dataesr/scientific-paragraphs-categorization huggingface dataset:
Before overlap analysis, human gene or protein lists were filtered for genes/proteins with mouse orthologs (5628 unique genes from all lists combined), and mouse microglia module genes were filtered for genes with human orthologs (12,487 unique genes). The background, excepting protein lists described above, was the sum of unique genes in these two lists  (13, 757)  and included both mRNAs and ncRNAs. For mouse gene comparisons between differentially expressed genes and microglia module genes, the background was 13,483 unique mouse genes from the filtered dataset. To create microglia module-overlap analysis with human synaptic, schizophrenia-and ASD-associated gene modules (Fig. 2f ), hypergeometric distribution was used to compare gene lists from microglial modules (IM, JM, MIA-IM, AM, and REP-AM) with publicly available lists of genes enriched in ASD brains, and genes associated with schizophrenia or synaptic interactions.
Physiological analysis PatchMaster acquisition software and EPC-9 or EPC-10 patch-clamp amplifiers (HEKA Elektronik) were used to acquire electrophysiological data including passive membrane, single action potential, and repetitive firing properties. A total of 37 physiological characteristics were assessed by experimenters blind to the treatment groups. FitMaster Analysis Software (HEKA Elektronik) was used to analyze passive and active membrane properties. The passive membrane properties, resting membrane potential (Vr), input resistance (Rn), and the membrane time constants (Tau) were recorded under current clamp conditions. Vr was measured as the voltage present when the current injection was zero. Rn and Tau were measured by injecting 20 mV steps starting at -160 mV and ending at + 100 mV.
Repetitive firing of APs was determined as the number of spikes evoked at each step in a series of current steps that increased by 20 mV from -100 to +120 mV. sEPSCs were recorded for 2 min at a holding potential of -80 mV. sIPSCs were recorded at a holding potential of -40 mV. Following acquisition of spontaneous synaptic data, tetrodotoxin (1 µM) was added to the Ringer's perfusion medium and, following a 5 min equilibration period, action potential independent mEPSCs were recorded as above. Synaptic data was assessed using MiniAnalysis software (Synaptosoft), with event detection threshold set at maximum RMS noise level (5 pA). The sEPSCs and sIPSCs were automatically detected and manually confirmed to yield frequency and mean amplitude data. Averaged waveforms were created from all the EPSCs or IPSCs acquired for each neuron.
To reduce signal blurring in the zplane, each acquired stack of images was deconvolved using AutoQuant software (Media Cybernetics). Adjacent deconvolved image stacks were imported into Neurolucida (MBF Bioscience), aligned in 3D, and integrated into a single volumetric dataset. For analysis of complete neuronal structure, neurons were manually traced and reconstructed in Neurolucida. Scholl analyses were performed using Neurolucida.
Spine density/subtype and microglia interaction analyses Spine subtype and microglia-neuron interactions were assessed from deconvolved and aligned 3D images in Neurolucida. Spine subtypes were assigned based on published methods  [76]. Spines were classified into four subtypes: thin (head and neck <3 μm), stubby (no neck), mushroom (neck with head ≥0. 6 μm) or filopodia (neck >3 μm in length). Microglia-spine and dendrite interactions were quantified from the same datasets and classified as follows: encapsulation (within 0. 3 μm; one z-plane, and microglia is surrounding at least ½ the spine head diameter), apposition (within 0. 9 μm; three z-planes, and microglia is surrounding less than ½ the spine head diameter), and proximity (within 1. 5 μm; five z-planes) to microglia (see diagram in Supplementary Fig.
Quantification and statistical analysis To determine the relationships between different behavioral tests, spine density, spine-microglia interactions, electrophysiological measurements and microglial morphology, pairwise correlation analyses were performed in JMP Pro 10 (SAS) by calculating Spearman's Rho, a correlation coefficient computed on the ranks of the data values. Color maps were created based on correlations and on p values of correlations. For all analyses including correlation analyses, alpha values for confidence intervals were 0. 05. Data was normally distributed, although this was not formally tested for every dataset. Two-group comparisons were made using two-sided unpaired t-tests. Variances were tested by the Brown-Forsythe test. Four group comparisons were made using 2-way ANOVAs, with main factors being poly(I:C) (MIA) and MG-REP (treatment) or paired t-test within the group with Sidak method for p value adjustment.
Acknowledgements We would like to thank  Clarence Schutt  at the  Nancy Lurie Marks Family Foundation (NLMFF) ,  Robert Landreth  at the Landreth Family Fund and  Susan Leeman  at  Boston University  for supporting this study,  BioMicro Center  at  Massachusetts Institute of Technology for RNA sequencing service, Plexxikon, Inc. for providing PLX5562 and control chows;  Michael Hasselmo ,  Helen Barbas ,  Tarik Haydar ,  Annina M. DeLeo  for their critical reading of the manuscript,  Tarik Haydar  and  Jose Luis Olmos Serrano  for their assistance in the two-photon confocal imaging and Noldus Ethovision system, and  Lisa Nguyen ,  Jonathan Ko , and  Melisha Budhathoki  for analysis of the morphology and behavioral data and  Keri Ngo  and  Angela Mei  for the electrophysiological data analysis.
Data availability RNA-sequencing and MG550 Nanostring data for this study will be deposited in the Gene Expression Omnibus with the primary accession codes (https://www. ncbi. nlm. nih. gov/ geo/). The human ASD gene network data that supports the findings of this study are available at https://asd. princeton. edu [72]. All other data supporting the findings of this study are available within the paper and its Supplementary Data information files.
Objectives In this paper, we introduced the Wear-IT framework  [21] , a software system designed to study and overcome the challenges of long-term engagement in mHealth settings. Wear-IT uses a combination of passive and active sensors, novel computational architecture, visualization for individual feedback and self-monitoring, and real-time responsiveness to optimize participant and technology interaction. These tools allow the development of mHealth solutions for measurement and intervention to deliver treatment, derive scientific inferences, and promote individual thriving through lifelong engagement in mHealth apps.
Survey rules also provide specifications about cases where the signal is not available. For example, if a participant opted out of location tracking, they might be removed from the study or might face altered rules for survey delivery that focus on different predictors. The modular balance between server and app and the ability to adapt to different situations makes Wear-IT a versatile platform for data collection and intervention delivery. The overall architecture of the Wear-IT program is centered around data collection items of different types, called item types, which are collected into data collection events. These events include any case where participant data are recorded, including timed surveys, passive collection events, or participant-triggered one-button labeling responses. Conceptually, a common scheduling process can be used to organize regular passive collection, random or semirandom, according to triggered events or through some combination of those characteristics.
When validated data collection is necessary, Wear-IT is also able to integrate data from scientific wearables (eg, the Empatica E4 device  [26] ). Wear-IT's approach to integration attempts to meet each device according to its own capabilities and affordances as developed by its manufacturer. As a framework for research, we have designed Wear-IT so that it accesses each tool in a modular fashion, such that common data streams can be harmonized across different devices. In practice, each device has different limitations and a different means of access. For example, the Empatica E4 wristband has a released software development kit for real-time monitoring that permits Wear-IT to directly interpret real-time data about movement, electrodermal activity, skin temperature, and heart rate. By contrast, the Oura ring  [27]  currently has no published direct access.
One simplified but common example might be an EMA data collection (costly in terms of participant burden), which is triggered by a randomized timer. More intricate collections are restricted by decision rules using arbitrary combinations of continuous or periodic data. For example, the app might request information about a location once the participant has visited the same place for a length of time that is determined by a predictive model from the cloud and adjusted regularly as updated estimates become available (eg, following an approach such as MIDDLE  [33] ). Simple linear models such as logistic regression or even support vector machine-based classification can be implemented in this same decision tree-like framework by simply replacing the single value (eg, heart rate is higher than 100) with a formula-based combination of values.
Evaluation Approach We provide two approaches to evaluation of the Wear-IT framework. First, we present a case study of visualization and feedback as a proof of concept and to demonstrate the types of data that Wear-IT can collect and process. Second, we held qualitative interviews with 4 users who are currently deploying studies using the Wear-IT framework. Interviewees included 3 faculty members and 1 graduate student in different departments across the health and behavioral sciences.
Visualization and Feedback Individualized visualization and feedback have been shown to be effective both as a means of engagement  [15]  and treatment  [14]. Although these visualizations must be tailored to the specific study or treatment, a variety of tools are available to display different types of data, such as those displayed in Figure  3. Note that these data are provided for demonstration purposes only and are modified from a true dataset to protect privacy. These do not represent research data and should not be used to drawn generalizable inferences; rather they are intended only to display the types of visualizations in use. In the top left, a multiline comparison shows a psychological state (here craving, but potentially affect, anxiety, or some other psychological state of interest) across time.
Qualitative Interview In total, 3 faculty members and 2 graduate students agreed to provide feedback in a qualitative interview to relate their experiences using the Wear-IT framework. Note that the resulting data are for product evaluation, and not a part of a formal study. It is not intended to be used for generalizable inference but rather to provide evaluation of the framework in its status as of early 2020. Some researchers additionally reported anonymous quotations from participants engaged in their studies-these data were collected under institutional review board (IRB) approval for those studies using Wear-IT. For clarity, we use the term users, meaning researchers using the Wear-IT framework, and participants to indicate participants in their studies. None of the users or participants whose comments are described here were part of the development team or authors on this paper.
Of course, limiting GPS data collection may reduce the ability of the app to respond to location-based cues. Hybrid approaches, where they do not conflict with scientific or clinical goals, may also be possible. For example, it is possible to provide users with a map of their GPS-recorded travels for the day and allow them to remove specific locations or routes before their data are uploaded to the server, although this may conflict with real-time responsiveness goals. Again, our goal is to balance participant risks and burden with the scientific and clinical goals of each project. Note that in some cases, it may even be possible to generate responses to data without uploading it. Finally, no matter how many security precautions are taken, there is some risk that data are disclosed. Although we take as many precautions as possible to reduce this risk, it must still be recognized and balanced against gains.
This type of data collection involves a variety of privacy risks. First, facial expression video of the participant themselves contains sensitive information about their identity, and there is some risk of the participant disclosing information that is more sensitive than they wish to share or information that might be overheard by others nearby. Second, the background behind the person's face includes potentially identifying location information. Third, it is possible that other people in the area might also be recorded, possibly without their express knowledge or permission. As with other cases, it is important to balance the value of the data against the risk and cost to the participant. A variety of options exist to manage this type of risk. For example, if we know the specific information we want to collect from the video, for example, affective state variables, all three concerns could be mitigated by applying facial expression tracking to the video on the phone and sending only a processed stream of coded affective signals (eg, happy and sad) or a version cropped only to the participant's face, in lieu of raw video.
Acknowledgments Some research reported in this publication was supported by the  Intensive Longitudinal Health Behavior Cooperative Agreement Program  funded jointly by the  Office of Behavioral and Social Sciences Research (OBSSR) ,  National Institute on Alcohol Abuse and Alcoholism (NIAAA) , and  National Institute of Biomedical Imaging and Bioengineering (NIBIB)  under Award Number  1U24AA027684-02. This study was supported by the  Penn State Social Science Research Institute  and the  Penn State Fund for Innovation.
Introduction Quantitative brain analysis is crucial to better understand the human brain and to analyze different brain pathologies. However, whole brain segmentation is still a very challenging problem, mostly due to the high number of anatomical labels compared to the limited number of available training data. Manual segmentation of the whole brain is indeed a very tedious and difficult task, preventing the production of large annotated datasets. To address this question, several methods have been proposed in the past years. By extending the single-atlas method paradigm, the multi-atlas framework has been successfully applied to whole brain segmentation  [1] ,  [2]. In such approaches, labeled templates are first nonlinearly registered to the target image. Afterwards, the estimated deformations are applied to the manual segmentations before fusing them.
e. , draft law) submitted to an assembly for consideration. As prior knowledge, we decided to use nonlinearly registered Atlas prior. Finally, we also propose modeling communication between both assemblies using an innovative multiscale strategy. In AssemblyNet, we use a multiscale cascade of assemblies where the first assembly produces a coarse decision at 2×2×2 mm 3. This coarse decision is transmitted to the second assembly for analysis at 1×1×1 mm 3. This amendment procedure is similar to an error correction or a refinement step. After consideration by both assemblies, the bill under consideration becomes a law which represents the final segmentation in our system. Our contributions are: i) the use of prior knowledge based on fast atlas registration, ii) a knowledge sharing between CNNs using nearest neighbor transfer learning, iii) iterative refinement process based on a multiscale cascade of assemblies and iv) the use of student-teacher semi-supervised learning based on a well-designed auxiliary dataset.
Datasets Training dataset: 45 T1w MRI from the OASIS dataset  [21]  manually labeled according to the BrainCOLOR protocol were used for training. The selected images were the same than the ones used in  [18]. The age range was 18-96y for this dataset. All the used images and manual segmentations were obtained from Neuromorphometrics Inc. During our experiments, we used the 132 anatomical labels consistent across subjects (see  [18] ). Testing dataset: 19 T1w MRI manually labeled according to the BrainCOLOR protocol were used for testing. These MRI came from three different datasets: 5 from the OASIS dataset, one from the colin27 cohort  [22]  and 13 from the CANDI database  [23]. This testing dataset is the same one used in  [18]. The age range was 20-89y for OASIS, 5-15y for CANDI and the age was 27y for Colin27.
Scan-Rescan dataset: 8 T1w MRI (from 4 subjects) manually labeled according to the BrainCOLOR protocol were used for scan-rescan experiment. The same expert segmented both the scan and the rescan image. These MRI came from 2 different datasets: 3 from the OASIS dataset (not included in the training) and one from a patient with Alzheimer's disease from the ADNI dataset  [24]. Pathological dataset: 29 T1w MRI manually labeled according to the BrainCOLOR protocol were included in the pathological dataset. We did not use the rescan image of the AD patient described in the scan-rescan dataset. These MRI came from the ADNI dataset. There were 15 cognitively control subjects and 14 patients with Alzheimer's disease in this dataset. The age of the subjects varied from 62y to 88y. Lifespan dataset: 360 unlabeled T1w MRI were randomly selected under constraints from the dataset used in our previous BigData studies  [25] ,  [26]  to build the lifespan dataset.
This dataset was based on 9 datasets publicly available (C-MIND 1  , NDAR 2  , ABIDE  3  , ICBM  4  , IXI  5  , OASIS 6  , AIBL  7  , ADNI1 and ADNI2 8  ). From 1y to 90y, we selected 2 females and 2 males for each age (i. e. , 2F and 2M of 1 year old, 2F and 2M of 2 years old and so on). Therefore, we obtained a balanced group with 50% of each gender uniformly distributed from 1y to 90y. We made sure that none of the training or testing subjects were selected in this auxiliary dataset.
AssemblyNet framework Preprocessing: To homogenize input orientations and intensities, all the images were first preprocessed using the volBrain pipeline  9  [27] with the following steps: i) denoising  [28] , ii) inhomogeneity correction  [29] , iii) affine registration into the MNI space (181×217×181 voxels at 1×1×1 mm 3 )  [2] , iv) tissue-based intensity normalization  [30]  and v) brain extraction  [31]. Finally, image intensities were centralized and normalized within the brain mask. Atlas priors: To obtain priors knowledge on the expected results, we performed a nonlinear registration of a MICCAI 2012 Multi-Atlas Labeling Challenge atlas (based on the 45 training images) to the subject under consideration. The Atlas prior was built using Ants  [2]  and majority voting. The non-inear registration of the Atlas prior to the subject under study was done with an unsupervised deep learning framework similar to VoxelMorph trained on the lifespan dataset  [32].
Here, we propose to follow the teacher-student paradigm  [34]  to take advantage of the fast processing capabilities and high segmentation accuracy of the proposed AssemblyNet. The teacher-student paradigm has been efficiently used for image classification task  [35]. In our SSL framework, an AssemblyNet teacher -trained on the 45 training images -was first used to segment unlabeled images (i. e. , the 360 images of the lifespan dataset). Then, these 360 pseudo-labeled images were used to train from scratch an AssemblyNet student. At the end, the AssemblyNet student was fine-tuned on the 45 manual segmentations of the training dataset. During our experiments, we investigated the iteration of this procedure considering that the obtained AssemblyNet student could be a good teacher for a second student generation.
Implementation details Data augmentation: First, the images of the training and lifespan datasets were flipped along mid sagittal plane in the MNI space. Then, we used MixUp data augmentation during training to minimize overfitting problems  [36]. This method performs a linear interpolation of a random pair of training examples and their corresponding labels. Training framework: For all the networks, we used the 3D U-Net architecture proposed in  [18] , but with a lower number of filters. Instead of using a basis of 32 filters of 3×3×3 -32 for the first layer, 64 for the second and so on -we selected a basis of 24 filters of 3×3×3 to reduce by 25% the network size. We experimentally found that this setting reduced memory consumption without impacting performance. Moreover, we used the same parameters for all the U-Nets with: batch size = 1, optimizer = Adam, epoch = 100, loss = Dice and dropout = 0.
Validation framework First, for each testing subject, we estimated the average Dice coefficient on the 132 considered anatomical labels (without background) in the native space. Afterwards, we estimated the global mean Dice in % over the 19 images of the testing dataset. In this experiment, we compared AssemblyNet with several state-of-the-art methods. First, the patch-based joint label fusion (JLF)  [4]  was used as reference. In addition, we included U-Net  [19] , SLANT-8 and SLANT-27 methods as proposed in  [18]. SLANT-8 is based on 8 U-Nets processing non-overlapping sub-volumes of 86×110×78 voxels while SLANT-27 is based on 27 U-Nets processing overlapping sub-volumes 96×128×88 voxels. All these methods were trained on the same 45 training images described in the section datasets. Finally, we included SLANT-27 trained on 5111 auxiliary images segmented using NLSS  [7]  and fined tuned on the 45 training images.
These are the best published results for whole brain segmentation to our knowledge. For all these methods we report the results published in  [18]. We also used the docker implementation of SLANT-27  10  to produce segmentations of all the considered datasets. For the scan-rescan reliability experiment, we rigidly registered the re-scan image into the native space of the scan images  [2]. We then interpolated the re-scan segmentations into the native space of the scan images using the estimated transformation matrix. By this way, we estimated the Dice coefficients between both manual segmentations (i. e. , intra-rater consistency) and both automatic segmentations (i. e. , intra-method consistency). Moreover, we estimated the methodexpert consistency as the Dice coefficients between the automatic segmentation of the rescan images and the manual segmentation of the scan image.
Impact of semi-supervised learning Second, we evaluated the impact of the proposed teacher-student semi-supervised learning (SSL) framework (see Table  2 ). The used of the lifespan dataset -labeled with teacher -in the training of the student lead to an improvement of 0. 6pp of mean Dice compared to the mean Dice of the teacher (Dice=73. 3%). The fine-tuning (FT) step further increased the mean Dice by 0. 3 pp. These results are in line with previous literature on the role of the FT step  [18] ,  [35]. Finally, the second iteration with FT produced marginal improvement and lead to a mean Dice of 74. 0%. This improvement was not significant. Therefore, in the following, we used the first student generation since the time required for the second iteration is not justified by the performance improvement.