Full Dynamic Range Research Articles

Bayesian hierarchical modelling of the M*–SFR relation from 1 ≲ z ≲ 6 in ASTRODEEP

ABSTRACT The Hubble Frontier Fields represent the opportunity to probe the high-redshift evolution of the main sequence of star-forming galaxies to lower masses than possible in blank fields thanks to foreground lensing of massive galaxy clusters. We use the beagle SED-fitting code to derive stellar masses, $\rm{{M_{\star }}}=\log ({\it M}/{\rm{M_{\odot }}})$, SFRs, $\rm{{\Psi }}=\log (\rm{{\psi }}/{\rm{M_{\odot }}}\, {\rm{yr}}^{-1})$, and redshifts from galaxies within the astrodeep catalogue. We fit a fully Bayesian hierarchical model of the main sequence over 1.25 < z < 6 of the form $\rm{{\Psi }}= \rm{\alpha _\mathrm{9.7}}(z) + \rm{\beta }({\rm{M_{\star }}}-9.7) + \mathcal {N}(0,\rm{\sigma }^2)$ while explicitly modelling the outlier distribution. The redshift-dependent intercept at $\rm{{M_{\star }}}=9.7$ is parametrized as $\rm{\alpha _\mathrm{9.7}}(z) = \log [{\it N}(1+{\it z})^{\rm{\gamma }}] + 0.7$. Our results agree with an increase in normalization of the main sequence to high redshifts that follows the redshift-dependent rate of accretion of gas on to dark matter haloes with $\rm{\gamma }=2.40^{+0.18}_{-0.18}$. We measure a slope and intrinsic scatter of $\rm{\beta }=0.79^{+0.03}_{-0.04}$ and $\rm{\sigma }=0.26^{+0.02}_{-0.02}$. We find that the sampling of the SED provided by the combination of filters (Hubble + ground-based Ks-band + Spitzer 3.6 and 4.5 μm) is insufficient to constrain M⋆ and Ψ over the full dynamic range of the observed main sequence, even at the lowest redshifts studied. While this filter set represents the best current sampling of high-redshift galaxy SEDs out to z > 3, measurements of the main sequence to low masses and high redshifts still strongly depend on priors employed in SED fitting (as well as other fitting assumptions). Future data sets with JWST should improve this.

Marine compressed air source array primary acoustic field characterization from at-sea measurements.

The primary acoustic field of a standard seismic survey source array is described based on a calibrated dataset collected in the Gulf of Mexico. Three vertical array moorings were deployed to measure the full dynamic range and bandwidth of the acoustic field emitted by the compressed air source array. The designated source vessel followed a specified set of survey lines to provide a dataset with broad coverage of ranges and departure angles from the array. Acoustic metrics relevant to criteria associated with potential impacts on marine life are calculated from the recorded data. Sound pressure levels from direct arrivals exhibit large variability for a fixed distance between source and receiver; this indicates that the distance cannot be reliably used as a single parameter to derive meaningful exposure levels for a moving source array. The far-field acoustic metrics' variations with distance along the true acoustic path for a narrow angular bin are accurately predicted using a simplified model of the surface-affected source waveform, which is a function of the direction. The presented acoustic metrics can be used for benchmarking existing source/propagation models for predicting acoustic fields of seismic source arrays and developing simplified data-supported models for environmental impact assessments.

Linear viscoelastic properties of the vertex model for epithelial tissues.

Epithelial tissues act as barriers and, therefore, must repair themselves, respond to environmental changes and grow without compromising their integrity. Consequently, they exhibit complex viscoelastic rheological behavior where constituent cells actively tune their mechanical properties to change the overall response of the tissue, e.g., from solid-like to fluid-like. Mesoscopic mechanical properties of epithelia are commonly modeled with the vertex model. While previous studies have predominantly focused on the rheological properties of the vertex model at long time scales, we systematically studied the full dynamic range by applying small oscillatory shear and bulk deformations in both solid-like and fluid-like phases for regular hexagonal and disordered cell configurations. We found that the shear and bulk responses in the fluid and solid phases can be described by standard spring-dashpot viscoelastic models. Furthermore, the solid-fluid transition can be tuned by applying pre-deformation to the system. Our study provides insights into the mechanisms by which epithelia can regulate their rich rheological behavior.

Luminance calibration of a full sky HDR imaging system using sky scanner measurements

A full sky High Dynamic Range imaging system, based on a Single-Lens Reflex camera with a fisheye lens, has been constructed and calibrated with a sky scanner luminance meter. The method considers the geometrical, spectral, timing and orientation issues between instruments. The calibration data sets, having nearly simultaneous measurements under stable sky conditions, were obtained from approximately one month of data using selection variables based in the experimental design. For luminance estimation we use the standard CIEY RGB combination and a Spectrally Matched Luminance (SML) predictor, matching the spectral response of the instruments. With 738 calibration points having luminances up to 23.6kcd/m2, covering 98.5% of the sky luminance range, CIEY is linearly correlated with sky scanner measurements with a coefficient of determination R2=0.9927 and a Root Mean Squared Error (RMSE) of 7.7%. SML gives better results, with R2=0.9973 and RMSE=5.3%. With 253 calibration points with luminances up to 12.9kcd/m2, comprising 94.1% of the sky luminance range, both predictors clearly improve, with R2=0.9964 and RMSE=4.1% in case of CIEY and R2=0.9982 and RMSE=2.9% in case of SML.

Long‐term minnow community trait shifts and metacommunity dynamics in a geomorphically unstable river

AbstractEcological disturbances may affect both habitat connectivity and heterogeneity, changing the dominant metacommunity processes affecting local communities. Altered fluvial sediment dynamics in rivers experiencing headcutting and accelerated erosion may foster morphologically unstable reaches in which supplies of habitat‐forming materials are spatially and temporally unpredictable. We hypothesise such environments will select for species with traits which promote dispersal or rapid resource use. We tested this hypothesis using historical and recent datasets from a biologically and functionally diverse taxonomical group, minnows (Leuciscidae and Cyprinidae) in the Bayou Pierre, Mississippi. Local communities decreased in both α and β‐diversity over time. Spatial autocorrelation of communities was consistently high through time, and more strongly related to regional community structure than local habitat variables in contemporary data. Communities shifted towards species with larger body sizes, less parental care, shorter age at maturity and higher fecundity. Communities also shifted towards more generalist species with lower potential trophic positions, and keystone species declined. Our data support the hypothesis of community‐level shifts towards species with dispersal trait syndromes and opportunistic resource utilisation. These shifts have led to a decrease in ecological complexity and overall homogenisation of minnow communities in the geomorphically evolving Bayou Pierre mainstem. The Bayou Pierre presents a unique window into ecological dynamics in a watershed characterised by altered sediment dynamics and sediment loss, in contrast to the large body of literature examining systems with increased sedimentation. These insights demonstrate the importance of studying the full range of fluvial sediment dynamics, including how broader processes of loss and transport may affect long‐term habitat stability, persistent trait syndromes and metacommunity dynamics.

Control of reactive collisions by quantum interference.

In this study, we achieved magnetic control of reactive scattering in an ultracold mixture of 23Na atoms and 23Na6Li molecules. In most molecular collisions, particles react or are lost near short range with unity probability, leading to the so-called universal rate. By contrast, the Na + NaLi system was shown to have only ~4% loss probability in a fully spin-polarized state. By controlling the phase of the scattering wave function via a Feshbach resonance, we modified the loss rate by more than a factor of 100, from far below to far above the universal limit. The results are explained in analogy with an optical Fabry-Perot resonator by interference of reflections at short and long range. Our work demonstrates quantum control of chemistry by magnetic fields with the full dynamic range predicted by our models.

Characterization of 128 × 128 MM-PAD-2.1 ASIC: a fast framing hard x-ray detector with high dynamic range

We characterize a new x-ray Mixed-Mode Pixel Array Detector (MM-PAD-2.1) Application Specific Integrated Circuit (ASIC). Using an integrating pixel front-end with dynamic charge removal architecture, the MM-PAD-2.1 ASIC extends the maximum measurable x-ray signal (in 20 keV photon units) to > 107 x-rays/pixel/frame while maintaining a low read noise across the full dynamic range, all while imaging continuously at a frame rate of up to 10 kHz. The in-pixel dynamic charge removal mechanism prevents saturation of the input amplifier and proceeds in parallel with signal integration to achieve deadtime-less measurements with incident x-ray rates of > 1010 x-rays/pixel/s. The ASIC format consists of 128 × 128 square pixels each 150 μm on a side and is designed to be 3-side buttable so large arrays can be effectively tiled. Here we use both laboratory x-ray sources and the Cornell High Energy Synchrotron Source (CHESS) to characterize two single ASIC prototype detectors for both low (single x-ray) and high incident flux detection. In the first detector the ASIC was solder bump-bonded to a 500 μm thick Si sensor for efficient detection of x-rays below 20 keV, whereas the second detector used a 750 μm thick CdTe sensor for x-rays above ∼ 20 keV.

A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron.

Advances in neuronal studies suggest that a single neuron can perform integration functions previously associated only with neuronal networks. Here, we proposed a dendritic abstraction employing a dynamic thresholding function that models the spatiotemporal dendritic integration process of a CA3 pyramidal neuron. First, we developed an input-output quantification process that considers the natural neuronal response and the full range of dendritic dynamics. We analyzed the IO curves and demonstrated that dendritic integration is branch-specific and dynamic rather than the commonly employed static nonlinearity. Second, we completed the integration model by creating a dendritic abstraction incorporating the spatiotemporal characteristics of the dendrites. Furthermore, we predicted the dendritic activity in each dendritic layer and the corresponding somatic firing activity by employing the dendritic abstraction in a multilayer-multiplexer information processing scheme comparable to a neuronal network. The subthreshold activity influences the suprathreshold regions via its dynamic threshold, a parameter that is dependent not only on the driving force but also on the number of activated synapses along the dendritic branch. An individual dendritic branch performs multiple integration modes by shifting from supralinear to linear then to sublinear. The abstraction includes synaptic input location-dependent voltage delay and decay, time-dependent linear summation, and dynamic thresholding function. The proposed dendritic abstraction can be used to create multilayer-multiplexer neurons that consider the spatiotemporal properties of the dendrites and with greater computational capacity than the conventional schemes.

Prototype characterization of a charge-integration pixel detector readout chip with in-pixel A/D conversion

HYLITE (High dYmamic range free electron Laser Imaging deTEctor) is a hybrid pixel detector readout chip, which is designed for advanced light sources such as X-ray Free Electron Laser (XFEL) and diffraction-limited storage rings. It is a charge-integration readout chip which has three gains for different dynamic ranges and automatic gain-switching function. The full dynamic range covered by HYLITE is 1 ∼ 104 photons with an energy of 12 keV for each pixel in every shot. In-pixel ADC is designed to achieve front-end digitization and a 10 kHz continuous frame rate. HYLITE0.1 is the first prototype chip for functional verification that was produced in CMOS 0.13 μm technology. It consists of a pixel array with 6 × 12 pixels and a periphery with full standalone operation features. The size of each pixel is 200 μm × 200 μm. Three design variations of pixels with different integrating capacitance and structures were designed to optimize between area and performance. A 10-bit Wilkinson ADC is integrated in each pixel to digitize the outputs of the pre-amplifier. Therefore, analog signal transmission of long distance is avoided and a frame rate of 10 kHz can be achieved. In this paper, we present the design of HYLITE0.1 and the test results of this prototype chip.

First Demonstration of Calibrated Color Imaging by the CAOS Camera

The Coded Access Optical Sensor (CAOS) camera is a novel, single unit, full spectrum (UV to short-wave IR bands), linear, high dynamic range (HDR) camera. In this paper, calibrated color target imaging using the CAOS camera and a comparison to a commercial HDR CMOS camera is demonstrated for the first time. The first experiment using a calibrated color check chart indicates that although the CMOS sensor-based camera has an 87 dB manufacturer-specified HDR range, unrestricted usage of this CMOS camera’s output range greatly fails quality color recovery. On the other hand, the intrinsically linear full dynamic range operation CAOS camera color image recovery generally matches the restricted linear-mode commercial CMOS sensor-based camera recovery for the presented 39.5 dB non-HDR target that also matches the near 40 dB linear camera response function (CRF) range of the CMOS camera. Specifically, compared to the color checker chart manufacturer provided XYZ values for the calibrated target, percentage XYZ mean errors of 8.3% and 10.9% are achieved for the restricted linear range CMOS camera and CAOS camera, respectively. An alternate color camera assessment gives CIE ΔE00 mean values of 4.59 and 5.7 for the restricted linear range CMOS camera and CAOS camera, respectively. Unlike the CMOS camera lens optics and its photo-detection electronics, no special linear response optics and photo-detector designs were used for the experimental CAOS camera, nevertheless, a good and equivalent color recovery was achieved. Given the limited HDR linear range capabilities of a CMOS camera and the intrinsically wide linear HDR capability of a CAOS camera, a combined CAOS-CMOS mode of the CAOS smart camera is prudent and can empower HDR color imaging. Applications for such a hybrid camera includes still photography imaging, especially for quantitative imaging of biological samples, valuable artworks and archaeological artefacts that require authentic color data generation for reliable medical decisions as well as forgery preventing verifications.

OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany.

Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventional measures during an ongoing outbreak. However, reliably inferring the epidemical dynamics by connecting complex models to real data is still hard and requires either laborious manual parameter fitting or expensive optimization methods which have to be repeated from scratch for every application of a given model. In this work, we address this problem with a novel combination of epidemiological modeling with specialized neural networks. Our approach entails two computational phases: In an initial training phase, a mathematical model describing the epidemic is used as a coach for a neural network, which acquires global knowledge about the full range of possible disease dynamics. In the subsequent inference phase, the trained neural network processes the observed data of an actual outbreak and infers the parameters of the model in order to realistically reproduce the observed dynamics and reliably predict future progression. With its flexible framework, our simulation-based approach is applicable to a variety of epidemiological models. Moreover, since our method is fully Bayesian, it is designed to incorporate all available prior knowledge about plausible parameter values and returns complete joint posterior distributions over these parameters. Application of our method to the early Covid-19 outbreak phase in Germany demonstrates that we are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.

Transmission Kikuchi diffraction: The impact of the signal-to-noise ratio

Signal optimization for transmission Kikuchi diffraction (TKD) measurements in the scanning electron microscope is investigated by a comparison of different sample holder designs. An optimized design is presented, which uses a metal shield to efficiently trap the electron beam after transmission through the sample. For comparison, a second holder configuration allows a significant number of the transmitted electrons to scatter back from the surface of the sample holder onto the diffraction camera screen. It is shown that the secondary interaction with the sample holder leads to a significant increase in the background level, as well as to additional noise in the final Kikuchi diffraction signal. The clean TKD signal of the optimized holder design with reduced background scattering makes it possible to use small signal changes in the range of 2% of the camera full dynamic range. As is shown by an analysis of the power spectrum, the signal-to-noise ratio in the processed Kikuchi diffraction patterns is improved by an order of magnitude. As a result, the optimized design allows an increase in pattern signal to noise ratio which may lead to increase in measurement speed and indexing reliability.

FIGS: Featured Ion-Guided Stoichiometry for Data-Independent Proteomics through Dynamic Deconvolution.

Data-independent acquisition (DIA) has significant advantages for mass spectrometry (MS)-based peptide quantification, while mixed spectra remain challenging for precise stoichiometry. We here choose to analyze the library spectra in specific sets preferentially and locally. Accordingly, the featured ions are defined as the fragment ions uniquely assigned to corresponding precursors in a given spectrum set, which are generated by dynamic deconvolution of the mixed mass spectra. Then, we present featured ion-guided stoichiometry (FIGS), a universal method for accurate and robust peptide quantification for the DIA-MS data. We validate the high performance on the quantification sensitivity, accuracy, and efficiency of FIGS. Notably, our FIGS dramatically improves the quantification accuracy for the full dynamic range, especially for low-abundance peptides.

UniverseMachine: Predicting Galaxy Star Formation over Seven Decades of Halo Mass with Zoom-in Simulations

Abstract We apply the empirical galaxy–halo connection model UniverseMachine to dark-matter-only zoom-in simulations of isolated Milky Way (MW)–mass halos, along with their parent cosmological simulations. This application extends UniverseMachine predictions into the ultrafaint dwarf galaxy regime (102 M ⊙ ≤ M * ≤ 105 M ⊙) and yields a well-resolved stellar mass–halo mass (SMHM) relation over the peak halo mass range of 108–1015 M ⊙. The extensive dynamic range provided by the zoom-in simulations allows us to assess specific aspects of dwarf galaxy evolution predicted by UniverseMachine. In particular, although UniverseMachine is not constrained for dwarf galaxies with M * ≲ 108 M ⊙, our predicted SMHM relation is consistent with that inferred for MW satellite galaxies at z = 0 using abundance matching. However, UniverseMachine predicts that nearly all galaxies are actively star-forming below M * ∼ 107 M ⊙ and that these systems typically form more than half of their stars at z ≲ 4, which is discrepant with the star formation histories of Local Group dwarf galaxies that favor early quenching. This indicates that the current UniverseMachine model does not fully capture galaxy quenching physics at the low-mass end. We highlight specific improvements necessary to incorporate environmental and reionization-driven quenching for dwarf galaxies, and we provide a new tool to connect dark matter accretion to star formation over the full dynamic range that hosts galaxies.

Balanced Dual Platinum Micro-Cantilever Thermal Conductivity Gas Sensor Using 3-Omega Technique

IntroductionThermal Conductivity Detector (TCD) is one of the commonly used sensors in Gas Chromatography (GC) systems. The principle behind TCD sensor is joule heating which heats the surrounding gas and the thermal-physical properties of the sensor changes based on the injected sample thermal conductivity [1, 2]. This technique can detect any gas that has different thermal conductivity with respect to the carrier gas. Therefore, TCD which is a universal gas detector have variety of applications including agriculture and environmental monitoring. TCD can also detect ammonia gas which has important role in agriculture [3].There have been considerable efforts to miniaturize gas sensors, to achieve portable systems which require low power consumption and simple to operate outside the laboratory. Among these sensors, TCD has several advantages such as simple operation principle, short response time, high corrosion resistance and CMOS compatible fabrication process. One of the limitations of this sensing method is Limit of Detection (LOD), therefore, the TCD sensors geometrical design and electrical measurement method have been continuously engineered to improve it. The concept of cantilever-based platinum balanced TCD was introduced by our group to increase the sensors working temperature and ability to tolerate high thermally induced stresses. This advancement allows us to use the 3-Omega [3-5] technique for electrical measurement of the sensor while not sacrificing the high operating temperature.MethodThe conventional TCD sensor used a single layer of thin film for both heating and sensing. In this work, a new design for the cantilever-based platinum balanced TCD is proposed that has two active layers, one for heating and another for sensing. The TCD structure composed of ALD aluminum oxide layer sandwiched between platinum layers, and these three layers are sandwiched between two aluminum oxide layers. In designing double active layers TCD, the heating layer of the sensor needed to be thick enough to be able to pass a sufficiently high current. On the contrary, for the sensing layer to be sensitive to the change of temperature, it needs to be thin enough to have a high resistance value. A lumped thermal model was developed to study the time delay between platinum layers assuming constant properties and steady-state response.Results and DiscussionThe new design had two active layers which can use different types of excitation current or voltages. It was found that the sensing layer can follow the TCD heater's temperature with a delay less than while the two active layers TCD's response time was 6 µs. By studying the four possible combinations of excitation current types, AC or DC, defined a method for adapting the 3-Omega technique to the 2-Omega technique. In the 2-Omega technique, an AC current excitation, with ω frequency, for the heating layer a DC current excitation for the sensing layer, while the lock-in-amplifier operates at 2ω to determine the sensing layer voltage. Figure 1 depicts the heating and sensing layer time dependent voltages, and Figure 2 presents the Fourier transform of those voltages. The increase in the sensing layer resistance and ease of eliminating the 1-ω frequency present in the 3-Omega technique, enhanced the LOD of the TCD sensor. and allows for the use of the full dynamic range of the lock-in amplifier. The higher precision and accuracy enhances the Limit of Detection (LOD) of the sensor which was achieved by separating the sensing and heating in a two active layers TCD design.REFERENCES[1] A. Mahdavifar, M. Navaei, P. J. Hesketh, M. Findlay, J. R. Stetter, and G. W. Hunter, "Transient thermal response of micro-thermal conductivity detector (µTCD) for the identification of gas mixtures: An ultra-fast and low power method," Microsystems & Nanoengineering, vol. 1, no. 1, 2015.[2] A. Lotfi, M. Navaei, and P. J. Hesketh, "A Platinum Cantilever-Based Thermal Conductivity Detector for Ammonia Sensing Using the 3-Omega Technique," ECS Journal of Solid State Science and Technology, vol. 8, no. 6, pp. Q126-Q133, 2019.[3] A. Lotfi, C. A. Heist, A. Warren, M. Navaei, and P. J. Hesketh, "Platinum Balanced Cantilever-based Thermal Conductivity Detector for Gas Chromatography Application," in 2019 IEEE SENSORS, 2019, pp. 1-4: IEEE.[4] S. Kommandur, A. Mahdavifar, P. J. Hesketh, and S. Yee, "A microbridge heater for low power gas sensing based on the 3-Omega technique," Sensors and Actuators A: Physical, vol. 233, pp. 231-238, 2015.[5] S. Kommandur, A. Mahdavifar, S. Jin, P. J. Hesketh, and S. Yee, "Metal-coated glass microfiber for concentration detection in gas mixtures using the 3-Omega excitation method," Sensors and Actuators A: Physical, vol. 250, pp. 243-249, 2016. Figure 1

Capturing hotspots of fresh submarine groundwater discharge using a coupled surface–subsurface model

Submarine groundwater discharge (SGD) contributes to the physical and chemical characters of coastal waters by discharging nutrients and contaminants, significantly impacting regional marine ecosystems and contributing to ocean chemical budgets. However, such groundwater discharge varies dramatically across scales and is often not comparable due to different model assumptions and field designs. We used a hydrologic model with integration of fundamental surface and subsurface processes to simulate the coastline level fresh SGD for the Crete Island in the Mediterranean Sea. The modeled hydrological processes suggested that fresh SGD substantially contributes to water flow entering the Mediterranean Sea (2.3 × 108 m3/yr), amounting to 31% of river discharge and 14% of precipitation. Spatially, fresh SGD varied from 2.4 m3/yr/m to 13.4 × 104 m3/yr/m, with an average of 2.6 × 103 m3/yr/m. The local maxima were commonly associated with river mouths reflecting larger hydraulic gradients and higher permeable structures. Temporally, fresh SGD was impacted by episodic precipitation in a delayed and prolonged pattern. We found that fresh SGD variability at the coastline segment level was compared to point measurements and fresh SGD magnitudes summered up to the catchment level were consistent with global products. Our results suggest the coupled surface–subsurface hydrologic modeling approach is a promising strategy to quantify and partition large-scale water budgets down to point observations that typically do not capture the full range of fresh SGD dynamics.

Transfer Deep Learning for Reconfigurable Snapshot HDR Imaging Using Coded Masks

AbstractHigh dynamic range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this paper we propose a joint design for snapshot HDR imaging by devising a spatially varying modulation mask in the hardware and building a deep learning algorithm to reconstruct the HDR image. We leverage transfer learning to overcome the lack of sufficiently large HDR datasets available. We show how transferring from a different large‐scale task (image classification on ImageNet) leads to considerable improvements in HDR reconstruction. We achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware–software so lution offers a flexible yet robust way to modulate per‐pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparison results show that our method outperforms the state of the art in terms of visual perception quality.

Simultaneous 4-phase-shifted full-field optical coherence microscopy.

A new method is presented for full-field optical coherence tomography imaging, which permits capturing single shot phase sensitive imaging through simultaneous acquisition of four phase-shifted images with a single camera using unpolarized light for object illumination. Our method retains the full dynamic range of the camera by using different areas of a single camera sensor to capture each image. We demonstrate the performance of our method by imaging phantoms and live cultures of fibroblast, cancer, and macrophage cells to achieve 59 dB sensitivity with isotropic resolution down to 1 μm, and displacement sensitivity down to 0.1 nm. Our method can serve as a platform for developing high resolution imaging systems because when used in conjunction with broadband spatially incoherent light sources, the resolution is not affected by optical aberrations or speckle noise.

Compensation of Strong Water Absorption in Infrared Spectroscopy Reveals the Secondary Structure of Proteins in Dilute Solutions.

Infrared (IR) absorption spectroscopy is a powerful tool that can quantify complex biomolecules and their structural conformations. However, conventional approaches to protein analysis in aqueous solutions have been significantly challenged because the strong IR absorption of water overwhelms the limited dynamic range of the detection system and thus allows only a very short path length and a limited concentration sensitivity. Here, we demonstrate a solvent absorption compensation (SAC) approach that can improve the concentration sensitivity and extend the available path length by distinguishing the analyte signal over the full dynamic range at each wavelength. Absorption spectra without any postprocessing show good linearity from 100 to 0.1 mg/mL protein concentration, allowing a >100 times enhanced signal-to-noise ratio in the amide I band compared to the non-SAC results. We apply this method to in situ investigate the isothermal kinetics of insulin fibrillation at two clinical concentrations at 74 °C for 18 h. Simultaneous monitoring of both reactants (native forms) and products (fibrils) allows quantitative discussion of the detailed fibrillation mechanisms, which are not accessible with other single modality measurements. This simple optical technique can be applied to other absorption spectroscopies of analytes in strongly absorbing solvents, allowing for enhanced sensitivity without changing the detection system.

Use of Naturalistic Driving Studies for Identification of Vehicle Dynamics

This paper discusses the feasibility of data captured in a long-term Naturalistic Driving Study (NDS) for identification of vehicle dynamics. Driving data were captured for over a year. In this data capture, there was minimal effort to define or control everyday driving practices. While the use of real-world data for model parameter identification is a well-known method, NDS are commonly used to explore the behavior of drivers or to analyze real-world traffic situations. Data from NDS have not yet been used for the purpose of parameterizing vehicle dynamics models since everyday drives commonly do not reflect the full range of vehicle dynamics. This leads to the question if the data from an NDS contains the needed information to describe vehicle dynamics accurately. This paper shows that data captured from long-term everyday vehicle usage is sufficient to characterize vehicle dynamics models. It uses lateral vehicle dynamics as an example to show how the data quantity changes the model accuracy and robustness. There is a point where any further data capture produces redundancy and does not add to the overall information. The well-known single-track model serves as the modeling example which offers options to simply compare the derived model behavior with a reference.