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A Combined Deep Learning System for Automatic Detection of "Bovine" Aortic Arch on Computed Tomography Scans
<p>A Combined Deep Learning System for Automatic Detection of “Bovine” Aortic Arch on Computed Tomography Scans</p> <p><em>Appl. Sci.</em> <strong>2022</strong>, <em>12</em>(4), 2056; <a href="https://doi.org/10.3390/app12042056">https://doi.org/10.3390/app12042056</a></p> <p>Abstract</p> <p>The “bovine” aortic arch is an anatomic variant consisting in a common origin of the innominate and left carotid artery (CILCA), associated with a greater risk of thoracic aortic diseases (aneurysms and dissections), stroke, and complications after endovascular procedures. CILCA can be detected by visual assessment of computed tomography (CT) chest scans, but it is rarely reported. We developed a deep learning (DL) segmentation-plus-classification system to automatically detect CILCA based on 302 CT studies acquired at 2 centers. One model (3D U-Net) was trained from scratch (supervised by manual segmentation), validated, and tested for the automatic segmentation of the aortic arch and supra-aortic vessels. Three DL architectures (ResNet50, DenseNet-201, and SqueezeNet), pre-trained over millions of common images, were trained, validated, and tested for the automatic classification of CILCA versus non-CILCA, supervised by radiologist’s classification. The 3D U-Net-plus-DenseNet-201 was found to be the best system (Dice index 0.912); its classification performance obtained from internal, independent testing on 126 patients gave a receiver operating characteristic area under the curve of 87.0%, sensitivity 66.7%, specificity 90.5%, positive predictive value 87.5%, negative predictive value 73.1%, positive likelihood ratio 7.0, and negative likelihood ratio 0.4. In conclusion, a combined DL system applied to chest CT scans was developed and proven to be an effective tool to detect individuals with “bovine” aortic arch with a low rate of false-positive findings.</p>
Computation of contrast-enhanced perfusion using only two CT scan phases: a proof-of-concept study on abdominal organs
<p>Cressoni M, Cozzi A, Schiaffino S, Cadringher P, Vitali P, Basso G, Ippolito D, Sardanelli F. Computation of contrast-enhanced perfusion using only two CT scan phases: a proof-of-concept study on abdominal organs. Eur Radiol Exp. 2022 Aug 29;6(1):37. doi: 10.1186/s41747-022-00292-y. PMID: 36031643; PMCID: PMC9420683.</p> <p>Abstract</p> <p><strong>Background: </strong>Computed tomography perfusion imaging (CTPI) by repeated scanning has clinical relevance but implies relatively high radiation exposure. We present a method to measure perfusion from two CT scan phases only, considering tissue enhancement, feeding vessel (aortic) peak enhancement, and bolus shape.</p> <p><strong>Methods: </strong>CTPI scans (each with 40 frames acquired every 1.5 s) of 11 patients with advanced hepatocellular carcinoma (HCC) enrolled between 2012 and 2016 were retrospectively analysed (aged 69 ± 9 years, 8/11 males). Perfusion was defined as the maximal slope of the time-enhancement curve divided by the peak enhancement of the feeding vessel (aorta). Perfusion was computed two times, first using the maximum slope derived from all data points and then using the peak tissue enhancement and the bolus shape obtained from the aortic curve.</p> <p><strong>Results: </strong>Perfusion values from the two methods were linearly related (r<sup>2</sup> = 0.92, p < 0.001; Bland-Altman analysis bias -0.12). The mathematical model showed that the perfusion ratio of two ROIs with the same feeding vessel (aorta) corresponds to their peak enhancement ratio (r<sup>2</sup> = 0.55, p < 0.001; Bland-Altman analysis bias -0.68). The relationship between perfusion and tissue enhancement is predicted to be linear in the clinical range of interest, being only function of perfusion, peak feeding vessel enhancement, and bolus shape.</p> <p><strong>Conclusions: </strong>This proof-of-concept study showed that perfusion values of HCC, kidney, and pancreas could be computed using enhancement measured only with two CT scan phases, if aortic peak enhancement and bolus shape are known.</p> <p> </p>
Outcomes of a computer-based cognitive training (CoRe) in early phases of cognitive decline: a data-driven cluster analysis
<p>This is the processed data for the manuscript<em> Outcomes of a computer-based cognitive training (CoRe) in early phases of cognitive decline: a data-driven cluster analysis.</em></p> <p>Dataset evaluated how patients’ baseline cognitive functioning could be associated to different individual characteristics and different trends of functioning across the cognitive rehabilitation process with CoRe. Identifying such predictors of responsiveness could promote to the development of a “personalized” or “stratified” precision medicine in the context of the broad spectrum of CT interventions. To this end, we used an unsupervised clustering technique in order to find subgroups of patients with different cognitive profiles and study how these clusters respond to the cognitive treatment. Cognitive profiles were developed by considering participants’ global cognition and processing speed (PS).</p> <p>Fifty-seven subjects underwent to a computer-based cognitive training (CCT) for 3 weeks and were evaluated at baseline (T0), post-intervention (T1), and after 6 (T2) and 12 (T3) months. Clusters of cognitive profiles were explored with k-means analysis. The analysis revealed two clusters, which were composed by 27 and 30 patients characterized by lower (Cluster 1) and higher (Cluster 2) cognitive functioning. At T1, cognitive performance improved in both groups, but Cluster 1 gained more benefits in global cognitive functioning than Cluster 2. However, at T3, Cluster 2 remained stable in its clinical condition, whereas Cluster 1 showed a pronounced worsening. In conclusion, Cluster 1 profile was associated with a more marked but also short-lasting responsiveness to CCT, whereas patients fitting with Cluster 2 characteristics seemed to obtain more CCT benefits in terms of stability or even delay of cognitive/functional decline. These findings may have relevant implications in informing the timing and modality of delivery of CCT.</p> <p> </p> <p> </p>
Dataset related to the article "Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion"
<p>This record contains raw data related to the article “Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion"</p> <p><strong>Purpose: </strong>To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation.</p> <p><strong>Methods: </strong>One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DL<sub>rest</sub>) and stress dataset (CTP-DL<sub>stress</sub>) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DL<sub>rest</sub>, and CCTA + CTP-DL<sub>stress</sub> was measured and compared. The time of analysis for CTP stress, CTP-DL<sub>rest</sub>, and CTP-DL<sub>Stress</sub> was recorded.</p> <p><strong>Results: </strong>Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTP<sub>Stress</sub> were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DL<sub>rest</sub> and CCTA + DL<sub>stress</sub> were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DL<sub>Rest</sub>, and CCTA + CTP-DL<sub>Stress</sub> significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001).</p> <p><strong>Conclusion: </strong>Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress<sub>.</sub>.</p>
Lung Ultrasound in Patients With SARS-COV-2 Pneumonia Correlations With Chest Computed Tomography, Respiratory Impairment, and Inflammatory Cascade
<p>Objectives—Lung ultrasound (LUS) might be comparable to chest computed tomography (CT) in detecting parenchymal and pleural pathology, and in monitoring interstitial lung disease. We aimed to describe LUS characteristics of patients during the hospitalization for COVID-19 pneumonia, and to compare the extent of lung involvement at LUS and chest-CT with inflammatory response and the severity of respiration impairment.<br> Methods—During a 2-week period, we performed LUS and chest CT in hospitalized patients affected by COVID-19 pneumonia. Dosages of high sensitivity C-reactive protein (HS-CRP), D-dimer, and interleukin-6 (IL-6) were also obtained. The index of lung function (P/F ratio) was calculated from the blood gas test. LUS and CT scoring were assessed using previously validated scores. <br> Results—Twenty-six consecutive patients (3 women) underwent LUS 34 - 14 days from the early symptoms. Among them, 21 underwent CT on the same day of LUS. A fair association was found between LUS and CT scores (R = 0.45, P = .049), which became stronger if the B-lines score on LUS was not considered (R = 0.57, P = .024). LUS B-lines score correlated with IL-6 levels (R = 0.75, P = 0.011), and the number of involved lung segments detected by LUS correlated with the P/F ratio (R = 0.60, P = .019) but not with HS-CRP and D-Dimer levels. No correlations were found between CT scores and inflammations markers or P/F.<br> Conclusion—In patients with COVID-19 pneumonia, LUS was correlated with both the extent of the inflammatory response and the P/F ratio. </p>
Computational models of immunity for pertussis boost (CMI-PB) multiomics datasets used for the first challenge
<p><strong>Systems vaccinology is increasingly generating massive datasets to characterize the immune state of individuals before and after vaccination, which require data science to interpret them. These studies are used to draw conclusions on what drives different individuals to have different vaccine responses. </strong></p>
Database of chemical structures of substances generally recognized as safe from the Computer Aided Science research group
<p>The databank of chemical structures was populated by drawing the two-dimensional structures of the Generally Recognised as Safe obtained by the list of the regulatory agency, Food and Drug Administration (FDA, http://www.fda.gov), n = 364 and from the Flavour and Extract Manufacturers Association (FEMA, https://www.femaflavor.org/), n = 2,500 in 2019. This databank was used to write the article "Evaluation of general toxicity in food constituents using in silico tools. Rev. Virtual Quim., 2019, 11 (2), 543-553.</p>
Dataset of the article "Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study"
<p>Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an ecological and cross-subject skill consisting of responding variably and flexibly to environmental cognitive-affective demands. Cross-sectional studies have mainly focused on samples composed of healthy individual and of patients with chronic conditions such as Mild Cognitive Impairment and Parkinson’s, emphasizing their behavioral rigidity. Our study is the first to consider a sample of healthy older subjects and to outline physiological and psychological markers typical of mental flexibility, to identify functional biomarkers associated with successful aging. Our results reveal that biomarkers (respiratory and heart rate variability assessments) distinguished between individuals high vs. low in mental flexibility more reliably than traditional neuropsychological tests. This unveiled the multifaceted nature of mental flexibility composed of both cognitive and affective aspects, which emerged only if non-linear multi-variate analytic approaches, such as Supervised Machine Learning, were used.</p>
Halogen-Bond-Based Organocatalysis Unveiled: Computational Design and Mechanistic Insights
<p>Please find the supporting information for the "Halogen-Bond-Based Organocatalysis Unveiled: Computational Design and Mechanistic Insights into Electronically Activated Donor Systems" paper here.</p> <p> </p> <p>Halogen-Bond-Based Organocatalysis Unveiled: Computational Design and Mechanistic Insights</p> <p>Nika Melnyk, <sup>1</sup> Marianne Rica Garcia <sup>1</sup> and Cristina Trujillo <sup>1,2</sup></p> <p><sup>1</sup>Trinity Biomedical Sciences Institute, School of Chemistry, The University of Dublin, Trinity College, D02 R590 Dublin 2, Ireland</p> <p><sup>2</sup>Department of Chemistry, University of Manchester, Oxford Road, Manchester, M139PL</p> <p>Email: cristina.trujillodelvalle@manchester.ac.uk</p> <p> </p> <p> </p> <p> </p>
THEMIS-E: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
THEMIS-D: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
THEMIS-C: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
THEMIS-B: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
THEMIS-A: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
A Normative Dataset of Healthy Pediatric Cranial Computed Tomography (CT) Images
This dataset contains cranial CT images from 100 healthy pediatric subjects, aged one month to ten years, to address the limited availability of public normative reference data for healthy pediatric cranial CT imaging. Supporting data includes subject demographics (age, sex, race/ethnicity) and a Python script for ease of data loading. It is designed to support research reproducibility by providing a common baseline for developing and validating AI algorithms, conducting normative studies of neurodevelopment, and serving as a control cohort for pediatric neurological research.
Database of Measurement and Computation for PhD project
<p>This database is created for Fu Liye's Ph.D. project. This database is an organized collection of data Fu Liye obtain during his Ph.D. period. </p> <p>Since pursuing the Ph.D. is a so-called general project, in this database, the data is classified into different groups according to various sub-projects. </p>
A Computational Approach for the Assessment of Executive Functions in Patients with Obsessive–Compulsive Disorder
<p>Previous studies on obsessive–compulsive disorder (OCD) showed impairments in executive domains, particularly in cognitive inhibition. In this perspective, the use of virtual reality showed huge potential in the assessment of executive functions; however, unfortunately, to date, no study on the assessment of these patients took advantage of the use of virtual environments. One of the main problems faced within assessment protocols is the use of a limited number of variables and tools when tailoring a personalized program. The main aim of this study was to provide a heuristic decision tree for the future development of tailored assessment protocols. To this purpose, we conducted a study that involved 58 participants (29 OCD patients and 29 controls) to collect both classic neuropsychological data and precise data based on a validated protocol in virtual reality for the assessment of executive functions, namely, the VMET (virtual multiple errands test). In order to provide clear indications for working on executive functions with these patients, we carried out a cross-validation based on three learning algorithms and computationally defined two decision trees.<br> We found that, by using three neuropsychological tests and two VMET scores, it was possible to discriminate OCD patients from controls, opening a novel scenario for future assessment protocols based on virtual reality and computational techniques.</p>
Looking at One's Self Through Facebook Increases Mental Stress: A Computational Psychometric Analysis by Using Eye-Tracking and Psychophysiology
<p>The aim of this study was to investigate if Mental Stress was superior, inferior, or equal navigating on Facebook own profile or others profiles. An experimental manipulation would invalidate the results since it would force the participants to navigate in only one condition each time. To overcome this problem, we used an eye-tracker to get clear time markers that identified the areas where the participants focused during all of the Facebook navigation. While the gazes were being recorded for 30 participants, we simultaneously recorded their psychophysiological signals, which were extracted and paired with each specific focus area. Consequently, we obtained the psychophysiological correlates of Facebook navigation for both the conditions related to “own” and “others.” The areas related to own were about the own profile (such as exploring and focusing on one's own information, posting one's own news, and similar activities). The areas related to others were about Facebook friends (e.g., exploring others' profiles and reading comments). The results showed that, based on cardiovascular measures (strong measurements of psychological stress), looking at one's own profile increased mental stress level. Bayesian analyses showed that these differences between the two conditions were not due to the cognitive load or the different attentional and emotional content in the two conditions. The study posed new questions about the expression of one's self to others, and indicated potential detrimental effects of chronic stress deriving from being more oriented to the self than the others.</p>
Risk factors for myocardial injury and death in patients with COVID-19: insights from a cohort study with chest computed tomography
<p>Clinical study about risk factors of myocardial injury and death in COVID-19</p> <p><a href="https://doi.org/10.1093/cvr/cvaa193">https://doi.org/10.1093/cvr/cvaa193</a></p> <p> </p>
Raw Data analyzed in the Paper "Computational analyses of YY1 and its target RKIP reveal their diagnostic and prognostic roles in lung cancer"
<p>The Excel file contains all the Raw Data analyzed in the Paper entitled "Computational analyses of YY1 and its target RKIP reveal their diagnostic and prognostic roles in lung cancer" under submission in Cancers Journal.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.