dataesr/scientific-paragraphs-categorization huggingface dataset:
Heterozygous genome assembly -Solanum tuberosum L. We downloaded the Nanopore data produced by the authors of a recent article by Zhou et al. (24)  describing the diploid assembly of Solanum tuberosum (Table  2 ). The long-read dataset was assembled using the Flye assembler (19) (v2. 8. 1) with the parameter '-g 1600m' to indicate a genome size of 1600Mb (representing the length of the diploid genome). The resulting assembly had a size of 1. 33Gb and a contig N50 of 440Kb (Table  2 ). Using this assembly, we performed two benchmarks: a first on the whole genome and a second on a specific genomic region which has been thoroughly analyzed in the Zhou et al. publication.
1) Background: Inter-tumour heterogeneity is one of cancer's most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer).
Processing Treatment Response Data for Modelling For each treated PDX, we retrieved its category from the processed response data. We also calculated its category from raw response data as indicated by Gao et al. [32]. Retrieved and calculated response categories differ in 277 of the 4758 PDX-treatment pairs. Although such discrepancies were small (mostly swaps between contiguous categories) and not numerous (5. 8% of the cases), we decided to use the calculated categories in these cases so that all PDX-treatment pairs were categorised following the same set of rules (Figure  S1 ). Gao et al. [32]  further subdivided PDX response into two classes: responders, as those PDXs exhibited some level of sensitivity to the treatment (CR, PR or SD), and non-responders (PD), as those resistant to the treatment.
On the other hand, RC is 0 when none of the sensitive PDXs is correctly identified as such, whereas RC is 1 if no sensitive PDX is missed by the classifier. An alternative global metric capturing both types of errors is the F-score (F1), which is the equally-weighted harmonic mean of PR and RC. The optimal value of F1 is 1 (when both PR and RC are maximal or 1), whereas its worst value is 0 (when either PR or RC is 0; when both PR and RC are 0, F1 is also 0). The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) were calculated using the R package ROCR version 1. 0-7. The ROC curve was built based on the response class predicted by the model (RF-OMC or SG) and the actual observed class of each test PDX. For a given model, the ROC curve shows all different pairs of values for the metrics True Positive Rate (TPR) and False Positive Rate (FPR) resulting from varying the operating threshold from 0 to 1 to cut-off the probability of the PDX to be a positive (responsive).
Figures  S2  and S3  show that RF-OMC predicts 21 of the 26 cases better than random (p < 0. 05; one-sided paired t-test). Highly significant p-values strongly suggest that the corresponding lists of selected features have a high predictive value. By contrast, Figures  S4  and S5  show that RF-all is only able to predict 16 of the 26 cases at this level (p < 0. 05; one-sided paired t-test). Each of the models in this study is evaluated using independent data by LOOCV (i. e. , the measured response of any test PDX was not used in any way to train or select the model). CV is a standard way to assess predictive accuracy not only in this topic  [13, [62] [63] [64] , but in any problem where there are few data instances (here PDXs) to train, select and test a machine-learning model  [65]. Additional experiments using data from a second PDX resource would be, in principle, appealing.
91, 0. 88 and 0. 81 (multi-gene) versus recalls of 0. 68, 0. 00 and 0. 56 (single-gene). Figure  6  shows that this is actually a strong general trend: 23 out of 26 studied cases have a higher proportion of correctly predicted sensitive PDXs using the multi-gene markers. We recently observed a similar trend when using the standard version of RF to predict the in vitro drug response from SNV data  [13] , which is here confirmed in vivo. Figure  6. ML multi-gene classifiers exhibit a higher recall than single-gene markers in 23 of the 26 treatment-cancer type pairs. (A) In 12 of the 13 treatments for BRCA, multi-gene markers achieve a higher recall than the single-gene marker. The exception was the BGJ398-KIF20B association. (B) In 11 of the 13 treatments for CRC, multi-gene markers achieve a higher recall than the single-gene marker.
Thus, the correlation of the noised-up feature with other features influences the ranking of the features by how predictive they are, at the cost of having to run RF with all features to estimate their importance. While investigating this additional algorithm is out of scope, we have illustrated this point in the case of one of the 102 datasets (BRCA-binimetinib, whose most predictive model has the expressions of 14 genes as features, as shown in Figure  3 ). Figure  S10  shows the ranking of these 14 features by p-values is similar to that of using RF's feature importance. The exception is FAM100A, which is much higher ranked when considering its correlation with the other 13 predictive features. Single-gene markers, such as actionable somatic mutations, are not generally strong predictors of treatment response  [5, 6, 10, 12, 13].
This represents a novelty with respect to the most often used static PMV/PPD model. Furthermore, the agent-based model is built on an activity and presence model and, therefore, is able to account for the diversity of the activities carried out by the occupants. User interaction data from about 9,000 connected Canadian thermostats included in the Donate Your Data (DYD) dataset are used to calibrate and establish the empirical foundation of the thermostat interaction model. Finally, we simulate typical DR-activated setpoint modulations in two residential buildings characterized by different levels of insulation and we use the framework to predict occupants' override rates as a function of the indoor temperature and the time since the start of the DR event. The derived relationship can be directly used to inform the design and control of setpoint modulations in residential buildings.
Furthermore, the agent-based model builds on an activity and presence model and, therefore, has the advantage of being able to account for the diversity of the activities carried out by the occupants, which leads to diverse setpoint schedules and metabolic rates. The three main components of the framework (occupant activity and presence model, thermostat adjustment model and dynamic thermal comfort model) are described in Section 2. The three models are coupled with the Building Energy Simulation (BES) tool incorporated within the python-based simulation platform DIMOSIM (DIstrict MOdeller and SIMulator), which can be used to predict energy demand at district level  [42]. Thus, all the models are developed in Python and are directly embedded within DIMOSIM. For calibrating the thermostat adjustment model we use data from about 9,000 connected Canadian thermostats included in the Donate Your Data (DYD) dataset and presented in Sections 3.
Data-driven ABM model foundation The empirical foundation of the ABM model is the DYD dataset which contains usage data from more than 100,000 ecobee connected thermostats mainly installed in North America and collected over multiple years  [74] [75] [76] [77]. The dataset is made of fiveminute time series of indoor air temperature, relative humidity and passive infrared (PIR) motion, which are sensed at the thermostat and at any connected remote sensors whose placement depends on the user. The monitored indoor air temperature (T a ) is the temperature compared to the setpoint temperature and, therefore, used to control the thermal system. In the simplest configuration, T a is given as a mean of the temperature sensed at the thermostat and at any connected remote sensors. However, depending on the choice of configuration made by the user, the sensed temperatures can be weighted by either the programmed thermostat schedule or the detected occupancy.
The dataset also includes user-reported metadata of home and occupant characteristics. An overview of the dataset can be found in  [74]. An Internet-connected thermostat, such as the ecobee thermostat, allows users to set a weekly operation schedule (i. e. to set different heating and cooling setpoint temperatures at different times of the day) and has manual control functionalities through web, mobile and voice platforms. For the derivation of the thermostat adjustment model we want to understand how occupants interact with their thermostats and, therefore, we focus on the analysis of the manual setpoint adjustments, which are deliberate user overrides of a scheduled/default setpoint temperature. For each manual setpoint adjustment, we compute the corresponding setpoint temperature change, which could be either an increase or decrease.
A time-step is considered occupied if any motion is detected by any motion sensor (remote, or on the thermostat itself), i. e. if the occupants are at home and are not sleeping. The resulting occupancy time series are further processed to 'fill in' short unoccupied periods of 30 min where the occupants may have been temporarily hidden from the sensor, using a similar approach of  [75]  and  [77]. The PIR sensors give many false negatives but have the advantage to have only few false positives  [75]. We apply the following selection criteria to the DYD dataset: we only consider thermostats in Canadian homes, we only take data from winter months characterized by a mean monthly outdoor temperature lower than 15 °C, we exclude thermostats containing less than 10 days of detected occupied time-steps.
The empirical values are coming from the DYD dataset, which has been already introduced in Section 2. 3 (see also Fig. 6 ). The simulated data origins from a combination of the following selected cases: Two residential buildings characterized by different levels of insulation: a typical existing single-family house (SFH 1982) and a newly-built single-family house (SFH 2012). For more details of the case study buildings see Section 3. 3. It is important to highlight that the calibration is done with respect to the indoor temperature and, therefore, we not need to simulate the actual 9,000 Canadian buildings where the thermostats are located. However, by simulating two very different buildings in terms of insulation  (SFH 1982 and SFH 2012 ) we can at least ensure them having a wide range of temperature and dynamics.
Furthermore, we equipped the buildings with convectors, which are mainly emitting heat by convection (80%) and, therefore, we can assume that the thermal dynamics are very similar to those associated with forced-air furnaces, which represents the large majority of the heating systems in the DYD dataset and, in general, in Canada. Four different types of households: family of 4 (2 employed + 2 students), couple of retired (2 retired), couple of employed (2 employed) and single adult living alone unemployed (1 unemployed). Given the stochastic nature of the occupant model each simulation is repeated 50 times.
However, the agent-based modelling framework can be extended to account for some of the above factors by adding new attributes to the agents. For example, by using the DYD dataset it would be possible to differently calibrate the probabilities p adj;clothing ; p adj;warm;setpoint and p adj;cold;setpoint depending on the composition of the household or the type of heating system. Also, these probabil- ities could be easily derived for the summer case and, therefore, the framework could be extended to model interactions with the air conditioning system. Another limitation of this study is that the calibration is done with the DYD dataset which contains data from a population able to afford an ecobee thermostat and which can, thus, be supposed to belong to a high socio-economic class. This also defines the boundaries of applicability of this model.
Conclusions In this paper, we describe and calibrate a novel agent-based stochastic model of occupants' interactions with thermostats in residential buildings in the heating season. The thermal discomfort predictions of the thermostat adjustment model are based on a two-node thermo-physiological model coupled with a dynamic thermal perception model. This represents a novelty with respect to the most often used static PMV-based discomfort predictions. Furthermore, the adjustment model is built on an activity and presence model and, therefore, is able to account for the diversity of the activities carried out by different types of occupants. The empirical foundation of the thermostat adjustments model is represented by user interaction data from about 9,000 connected Canadian thermostats included in the Donate Your Data (DYD) dataset, which are also used to calibrate the model.
This characteristic of medulla development provides a distinct opportunity to study neuroblast and neuronal trajectories in unparalleled detail since all developmental stages coexist in the same brain. To achieve that, we performed scRNASeq on optic lobes microdissected from the central brain using the Chromium system (10x Genomics). We obtained 49,893 single-cell transcriptomes from 40 L3 optic lobes. We used the Seurat v3 integration. CC-BY 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted November 7, 2021. ; https://doi. org/10. 1101/2021. 06. 13. 448242 doi: bioRxiv preprint pipeline  24  to remove batch effects between the ten different libraries that were generated (Extended Data Figure  1 ).
To verify whether these trajectories retain the information of time, as suggested by the progression of neuroblasts to GMCs to neurons, we merged the larval single-cell dataset with the early pupal stage 15 (P15) dataset that we had previously generated  21. These P15 neurons fell at the tip of each of the neuronal branches, confirming that these branches indeed represent neuronal trajectories (Figure  1C ). Importantly, since the P15 dataset is annotated, this allowed us to identify the neuronal types at the tip of the trajectories. We could in fact identify neurons from all the neuropils of the optic lobe (lamina, medulla, lobula, and lobula plate) in the larval UMAP, which together accounted for 85% of the dataset. The annotation of the different neuropils was confirmed by looking at known markers of the lamina (dac, gcm, so, eya, sim  33  ) and lobula plate (D, tll, acj6, dac  34, 35  ) (Extended Data Figure  2B ).
We could confirm that there are cells in the UMAP plot that express none of the known tTFs (Figure  1F  -arrow). The larval scRNAseq dataset gave us the opportunity to look for all potential tTFs in an unbiased and comprehensive way. We isolated the cluster of medulla neuroblasts from the scRNASeq data and used Monocle 37 to reconstruct their developmental trajectory. To confirm the accuracy of the trajectory, we looked at the expression of the known tTFs: Hth, Ey, Slp, D, and Tll. Indeed, as was already clear from the UMAP plot, these tTFs were expressed in the correct temporal order along the trajectory (Figure  2A  -TFs in purple). We then examined the expression dynamics of all Drosophila TFs and identified 39 candidate tTFs that exhibited expression restricted to a temporal window. These fell into two distinct categories: 14 of them were expressed at relatively high levels and included the 6 known tTFs (Slp includes two genes, Slp1 and Slp2) (Extended Data Figure  3A ), while 25 of them were expressed at lower and fluctuating levels along the trajectory (Extended Data Figure  3B ).
We tested the expression pattern of four of the 25 lowly expressed candidates (ap, cut, gcm, and gem) in the developing optic lobes using antibodies against Ap and the cut-Gal4, gcm-Gal4, and gem-Gal4 lines, but none were expressed in a temporal manner; therefore, we decided not to pursue these candidates further as their fluctuations likely represent noise. Moreover, although Klumpfuss (Klu) was previously suggested to be a tTF  38  , Klu mRNA was found to be continuously expressed throughout neuroblast life in our dataset.
The implementation of neuronal identity occurs very early in neuronal life and follows a fixed trajectory Drosophila neurons are already specified at birth, based on the spatial and temporal identity of the neuroblasts from which they were born, as well as the Notch status of the neuron. However, it remains unclear how neurons proceed to differentiate. To study the very first steps of neuronal differentiation after specification, we initially focused on a well-studied and easily identifiable neuronal type, Mi1 (Mi1 is the only adult neuronal type in the medulla that expresses Bsh). We selected from the scRNASeq dataset of the L3 developing optic lobes the Bsh-positive clusters that correspond to Mi1 neurons at different levels of maturity, as well as the GMCs most closely linked to them in the UMAP plot (Extended Data Figure  6A-A' ).
We merged this dataset with the Mi1 clusters from pupal stages P15, P30, P40, P50, and P70, and used Monocle3 42 to reconstruct their differentiation trajectory (Figure  5A ). We used the expression of Ase in GMCs and Bsh in neurons to mark the beginning of the elongated trajectory at L3 and P15 (Extended Data Figure  6B-B' ). At all later stages, the neurons formed a tight cluster with no clear trajectory. The larval cells were isolated from late L3 brains about 20 hours after neuroblasts started dividing and producing neurons. We therefore estimate that the L3 trajectory corresponds to the first ~20 hours of neuronal life. We identified groups of genes (modules) that are co-regulated along the entire trajectory from L3 to P70 and searched for the GO terms enriched in each gene module (Figure  5B ).
We first verified that the correct neurotransmitter identity was indeed established as early as L3. We looked for the expression of ChAT, VGlut, and Gad1 (markers for cholinergic, glutamatergic, and GABAergic cell types, respectively) in the scRNASeq data and observed that they were expressed in the medulla in non-overlapping neuronal sets (Figure  5D ). We confirmed using the adult scRNASeq data that we published recently  21  that the neurotransmitter identity of each cell type at L3 was the same as in the adult (Table  S1 ). We then stained late L3 brains with antibodies for choline acetyltransferase (ChAT) and the vesicular glutamate transporter (VGlut) in late L3. While we observed their expression in the mature neurons of the larval ventral nerve cord, we saw no expression in the developing optic lobe (Figure  5E ).
Drosophila tTF expression in mouse cortical radial glia While similarities in spatial patterning between vertebrates and flies has long been noted  3, 6  , it is not clear how temporal patterning has evolved  48, 49. Sporadic evidence suggests that temporal factors identified in the Drosophila ventral nerve cord neuroblasts pattern stem cells that generate the mouse retina and even the cortex  [12] [13] [14] [15] [16]. To address the similarities and differences between Drosophila neuroblasts and mouse cortical radial glia, we probed for the expression of known Drosophila tTFs a recently published scRNASeq dataset from the mouse cortex, where radial glia at different stages of development (E12-E15) were sequenced  18. We first looked for the orthologs of the known tTFs of the Drosophila optic lobes described earlier, as well as in this study.
None of the medulla neuroblast tTFs were expressed in strict temporal windows in ageing radial glia between day 12, where they produce deep cortical layers, and day 15, when more superficial layers are generated (Extended Data Figure  7A ), with the exception of the Ey ortholog, Pax6, which was enriched in older progenitors. Foxg1, an ortholog of Slp was previously described to be enriched in young radial glia  49, 50  ; while it is slightly reduced in older radial glia in this dataset, this reduction was not statistically significant. We also looked at the Drosophila orthologs of the mouse TFs that were described to be expressed in a temporal manner in the mouse CNS  51  ; none of them were found to be tTFs in our trajectory analysis. The lack of conservation of a common temporal series of transcription factors sensu stricto between flies and mice shows that the acquisition of the specific temporal series occurred later and independently in each phylum, which is consistent with the several cascades of tTFs observed in various brain structures, such as the ventral nerve cord  11  , Type II central brain neuroblasts  52  or optic lobe neuroblasts.
Discussion We present here a comprehensive series of transcription factors that temporally pattern a developing neural structure. We took advantage of the unique structure of the developing Drosophila optic lobe and generated detailed trajectories of neural stem cells starting from the time they are born to the time they terminally differentiate. All known tTFs were confirmed using in this approach; moreover, all the candidate tTFs that were identified computationally were verified experimentally, thus providing a proof-ofprinciple for the combination of scRNASeq and trajectory inference that can be applicable to most other neuronal tissues in different animals that lack the genetic toolkit of Drosophila. We show that most tTFs are expressed in overlapping windows creating a combinatorial code that differentiates neural stem cells of different ages and therefore provide them with the ability to generate diverse neurons after every division.
g. neurotransmitter identity is established at the same relative time, independent of whether the neuron is cholinergic or glutamatergic) which is reminiscent of neurogenesis in vertebrates, as we show by analyzing singlenucleus sequencing data from the developing human cortex. This shows that, while specification mechanisms decide which genes are going to be expressed, the timing of the expression of genes of a specific function is preset, and this attribute of neurons is conserved between mammals and flies. Importantly, this highlights that understanding the mechanisms of neuronal differentiation in flies can generate insight for the equivalent process in humans, which is necessary for the generation of in vitro neuronal differentiation protocols  53. Finally, we probed a scRNASeq dataset of mouse radial glia for the expression of the optic lobe tTFs.
Antibody generation Polyclonal antibodies were generated by Genscript (https://www. genscript. com/). The epitopes used for each immunization are listed below. Wandering third instar larval Drosophila optic lobes were fixed in 4% formaldehyde for 15 minutes at room temperature (with the exception of one staining using the mouse anti-eyeless, for which fixation was on ice for 30 minutes). After washing, they were incubated for 2 days with primary antibodies at 4 o C. After washing the primary antibody, the brains were incubated with the secondary antibodies overnight at 4 o C. The secondary antibodies were washed and the brains were mounted in Slowfade and imaged at a confocal microscope (Leica SP8) using a 63x glycerol objective. Images were processed in Fiji and Illustrator. At least four brains were analyzed for each experiment.