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restrictedMay 2018View details →

A ferroelectric liquid crystal confined in cylindrical nanopores: reversible smectic layer buckling, enhanced light rotation and extremely fast electro-optically active Goldstone excitations

The orientational and translational order of a thermotropic ferroelectric liquid crystal (2MBOCBC) imbibed in self-organized, parallel, cylindrical pores with radii of 10, 15, or 20 nm in anodic aluminium oxide monoliths (AAO) are explored by high-resolution linear and circular optical birefringence as well as neutron diffraction texture analysis. The results are compared to experiments on the bulk system. The native oxidic pore walls do not provide a stable smectogen wall anchoring. By contrast, a polymeric wall grafting enforcing planar molecular anchoring results in a thermal-history independent formation of smectic C* helices and a reversible chevron-like layer buckling. An enhancement of the optical rotatory power by up to one order of magnitude of the confined compared to the bulk liquid crystal is traced to the pretransitional formation of helical structures at the smectic-A*-to-smectic-C* transformation. A linear electro-optical birefringence effect evidences collective fluctuations in the molecular tilt vector direction along the confined helical superstructures, i.e. the Goldstone phason excitations typical of the para-to-ferroelectric transition. Their relaxation frequencies increase with the square of the inverse pore radii as characteristic of plane-wave excitations and are two orders of magnitude larger than in the bulk, evidencing an exceptionally fast electro-optical functionality of the liquid-crystalline-AAO nanohybrids.

restrictedcc-by-4.0Aug 2024View details →

How visual cues affect consumers' fast-food choices

Open the record for dataset details and reuse information.

restrictedcc-by-4.0Aug 2024View details →

CSS161010: a luminous, fast blue optical transient with broad blueshifted hydrogen lines

CSS161010 spectra and photometry Paper available at: https://arxiv.org/abs/2408.04698

restrictedJan 2023View details →

Dataset related to the article "AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients"

This record contains partial raw data related to the article “AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients” Purpose: To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients' risk stratification. Material and methods: In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web-mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results: The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816-0.867) on wave 1 and was used to build a 0-100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402-0.8766). Conclusions: AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

restrictedJan 2023View details →

Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant

Sturla F, Caimi A, Romarowski RM, Nano G, Glauber M, Redaelli A, Votta E, Marrocco-Trischitta MM. Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant. J Endovasc Ther. 2022 May 19:15266028221095403. doi: 10.1177/15266028221095403. Epub ahead of print. PMID: 35588222. Abstract Purpose: Displacement forces (DFs) identify hostile landing zones for stent graft deployment in thoracic endovascular aortic repair (TEVAR). However, their use in TEVAR planning is hampered by the need for time-expensive computational fluid dynamics (CFD). We propose a novel fast-approximate computation of DFs merely exploiting aortic arch anatomy, as derived from the computed tomography (CT) and a measure of central aortic pressure. Materials and methods: We tested the fast-approximate approach against CFD gold-standard in 34 subjects with the "bovine" aortic arch variant. For each dataset, a 3-dimensional (3D) model of the aortic arch lumen was reconstructed from computed tomography angiography and CFD then employed to compute DFs within the aortic proximal landing zones. To quantify fast-approximate DFs, the wall shear stress contribution to the DF was neglected and blood pressure space-distribution was averaged on the entire aortic wall to reliably approximate the patient-specific central blood pressure. Also, DF values were normalized on the corresponding proximal landing zone area to obtain the equivalent surface traction (EST). Results: Fast-approximate approach consistently reflected (r2=0.99, p<0.0001) the DF pattern obtained by CFD, with a -1.1% and 0.7° bias in DFs magnitude and orientation, respectively. The normalized EST progressively increased (p<0.0001) from zone 0 to zone 3 regardless of the type of arch, with proximal landing zone 3 showing significantly greater forces than zone 2 (p<0.0001). Upon DF normalization to the corresponding aortic surface, fast-approximate EST was decoupled in blood pressure and a dimensionless shape vector (S) reflecting aortic arch morphology. S showed a zone-specific pattern of orientation and proved a valid biomechanical blueprint of DF impact on the thoracic aortic wall. Conclusion: Requiring only a few seconds and quantifying clinically relevant biomechanical parameters of proximal landing zones for arch TEVAR, our method suits the real preoperative decision-making process. It paves the way toward analyzing large population of patients and hence to define threshold values for a future patient-specific preoperative TEVAR planning.

geo12/100

Wildtype Mouse Hypothalamus Cerebellum Fed Fast

GEO Series GSE4221. Mus musculus. 11 samples. Type: Expression profiling by array.

openGEO-OpenFeb 2007View details →

geo12/100

Gene expression data of livers from C57BL/6J mice fed ad libitum or were fasted for 16 hour.

GEO Series GSE159038. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenFeb 2021View details →

MMS 4 Electric Double Probe (EDP) Spacecraft Potential, Level 2 (L2), Fast Mode, 31.25 ms Data

Electric Double Probe, Dual Probe Spacecraft Potential, Level 2, Fast Survey Data. Spacecraft potential is etstimatrd by averaging measurements of the probe-to-spacecraft potential from several probes.

MMS 1 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data

The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system, see SCM data product guide for details, https://lasp.colorado.edu/mms/sdc/public/datasets/fields/. This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The DSP and SCM instrument papers can be found at https://link.springer.com/article/10.1007/s11214-014-0115-x and https://link.springer.com/article/10.1007/s11214-014-0096-9, respectively. The DSP and SCM data product guides can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.

THEMIS-A: On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements, for particle and wave burst survey modes.

On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements for particle and wave burst survey modes. Spectra are produced only in Particle Burst and Wave Burst modes; only a preselected four of the signals listed in Table 1 are input at any time. Data fed through the FFT while not in Particle or Wave Burst modes is automatically disgarded. The FFTs (Cooley-Tukey algorithm) are conducted as an integral part of the power spectrum calculation by the Field Programable Gate Arrays (FPGAs). A CORDIC algorithm is used for sine/cosine calculations. The data has raw resolution of 1024 pts for 8,192 sample/sec signals and 2048 pts for 16,384 sample/sec signals (EAC measurements only). Signals at 8,192 samples/sec are handled by 1024-point FFTs, while those at 16,384 samples/sec go through 2048-point FFTs. Past and current signal configurations for specific spacecraft are listed bellow in Table 2. The spectra are arranged into log spaced frequency bins in steps of 16, 32, or 64. Cadence is adjusted to keep packet size constant (i.e. increasing the fequency resolution by a factor of 2 decreases the sampling rate by 1/2). The frequency bins cover a range of 0 Hz to 4 kHz. Table 1: FFT Input Signals. Signal Description SCMX, SCMY, SCMZ: Three axis magnetic fiend from SCM V1 through V6: Probe-spacecraft voltage for all six EFI sensors E12DC, E34DC, E56DC: DC-coupled electric field measured from opposing EFI sensors E12AC, E34AV, E56AC: AC-coupled electric field measured from opposing EFI sensors E12HF: High frequency electric field from EFI Table 2: Spacecraft specific configurations. All probes were initially set to use EDC34, EDC56, SCM2, and SCM3 signals for both particle and wave burst modes. Output was set to 16 frequency bins at 4 Hz. Configuration Changes: 9 June 2008: Particle burst spectra resolution increased from 16 to 64 bins and cadence slowed from 4 Hz to 1 Hz for closer analysis of spectral features. N/A: Particle burst spectra on all probes reconfigured to 64 bins at 1 Hz. 15 Aug 2008: E34DC and E56DC inputs switched to E34AC and E56AC to reduce impact of wake fields on FFT spectral measurements. 15 Oct 2008: Particle burst spectra resolution reduced from 64 to 32 bins and the rate increased from 1 Hz to 2 Hz; a response to noise from plasma wake fields in the 64 bin spectra. Table 3: Instrument-Spacecraft Physical Configuration Instrument Alignment in Spacecraft Geometric coordinates (SPG). See THEMIS website for coordinate system details and mechanical drawings. EFI boom 1: Along positive X-axis EFI boom 2: Along negative X-axis EFI boom 3: Along positive Y-axis EFI boom 4: Along negative Y-axis EFI boom 5: Along positive Z-axis EFI boom 6: Along negative Z-axis SCM *The SCM uses an instrument specific set of axes; an orthogonal system centered instrument with the X-axis 12.1 degrees from the SPG X-axis.

ISEE 1 Linearly Interpolated 60 s Resolution Fast Plasma Experiment data in GSE Coordinates

ISEE-1 Fast Plasma Experiment data linearly interpolated to have the measurements on the minute at 60 s resolution data in GSE coordinates. This data set consists of processed solar wind data that has been linearly interpolated to 1 min resolution at the position of the spacecraft using the interp1.m function in MATLAB. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies and cross correlation studies on solar wind.

MMS 1 Electric Double Probe (EDP) Spacecraft Potential, Level 2 (L2), Fast Mode, 31.25 ms Data

Characteristics of Reconnection Sites and Fast Flow Channels in an MHD Simulation: Simulation Results

The simulation outputs are asci files containing out from the MHD simulation mapped to the minimum magnetic field surface in the magnetotail during the March 14,2008 substorms. There is one time in each file and 2161 files each separated by 10s. The results span 6 hours from 0200 to 0800. The results are in GSM coordinates.

ISEE 1 Linearly Interpolated 60 s Resolution Fast Plasma Experiment data in GSM Coordinates

ISEE-1 Fast Plasma Experiment data linearly interpolated to have the measurements on the minute at 60 s resolution data in GSM coordinates. This data set consists of processed solar wind data that has been linearly interpolated to 1 min resolution at the position of the spacecraft using the interp1.m function in MATLAB. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies and cross correlation studies on solar wind.

MMS 3 Electric Double Probe (EDP) Spacecraft Potential, Level 2 (L2), Fast Mode, 31.25 ms Data

THEMIS-B: On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements, for particle and wave burst survey modes.

On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements for particle and wave burst survey modes. Spectra are produced only in Particle Burst and Wave Burst modes; only a preselected four of the signals listed in Table 1 are input at any time. Data fed through the FFT while not in Particle or Wave Burst modes is automatically disgarded. The FFTs (Cooley-Tukey algorithm) are conducted as an integral part of the power spectrum calculation by the Field Programable Gate Arrays (FPGAs). A CORDIC algorithm is used for sine/cosine calculations. The data has raw resolution of 1024 pts for 8,192 sample/sec signals and 2048 pts for 16,384 sample/sec signals (EAC measurements only). Signals at 8,192 samples/sec are handled by 1024-point FFTs, while those at 16,384 samples/sec go through 2048-point FFTs. Past and current signal configurations for specific spacecraft are listed bellow in Table 2. The spectra are arranged into log spaced frequency bins in steps of 16, 32, or 64. Cadence is adjusted to keep packet size constant (i.e. increasing the fequency resolution by a factor of 2 decreases the sampling rate by 1/2). The frequency bins cover a range of 0 Hz to 4 kHz. Table 1: FFT Input Signals. Signal Description SCMX, SCMY, SCMZ: Three axis magnetic fiend from SCM V1 through V6: Probe-spacecraft voltage for all six EFI sensors E12DC, E34DC, E56DC: DC-coupled electric field measured from opposing EFI sensors E12AC, E34AV, E56AC: AC-coupled electric field measured from opposing EFI sensors E12HF: High frequency electric field from EFI Table 2: Spacecraft specific configurations. All probes were initially set to use EDC34, EDC56, SCM2, and SCM3 signals for both particle and wave burst modes. Output was set to 16 frequency bins at 4 Hz. Configuration Changes: 30 June - 3 July 2008: Particle burst spectra on all probes reconfigured to 64 bins at 1 Hz. Table 3: Instrument-Spacecraft Physical Configuration Instrument Alignment in Spacecraft Geometric coordinates (SPG). See THEMIS website for coordinate system details and mechanical drawings. EFI boom 1: Along positive X-axis EFI boom 2: Along negative X-axis EFI boom 3: Along positive Y-axis EFI boom 4: Along negative Y-axis EFI boom 5: Along positive Z-axis EFI boom 6: Along negative Z-axis SCM: *The SCM uses an instrument specific set of axes; an orthogonal system centered instrument with the X-axis 12.1 degrees from the SPG X-axis.

THEMIS-D: On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements, for particle and wave burst survey modes.

On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements for particle and wave burst survey modes. Spectra are produced only in Particle Burst and Wave Burst modes; only a preselected four of the signals listed in Table 1 are input at any time. Data fed through the FFT while not in Particle or Wave Burst modes is automatically disgarded. The FFTs (Cooley-Tukey algorithm) are conducted as an integral part of the power spectrum calculation by the Field Programable Gate Arrays (FPGAs). A CORDIC algorithm is used for sine/cosine calculations. The data has raw resolution of 1024 pts for 8,192 sample/sec signals and 2048 pts for 16,384 sample/sec signals (EAC measurements only). Signals at 8,192 samples/sec are handled by 1024-point FFTs, while those at 16,384 samples/sec go through 2048-point FFTs. Past and current signal configurations for specific spacecraft are listed bellow in Table 2. The spectra are arranged into log spaced frequency bins in steps of 16, 32, or 64. Cadence is adjusted to keep packet size constant (i.e. increasing the fequency resolution by a factor of 2 decreases the sampling rate by 1/2). The frequency bins cover a range of 0 Hz to 4 kHz. Table 1: FFT Input Signals. Signal Description SCMX, SCMY, SCMZ: Three axis magnetic fiend from SCM V1 through V6: Probe-spacecraft voltage for all six EFI sensors E12DC, E34DC, E56DC: DC-coupled electric field measured from opposing EFI sensors E12AC, E34AV, E56AC: AC-coupled electric field measured from opposing EFI sensors E12HF: High frequency electric field from EFI Table 2: Spacecraft specific configurations. All probes were initially set to use EDC34, EDC56, SCM2, and SCM3 signals for both particle and wave burst modes. Output was set to 16 frequency bins at 4 Hz. Configuration Changes: 23-27 June 2008: Particle burst spectra on all probes reconfigured to 64 bins at 1 Hz. Table 3: Instrument-Spacecraft Physical Configuration Instrument Alignment in Spacecraft Geometric coordinates (SPG). See THEMIS website for coordinate system details and mechanical drawings. EFI boom 1: Along positive X-axis EFI boom 2: Along negative X-axis EFI boom 3: Along positive Y-axis EFI boom 4: Along negative Y-axis EFI boom 5: Along positive Z-axis EFI boom 6: Along negative Z-axis SCM *The SCM uses an instrument specific set of axes; an orthogonal system centered instrument with the X-axis 12.1 degrees from the SPG X-axis.

THEMIS-C: On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements, for particle and wave burst survey modes.

On Board Fast Fourier Transform (FFT) power spectra of Electric (EFI) and Magnetic (SCM) field measurements for particle and wave burst survey modes. Spectra are produced only in Particle Burst and Wave Burst modes; only a preselected four of the signals listed in Table 1 are input at any time. Data fed through the FFT while not in Particle or Wave Burst modes is automatically disgarded. The FFTs (Cooley-Tukey algorithm) are conducted as an integral part of the power spectrum calculation by the Field Programable Gate Arrays (FPGAs). A CORDIC algorithm is used for sine/cosine calculations. The data has raw resolution of 1024 pts for 8,192 sample/sec signals and 2048 pts for 16,384 sample/sec signals (EAC measurements only). Signals at 8,192 samples/sec are handled by 1024-point FFTs, while those at 16,384 samples/sec go through 2048-point FFTs. Past and current signal configurations for specific spacecraft are listed bellow in Table 2. The spectra are arranged into log spaced frequency bins in steps of 16, 32, or 64. Cadence is adjusted to keep packet size constant (i.e. increasing the fequency resolution by a factor of 2 decreases the sampling rate by 1/2). The frequency bins cover a range of 0 Hz to 4 kHz. Table 1: FFT Input Signals. Signal Description SCMX, SCMY, SCMZ: Three axis magnetic fiend from SCM V1 through V6: Probe-spacecraft voltage for all six EFI sensors E12DC, E34DC, E56DC: DC-coupled electric field measured from opposing EFI sensors E12AC, E34AV, E56AC: AC-coupled electric field measured from opposing EFI sensors E12HF: High frequency electric field from EFI Table 2: Spacecraft specific configurations. All probes were initially set to use EDC34, EDC56, SCM2, and SCM3 signals for both particle and wave burst modes. Output was set to 16 frequency bins at 4 Hz. Configuration Changes: 30 June - 3 July 2008: Particle burst spectra on all probes reconfigured to 64 bins at 1 Hz. Table 3: Instrument-Spacecraft Physical Configuration Instrument Alignment in Spacecraft Geometric coordinates (SPG). See THEMIS website for coordinate system details and mechanical drawings. EFI boom 1: Along positive X-axis EFI boom 2: Along negative X-axis EFI boom 3: Along positive Y-axis EFI boom 4: Along negative Y-axis EFI boom 5: Along positive Z-axis EFI boom 6: Along negative Z-axis SCM: *The SCM uses an instrument specific set of axes; an orthogonal system centered instrument with the X-axis 12.1 degrees from the SPG X-axis.

MMS 3 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data

ISEE 1 Fast Plasma Experiment Solar Wind Weimer Propagated 60 s Resolution Data in GSM Coordinates

ISEE-1 Weimer propagated solar wind data and linearly interpolated to have the measurements on the minute at 60 s resolution FPE data in GSM coordinates. This data set consists of propagated solar wind data that has first been propagated to a position just outside of the nominal bow shock (about 17, 0, 0 Re) and then linearly interpolated to 1 min resolution using the interp1.m function in MATLAB. The input data for this data set is a 1 min resolution processed solar wind data constructed by Dr. J.M. Weygand. The method of propagation is similar to the minimum variance technique and is outlined in Dan Weimer et al. [2003; 2004]. The basic method is to find the minimum variance direction of the magnetic field in the plane orthogonal to the mean magnetic field direction. This minimum variance direction is then dotted with the difference between final position vector minus the original position vector and the quantity is divided by the minimum variance dotted with the solar wind velocity vector, which gives the propagation time. This method does not work well for shocks and minimum variance directions with tilts greater than 70 degrees of the sun-earth line. This data set was originally constructed by Dr. J.M. Weygand for Prof. R.L. McPherron, who was the principle investigator of two National Science Foundation studies: GEM Grant ATM 02-1798 and a Space Weather Grant ATM 02-08501. These data were primarily used in superposed epoch studies References: Weimer, D. R. (2004), Correction to ‘‘Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique,’’ J. Geophys. Res., 109, A12104, doi:10.1029/2004JA010691. Weimer, D.R., D.M. Ober, N.C. Maynard, M.R. Collier, D.J. McComas, N.F. Ness, C. W. Smith, and J. Watermann (2003), Predicting interplanetary magnetic field (IMF) propagation delay times using the minimum variance technique, J. Geophys. Res., 108, 1026, doi:10.1029/2002JA009405.