dataesr/scientific-paragraphs-categorization huggingface dataset:
In-vivo VSDI and optogenetic stimulation in the mouse brain To perform the VSD imaging experiments, animals were anesthetized with 0. 5% isoflurane, and a large portion of the dorsal skull on the right hemisphere as well as the dura matter were removed. Then, the exposed brain was incubated for 60 -90 minutes with the dye RH1692 solution prepared with the HEPES-buffered saline (1 mg/ml). After the unbounded dye solution was washed out from the brain, the stained brain surface was covered with the 1. 5% agarose dissolved in HEPES-buffered saline and sealed with a glass coverslip. The VSD data was collected as 12-bit images using a CCD camera (1M60 Pantera, Dalsa, Waterloo, ON) and EPIX E4DB frame grabber with XCAP 3. 1 imaging software (EPIX, Inc. , Buffalo Grove IL). The focal plane was set to about 1 mm bellow the cortical surface in order to reduce the potential brain surface pulsation artifacts.
The laser beam was directed to pre-defined coordinates on the cortical surface using custom software written in IGOR PRO (Portland, OR), which controlled galvanometer scan mirrors (Cambridge Tech, Lexington, MA), via analog output voltage from PCI-6110 DAQ (National Instruments, Austin, TX). Low amplitude and short duration single laser pulses were applied to the cortical surface to ensure sufficient activation, low laser stimulus artifact, and low photo-bleaching. white for not significant correlations and shades: dark, medium, light for significant correlation values greater than 0. 8, 0. 6, 0. 5). Six components are unique (i. e. , parts in motifs 2, 4, 6, 7, and 9) and not correlated to other motif parts. This makes motif 7 the most unique one and motif 1 the most entangled motif (followed by the entanglements in motifs 5, 11, and 12).
The membranes were washed in TBST (3 ϫ 15 min) and revealed with ECl (PerkinElmer, catalog #NEL_103001EA). Membranes were exposed to Bioflex MSI autoradiography/x-ray film for different time intervals, and only the films that showed easily identifiable, but not saturated, bands for every sample were used for quantification, using ImageJ software (RRID:SCR_003070; http://imagej. nih. gov/ij). Background mean gray value was subtracted, and then values were normalized on GAPDH mean gray value. The average of normalized mean gray value of control experiments was calculated and assigned a value of 1. The normalized values of the PPACK and tPA treatments were then expressed as the relative of the control samples. Specificity of the anti-BDNF antibody was verified using brain lysates from CaMKII_ Cre;BDNF lox/lox and their BDNF lox/lox adult littermates.
The same threshold was applied to all images acquired during the same imaging session. The number of individual points was quantified using the macro "analyze particles" of ImageJ (RRID:SCR_003070). The "analyze particles" feature was applied with a minimum area of 15 pixels to calculate numbers and total areas of identified objects. Data were exported to Prism (GraphPad, RRID:SCR_002798) for further analysis. For colocalization analysis, we used the Mander's split coefficient to identify the fraction of PLA spots that colocalized with PV ϩ boutons. We confirmed by using the Costes randomization method with 100 iterations that any colocalization was more than expected from randomly distributed fluorophores (ImageJ colocalization analysis with JACop macro)  (Bolte and Cordelieres, 2006). Each colocalization between PV and PLA signals was further verified manually using Imaris software (Bitplane) by an experimenter blind to the genotype.
On average, we acquired only one PV cell from each successfully transfected organotypic culture. Confocal images of the PV cell axon arbors were taken in the first 150 m from the PV cell soma using a 63ϫ glycerol objective (NA 1. 3, Leica Microsystems) and a Leica Microsystems SPE or a Leica Microsystems SP8 confocal microscope. Analysis of PV cell perisomatic innervation was performed as described previously  (Chattopadhyaya et al. , 2013) , using Neurolucida software (MBF, RRID:SCR_001775). Briefly, neuron somata were identified by NeuN immunofluorescence, and the axon of PV cells was traced in 3D. Only innervated NeuN-positive cells were included in this analysis. The following parameters were analyzed for each PV cell: (1) perisomatic bouton density, (2) axonal terminal branching around innervated somata, and (3) percentage of NeuN-positive somata innervated by a PV cell.
Imaging and analysis of immunolabeling in vivo For the analysis of PV, vGAT, and PNN expression in minipumpimplanted brains, sections were processed in parallel, and all images were acquired the same day using identical confocal parameters. Confocal images (Leica Microsystems, either SPE or SP8 confocal microscope) were acquired using either a 20ϫ water-immersion objective (NA 0. 7; Leica Microsystems) or a 63ϫ glycerol objective (NA 1. 3; Leica Microsystems). For each animal, we acquired two confocal stacks in layer 5 in both hemispheres (infused, ipsilateral vs noninfused, contralateral). Data were obtained from 3 or 4 brain sections per animal. z stacks were acquired with a 1 m step, exported as TIFF files, and analyzed using ImageJ software (RRID:SCR_003070). Briefly, PV, vGAT, or PNN perisomatic rings (between 7 and 10 in each stack) were outlined, and the mean gray values were measured, after background subtraction.
Visual-evoked potentials (VEPs) VEPs were recorded as described by  Porciatti et al. (1999). We measured contralateral to ipsilateral ratio (C/I) of VEP amplitude to measure ocular dominance plasticity. Extracellular signal was filtered from 1 to 275 Hz. VEPs in response to square wave patterns with a spatial frequency of 0. 06 cycles per degree and abrupt phase inversion (1 Hz temporal period) were evaluated in the time domain by measuring the P1 peak-to-baseline amplitude and latency. Computer-controlled mechanical shutters were used to collect data from each eye.
Single units For single-unit recording, extracellular signal was filtered from 0. 6 to 6 kHz. Sampling rate was 33 kHz. Spiking events were detected online by voltage threshold crossing, and waveforms of 1 ms were acquired around the time of threshold crossing. To improve isolation of units, recordings from groups of four neighboring sites (tetrode) were linked, so that each spike was composed by 4 waveforms. Then waveforms were processed using the OffLine Sorter software (Plexon). Drifting sinusoidal gratings were used as visual stimuli (1. 5 s duration, temporal frequency of 2 Hz, 12 directions, 6 spatial frequency: 0. 01, 0. 02, 0. 04, 0. 08, 0. 16, 0. 32 cycles per degree). Stimulation was repeated five times per eye, with stimulus conditions randomly interleaved, and two gray blank conditions (mean luminance) were included in all stimulus sets to estimate the spontaneous firing rate.
Optical images were recorded using a 12-bit CCD camera (1M60, Dalsa) driven by the Imager 3001 system (Optical Imaging) and fitted with a macroscopic lens (AF Micro Nikon, 60 mm, 1:2:8D). Frames of 512 ϫ 512 pixels were acquired at a rate of 4 Hz, giving a spatial resolution of 28 m/pixel. The acquisition was sustained for 10 min during the continuous stimulation paradigm. During episodic stimulation, frames were acquired for 20 s for every contrast and spatial frequency tested. An average of 10 repetitions was used to obtain a good signal-to-noise ratio. Data analysis. Optical intrinsic signal (OIS) data were analyzed with MATLAB (The MathWorks). For each pixel of the cortex, a Fourier transform was applied on temporal signals collected during continuous stimulation. Fourier phase and amplitude were generated for each frequency and used to map the retinotopy and perform quantification.
The amplitude of neuronal activity was used to generate the "neuronal activation" map. In parallel, the phase at the stimulus frequency was related to the delay to activate the receptive field and was associated with the relative retinotopic position. The "retinotopic" map was obtained by multiplying the amplitude and phase maps. ROIs located in the occipital cortex were manually delineated in the activation maps for each hemisphere. The area of V1 was calculated from the ROI borders. The shape of the ROI was fitted to an ellipse with MATLAB, and the ratio of length of the two main axes of the ellipse determined (height/width) was calculated to measure the "ovality index. " The ratio of the number of the phases detected in the retinotopic maps over 2 (i. e. , the range of the phases displayed) was used to estimate the "apparent visual field" (i.
A bipolar 125 µm stimulating electrode was inserted into the rat Schaffer collateral (Juvenile: -3 mm posterior, 0. 2 mm lateral, 3. 2 mm ventral to bregma; Adults: -3. 1 mm posterior, 0. 3-0. 5 mm lateral, 3. 5 mm ventral to bregma  30. A single recording microelectrode (glass, tip diameter 2-5 mm, filled with 2 M NaCl, resistance 1-4 MO) was slowly lowered into the CA1(Juvenile -4. 1 mm anterior, 2. 1 mm lateral, and 2. 8 mm ventral to bregma; Adults: -4. 2 mm anterior, 2. 5 mm lateral, and 3 mm ventral to bregma). The evoked responses were digitized (10 kHz) and analyzed using a Cambridge Electronic Design 1401-plus data acquisition system (Cambridge, UK) and its Spike2 software. Offline measurements were made of the amplitude of field post-synaptic potentials (fPSPs) using averages of five successive responses to a given stimulation intensity applied at 0.
Statistical analysis. Data were expressed as the mean ± SEM, and statistical analyses were conducted using SPSS (version 21) or Prism 7 software with a threshold for considering statistically significant difference being set up at p ≤ 0. 05. Body weight data were analyzed using unpaired Student's t tests. The OLM, LTP and corticosterone results were analyzed using ANOVAs with diet (CD vs HFD), age (juvenile vs adult) or drug treatment (RU486 vs vehicle) as between-subject factors and with repeated measurement on the time factor when appropriate. This was followed by simple effects analyses to establish the source of any significant interactions. Analysis of Western blot data was performed using Quantity One computer software. Statistical analysis was performed using independent sample t tests (t tests were performed only on data from the same gel).
Methods: We reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias. Results: Circular analysis was detected in 15. 9% of ADHD classification studies, lack of independent test set was noted in 13% and insufficient methodological detail to establish its presence in another 11. 6%. Accuracy of classification ranged from 60% to 80% in the 59. 4% of reviewed studies that met criteria for independence of feature selection, model construction and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes.
Thus, we examine this question using ADHD as an exemplar given the large existing literature base on neuroimaging classifiers of diagnosis. Potential pitfalls of applying classification approaches to neuropsychiatric data have been discussed extensively  (1, 3, 41, 42). Two that are particularly revelant include circular analysis and sample size. First, to evaluate its role in clinical medicine, a machine learning classifier needs to have good generalizability: defined by good performance on patients not included in the study (i. e. , new patients). In the experimental setting, this is assessed by cross-validation, whereby a subset of a dataset is not included in construction of the classification model ("training") and subsequently used to assess the performance of the model ("testing"). The testing accuracy, however, can be inflated due to a common error of including all data when selecting features to be used for classification (i.
MATERIALS AND METHODS. We performed a literature search using multiple databases (PubMed, Web of Science) and search engines (Google Scholar), with key words including "ADHD", "ADD", "classification", "machine learning", "classifier", "prediction", "accuracy", retaining publications that explicitly described a classification framework to distinguish between ADHD and comparison groups (n = 95 studies) based on neuroimaging features. Studies were excluded if: (a) no control group was examined (ADHD only or ADHD versus other disorder groups) (n = 5); (b) sample size per class or age group was not specified (n = 5); (c) total sample was <6, limiting within-group variance (n = 2); (d) accuracy was shown graphically but not reported in the text (n = 3); (e) the model did not use neuroimaging features (n = 9); and, (f) classification was not performed based on original ADHD diagnostic labels (n = 1).
Frequency of Circular Analysis To assess the frequency of circular analysis, we evaluated the methods section of each study. We identified procedures for feature selection and those for classification, with the goal being to identify if the same data set was used for feature selection and in the testing of the classification model. If this was unambiguously the case, the study was labeled as nonindependent (NI, see Table  1 ) with respect to model testing. In many instances, there was ambiguity regarding non-independence given the methods description and/or presented workflow. Such studies were labeled as unknown (UN), with respect to non-independence. For all such studies, we contacted the primary author to seek additional details in order to reduce the size of the UN category. Some studies presented rationale for including all subjects' data in model training because the algorithm of feature selection analysis was independent from the analysis of the classifier, and thus should not affect classifier performance  (14, 51).
Prevalence of Circular Analysis. A total of 15. 9% (11/69) presented methods that were consistent with circular analysis, whereby feature selection was performed on the full dataset including the test data. Nine studies (13. 0%) did not employ any cross-validation. Hence, the reported accuracies were untested with respect to generalizability. In 8 studies (11. 6%, 8/69) independence was unclear (UN). That is, the methods provided insufficient information to determine if circular analysis was present. For example, some studies used linear discriminant analysis trained on half the dataset but t-tests were used to determine which features were considered by the linear discriminant analysis. Importantly, it was not specified which data were used to perform the t-tests (training sample only or full sample).
In the Monastra et al  (10, 55)  studies, the 1999 study identified the threshold, whereas the 2001 study provided the cross-validation result using new participants. In the Snyder et al  (18, 20)  studies, the thresholds were defined based on an external database in the 2008 study, and based on the 2008 result in the 2015 study. Thus, the Snyder et al  (18, 20)  studies and the 2001 Monastra et al study  (10)  can be considered independent cross-validation and by this definition do not fall under circular analysis. However, these studies had limitations with respect to estimation of specificity. The non-ADHD comparison sample size averaged 16 individuals per age group (i. e. , n=7, 11 and 15 per tested age group in  Monastra, 2001   (10) , n=9, 20 and 33 per tested age group in Snyder 2008 (  18 )).
DISCUSSION The aim of our study was to assess the contribution of circular analysis and small-sample bias to accuracy of diagnostic classification studies in ADHD using neuroimaging biomarkers. We found circular analysis in 15. 9% of ADHD classification studies, lack of cross-validation in 13% and insufficient methodological detail to establish its presence in another 11. 6%. Our results reveal that accuracy of classification is 60-80% in the 59. 4% of studies that met our criteria for independence of feature selection, model construction and test datasets. There was a negative relationship between accuracy and sample size even in the presence of independent testing, suggesting that small sample accuracies may be subject to bias.
Methodological factors and classification accuracy. A key conclusion from our anlaysis is that in 28. 9% of the studies reviewed, reported accuracy was likely inflated due to presence of circular analysis or lack of internal validation (test set). In some cases, the use of a full dataset for feature selection was justified by using an analysis thought to be independent from the contrast of ADHD versus controls (e. g. , mean effect across all subjects within a condition  (14) , PCA (  51 )). However, the independence of such approaches is difficult to guarantee, can still contribute to bias during testing  (43)  and therefore should be avoided. External validation, an even stronger test on generalizability, was absent in 55 (79. 7%) studies, suggesting that our estimates of true accuracy in classification of ADHD may be optimistic still.
Conclusions. In this study, we found that unbiased classification accuracy in ADHD diagnosis in the range of 60-80%, too low to be viewed as an independently useful biomarker of disease, is consistent with known heterogeneity in this disorder. These data are also consistent with contributions of circular analysis and small-sample bias to inflation of higher accuracies, thus accounting for the discrepancy. We conclude that steps to resolve these issues, as well as a shift towards accounting for sample heterogeneity and prediction of future outcomes, will be crucial in increasing the utility of classification in ADHD.
Acknowledgements: We would like to thank  Francisco Pereira ,  Scott Makeig  and  Iman Rezazadeh  for helpful discussions. This work was supported by  National Institutes of Health  grants  MH101282  and  NS97484  (to SKL), as well as the  UCLA-California Institute of Technology   Medical Scientist Training Program  ( NIH T32 GM08042 ) and the  Eisenhower Medical Center Department of Internal Medicine.
Background: This review discusses the findings in the literature on pain and distraction tasks according to their sensory modality. Distraction tasks have been shown to reduce (experimentally induced) acute pain and chronic pain. This can be influenced by nature and by the sensory modalities used in the distraction tasks. Yet the effect on reducing pain according to the sensory modality of the distraction task has received little attention. Methods: A bibliographic search was performed in different databases. The studies will be systematized according to the sensory modality in which the distraction task was applied.
Literature Review The following databases were consulted independently: EMBASE (1980 to 2019), Medline (via EBSCOhost, 1982 to 2019), the Cumulative Index to Nursing and Allied Health Literature (via EBSCOhost, 1996 to 2019), and PsycINFO (1967 to 2019). A unique search strategy was designed for each of the 4 databases. The review was limited to studies on humans, published in English. The search was performed using the combined terms "attention," "distraction task," and "pain," joined by the Boolean operator "AND. " Duplicate articles were eliminated. This review was conducted in March 2019.
Study Selection A bibliographic search was done in the databases mentioned, identifying 67 publications, of which 34 were discarded as duplicates. A total of 33 publications were analyzed based on the title and abstract. Of these, 9 were discarded for being conference abstracts (n = 3), not measuring the variable pain (n = 5), and not manipulating the variable attention (n =1). Consequently, 24 studies fulfilled the previously established selection criteria and were included in this review (Figure  1 ). It should be noted that performing a metaanalysis with these studies becomes very difficult owing to the heterogeneity of the participants of the different studies (ie, healthy, with specific pathologies) and the few investigations addressing sensory modalities (ie, there is only 1 study on tactile modality).
GENERAL DISCUSSION The primary aim of this article was to review the main findings in the literature on distraction and pain, emphasizing the sensory modality of the distraction tasks used. The reviewed studies are detailed in Table  1. We will first discuss the main findings by sensory modality, addressing the possible etiologies of the data described, grouping the data into adults with acute pain, children with acute pain, and adults with chronic pain. Second, we will set out the differences found between induced and chronic pain, with a clear emphasis on the reasons for their differential pattern. Third, we will review the differences between adults and children. Finally, an additional section is included in which other limitations and future lines of enquiry are described.
Knowledge of scene illumination is one of the constraints and generally it is either assumed to be constant (as in case of remote sensing) or measured separately, before processing the acquired multispectral data. Extending the use of multispectral imaging system from heavily constrained environments to real world applications is still an open challenge. For the demonstration of multispectral constancy, three scenes are created in a viewing booth and are acquired through a multispectral camera under five different illuminations. Estimation of scene illuminant is performed and the multispectral data is transformed into a canonical representation. A linear method is used for spectral reconstruction by using a calibration matrix which is formed with the canonical illuminant. This method allows the use of multispectral camera as a spectrophotometer and its use in the uncontrolled imaging environment.
Details of W c is provided in Section 3. 5. Multispectral constancy is analyzed in simulations by Khan et al. [14]  and it is taken at the experimental level in this paper. Multispectral data is acquired for three scenes with varying illuminations and processed through the mltispectral constancy framework. The results are through spectral reconstruction of material surface reflectances in the scene. Details of the experimental setup are discussed in the following section.
In our experiments, we use p = 5 and σ = 2. These values are obtained in the simulations performed in  [18]. Scene illuminant is estimated in the sensor domain after masking out the ColorChecker from the scene. In this work, we assume uniform illumination all over the scene. However, in a real scenario, the spectral power distribution of illumination can differ at various locations in an image. There are various methods for dealing with such situations that address illuminant estimation a per-pixel level  [29] [30] [31]. In our current work, we are demonstrating the multispectral constancy pipeline from image acquisition to the spectral reconstruction. This pipeline consist of several parts, and there is much room for further improvements in each module of this pipeline. Since our aim is to verify the effectiveness of multispectral constancy through real multispectral images in this work, we do so by limiting the experiments by assuming uniform illumination over the scene.