Limited Dynamic Range Research Articles

Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System

Pushing the frontiers of time-series information processing in the ever-growing domain of edge devices with stringent resources has been impeded by the systems’ ability to process information and learn locally on the device. Local processing and learning of time-series information typically demand intensive computations and massive storage as the process involves retrieving information and tuning hundreds of parameters back in time. In this work, we developed a memristor-based echo state network accelerator that features efficient temporal data processing and in-situ online learning. The proposed design is benchmarked using various datasets involving real-world tasks, such as forecasting the load energy consumption and weather conditions. The experimental results illustrate that the hardware model experiences a marginal degradation in performance as compared to the software counterpart. This is mainly attributed to the limited precision and dynamic range of network parameters when emulated using memristor devices. The proposed system is evaluated for lifespan, robustness, and energy-delay product. It is observed that the system demonstrates reasonable robustness for device failure below 10%, which may occur due to stuck-at faults. Furthermore, 247 × reduction in energy consumption is achieved when compared to a custom CMOS digital design implemented at the same technology node.

Research on a signal demodulation algorithm for fiber optic acoustic sensors based on the amplitude ratio of harmonic components

Fiber optic sensing technology's performance is crucially influenced by the choice of demodulation algorithms. Traditional intensity demodulation methods rely on precisely controlling the working point to the quadrature point (Q point) by adjusting the laser wavelength. However, issues like laser wavelength adjustment accuracy and laser power variations with wavelength affect demodulation accuracy, limiting its application in complex scenarios. Additionally, this method requires sensitivity calibration and has a limited dynamic range. To address these limitations, this paper proposes an amplitude ratio of harmonic components (ARHC) demodulation algorithm. It analyzes the harmonic components of the interference light intensity output by the sensor to calculate Fabry-Perot cavity length changes, achieving real-time demodulation of dynamic signals. Compared to traditional methods, the ARHC algorithm simplifies system adjustment, has a larger demodulation range and higher accuracy, and is more adaptable to environmental changes. Simulations and experiments demonstrate its effectiveness and high-precision, large-dynamic-range signal demodulation capabilities under different interference conditions. The EFPI ultrasonic sensitivity based on the ARHC algorithm is 2.5 nm/Pa@23kHz, and it can linearly demodulate up to a vibration amplitude of 372.8 nm for the sensitive diaphragm, with a demodulation error of less than or equal to 0.8 nm.

An Antagonistic Photovoltaic Memristor for Bioinspired Active Contrast Adaptation.

Machine vision systems that consist of cameras and image-processing components for visual inspection and identification tasks play a critical role in various intelligent applications, including pilotless vehicles and surveillance systems. However, current systems usually possess a limited dynamic range and fixed photoresponsivity, restricting their capability of gaining high-fidelity images when encoding a high-contrast scene. Here, it is shown that a photovoltaic memristor incorporating two antagonistic photovoltaic junctions can autonomously adjust its response to varying light stimuli, enabling the amplification of shadows and inhibition of highlight saturation. Due to the dynamic photodoping effect at the p-n junction with an asymmetrical profile, the photocurrent polarities of the antagonistic memristor can be changed as the light intensity increases. The light-intensity-dependent switchable photovoltaic behaviors match Weber's law where photosensitivity is inversely proportional to the light stimuli. An 11×11 memristor array is used to detect a high-contrast scene with light intensities ranging from 1 to 5×104 µW cm-2, achieving a similar active contrast adaptation performance compared with the human visual systems (less than 1.2 s at 94 dB). This work paves the way for innovative neuromorphic device designs and may lead to the development of state-of-the-art active visual adaptation photosensors.

3D shape measurement method for high-reflective surface based on a color camera

Fringe projection profilometry (FPP) has been widely utilized in many fields due to its non-contact, high accuracy, high resolution and full-field measurement capabilities. However, the limited dynamic range of the camera sensor can result in overexposure of high-reflective parts in industrial production measurement. To effectively solve the above issue, this paper proposes a 3D shape measurement method for the high-reflective surface based on a color camera. Firstly, the optimal exposure time for the low-reflective region is estimated using the relationship between the standard variance of the phase error and the modulation. Twelve blue phase-shifted fringe patterns and a uniform blue image are utilized to obtain 3D shape of high-reflective surface. Secondly, captured images are separated into red, green and blue components. The fused Gaussian low-pass filtering method with different cut-off frequencies is used to filter and denoise the green or red components and the true intensity of the blue component in the saturated regions is calculated by the unsaturated intensity of the green or red components based on the calibrated crosstalk matrix. Then the image in saturated regions is fused and normalized with the unsaturated region. The absolute phase obtained from the fused normalized fringe patterns is converted into 3D data. Finally, experiments have been carried out on measuring. The experimental results show that the proposed method is capable of obtaining the 3D shape of the surface of a high-reflective object with fewer patterns and high measurement accuracy.

NeIle, a Genetically Encoded Indicator for Branched-Chain Amino Acids Based on mNeonGreen Fluorescent Protein and LIVBP Protein.

Branched-chain amino acids (BCAAs) play an important role in the functioning of mammalian cells and the central nervous system. However, available genetically encoded indicators for BCAAs are based on Förster resonance energy transfer and have a limited dynamic range. We developed a single fluorescent protein-based sensor for BCAAs, called NeIle, which is composed of circularly permutated mNeonGreen protein inserted into the leucine-isoleucine-valine binding protein (LIVBP) from Escherichia coli bacteria. In solution, the NeIle indicator displayed a positive fluorescence response to adding isoleucine, leucine, and valin amino acids with high ΔF/F dynamic ranges of 27-, 19-, and 11-fold and the corresponding affinity values of 5.0, 2.9, and 75 mM, respectively. The spectral and biochemical properties of the NeIle indicator were characterized in solution. We characterized the brightness of the NeIle indicator in living mammalian cells, including cultured neurons. Using the NeIle indicator, we successfully visualized the dynamics of isoleucine transients in different organelles of mammalian cells. We obtained and analyzed the X-ray crystal structure of the NeIle indicator in an isoleucine-bound state. Structure-guided directed mutagenesis of the NeIle indicator revealed the basis of its fluorescence response and selectivity to isoleucine.

Calibration approach of non-modulated pyramid wavefront sensors for improving the dynamic range

The pyramid wavefront sensor (PWFS) can provide high sensitivity in demanding adaptive optics applications. However, its exquisite sensitivity has a limited dynamic range, which relies on the use of beam modulation to improve the dynamic range of the PWFS—despite this task being achieved by moving optical components in the PWFS, increasing the complexity of the system. An attractive idea to simplify the optical and mechanical design of a PWFS is to work without any dynamic modulation. This paper proposes a new method, to the best of our knowledge, called the pyramid wavefront sensor with truncated axicon (TA-PWFS), which is used in the non-modulated PWFS in the closed-loop adaptive optics system with high sensitivity and high dynamic range without the need for cumbersome modulation. The new approach uses a diffractive element placed at a conjugated Fourier plane of the pyramid prism to shape part of the incident light in a ring pattern around the pyramid pin. The radius of the circular-shaped image is identical to that of the modulation radius in the sinusoidal modulation, offering a high dynamic range, while the remaining portion of the light continues un-diffracted, producing high sensitivity through a spot on the pyramid pin. Simulation results with different kinds of aberrations containing large global tilts reveal that the use of the TA-PWFS has a noticeable improvement in the aberration estimation performance. With a highly simplified structure, elements without mechanical modulation, and aberration reconstruction without precorrection of global tilt, the TA-PWFS could present an innovative method for the design of non-modulated pyramid wavefront sensors that can better suit the higher requirements of adaptive optics applications.

Dynamic range limitations of non-coherent continous-wave THz photomixing systems with broadband detectors

The dynamic range of non-coherent continuous-wave (CW) THz photomixing (PM) systems with broadband detectors can be significantly limited by various parasitic effects. Specifically, we examine the generation of parasitic (i) THz and (ii) IR radiation, and (iii) higher harmonics in CW THz PM emitters. (i) The parasitic broadband THz radiation, spanning from 100 to 250 GHz with a total output power of 20 nW, results from not perfectly clean laser spectra. As a result, for a frequency-flat Golay cell detector, the PM-system dynamic range is limited to 32.8 dB at 500 GHz, 26.7 dB at 1 THz, and 8.5 dB at 2.3 THz. In the case of detectors with a frequency-declining responsivity, the dynamic range can drop by ∼10 dB more. (ii) The IR radiation leaking from a PM emitter (≈20 μW) is sensitive to the PM emitter bias, which results in its modulation with an amplitude of about 1.3 μW, when a standard PM-emitter bias modulation is applied. The detected IR radiation could be confused for the THz signal. (iii) Parasitic generation of higher harmonics in PM systems can also limit the system’s dynamic range or create spectral artifacts. However, we show that the harmonics are low at least at ∼1 THz and above. Specifically, they are less than 400 pW for fundamental frequencies above 750 GHz, which is more than 43 dB below the power of the fundamental harmonic. The above-stated values were obtained for a commonly-used PIN-diode photomixer mounted on a Si lens and 1.5 μm distributed-feedback lasers. In general, suppression of these parasitic signals is crucial for non-coherent CW THz PM systems.

Fiber Fabry-Perot accelerometer with extended dynamic range and low noise floor

Optical interferometric accelerometers are widely used in seismic monitoring, petroleum resources exploration, and structural health monitoring due to their low noise floor and resistance to electromagnetic interference (EMI). However, their small working range limits further applications. To broaden the working range of the sensor while ensuring the inherent anti-electromagnetic interference capability of the optical sensor, this paper proposes an orthogonal optical path (OP) range broadening scheme (OORBS). The linear working range is widened by splicing the linear intervals in the two interferometric signals. Subsequently, a platform based on a nano-displacement unit was built to validate the feasibility of the OORBS under static and AC cavity length variations. The experimental results show that the OORBS can recover the cavity length completely. Finally, the OORBS was combined with an accelerometer to realize the range broadening. The OORBS extends the accelerometer’s working range from 0.42 mg to 68 mg while maintaining the high sensitivity, which is about a 162-time improvement. The accelerometer’s noise floor reaches 4.8 ng/Hz1/2 at 15 Hz and accordingly, the dynamic range of the accelerometer increases from 98.8 dB to 143 dB. The proposed method is general to address the Fabry-Perot-based dynamic range limitation and can be adapted for various interferometric sensors, such as Fabry-Perot, grating, and Mach–Zehnder.

B-043 Q-nab™ Igg kit: verification and validation of a novel cell-free quantitative neutralizing antibody assay

Abstract Background As the world exits the COVID-19 pandemic era, it is very important to assess population-level and individual seroprotectivity against multiple circulating and emerging SARS-CoV-2 strains, especially in vulnerable populations. Current serological measurements for neutralizing antibodies (NAbs) are either done using complex and time-intensive pseudovirus- or micro-neutralization assays or as a high throughput competitive ELISA assay. These assays, by design, cannot be multiplexed to measure NAb titers against multiple strains and suffer from a limited dynamic range. The Q-NAb IgG Tests, which rely on blocking the non-neutralizing antibodies (NNAbs) using a novel fusion protein, were developed and validated to quantitatively measure NAb titer against the SARS-CoV-2 strains used to develop the first two mRNA vaccines viz. ancestral and BA.4/5 strains. Methods The Q-NAb assay measures the NAb titer in a sample by first sequestering the NNAbs using the fusion protein (made by covalently coupling SARS-CoV-2 BA.4/5 RBD and h-ACE2 protein) thereby enriching the NAbs in the sample. Variant-specific NAbs are then detected using an indirect ELISA where the wells are coated with the variant RBD and yield a signal that is directly proportional to the concentration of NAb. Clinical samples from subjects that have either had a SARS-CoV-2 infection (N=27), taken the initial vaccine series (N=123) and/or the bivalent booster (N=151) and pre-pandemic samples (N=29) were commercially sourced. Q-NAb NAb titers were measured using the ancestral and BA.4/5 kits and were compared against corresponding MN50 values using both parametric and non-parametric models. Single-site precision, low-end sensitivity, dilutional linearity, and interference screening of Q-NAb IgG Kits were evaluated in accordance with appropriate CLSI guidelines. Traceability and total uncertainty of the Q-NAb IgG Kits (Ancestral and BA.4/5) to WHO International Standard 21/340 and 21/338 respectively was established using a calibration hierarchy adapted from ISO 17511:2020. Results Both the ancestral and BA.4/5 Q-NAb IgG Test Kits correlated with MN50 titers with a Spearman correlation coefficient of 0.90 and 0.89 respectively. The total uncertainty in traceability of calibration to WHO International standards 21/340 and 21/338 was found to be less than 10% for both tests. Single site precision across the assay range for BA.4/5 kit was found to be 6-10% with a limit of quantitation of 312 IU/mL. Assays exhibited dilutional linearity across their respective dynamic range and had no significant interference from common endogenous and exogenous substances. Conclusions We have successfully validated the novel Q-NAb IgG neutralization assay platform as individual assays for measuring neutralizing titers against ancestral and BA.4/5 SARS-CoV-2 strains. The assays have expanded dynamic range, excellent analytical performance and correlation to accepted gold-standard microneutralization assays. Both assays are calibrated and traceable to WHO international standards, thereby enabling the compilation and comparison of data from multiple clinical cohorts run across multiple laboratories. The design of the Q-NAb IgG Test kit makes it amenable to multiplexing (for example, on an MSD platform) to assess neutralization titers against multiple variants at the same time. Such a multiplexed platform can be used to assess vaccine efficacy and seroprotection against multiple variants including emerging variants.

B-155 Analysis of Monoclonal Antibodies Using a Quantification LC-MS/MS Kit - Spotlight on Infliximab

Abstract Background The use of immunoassays for measuring mAbs is well established but has some limitations, including poor performance, lack of standardization, a high cost when processing a limited number of samples, limited dynamic range, and the potential for cross-reactivity. Using LC-MS/MS associated with the ready-to-use commercial mAbXmise Kit is a simple way to implement mAbs measurement for infliximab, providing high analytical performance, ease of use, and flexibility. Methods The Promise Proteomics Inflammation mAbXmise Kit contains all the calibrators, QCs, internal standard and sample preparation consumables to run the LC-MS/MS method for infliximab. Briefly, 20µL samples are dispensed into the mAbXmise plates containing lyophilized stable labelled infliximab. Samples are transferred to the PuriXmise plate to perform the immunocapture of infliximab and allow washing of the samples. Samples are eluted into a collection plate, evaporated to dryness, resuspended, and the protease (CutXmise) is added to the sample to digest infliximab overnight. The digestion is quenched, with the samples immediately ready for analysis. Using an ACQUITY™ UPLC™ I-Class PLUS FL System, samples were injected onto a Waters™ XSelect™ Premier HSS T3 Column, eluted using a water/acetonitrile/formic acid gradient profile and quantified with both a Waters Xevo™ TQ-XS and Xevo TQ-S micro Mass Spectrometer. Results The method was shown to be linear from 2 - 100 µg/mL. Analytical sensitivity at lowest calibrator of 2µg/mL provided S/N (PtP) ≥15:1 for all tryptic peptides on both mass spectrometers. Coefficients of variation (CV) at 4 and 25µg/mL QC concentrations were all ≤ 10.0% (n = 5). The relative concentrations of the tryptic peptides were compared across 29 anonymized samples and good agreement was observed for GLE, SAV, DIL and ASA peptides. Conclusions We have demonstrated a clinical research method for quantifying Infliximab in plasma using a commercially available kit for sample preparation followed by LC-MS/MS analysis. The Promise Proteomics mAbXmise Kit aids in the day-to-day reproducibility of the immunocapture and tryptic digestion of IFX for targeted LC-MS/MS analysis with the correct peptide selection. Compared to a direct digest surrogate peptide workflow, the process used in the kit reduces matrix interference, improves analytical sensitivity, and enables the use of lower sample volumes. The method can be run on both the Xevo TQ-XS and Xevo TQ-S micro Mass Spectrometers which provide the dynamic range to quantify IFX across the expected range, and the selectivity and analytical sensitivity to obtain low-level quantification of the IFX surrogate peptide in plasma samples. The data presented combines the use of a kit dedicated to the preparation of samples and the use of liquid chromatography and mass spectrometry instrumentation to perform the quantitative analyses. The mAbXmise Kit described has not been cleared by any regulatory entity for diagnostic purposes outside of Europe. Analytical performance depends on the instrument characteristics and its settings. The end user is responsible for completion of the method development and validation. Proteomics mAbXmise Kits are not available for sale in all countries. For Research Use Only. Not for use in diagnostic procedures.

Establishment of a Sensitive and Reliable Droplet Digital PCR Assay for the Detection of Bursaphelenchus xylophilus.

Pine wilt disease (PWD), which poses a significant risk to pine plantations across the globe, is caused by the pathogenic agent Bursaphelenchus xylophilus, also referred to as the pine wood nematode (PWN). A droplet digital PCR (ddPCR) assay was developed for the quick identification of the PWN in order to improve detection sensitivity. The research findings indicate that the ddPCR assay demonstrated significantly higher analysis sensitivity and detection sensitivity in comparison to traditional quantitative PCR (qPCR). However, it had a more limited dynamic range. High specificity was shown by both the ddPCR and qPCR techniques in the diagnosis of the PWN. Assessments of reproducibility revealed that ddPCR had lower coefficients of variation at every template concentration. Inhibition tests showed that ddPCR was less susceptible to inhibitors. There was a strong linear association between standard template measurements obtained using ddPCR and qPCR (Pearson correlation = 0.9317; p < 0.001). Likewise, there was strong agreement (Pearson correlation = 0.9348; p < 0.001) between ddPCR and qPCR measurements in the evaluation of pine wood samples. Additionally, wood samples from symptomatic (100% versus 86.67%) and asymptomatic (31.43% versus 2.9%) pine trees were diagnosed with greater detection rates using ddPCR. This study's conclusions highlight the advantages of the ddPCR assay over qPCR for the quantitative detection of the PWN. This method has a lot of potential for ecological research on PWD and use in quarantines.

High-energy insights from an escaping coronal mass ejection with Solar Orbiter/STIX observations

Solar eruptive events, including solar flares and coronal mass ejections (CMEs), are typically characterised by energetically significant X-ray emissions from flare-accelerated electrons and hot thermal plasmas. However, the intense brightness of solar flares often overshadows high-coronal X-ray emissions from the associated eruptions due to the limited dynamic range of current instrumentation. Occulted events, where the main flare is blocked by the solar limb, provide an opportunity to observe and analyse the X-ray emissions specifically associated with CMEs. This study investigates the X-ray and extreme ultraviolet (EUV) emissions associated with a large filament eruption and CME that occurred on February 15, 2022. This event was highly occulted from the three vantage points of Solar Orbiter (sim 45$^ circ $ behind the limb), Solar–TErrestrial RElations Observatory (STEREO-A), and Earth. We utilised X-ray observations from the Spectrometer/Telescope for Imaging X-rays (STIX) and EUV observations from the Full Sun Imager (FSI) of the Extreme Ultraviolet Imager (EUI) on board Solar Orbiter, supplemented by multi-viewpoint observations from STEREO-A/Extreme-UltraViolet Imager (EUVI). This enabled a comprehensive analysis of the X-ray emissions in relation to the filament structure observed in the EUV. We used STIX's imaging and spectroscopy capabilities to characterise the X-ray source associated with the eruption. Our analysis reveals that the X-ray emissions associated with the occulted eruption originate from an altitude exceeding 0.3 \(R_ above the main flare site. The X-ray time profile shows a sharp increase and exponential decay, and consists of both a hot thermal component at 17pm 2 MK and non-thermal emissions ($&gt;11.4 keV) characterised by an electron spectral index of 3.9pm 0.2. Imaging analysis shows an extended X-ray source that coincides with the EUV emission as observed from EUI, and was imaged until the source grew to a size exceeding the STIX imaging limit (180 arcsec). Filament eruptions and associated CMEs have hot and non-thermal components, and the associated X-ray emissions are energetically significant. Our findings demonstrate that STIX combined with EUI provides a unique and powerful tool for examining the energetic properties of the CME component of solar energetic eruptions. Multi-viewpoint and multi-instrument observations are crucial for revealing such energetically significant sources in solar eruptions that might otherwise remain obscured.

Defect-engineered monolayer MoS2 with enhanced memristive and synaptic functionality for neuromorphic computing

Two-dimensional transition metal dichalcogenides (TMDs)-based memristors are promising candidates for realizing artificial synapses in next-generation computing. However, practical implementation faces several challenges, such as high non-linearity and asymmetry in synaptic weight updates, limited dynamic range, and cycle-to-cycle variability. Here, utilizing optimal-power argon plasma treatment, we significantly enhance the performance matrix of memristors fabricated from monolayer MoS2. Our approach not only improves linearity and symmetry in synaptic weight updates but also increases the number of available synaptic weight updates and enhances Spike-Time Dependent Plasticity. Notably, it broadens the switching ratio by two orders, minimizes cycle-to-cycle variability, reduces non-linear factors, and achieves an energy consumption of ~30 fJ per synaptic event. Implementation of these enhancements is demonstrated through Artificial Neural Network simulations, yielding a learning accuracy of ~97% on the MNIST hand-written digits dataset. Our findings underscore the significance of defect engineering as a powerful tool in advancing the synaptic functionality of memristors.

The simplicity of protein sequence-function relationships

How complex are the rules by which a protein’s sequence determines its function? High-order epistatic interactions among residues are thought to be pervasive, suggesting an idiosyncratic and unpredictable sequence-function relationship. But many prior studies may have overestimated epistasis, because they analyzed sequence-function relationships relative to a single reference sequence—which causes measurement noise and local idiosyncrasies to snowball into high-order epistasis—or they did not fully account for global nonlinearities. Here we present a reference-free method that jointly infers specific epistatic interactions and global nonlinearity using a bird’s-eye view of sequence space. This technique yields the simplest explanation of sequence-function relationships and is more robust than existing methods to measurement noise, missing data, and model misspecification. We reanalyze 20 experimental datasets and find that context-independent amino acid effects and pairwise interactions, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of phenotypic variance and over 92% in every case. Only a tiny fraction of genotypes are strongly affected by higher-order epistasis. Sequence-function relationships are also sparse: a miniscule fraction of amino acids and interactions account for 90% of phenotypic variance. Sequence-function causality across these datasets is therefore simple, opening the way for tractable approaches to characterize proteins’ genetic architecture.

DNIM: Deep-sea netting intelligent enhancement and exposure monitoring using bio-vision

Intelligent monitoring of deep-sea nets is affected by light attenuation, light scattering, and limited dynamic range factors of the camera, which can cause color shift, low visibility, and over/underexposure in monitoring, resulting in reduced farming efficiency, and misdetection of biological behavior. Therefore, we propose a method for underwater net tank scene enhancement and exposure using multicolor space and bio-vision, called DNIM. Specifically, we first propose an intelligent enhancement and exposure monitoring method for deep-sea nets using bio-vision, which eliminates the effect of exposure on underwater imaging by introducing a bio-vision system that uses a brightness transformation function to generate a series of exposure images. Inspired by the image fusion method, we designed a color correction and visibility recovery method using bio-vision. In the Lab color model, color shifts in bio-vision are corrected by guiding feature transformations with contrast-constrained adaptive histogram equalization; in the RGB color model, depth information is recovered by creating a linear model for the depth of field of underwater blurred images based on the characteristics of the bio-vision system. To obtain more qualified results, we also propose an efficient perceptual fusion module to mix the output of visibility recovery and color correction. Finally, we propose two deep-sea net tank culture datasets: salmon bait-eating behavior dataset (SBBD) and salmon non-bait-eating behavior dataset (SNBD). By comparing the experimental analysis in four real scenario datasets, our DNIM achieved superior results compared to 16 advanced underwater enhancement methods. The code is publicly available at: https://github.com/An-Shunmin/DNIM.

Effects of salinity on nitrogen reduction pathways in estuarine wetland sediments

Denitrification, anammox, and DNRA are three important nitrogen (N) reduction pathways in estuarine sediments. Although salinity is an important variables controlling microbial growth and activities, knowledge about the effects of changing salinity on those three processes in estuarine and coastal wetland sediments are not well understood. Herein, we performed a 60-d microcosms experiment with different salinities (0, 5, 15, 25 and 35 ‰) to explore the vital role of salinity in controlling N-loss and N retention in estuarine wetland sediments. The results showed that sediment organic matter, sulfide, and nitrate (NO3−) were profoundly decreased with increasing salinity, while sediment ammonium (NH4+) and ferrous (Fe2+) varied in reverse patterns. Meanwhile, N-loss and N retention rates and associated gene abundances were differentially inhibited with increasing salinity, while the contributions of denitrification, anammox, and DNRA to total nitrate reduction were apparently unaffected. Moreover, denitrification rate was the most sensitive to salinity, and then followed by DNRA, while anammox was the weakest among these three processes. In other words, anammox bacteria showed a wide range of salinity tolerance, while both denitrification and DNRA reflected a relatively limited dynamic range of it. Our findings could provide insights into temporal interactive effects of salinity on sediment physico-chemical properties, N reduction rates and associated gene abundances. Our findings can improve understanding of the effects of saltwater incursion on the N fate and N balance in estuarine and coastal sediments.

Bayesian multi-exposure image fusion for robust high dynamic range ptychography.

The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times, spanning from under to overexposure and fusing them into a single HDR image. The conventional MEF technique in ptychography typically involves subtracting the background noise, ignoring the saturated pixels and then merging the acquisitions. However, this approach is inadequate under conditions of low signal-to-noise ratio (SNR). Additionally, variations in illumination intensity significantly affect the phase retrieval process. To address these issues, we propose a Bayesian MEF modeling approach based on a modified Poisson distribution that takes the background and saturation into account. The expectation-maximization (EM) algorithm is employed to infer the model parameters. As demonstrated with synthetic and experimental data, our approach outperforms the conventional MEF method, offering superior phase retrieval under challenging experimental conditions. This work underscores the significance of robust multi-exposure image fusion for ptychography, particularly in imaging shot-noise-dominated weakly scattering specimens or in cases where access to HDR detectors with high SNR is limited. Furthermore, the applicability of the Bayesian MEF approach extends beyond CDI to any imaging scheme that requires HDR treatment. Given this versatility, we provide the implementation of our algorithm as a Python package.

Quality assessment for multi-exposure fusion light field images with dynamic region segmentation

The luminance dynamic range of real scenes can reach up to nine orders, whereas the existing light field camera captures light field images (LFIs) with a limited luminance dynamic range of about two orders. Although LFIs can be enhanced using multi-exposure fusion (MEF) technologies, this process introduces various distortions that affect human visual perception. Therefore, it is crucial to develop an effective quality assessment tool for multi-exposure fusion light field images (MEF-LFIs). Starting from the complex distortion characteristics generated by MEF with dynamic scenes, this paper proposes an MEF-LFI quality metric with dynamic region segmentation. The proposed metric consists of three main modules. The first module focuses on spatial feature extraction and incorporates a dynamic region segmentation scheme to address artifacts in MEF-LFIs. The second module utilizes Shearlet transform for spectrum feature extraction to characterize detail loss. Additionally, the third module addresses distortion in the angular domain of MEF-LFIs through angular feature extraction using epipolar plane images and tensor decomposition. Finally, the quality of the MEF-LFIs is evaluated by integrating spatial features, spectrum features and angular features into feature vectors which are then inputted into a regression model. The experimental results show that the proposed metric is superior to the representative quality metrics and has better consistency with the human visual perception.

Investigating and Engineering an 1,2-Propanediol-Responsive Transcription Factor-Based Biosensor.

Transcription factor (TF)-based biosensors have arisen as powerful tools in the advancement of metabolic engineering. However, with the emergence of numerous bioproduction targets, the variety of applicable TF-based biosensors remains severely limited. In this study, we investigated and engineered an 1,2-propanediol (1,2-PD)-responsive transcription activator, PocR, from Salmonella typhimurium to enrich the current biosensor repertoire. Heterologous characterization of PocR in E. coli revealed a significantly limited operational range and dynamic range, primarily attributed to the leaky binding between PocR and its corresponding promoters in the absence of the 1,2-PD inducer. Promiscuity characterization uncovered the minor responsiveness of PocR toward glycerol and 1,2-butanediol (1,2-BD). Using AlphaFold-predicted structure and protein mutagenesis, we preliminarily explored the underlying mechanism of PocR. Based on the investigated mechanism, we engineered a PcoR-F46R/G105D variant with an altered inducer specificity to glycerol, as well as a PocR-ARE (Q107A/S192R/A203E) variant with nearly a 4-fold higher dynamic range (6.7-fold activation) and a 20-fold wider operational range (0-20 mM 1,2-PD). Finally, we successfully converted PocR to a repressor through promoter engineering. Integrating the activation and repression functions established a versatile 1,2-PD-induced bifunctional regulation system based on PocR-ARE. Our work showcases the exploration and exploitation of an underexplored type of transcriptional activator capable of recruiting RNA polymerase. It also expands the biosensor toolbox by providing a 1,2-PD-responsive bifunctional regulator and glycerol-responsive activator.

Optimizing immunofluorescence with high-dynamic-range imaging to enhance PD-L1 expression evaluation for 3D pathology assessment from NSCLC tumor tissue

Assessing programmed death ligand 1 (PD-L1) expression through immunohistochemistry (IHC) is the golden standard in predicting immunotherapy response of non-small cell lung cancer (NSCLC). However, observation of heterogeneous PD-L1 distribution in tumor space is a challenge using IHC only. Meanwhile, immunofluorescence (IF) could support both planar and three-dimensional (3D) histological analyses by combining tissue optical clearing with confocal microscopy. We optimized clinical tissue preparation for the IF assay focusing on staining, imaging, and post-processing to achieve quality identical to traditional IHC assay. To overcome limited dynamic range of the fluorescence microscope’s detection system, we incorporated a high dynamic range (HDR) algorithm to restore the post imaging IF expression pattern and further 3D IF images. Following HDR processing, a noticeable improvement in the accuracy of diagnosis (85.7%) was achieved using IF images by pathologists. Moreover, 3D IF images revealed a 25% change in tumor proportion score for PD-L1 expression at various depths within tumors. We have established an optimal and reproducible process for PD-L1 IF images in NSCLC, yielding high quality data comparable to traditional IHC assays. The ability to discern accurate spatial PD-L1 distribution through 3D pathology analysis could provide more precise evaluation and prediction for immunotherapy targeting advanced NSCLC.