidance on establishing ATAC-Seq in holosteans.
Adult ebGABAs exhibit features of long-range projecting cells. Given that ebGABAs displayed unique anatomical and functional features in the immature CA1, we asked whether they maintained distinct properties in adulthood. We examined the molecular content of CA1 ebGABAs to infer the putative cell types comprising this GABAergic population. Staining for single neurochemical markers, we found that many ebGABAs expressed SOM (49 ± 16%, mean ± SD, four mice) and, in a progressively lower extent, PV (29 ± 7%, five mice), NPY (24 ± 11%, five mice) and M2R (22 ± 12%, three mice, Fig. 3a  and Supplementary Fig. 3a, b ). These data are in line with previously published results on the whole hippocampus  10. Using an antibody that allows discrimination between weak and strong levels of nNOS expression, we found that a small but consistent proportion of ebGABAs (8 ± 4%, six mice) expressed strong nNOS levels, a marker of longrange projection cells  18  (Fig.
Source data are provided as a Source Data file. Furthermore, we examined spontaneous EPSCs (sEPSCs), many of which are likely to be action potential-dependent, and thereby arise from intra-hippocampal connections in coronal slices. We confirmed that sEPSCs were mediated by AMPA/KA receptors because they were completely blocked by NBQX (10-20 µM, 4/4 cells). CtrlGABA and ebGABA displayed similar sEPSC parameters, in particular frequency and amplitude (Fig. 4k , l and Supplementary Fig. 5h ), indicating similar recruitment of these cells by putative intra-hippocampal excitatory afferents. Taken together, these data indicate that the deep location and dendritic arborization of CA1 ebGABA favor their recruitment by intra-hippocampal inputs. cells  24  , we also asked whether an early birth date biases GABA cells toward specific inhibitory wiring schemes.
Slices were imaged using a 20×, NA 0. 95 objective (Olympus). Imaging depth was on average 80 µm below the surface (range: 50-100 µm). Patch recording electrodes (4-8 MΩ resistance) were pulled using a PC-10 puller (Narishige) from borosilicate glass capillaries (GC150F-10, Harvard Apparatus) and filled with a filtered solution consisting of the following compounds (in mM): 130 K-methylSO4, 5 KCl, 5 NaCl, 10 HEPES, 2. 5 Mg-ATP, 0. 3 GTP, and 0. 5% neurobiotin (265-275 mOsm, pH 7. 3). Electrophysiological signals were amplified (EPC10 amplifier; HEKA Electronik), low-pass filtered at 2. 9 kHz, digitized at 10 kHz and acquired using a Digidata 1550 Digitizer and pClamp 10 software (Molecular Devices). For most stimulation experiments, imaging acquisition was separated between: (1) a baseline period during which the cell was held close to resting membrane potential (i.
e. , zero current injection); (2) a stimulation period during which phasic stimulation protocols were applied; and (3) a 3 min recovery period during which the cell was brought back to resting membrane potential. The stimulation protocol consisted of suprathreshold current pulses (amplitude: 100-150 pA) of 200 ms duration repeated at 0. 1, 0. 2, and 0. 4 Hz. Cells were discarded if they did not meet the following criteria: (1) stable resting membrane potential; (2) stable network dynamics measured with calcium imaging (i. e. , the coefficient of variation of the inter-GDP interval did not exceed 1); (3) cells displaying healthy shape and good Fura2-AM loading throughout the entire field of view. Analysis of ex vivo calcium imaging data during development. We used custom designed MATLAB software allowing automatic identification of loaded cells, measurement of the average fluorescence transients from each cell as a function of time and detection of onsets and offsets of calcium signals  11.
The cell was included in the dataset only if the difference between expected and observed cycles did not exceed ±2 cycles during resting conditions. To assess whether single cell stimulation was able to lock GDP occurrence, we isolated the trials following each stimulation (trial length was either 2. 5 or 5 s). Next, we calculated an average histogram of single cell calcium onsets including all trials. The histogram was then normalized by the number of GDPs during the stimulation protocol. The height of the highest peak in the histogram was stored. To estimate the significance of the time locking, we created surrogate data consisting of concatenated control and post-stimulation frames (without stimulation trials). The same histogram was then produced from the surrogate data. Since no stimulation was present in this case, we used an arbitrary frame as stimulation time.
05% neurobiotin (pH 7. 3 and ~280 mOsm). For voltage clamp experiments, an intracellular solution with the following ingredients (in mM) was used: 152 Csmethanesulfonate, 4 CsCl, 4 ATP-Mg, 0. 3 GTP-Na 2 , 10 Na 2 -phosphocreatine, 10 HEPES (pH 7. 3 and ~280 mOsm). Electrophysiological signals were amplified (Multiclamp 700B), low-pass filtered at 2. 9 kHz, digitized at 5-20 kHz and acquired using a Digidata 1440 A digitizer and pClamp 10 software (all from Molecular Devices). Patch clamp recordings were performed from visually identified neurons (GFP+ cells for ebGABAs, random putative GABAergic cells for ctrlGABAs). EbGABAs and ctrlGABAs were sampled in roughly equal numbers from all layers of both the dorsal and the intermediate/ventral CA1 to avoid biases toward specific cell types or inputs.
Cells were tested for postsynaptic responses and included in connectivity data only if located in a part of CA1 densely innervated by tdTomato+ axons. Light was delivered using a 40× objective, leading to a light spot size of ~1 mm, which was able to illuminate all CA1 layers. Electrical stimulation of the Schaffer collateral and evoked local IPSC experiment were performed by placing a bipolar stimulating electrode made by two twisted nichrome wires in the stratum radiatum of CA1 (between the recorded cell and CA3). The bipolar electrode was connected to a DS2A Isolated Voltage Stimulator (Digitimer Ltd. ) delivering 0. 2 ms-long stimuli. Postsynaptic current amplitude and paired pulse ratio were assessed by two stimulations separated by 50 ms. Sweeps were separated by 20 s delay to avoid the induction of plasticity and ensure stable responses.
Maximum PSCs were determined by delivering increasing stimulation powers and constructing stimulation power vs. PSC amplitude curves. Since these curves usually saturated, the maximum PSC amplitude was measured from the first PSC of the plateau. In the few cases in which the amplitude did not saturate, the response obtained from the maximum stimulation power was used to calculate the maximum amplitude. For sPSCs experiments, R s was not compensated because this allowed to monitor more precisely its changes throughout the experiment. For evoked PSC experiments, 60% compensation was applied to the R s. Only recordings with R s < 30 MΩ were included in the dataset. The cell was discarded if R s changed by more than 20% throughout the protocol. For electrically evoked and optically evoked PSCs, PSC onset delays were consistent with monosynaptic responses (3.
3 ± 1. 6 ms, mean ± SD). Analysis of ex vivo patch clamp recordings in adulthood. Analysis of intrinsic membrane properties was performed using custom-made MATLAB scripts. The resting membrane potential was estimated by averaging a 60 s current-clamp trace recorded at 0 pA holding current. The input resistance was calculated from the slope of steady-state voltage responses to a series of subthreshold current injections lasting 500 ms (from -50 pA to last sweep with a subthreshold response, 5 or 10 pA step size). The membrane time constant (τ) was estimated from a biexponential fit of the voltage response to a -30 pA hyperpolarizing pulse. The membrane capacitance was calculated as the ratio between membrane τ and input resistance. The first spike in response to a juxta-threshold positive current injection was used to determine: the threshold potential (the first point > 0.
The maximum firing rate was defined as the maximum value reached in a f/I curve with positive current injections from +20 to +820 pA (step size: 50 pA). Analysis of spontaneous postsynaptic currents (sPSCs) was performed with MiniAnalysis (Synaptosoft). PSC parameters were calculated from 2 min recording time. The decay time (10-90%) was calculated from a mono-exponential fit of mean sPSCs. Analysis of evoked postsynaptic current data were performed with Clampfit 10 (Molecular Devices) and MATLAB custom-made scripts. Since membrane and synaptic parameters may be affected by age, for all adult datasets we tested that the distributions of the ages of the mice used did not differ statistically between ctrlGABA and ebGABA. Additionally, for optogenetics experiments we verified that the time of opsin expression did not differ between the groups.
Imaging was performed with a single beam multiphoton laser scanning system coupled to a microscope (TriM Scope II, Lavision Biotech). The Ti: sapphire excitation laser (Chameleon Ultra II, Coherent) was operated at 1030 nm for jRGECO1a excitation and 920 nm for GFP excitation. Fluorescence emission was acquired using a 16× objective (Nikon, NA 0. 8) and split in two detectors (GaSP PMT, H7422-40, Hamamatsu) with bandpass filters of 510/10 nm for GFP and 580/20 nm for jRGECO1a. Scanner and PMTs were controlled by a commercial software (Imspector, Lavision Biotech). To optimize the signal-tonoise ratio of fluorescence variation, we used a dwell time exposition of 1. 85 µs and a spatial resolution of 2 µm/pixel that allowed us to acquire at 9. 85 Hz at a field of view of 400 × 400 µm. Locomotion and imaging triggers were synchronously acquired and digitized using a 1440A Digidata (Axon instrument, 2 kHz sampling) and the pClamp 10 software (Molecular Devices).
Analysis of in vivo calcium imaging data in adulthood. In vivo calcium movies were pre-processed using the CaImAn toolbox for MATLAB  49. First, movies were motion-corrected using a rigid registration method  50. Then, contours and calcium transients were detected using a constrained nonnegative matrix factorization framework allowing denoising and demixing of fluorescence signals  51. To ensure correct segmentation of somatic calcium activity, the automatic detection was manually refined by adding and removing region of interests (ROIs) using the correlation image based on neighboring pixels. ΔF/F fluorescence signals were then denoised with a median filter, detrended and extraction of the 20% ΔF/F. An additional manual refinement was carried out with visual inspection of each ROI in the correlation image and the corresponding trace.
Unstable or noisy traces were removed because this led to spurious spike inference. A Markov Chain Monte Carlo approach  52  initialized by the fast OOPSI algorithm  53  was used to model rise and decay time constants using a second order autoregressive process, which allowed spike inference from the fluorescence traces. A final visual inspection of overlapping calcium traces and spike raster plots was performed to ensure that the spike times reflected the dynamics of the fluorescence traces. Temporal alignment of the treadmill movement signal and spike raster as well as subsequent analyses were performed using custom-made MATLAB scripts. Locomotion epochs were defined as time periods with deflections in the photosensors signal reading the treadmill movement. Rest epochs were defined as periods >200 ms without treadmill movement.
Functional connectivity analysis was performed in Python using our CICADA toolbox (https://gitlab. com/cossartlab/cicada/). This analysis was restricted to FOVs imaged from the stratum pyramidale (n = 5, including 7 ebGABAs and 776 control cells) because these had more cells (77-236) than movies recorded from the stratum oriens (n = 2, 8-24 cells). The principle of output functional connectivity is to establish a connection from cell A to cell B if the firing of cell A precedes in a repetitive way the firing of cell B. Given the spike times of neuron A, we calculated the distribution of spikes of neuron B occurring at different time lags (between -1000 and +1000 ms). This measure is equivalent to the PSTH of cell B but centered on the firing onsets of cell A. Zero delay correlations were discarded by excluding normal distributions (Gaussians centered at zero) identified using the D'Agostino and Pearson's test with a two-sided chi squared probability threshold of 0.
05 (normaltest function in Python). We also excluded cases in which the activation of two neurons was completely uncorrelated (uniform distributions, identified using the Kolmogorov-Smirnov test from the Python "scipy" package with a two-tailed P value threshold of 0. 05). A connection was drawn from A to B if the average time lag was greater than zero, or from B to A if it was lower than zero. This procedure was applied to all possible pairs of neurons. The network graph was built with the "Networkx" package in Python. Hub cells were defined based on the following criteria: (1) being functionally connected to at least 5% of the cells in the network; (2) being among the 5% most connected cells in the distribution including cells from all movies; (3) being among the cells with betweenness centrality above the 80th percentile in the distribution including cells from all movies.
Acknowledgements We thank  L. Cagnacci ,  C. Pauchet-Lopez ,  F. Michel  ( InMAGIC imaging facility ) and  INMED 's animal facility technicians for excellent technical support;  V. Crepel  and  A. Represa  for sharing equipment;  J. Denis ,  R. Dard ,  M. Picardo ,  E. Leprince  and  Y. Bollmann  for experimental and/or analytical help;  V. Lopes-Dos Santos  for sharing the assembly detection code and useful discussions;  R. Khazipov ,  T. Marissal ,  R. Boyce , for useful comments on this paper. This work was supported by the  European Research Council  under the European Union's  FP7 and Horizon 2020 research and innovation program  (grant No. 646925 ) and by the  Bettencourt-Schueller Foundation  (Coup d'Elan). M. B. was supported by the  Fyssen Foundation , the  Fondation pour la Recherche Medicale  (grant No.
SPF20170938593 ), and by the  European Union  ( Marie Skłodowska-Curie individual fellowship , grant No. 794861-IF-2017 ). D. A. was supported by an  A*MIDEX  grant (grant No. ANR-11-IDEX-0001-02 ) and by the  I-Site Paris Seine Excellence Initiative  (grant No. ANR-16-IDEX-0008 ). T. Tr. was supported by The  French National Research Agency  (grant No. ANR-14-CE13-0016 ).
Like many other search engines, Amadeus' flight search engine is a large scale distributed system comprising many different components. The Minimum Connection Time (MCT) module is one such component and it is implemented atop a BRMS. It is used in the early stages of the search (in the so called Domain Explorer component) to determine the validity of a connection between two flights in terms of the time needed between the arrival of a plane and the departure of the connecting flight. The module plays a key role in terms of the performance and total cost of operating the search engine. When a query looking for flights between a departure and a destination airport needs to be processed, a large number of potential routes has to be computed. For all routes that are non-direct flights, the MCT module is invoked to ascertain the minimum connection time to the next flight.
Thus, the MCT module needs to fulfil stringent performance requirements on both latency per query and overall query throughput. Because it is used as a module within a larger system, there are additional constraints in terms of the amount of memory used that determine what type of BRMS can be employed in practice. In the current deployment, the MCT module is responsible for 40% of the computing resources used by the Domain Explorer. In what follows, we describe our efforts to redesign the MCT module to achieve several goals. First, to reduce the latency involved in computing the MCT and, if possible, to also increase the throughput without having to use additional computing resources. Second, to provide a scalability path at a time where the number and complexity of the rules used in MCT is increasing; while the latency margin for query processing remains constant, since the flight search is an online interactive system.
Business Rule Management Systems Business Rule Management Systems are widely used both as stand alone engines as well as components of larger systems. Examples of BRMSs include Oracle Business Rules  [20] , IBM's Operational Decision Manager  [13] , Microsoft's BizTalk Business Rule Engine  [17] , or open-source systems such as Drools  [4]. These engines capture complex and evolving business logic as a set of rules expressed in a high level syntax. Rule Engines are then used similarly to a database by posing queries that return a decision or trigger an action according to the parameters set in the query. In a rule engine, the simplest form of matching consists of retrieving the rules in the database fitting the query by iterating over the rules, a process that can be accelerated using indexes  [16].
Unlike the former ones, however, the MCT use case involves a wider variety of data types and matching conditions, as well as having no ordering restrictions on the processing of the attributes. Limiting as these constraints might seem, they are actually met in many situations. Within the airline industry, stateless rules over a collection of attributes are used to determine, for instance, whether a passenger is eligible for an upgrade, whether a ticket can be changed, or whether overbooking is allowed in a flight and by how much. Amadeus alone has a total of 783 use cases of stateless business rule engines used in production. More generally, our approach matches almost the entire eXtensible Access Control Markup Language  [22] , an OASIS standard for stateless attribute-based access control, used to define and process access control rules of the type "If the user meets conditions A, B, C, D; and the access meets conditions E, F; then grant access".