dataesr/scientific-paragraphs-categorization huggingface dataset:
Of note, these considerations are limited to the 3D extracted cube surrounding each neuron (~350 × 550 × 350 px 3 , xy pixel size -0. 816 lm, z-step -1 lm), and possible differences between these cell clusters could exist for dendrites extending beyond 240 microns from the soma. Specifically, biVChI and mVChI clusters presented fundamentally distinct morphological features. For example, the SD compression feature that provides a measure of SD linearity was larger in mVChIs (beta = 0. 1 AE 0. 0043) than that in biVChIs. In comparison, the SD were shorter in biVChIs (beta = À235. 44 AE 7. 13 lm), such that the cumulative length of all SD in mVChI and biVChI was similar (Fig 3F ). Next, we explored online PatchSeq datasets that included all major inhibitory cortical interneurons in the visual cortex  (Gouwens et al, 2020).
Out of 141 ChAT-positive layer 2/3 cells (> 1 read), 14 had reconstruction data. Interestingly, in the dimensionalityreduced UMAP space, single bipolar and multipolar cells were separated according to their transcriptomic profiles alone, indicating a divergence in gene expression between these cell types (Fig 3G ). However, the low number and experimental constraints did not allow us to assess specific genes or pathways as particular to these cell type sub-populations. We conclude that the barrel cortex biVChIs and mVChIs differ in their cortical distribution, in their proximal and distal dendritic branching patterns, and in their transcriptomic profiles, compatible with distinct innervation features and potentially unique functions of these two VChI types in the cortical microcircuits (See Dataset EV4 for full feature statistics).
After 21 days we executed the complete imaging and PopRec pipeline (. However, following whisker deprivation, we observed a shift in the proportion from biVChIs to mVChIs, in contralateral cortices but strikingly, also in ipsilateral cortices from whisker-deprived compared with control mice. Further, we observed a similar shift in the cortical depth distributions of VChIs, with a higher proportion of deep VChIs (Fig  4C  and D ). Both biVChIs and mVChIs from whisker-deprived mice showed more elaborate branching and extended dendrite sections, and less curved dendrites compared with control mice; however, mVChIs showed more prominent dendritic differences  (Fig 4E, Dataset EV5). To estimate differences between experimental conditions at the level of the single cells, we trained a Gaussian regression model (MATLAB) with 20-fold cross-validation to separate cells according to their condition.
Similar to individual feature differences, these characteristics were evident in both the ipsi-and contralateral hemispheres while showing a stronger divergence in the contralateral side  (Fig 4F). To probe if the spatial order and scattering pattern of VChIs was affected by deprivation, we measured the expected distribution of cell somata in the 3D space and found that in both ipsi-and contralateral hemispheres, VChIs in deprived mice showed a more clustered, less random distribution compared with control mice  (Fig 4G). Finally, to ask whether the observed effects were specific to the barrel regions, we estimated morphological differences in peripheral VChIs of the barrels by only including cells that are in the 20% periphery of the barrel fields. We found no difference between bipolar to multipolar proportion, contrary to what we observed inside the barrel regions, and as a whole, all changes were only observed between the control and deprived mouse and not within the deprived mouse itself  (Fig 4H, Dataset EV10).
We hence sought transcripts and pathways whose expression is altered 3 weeks postdeprivation; the time point at which we assessed the morphological changes using PopRec (Fig  4 ). To study the transcriptomic response of VChIs to whisker deprivation, we performed the same unilateral whisker plucking procedure presented above, sacrificed the mice at p28 and immediately dissected cortical regions around the barrel cortex. Cortical tissues were subjected to the NuNeX protocol for isolating barrel cortex VChIs from both cortical hemispheres of mice that underwent unilateral whisker deprivation and controls (Materials and Methods). Further, our extraction procedure enabled the detection of numerous cholinergic mRNA transcripts per sample, including transcripts of relatively low levels (e. g. , ChAT and VAChT) and VIP-related transcripts, supporting the neuronal source of this dataset.
Further, we compared the expression of preparations from control mice to known datasets and cortical cell type profiles (Dataset EV6) and found that NuNeX-isolated neurons from control mice were more A Experimental design and NuNeX RNA isolation strategy. Mice underwent unilateral whisker deprivation on day p7, were sacrificed on day 30 and their barrel cortices were isolated, lightly fixed, homogenized, and subjected to fluorescence-activated cell sorting (FACS). RNA was extracted from pool-sorted tdTomato and DAPIpositive neurons from left and right cortices of different mice, subjected to RNA-seq and downstream analysis. B RNA-sequencing and mapping statistics. C Correlation analysis of Cholinergic and VIP-related transcript profiles from control mice to a web-available dataset (Allen Institute) of 246 neuron types.
Discussion By constructing the structural "morpheme" of VChIs from control and whisker-deprived mice and seeking the corresponding RNA-seq differences, we found new links between the structuraltranscriptomics features of VChIs while accounting for the corresponding experimental variability in multiple mice and extracting hundreds of spatially preserved cell structures per mouse. To challenge the validity of these VChI differences, we mined web-available single-cell transcriptomic datasets and performed experimental validation tests in cultured human-derived neuroblastoma cells  (Appendix Fig S8) , as well as murine primary neuronal cultures. Our approach enabled the exploration of the links between morphology, spatial distribution, and cellular transcriptomic differences at a single neuron resolution level, adding a new possibility of high-throughput neuronal morphology analysis and in-depth transcriptomic profiling.
To enable the rapid automated reconstruction of thousands of neurons with multiple structural parameters, and to handle huge imaging datasets, we "broke down" the data into small "cube" pieces that could be analyzed as CPU clusters. As our approach keeps the original cortical coordinates of each neuron, it further enables regional analysis of neurons such as those residing in specific cortical columns. Our strategy is amenable to use with many other automated reconstruction tools for single neurons. Furthermore, transforming large image datasets to mini-cubes can assist in benchmarking the performance of machine learning reconstruction tools in a well-controlled manner. Notably, the size of these 3D cubes is critical for the type of questions explored. For example, if the cells are very sparse, e.
Light-sheet microscopy Cortices were subjected to light-sheet microscopy (UltraMicroscope II, LaVision BioTec) operated by the Inspector Pro software (LaVision BioTec) using a fixed objective lens with 2× magnification of 4× 0. 3NA. Images were acquired with a Neo sCMOS camera (2,560 × 2,160, pixel size 6. 5 lm × 6. 5 lm, Andor) in 16 bit. Cortical samples were attached to the sample holder with epoxy glue and placed in a 100% quartz imaging chamber (LaVision BioTec). The light sheet was generated by independent lasers (Module Gen. II, LaVision BioTec); autofluorescence and VChIs were excited by 488 and 639 nm lasers, respectively. All autofluorescence scans were taken at estimated sheets of 10 lm and 4 lm z-step size with no horizontal focusing and signals were detected with Em 525 nm/ 50 nm filters.
PopRec pipeline Each light-sheet scan was cropped such that only the cortex region was kept, and the barrel cortex region was traced and defined as an ROI (FIJI;  Schindelin et al, 2012)  based on normalized (Intensify3D) autofluorescent scans. Peripheral barrel cells were defined  (Fig 4H)  by expanding the region volume outward by 20% and ignoring cells in the barrel region. Processed signal scans were normalized across experimental groups with (Intensify3D + ) and neuron somata were detected by a Somas to Cubes App. Somata detection coordinates were overlaid on a Z-projected image and accurate detection was determined depending on whether the spot had clear neurites or not (Dataset EV1). A cube was isolated around each neuron, and the data were saved as individual 3D tiff files (MATLAB).
Cubes were sized 350 × 500 × 350 pixels (x, y resolution -0. 812pixel/micron, z steps -1pixel/micron). Samples were oriented before imaging such that the y-axis was perpendicular to the cortical surface. Image cubes were fed to a MATLAB script running on a Linux cluster (see codes on repository). Morphological features from candidate reconstructions were extracted for each cell (Python) and were filtered using a dedicated App -Reconstruction Validation (MATLAB and Vaa3D;  Peng et al, 2014). Cortical surface and bottom border (Callosum) were measured from autofluorescent scans. All MATLAB and python codes are available as Codes. zip file in the repository.
Statistical analysis of morphological differences and clustering Statistical analysis was performed with MATLAB (MathWorks). tSNE clustering was performed on 30 standardized (z-score, MATLAB) morphological features. To account for known biological variabilities such as repeated measures (animals) or animal sex, we used a linear mixed effects approach as described in https://www. mathworks. com/help/stats/fitlme. html. Detailed models are described in Datasets EV3 and EV4.
VChI FACS enrichment To enrich barrel cortex VChIs, we sorted cells based on DAPI and tdTomato signals. We combined cells from several mice to reach roughly 200 VChIs per pool, ensuring that each pool was derived from the same mouse strain, treatment condition, sex, and the same hemisphere. To ensure reproducible experimental conditions, we limited whisker plucking to the right side only. RNA was extracted from the pools of sorted cells with a dedicated kit for formalin-fixed tissues (Qiagen, 217504) according to the manufacturer's instructions. See Dataset EV6 for details on sample characteristics and RIN values. Before cell sorting, NeuN antibody was added for staining (1:500, Alexa Fluor â 488 conjugated Sigma MAB377X) and incubated for 30 min. Cells were then pelleted by centrifugation at 900 g for 5 min, resuspended in staining buffer, and stained with DAPI (Santa Cruz, DAPI | CAS 28718-90-3) for 10 min.
All steps were performed on ice. Cholinergic cells and NeuN-positive/negative cells were sorted through a 70 lm nozzle at a sheath pressure of 70 psi and a drop drive frequency of 90-95 kHz. A highly pure sorting modality (2-way purity sorting) was chosen for both rounds of sorting. The flow rate during the first-round sorting was approximately 5,000 events/s. Sorted cholinergic and DAPI-positive cells were collected into tubes containing 100 ll PKD (< 1,000 cells). The second sort of NeuN-positive/ negative from the fixed and fresh tissue cells was sorted into 500 ll staining buffer (> 1,000 cells). Cells sorted from the fixed tissue were centrifuged at 900 g for 5 min and resuspended in 100 ll PKD, from the fresh tissue cells were resuspended in 700 ll Qiazol. Samples were collected with BD FACSAria III (BD Biosciences) and analyzed with FCS Express 7 Software (De Novo Software).
PatchSeq RNA-seq analysis Single nuclei RNA-seq data from the PatchSeq experiment was accessed from the repositories specified in the original study  (Gouwens et al, 2020). Data were analyzed using python3. 9 and Scanpy package  (Wolf et al, 2018). The initial dataset consisted of 3,840 cells and 45,768 genes. After sorting genes with less than 1,000 total counts, 22,250 genes were left for downstream analysis. Initial data preprocessing included normalization of counts per sample (target sum = 1e4) and log transformation of the normalized counts.
E PopRec pipeline step 3: feature extraction (BlueBrain/NeuroM) and ranking of candidate reconstruction in a randomized manner with the Reconstruction validationApp where the user decides if the current reconstruction displayed is accurate or not with full 3D visualization of original imaging. F The output of the PopRec pipeline produces the reconstruction cubes, neuronal reconstructions, a list of features from the reconstructed neurons, and the predicted rank of each neuron in the training dataset. In this study, we used this subset rank to train a regression model (MATLAB) and filter out neurons that do not pass a certain quality threshold from downstream analysis.
F Distribution of biVChIs vs mVChIs for total neuron length, mean stem dendrite length per neuron, mean number of bifurcations per neuron, stem dendrite (SD) compression as a measure of dendrite curvature, and the total number of sections per neuron. See Dataset EV4 for full statistical description. G PatchSeq analysis of VChIs. UMAP embedding of expression profiles from the entire study highlighting marker genes for inhibitory interneurons. Bottom right-only ChAT-positive neurons out of which 14 VChIs from layer 2/3 (n = 8 bipolar and n = 6 multipolar) labeled as bipolar and multipolar. Data information: In (D-F) linear mixed effects (LME) models were used in all panels unless stated otherwise. n = 7 mice in total with 14 cortical hemispheres. n = 2,355 total cells, n-bipolar = 1,016, n-multipolar = 1,339.
F Regression model for predicting cortical condition at the single-cell level. Left-prediction value for each cell. Top right-confusion matrix for classification of neurons to condition chance level is 33%. Bottom right-prediction summary histogram. G Ripley's L statistics for spatial distribution in 3D as a function of relative distance. H Peripheral barrel selection and proportion of bipolar and multipolar VChIs as well as depth distribution of peripheral VChIs (Dataset EV10, Cross tab test with post hoc). Data information: n = 12 mice in total with 24 cortical hemispheres as depicted in (A). ***P < 0. 001, **P < 0. 01, *P < 0. 05. (A-F) n-bipolar = 1,167, n-multipolar = 1,561 neurons inside the barrel region (Dataset EV5). (G and H) n-bipolar = 426, n-multipolar = 517 neurons in the barrel periphery region (Dataset EV10).
Samples were sorted by FACS based on DAPI and tdTomato signals. Due to the sparsity of VChIs, we pooled 1-4 cortices per sample to extract RNA and performed low input RNA library preparation and long RNA-sequencing (Fig 5A; Dataset EV6, and Materials and Methods). RNA-sequencing from VChIs isolated using our NuNeX procedure resulted in an average of 4. 04e7 reads per sample (min = 2. 49e7, max = 5. 05e7 reads) and mean 62. 2% mapping coverage (corresponding to an average of 2. 51e7 mapped reads; min = 6. 27e6 and max = 3. 51e7 reads per sample, Fig 5D, Dataset EV6). Notably, the mean RIN was 7. 2, reflecting goodquality RNA despite the relatively long isolation procedure (Fig 5B).
Acknowledgements The authors would like to dedicate this manuscript to the memory of  Ms Lili Safra , whose generous support of our Brain center enabled this work and many others. We are also grateful to  Dr Sebastian Lobentanzer , Frankfurt, and  Shani Vaknine ,  Jerusalem  for advice and assistance in RNA-seq analysis; and to  Drs Yoseph Addadi ,  Rehovot , and  Ester R Bennett ,  David S Greenberg ,  Sagiv Shifman ,  Yael Kalisman  and  Ron Refaeli, Jerusalem  for their contributions toward this study. We acknowledge the support of this study by the  European Research Council Advanced Award 321501  and the  Israel Science Foundation  Grant no. 1016/18  (to HS), and a  joint Edmond and Lily Safra Center of Brain Sciences (ELSC)  grant (to ML and HS) as well as the Gatsby charitable foundation, by the  Drahi family foundation  (to IS).
Data availability All codes used to generate the results are publicly available by a GitHub repository and is where any updates on code and data would be made (https://github. com/nadavyayon/PopRec), RNAseq files and metadata table are available in ArrayExpress (https://www. ebi. ac. uk/fg/annotare/E-MTAB-12395). The raw and partially processed Light-Sheet images can be found in https://www. ebi. ac. uk/biostudies/bioimages/studies/S-BIAD560. Any additional information or potentially missing information.
+ , (Appendix Fig S1), https://github. com/nadavyayon/Intensify3D) and corrected the observed signals within and between biological samples (Fig 1B). Data augmentation to smaller 3D volumes Postnormalization, we applied another application-Soma to Cubes. This Application first automatically detects the soma locations based on predefined parameters. Subjecting the imaged volumes to this Application enabled the automatic detection of VChI somata with an accuracy of 97. 94 AE 0. 012% determined by manual validation (14 cortical samples of na€ ıve mice, Fig 1C, Dataset EV1, see Materials and Methods). Current tracing and reconstruction tools are often limited by imaging size and have significant RAM limitations (BitPlane-IMARIS.
Second, to raise the level of students' attitude towards learning Mathematics to very high, the teachers may provide an authentic learning experience to elicit active participation among the students. The integration of Mathematics software such as Geogebra and the likes were recommended to increase students' engagement in Mathematics. Furthermore, considering the present situation in this new normal, teachers are encouraged to maximize the utilization of their respective Learning Management System (LMS) as this will be the channel/ medium of most of the students' activities. Third, to raise the level of skill proficiency development in Mathematics to very high, teachers may provide the step-by-step process on how a particular problem was solved, visible to the learners. Also, giving feedback to students' submitted output is recommended for them to be aware of the errors and mistakes they have committed.
The execution of a behavior may depend on the state of other components: such dependencies are denoted by ports that form the interface of components, indicating their provisions and requirements towards each other. Section 2 gives an overview of Concerto, for a more complete presentation, the reader can refer to  [9]. Our goal with this work is to automatically generate reconfiguration scripts for systems of Concerto components, i. e. , determine required behaviors and coordinate their execution. We take as starting point a reconfiguration goal composed of behaviors to execute over some components and a specification of the final state of the system, particularly the statuses of ports. That goal may be provided by a system administrator, or could have been generated in the context of an autonomic control loop  [19].
Reconfiguration With Concerto Components and Assemblies. A distributed system in Concerto is represented as an assembly, i. e. , a collection of components that correspond to control entities for the elements of the system. Components are not intended to represent the functional aspects of those elements, but instead to pilot the actions (installation, maintenance, suspension of service, etc. ) required to operate them during their lifespan. In other words, a Concerto component is a wrapper around a new or legacy piece of software (e. g. , service, module), typically written by its developer, that acts as replacement for scripts to install and maintain it. The structural interface of a component is provided by its provide ports and use ports. Provide ports denote services or data provided by that component when those ports are active, while use ports denote requirements that the component has when those ports are active.
Both ports are connected to use ports belonging to server. Reconfiguration Scripts. Concerto is equipped with a simple language to execute reconfigurations. Whereas a Concerto component is written by a developer, the reconfiguration language is intended to be used by system administrators or DevOps engineers. Components are piloted through asynchronous requests via the command pushB(id, b) that asks the component identified by id to execute behavior b. The command takes its name from the fact that requests received by a component are queued and asynchronously executed by that component in the order in which they were received. While a component executes a behavior request, transitions in that behavior are fired until the component reaches a state where none of them can be fired. The behavior request is then considered complete, and the component executes the next one, until no more requests remain.
The Concerto language also provides synchronization commands: wait(id) blocks the execution of the reconfiguration program until the component identified by id has executed all behaviors requests submitted to it, and waitAll() blocks the execution until all components have executed all pending behavior requests. These three commands allow parallel asynchronous execution in Concerto, leading to more efficient reconfigurations. Based on the description of the components provided by their developers, Concerto can execute reconfiguration scripts , allowing for empirical performance comparisons  [10]. The goal of this work is to generate a reconfiguration script using the three aforementioned commands to execute behaviors over components and bring them to a desired state. In addition to those three commands, the Concerto language also provides four usual commands to modify the topology of an assembly: create and delete components, connect and disconnect them.
Computing a reconfiguration program to lead components to a desired state (or to have them perform some required operations) is the focus of this paper. As an example, consider the assembly in Figure  1 , where all the components are running. We wish to run software updates on dep1 and dep2, but this will deactivate their provide port service. To carry out the updates, component server must first deactivate its corresponding use ports, which is accomplished by executing its behavior suspend. Figure  2a  depicts a reconfiguration script that performs this, then returns the components to a running state. No explicit synchronization is needed between the suspension of server and the updates: the execution model of Concerto ensures that the updates cannot be executed as long as the provide ports are in use.
Sequences of behaviors to execute are thus extended ([ ] denotes the empty sequence, and s 1 • s 2 the concatenation of two sequences). To ensure a monotonic search, sequences are extended only for components c without unscheduled behaviors in S, i. e. , not the components that brought about the intermediary goal Γ' ports. If no such extension can be found (¬progress), the scheduling of S is blocked by a circular dependency between components and the synthesis process fails. If the procedure terminates, it returns a reconfiguration script corresponding to a solution of the scheduling problem of S. Consider the example of running updates in the assembly of Figure  1. Initially (see Subsection 3. 1), the mapping S of sequences of behaviors computed with localSeq is defined by S(dep1) = S(dep2) = [update, deploy], and S(server) = [ ], because Γ bhv does not include any behavior for that component, and the component is already in a state that satisfies Γ ports.