Full Dynamic Range Research Articles

Tensor product random matrix theory

The evolution of complex correlated quantum systems such as random circuit networks is governed by the dynamical buildup of both entanglement and entropy. We here introduce a real-time field theory approach—essentially a fusion of the GΣ functional of the SYK model and the field theory of disordered systems—engineered to microscopically describe the full range of such crossover dynamics: from initial product states to a maximum entropy ergodic state. To showcase this approach in the simplest nontrivial setting, we consider a tensor product of coupled random matrices, and compare to exact diagonalization. Published by the American Physical Society 2024

Data Analysis of Dynamics in Protein Solutions Using Quasi-Elastic Neutron Scattering─Important Insights from Polarized Neutrons.

Protein dynamics play a vital role in biology. Quasi elastic neutron scattering (QENS) is an ideal method to access these dynamics. To isolate protein dynamics, it is important to separate the signal of the buffer and the protein. Normally data analysis is performed based on the assumption that the scattering spectrum is incoherent. To observe the full range of protein dynamics, it is necessary to perform the experiments in solution. This solution is usually a fully deuterated buffer, while the protein remains protonated. It is generally assumed that subtracting the buffer contribution removes all coherent signal from the measured spectrum, and the rest can be considered as purely incoherent. Up until recently, there was no way to experimentally verify this assumption. Polarized QENS experiments allow for the coherent and incoherent contributions to be separated. By comparing the results from the polarized QENS experiment and the standard analysis method from unpolarized QENS, we are thus able to check this assumption experimentally. We show that the pure incoherent spectrum obtained from polarization analysis does not match the results for unpolarized QENS. We discuss the implications of this for data analysis and possible solutions to the problem, as well as mitigation techniques for standard QENS.

Towards Interpretable Physical‐Conceptual Catchment‐Scale Hydrological Modeling Using the Mass‐Conserving‐Perceptron

AbstractWe investigate the applicability of machine learning technologies to the development of parsimonious, interpretable, catchment‐scale hydrologic models using directed‐graph architectures based on the mass‐conserving perceptron (MCP) as the fundamental computational unit. Here, we focus on architectural complexity (depth) at a single location, rather than universal applicability (breadth) across large samples of catchments. The goal is to discover a minimal representation (numbers of cell‐states and flow paths) that represents the dominant processes that can explain the input‐state‐output behaviors of a given catchment, with particular emphasis given to simulating the full range (high, medium, and low) of flow dynamics. We find that a “HyMod Like” architecture with three cell‐states and two major flow pathways achieves such a representation at our study location, but that the additional incorporation of an input‐bypass mechanism significantly improves the timing and shape of the hydrograph, while the inclusion of bi‐directional groundwater mass exchanges significantly enhances the simulation of baseflow. Overall, our results demonstrate the importance of using multiple diagnostic metrics for model evaluation, while highlighting the need for properly selecting and designing the training metrics based on information‐theoretic foundations that are better suited to extracting information across the full range of flow dynamics. This study sets the stage for interpretable regional‐scale MCP‐based hydrological modeling (using large sample data) by using neural architecture search to determine appropriate minimal representations for catchments in different hydroclimatic regimes.

On-demand transposition across light-matter interaction regimes in bosonic cQED

The diverse applications of light-matter interactions in science and technology stem from the qualitatively distinct ways these interactions manifest, prompting the development of physical platforms that can interchange between regimes on demand. Bosonic cQED employs the light field of high-Q superconducting cavities coupled to nonlinear circuit elements, harnessing the rich dynamics of their interaction for quantum information processing. However, implementing fast switching of the interaction regime without deteriorating the cavity coherence is a significant challenge. We present an experiment that achieves this feat, combining nanosecond-scale frequency tunability of a transmon coupled to a cavity with lifetime of hundreds of microseconds. Our implementation affords a range of useful capabilities for quantum information processing; from fast creation of cavity Fock states using resonant interaction and interchanging tomography techniques at qualitatively distinct interaction regimes on the fly, to the suppression of unwanted cavity-transmon dynamics during idle evolution. By bringing flux tunability into the bosonic cQED toolkit, our work opens up the possibility to probe the full range of light-matter interaction dynamics within a single platform and provides valuable pathways towards robust and versatile quantum information processing.

The spiking output of the mouse olfactory bulb encodes large-scale temporal features of natural odor environments.

In natural odor environments, odor travels in plumes. Odor concentration dynamics change in characteristic ways across the width and length of a plume. Thus, spatiotemporal dynamics of plumes have informative features for animals navigating to an odor source. Population activity in the olfactory bulb (OB) has been shown to follow odor concentration across plumes to a moderate degree (Lewis et al., 2021). However, it is unknown whether the ability to follow plume dynamics is driven by individual cells or whether it emerges at the population level. Previous research has explored the responses of individual OB cells to isolated features of plumes, but it is difficult to adequately sample the full feature space of plumes as it is still undetermined which features navigating mice employ during olfactory guided search. Here we released odor from an upwind odor source and simultaneously recorded both odor concentration dynamics and cellular response dynamics in awake, head-fixed mice. We found that longer timescale features of odor concentration dynamics were encoded at both the cellular and population level. At the cellular level, responses were elicited at the beginning of the plume for each trial, signaling plume onset. Plumes with high odor concentration elicited responses at the end of the plume, signaling plume offset. Although cellular level tracking of plume dynamics was observed to be weak, we found that at the population level, OB activity distinguished whiffs and blanks (accurately detected odor presence versus absence) throughout the duration of a plume. Even ~20 OB cells were enough to accurately discern odor presence throughout a plume. Our findings indicate that the full range of odor concentration dynamics and high frequency fluctuations are not encoded by OB spiking activity. Instead, relatively lower-frequency temporal features of plumes, such as plume onset, plume offset, whiffs, and blanks, are represented in the OB.

Mathematical generation of data-driven hippocampal CA1 pyramidal neurons and interneurons copies via A-GLIF models for large-scale networks covering the experimental variability range

Efficient and accurate large-scale networks are a fundamental tool in modeling brain areas, to advance our understanding of neuronal dynamics. However, their implementation faces two key issues: computational efficiency and heterogeneity. Computational efficiency is achieved using simplified neurons, whereas there are no practical solutions available to solve the problem of reproducing in a large-scale network the experimentally observed heterogeneity of the intrinsic properties of neurons. This is important, because the use of identical nodes in a network can generate artifacts which can hinder an adequate representation of the properties of a real network.To this aim, we introduce a mathematical procedure to generate an arbitrary large number of copies of simplified hippocampal CA1 pyramidal neurons and interneurons models, which exhibit the full range of firing dynamics observed in these cells — including adapting, non-adapting and bursting. For this purpose, we rely on a recently published adaptive generalized leaky integrate-and-fire (A-GLIF) modeling approach, leveraging on its ability to reproduce the rich set of electrophysiological behaviors of these types of neurons under a variety of different stimulation currents.The generation procedure is based on a perturbation of model’s parameters related to the initial data, firing block, and internal dynamics, and suitably validated against experimental data to ensure that the firing dynamics of any given cell copy remains within the experimental range. A classification procedure confirmed that the firing behavior of most of the pyramidal/interneuron copies was consistent with the experimental data. This approach allows to obtain heterogeneous copies with mathematically controlled firing properties. A full set of heterogeneous neurons composing the CA1 region of a rat hippocampus (approximately 1.2 million neurons), are provided in a database freely available in the live paper section of the EBRAINS platform.By adapting the underlying A-GLIF framework, it will be possible to extend the numerical approach presented here to create, in a mathematically controlled manner, an arbitrarily large number of non-identical copies of cell populations with firing properties related to other brain areas.

Linearity and bias of proton density fat fraction across the full dynamic range of 0-100%: a multiplatform, multivendor phantom study using 1.5T and 3T MRI at two sites.

Performance assessments of quantitative determinations of proton density fat fraction (PDFF) have largely focused on the range between 0 and 50%. We evaluate PDFF in a two-site phantom study across the full 0-100% PDFF range. We used commercially available 3D chemical-shift-encoded water-fat MRI sequences from three MRI system vendors at 1.5T and 3T and conducted the study across two sites. A spherical phantom housing 18 vials spanning the full 0-100% PDFF range was used. Data at each site were acquired using default parameters to determine same-day and different-day intra-scanner repeatability, and inter-system and inter-site reproducibility, in addition to linear regression between reference and measured PDFF values. Across all systems, results demonstrated strong linearity and minimal bias. For 1.5T systems, a pooled slope of 0.99 with a 95% confidence interval (CI) of 0.981-0.997 and a pooled intercept of 0.61% PDFF with a 95% CI of 0.17-1.04 were obtained. Results for pooled 3T data included a slope of 1.00 (95% CI 0.995-1.005) and an intercept of 0.69% PDFF (95% CI 0.39-0.97). Inter-site and inter-system reproducibility coefficients ranged from 2.9 to 6.2 (in units of PDFF), while intra-scanner same-day and different-day repeatability ranged from 0.6 to 7.8. PDFF across the 0-100% range can be reliably estimated using current commercial offerings at 1.5T and 3T.

The physical logic of protein machines

Proteins are intricate molecular machines whose complexity arises from the heterogeneity of the amino acid building blocks and their dynamic network of many-body interactions. These nanomachines gain function when put in the context of a whole organism through interaction with other inhabitants of the biological realm. And this functionality shapes their evolutionary histories through intertwined paths of selection and adaptation. Recent advances in machine learning have solved the decades-old problem of how protein sequence determines their structure. However, the ultimate question regarding the basic logic of protein machines remains open: how does the collective physics of proteins lead to their functionality? and how does a sequence encode the full range of dynamics and chemical interactions that facilitate function? Here, we explore these questions within a physical approach that treats proteins as mechano-chemical machines, which are adapted to function via concerted evolution of structure, motion, and chemical interactions.

Gnuastro: Visualizing the Full Dynamic Range in Color Images

Color plays a crucial role in the visualization, interpretation, and analysis of multi-wavelength astronomical images. However, generating color images that accurately represent the full dynamic range of astronomical sources is challenging. In response, Gnuastro v0.22 introduces the program astscript-color-faint-gray, which is extensively documented in the Gnuastro manual. It employs a nonlinear transformation to assign an 8-bit RGB (Red–Green–Blue) value to brighter pixels, while the fainter ones are shown in an inverse grayscale. This approach enables the simultaneous visualization of low surface brightness features within the same image. This research note is reproducible with Maneage, on the Git commit c206945.

Strong Backreaction Regime in Axion Inflation.

We study the nonlinear dynamics of axion inflation, capturing for the first time the inhomogeneity and full dynamical range during strong backreaction, till the end of inflation. Accounting for inhomogeneous effects leads to a number of new relevant results, compared to spatially homogeneous studies: (i)the number of extra efoldings beyond slow-roll inflation increases very rapidly with the coupling, (ii)oscillations of the inflaton velocity are attenuated, (iii)the tachyonic gauge field helicity spectrum is smoothed out (i.e., the spectral oscillatory features disappear), broadened, and shifted to smaller scales, and (iv)the nontachyonic helicity is excited, reducing the chiral asymmetry, now scale dependent. Our results are expected to impact strongly on the phenomenology and observability of axion inflation, including gravitational wave generation and primordial black hole production.

Distributions and Physical Properties of Molecular Clouds in the Third Galactic Quadrant: l = [219.°75, 229.°75] and b = [−5.°25, 5.°25

We present the results of an unbiased 12CO/13CO/C18O (J = 1–0) survey in a portion of the third Galactic quadrant (TGQ): 219.°75 ≤ l ≤ 229.°75 and −5.°25 ≤ b ≤ 5.°25. The high-resolution and high-sensitivity data sets help to unravel the distributions and physical properties of the molecular clouds (MCs) in the mapped area. In the LSR velocity range from ∼−1 to ∼85 km s−1, the molecular material successfully traces the Local, Perseus, and Outer arms. In the TGQ, the Outer arm appears to be more prominent than that in the second Galactic quadrant (SGQ), but the Perseus arm is not as conspicuous as that in the SGQ. A total of 1,502 12CO, 570 13CO, and 53 C18O molecular structures are identified, spanning over ∼2 and ∼6 orders of magnitude in size and mass, respectively. Tight mass–radius correlations and virial parameter–mass anticorrelations are observable. Yet, it seems that no clear correlations between velocity dispersion and effective radius can be found over the full dynamic range. The vertical distribution of the MCs renders evident pictures of the Galactic warp and flare.

Islamism, party change, and strategic conciliation: Evidence from Tunisia

What happens to an Islamist party after moderating its behaviour and ideology? Existing work on Islamist parties has elaborated the varied causes of moderation. Yet, the mixed findings do not capture the full range of Islamist dynamics. This article draws on a multiyear, interview-based study of the Tunisian Islamist party Ennahda to interrogate the process of intraparty change after moderation. Islamist parties face a two-level problem with external and internal trade-offs. I argue that the intraparty characteristics that enable moderation may also contribute to undermining a party’s institutional structure and identity as it responds to an uncertain political context. These findings bring processual evidence from Islamist parties into broader explanations of party change and highlight the ongoing effects of moderation, not just its causes.

PicoTDC: a flexible 64 channel TDC with picosecond resolution

The PicoTDC is a 64 channel TDC (Time to Digital Converter) ASIC, with 3ps or 12ps time binning, developed at CERN for use in a large variety of high channel count scientific instrumentation. Acquired time measurements are processed on-chip with a programmable buffering and triggered data flow architecture before being read out on 1 to 4 byte-wise readout ports with a total readout bandwidth of up to 10 Gbps. Leading edge, falling edge or leading edge together with pulse width can be captured at burst rates as high as 1.2 GHz and sustained rate of 320 MHz per channel. Effective single-shot time resolution has been measured to be 3.7 ps RMS across all 64 channels over its full dynamic range and as good as 1.35 ps RMS when using an on-chip adjustment feature optimized for a specific channel (0.43 ps if averaged over multiple measurements). The PicoTDC is implemented in a 65 nm CMOS process and 20 k chips have been produced and packaged in a 400 pin plastic BGA package.

Abstract 5402: Using computer vision to resolve proliferative dynamics within therapeutic responses in large scale screens of patient derived models

Abstract IC50, Emax, and AUC values are commonly used to assess susceptibility for a given therapeutic candidate but are subject to the number of cell divisions for a given in vitro model. When comparing multiple in vitro models and especially patient-derived models such as patient-derived tumor organoids, the degree of intrinsic cell proliferation or doubling time substantially differs and therefore limits conventional metrics as above. Furthermore, assays that directly measure cellular viability can oftentimes overlook therapeutic agents that have cytostatic, versus cytotoxic mechanisms of action and require assays with similar sensitivity and precision to account for this phenotype. Finally, terminal endpoint assays can require tedious temporal optimization to capture the full dynamic range of a given therapeutic which makes cross-therapeutic and cross-model comparisons difficult to interpret. An assay that incorporates these multiple features and in vitro specific doubling time would lead to highly accurate measurements of drug effects and allow the calculation of growth-adjusted IC50 values i.e. GI50 values. We, therefore, sought to develop a computer vision model that utilizes label-free longitudinal light microscopy images as input to report the total number of nuclei present within a given experimental well, allowing for the monitoring of cell division and any cytostatic effects. To train the neural network, we use Hoechst-stained images as our ground truth. The model used is an extension of the Regularized Conditional Adversarial Network (PMID: 34320344). It takes as input Brightfield images along with the registered Hoescht stained wells. The label for each well is obtained using commercial image processing software to count the total number of nuclei present in each well from the Hoescht stained channel. The model was trained to predict the virtually stained Hoescht well for every Brightfield, as well as the aggregate nuclei count for all organoids present inside the well. The model was trained on 29000 individual sites across 9 distinct cancer types. Five experiments were conducted during training to test for bit depth and tonality effects of the input training data. Inference on 9240 images showed a pearsonR score of 0.81 on the best-performing model. Results show that the model performs well and can be applied in real-time using only experimental data to correct for differing cell division rates and measure cytostatic phenotypes all while using simple light microscopy. The tool will enable cross-comparison of different therapeutic MoAs as well as enable cross-cancer type/indication comparison for a therapeutic in development to inform early development clinical strategy. Citation Format: Madhavi Kannan, Brian Larsen, Chi-Sing Ho, Jagadish Venkataraman, Martin Stumpe, Ameen Salahudeen. Using computer vision to resolve proliferative dynamics within therapeutic responses in large scale screens of patient derived models. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5402.

A SiPM-Based Gamma Spectrometer With Field-Programmable Energy Binning for Data-Efficient Isotope Analysis

A highly reconfigurable gamma spectrometer with a silicon photomultiplier detector and a custom integrated circuit (IC)-based multi-channel analyzer (MCA) is presented. The core of the MCA comprises a custom analog front-end IC and a piecewise-linear analog-to-digital converter (ADC) IC, both fabricated in a 180nm complementary metal-oxide-semiconductor (CMOS) process. Along with a field-programmable gate array (FPGA)-based digital back-end, the proposed architecture allows pulse-height analysis with reconfigurable analog gain and ADC resolution across the full dynamic range. Specifically, the piecewise-linear ADC can increase resolution in selected regions of interest while utilizing a fixed 7-bit digital word, thus enabling data reduction and spectrum feature enhancement. Reconfigurability also allows the MCA to be tuned for different scintillator materials used in the detector. The system architecture is introduced at a conceptual level, followed by detector and circuit-level MCA implementation details and design trade-offs. Several isotope spectra were recorded with two common scintillators, LYSO and CsI(Tl), spanning energy peaks from 32keV to 1.33MeV. Variable energy bin-widths were measured across different AFE gains, ranging from 3keV to 58keV for LYSO, and 1.4keV to 51keV for CsI(Tl), demonstrating the versatility of the proposed system and its ability to provide reconfigurable peak enhancement for radiation spectroscopy.

Parent-Adult Child Estrangement in the United States by Gender, Race/ethnicity, and Sexuality.

To provide nationally-representative estimates of parent-adult child estrangement. Population-level research is needed on parent-adult child estrangement to understand the full range of family dynamics in the U.S. We estimate logistic regression models using data from the National Longitudinal Survey of Youth 1979 Child and Young Adult supplement to determine estimates of estrangement (and subsequent unestrangement) from mothers (N=8,495) and fathers (N=8,119) by children's gender, race/ethnicity, and sexuality. We then estimate hazards of first estrangement from mothers (N = 7,919) and fathers (N = 6,410), adjusting for adult child's and parents' social and economic characteristics. Six percent of respondents report a period of estrangement from mothers, with an average age of first maternal estrangement of 26 years old; 26 percent of respondents report estrangement from fathers, with an average age of first paternal estrangement of 23 years old. Results further show heterogeneity by gender, race/ethnicity, and sexuality; for example, daughters are less likely to be estranged from their mothers than are sons, Black adult children are less likely than White adult children to be estranged from their mothers but more likely to be estranged from fathers, and gay, lesbian, and bisexual adult children are more likely than heterosexuals to be estranged from fathers. The majority of estranged adult children become unestranged from mothers (81%) and fathers (69%) in subsequent waves. This study provides compelling new evidence on an overlooked aspect of intergenerational relationships, concluding with insight into the structural forces that unequally contribute to estrangement patterns.

Improvements in image quality after optimization in digital intraoral radiographs

BackgroundDigital intraoral radiographic exposures are optimized largely on the basis of subjective assessment of diagnostic image quality. This study presents an objective approach to optimize radiographic exposure settings for digital intraoral radiographic systems. MethodsSeven size 2 digital intraoral systems were assessed for image quality and determination of optimal exposure following the protocol specified in American National Standard Institute/American Dental Association Standard No. 1094: Quality Assurance for Digital Intra-Oral Radiographic Systems. A ProX radiograph unit (Planmeca) at 63 kVp and 6 mA was used to obtain radiographs of the Dental Digital Quality Assurance phantom. ImageJ software (National Institutes of Health) was used to quantify dynamic range and spatial resolution, and contrast perceptibility was evaluated visually. Optimal exposure is the setting with the maximal contrast perceptibility and spatial resolution while displaying the full dynamic range. After image optimization, a custom phantom consisting of an endodontically prepared tooth was imaged to evaluate the file position relative to the apex for each system. Differences in distances between file position relative to the root apex at the optimal exposure as well as 1 increment above and below were measured. ResultsRadiographic images obtained at the optimal exposure yielded better visualization and more accurate measurements of the file tip relative to the apex. ConclusionsOptimizing radiographic exposures improves image quality and accuracy in clinical decisions. Practical ImplicationsImprovement in image quality and better accuracy in actual distance of the endodontic file to the radiographic apex coupled with complete cleaning, shaping, and obturation of the canal should lead to better endodontic treatment outcomes.

Architectures and complex functions of tandem riboswitches

ABSTRACT Riboswitch architectures that involve the binding of a single ligand to a single RNA aptamer domain result in ordinary dose-response curves that require approximately a 100-fold change in ligand concentration to cover nearly the full dynamic range for gene regulation. However, by using multiple riboswitches or aptamer domains in tandem, these ligand-sensing structures can produce additional, complex gene control outcomes. In the current study, we have computationally searched for tandem riboswitch architectures in bacteria to provide a more complete understanding of the diverse biological and biochemical functions of gene control elements that are made exclusively of RNA. Numerous different arrangements of tandem homologous riboswitch architectures are exploited by bacteria to create more ‘digital’ gene control devices, which operate over a narrower ligand concentration range. Also, two heterologous riboswitch aptamers are sometimes employed to create two-input Boolean logic gates with various types of genetic outputs. These findings illustrate the sophisticated genetic decisions that can be made by using molecular sensors and switches based only on RNA.

Optical time domain backscattering of antiresonant hollow core fibers.

Today's lowest-loss hollow core fibers are based on antiresonance guidance. They have been shown both theoretically and experimentally to have very low levels of backscattering arising from the fiber structure - 45 dB below that of traditional optical fibers with a solid silica glass core. This makes their longitudinal characterization using conventional reflectometric techniques very challenging. However, it was recently estimated that when filled with air, their backscattering coefficient increases to about 30 dB below that of standard solid core fibers. This level should be measurable with commercially available high performance optical time domain reflectometers (OTDR). Here we demonstrate - for the first time to the best of our knowledge - the measurement of backscattering from the air inside a hollow core fiber. We show that the characterization of multi-km long hollow core fibers with 15 m spatial resolution is possible using a commercial OTDR instrument. To benefit from its full dynamic range, we strongly suppress the 4% back-reflections that ordinarily occur at the OTDR's standard fiber output when directly-connected to a hollow core fiber. Furthermore, low coupling loss into the hollow core fiber (0.3 dB in our experiment) also helps to maximize the achievable OTDR signal-to-noise ratio. This approach enables distributed characterization and fault-finding in low-loss hollow core fibers, a topic of increasing importance as these fibers are now starting to be installed in commercial optical communication networks.

Dynamics and Mechanisms of Contrast-Dependent Modulation of Spatial-Frequency Tuning in the Early Visual Cortex.

The spatial-frequency (SF) tuning of neurons in the early visual cortex is adjusted for stimulus contrast. As the contrast increases, SF tuning is modulated so that the transmission of fine features is facilitated. A variety of mechanisms are involved in shaping SF tunings, but those responsible for the contrast-dependent modulations are unclear. To address this, we measured the time course of SF tunings of area 17 neurons in male cats under different contrasts with a reverse correlation. After response onset, the optimal SF continuously shifted to a higher SF over time, with a larger shift for higher contrast. At high contrast, whereas neurons with a large shift of optimal SF exhibited a large bandwidth decrease, those with a negligible shift increased the bandwidth over time. Between these two extremes, the degree of SF shift and bandwidth change continuously varied. At low contrast, bandwidth generally decreased over time. These dynamic effects enhanced the processing of high-frequency range under a high-contrast condition and allowed time-average SF tuning curves to show contrast-dependent modulation, like that of steady-state SF tuning curves reported previously. Combinations of two mechanisms, one that decreases bandwidth and shifts optimal SF, and another that increases bandwidth without shifting optimal SF, would explain the full range of SF tuning dynamics. Our results indicate that one of the essential roles of tuning dynamics of area 17 neurons, which have been observed for various visual features, is to adjust tunings depending on contrast.SIGNIFICANCE STATEMENT The spatial scales of features transmitted by cortical neurons are adjusted depending on stimulus contrast. However, the underlying mechanism is not fully understood. We measured the time course of spatial frequency tunings of cat area 17 neurons under different contrast conditions and observed a variety of dynamic effects that contributed to spatial-scale adjustment, allowing neurons to adjust their spatial frequency tuning range depending on contrast. Our results suggest that one of the essential roles of tuning dynamics of area 17 neurons, which have been observed for various visual features, is to adjust tunings depending on contrast.