Light Fluctuations Research Articles

Mortality patterns and recovery challenges in Millepora alcicornis after mass bleaching event on Northeast Brazilian reefs

Coral reefs are suffering globally from the increased frequency and intensification of thermal anomalies, caused by anthropogenic climate change, leading to major mass bleaching events over the past three decades. Environmental factors, including temperature, geomorphology, interspecific competition, protection status and local settings, can modulate the severity of bleaching and the subsequent survival capacity of corals and hydrocorals after mass bleaching events. However, the complexity of environmental factors interacting over fine-scale spatial-temporal scales is still a major gap in understanding coral bleaching events of South Atlantic reefs. Here, we examined mortality and recovery patterns of the predominant hydrocoral species Millepora alcicornis after a mass bleaching event at the Northeastern coast of Brazil in 2019–2020. The ecological impact was evaluated by analyzing spatial factors, coral morphology, protection status and mortality rates in combination with the subsequent recovery potential influenced by overgrowth competition of dominant benthic organisms. The results indicate that hydrocorals located in proximity to the shore and shallow depths were more vulnerable with mortality rates of up to 90%, presumably related to higher light and temperature fluctuations. A total coral cover loss of approx. 50% was estimated for M. alcicornis within the study area and dead skeletons were overgrown by algal turfs and crustose coralline algae with the former being the predominant colonizer. In summary, our findings reveal fin-scale heterogeneous spatial vulnerability within the same coastal reef complex, indicating zones of high coral mortality. The described heterogeneous spatial vulnerability of the studied reef complex is an important factor to be considered in coral reef restauration and management plans to secure coral ecosystem services for the coming decades.

Illuminating microbial mat assembly: Cyanobacteria and Chloroflexota cooperate to structure light-responsive biofilms.

Microbial mats are stratified communities often dominated by unicellular and filamentous phototrophs within an exopolymer matrix. It is challenging to quantify the dynamic responses of community members in situ as they experience steep gradients and rapid fluctuations of light. To address this, we developed a binary consortium using two representative isolates from hot spring mats: the unicellular oxygenic phototrophic cyanobacterium Synechococcus OS-B' (Syn OS-B') and the filamentous anoxygenic phototroph Chloroflexus MS-CIW-1 (Chfl MS-1). We quantified the motility of individual cells and entire colonies and demonstrated that Chfl MS-1 formed bundles of filaments that moved in all directions with no directional bias to light. Syn OS-B' was slightly less motile but exhibited positive phototaxis. This binary consortium displayed cooperative behavior by moving further than either species alone and formed ordered arrays where both species aligned with the light source. No cooperative motility was observed when a non-motile pilB mutant of Syn OS-B' was used instead of Syn OS-B'. The binary consortium also produced more adherent biofilm than individual species, consistent with the close interspecies association revealed by electron microscopy. We propose that cyanobacteria and Chloroflexota cooperate in forming natural microbial mats, by colonizing new niches and building robust biofilms.

Frequency shift caused by a magnetic field and light intensity inhomogeneity in an optically detected clock.

We present a novel, to the best of our knowledge, frequency shift mechanism in the optically detected atomic clock. This frequency shift is analogous to the light shift that is associated with a detecting light power. However, this shift arises from the inhomogeneity of the magnetic field (C-field) and the detecting light intensity. We call this shift the "pseudo-light shift" (p-LS). This shift allows the correlation between the clock output frequency and the detecting light power to switch between positive and negative, depending on the magnetic field. The mechanism is described and experimentally validated in our cesium beam clock through two experiments. The study of this frequency shift can enhance the accuracy of light shift assessments in atom-laser interaction systems and suppress long-term stability deterioration caused by the light power fluctuation.

Have We Selected for Higher Mesophyll Conductance in Domesticating Soybean?

Soybean (Glycine max) is the single most important global source of vegetable protein. Yield improvements per unit land area are needed to avoid further expansion onto natural systems. Mesophyll conductance (gm) quantifies the ease with which CO2 can diffuse from the sub-stomatal cavity to Rubisco. Increasing gm is attractive since it increases photosynthesis without increasing water use. Most measurements of gm have been made during steady-state light saturated photosynthesis. In field crop canopies, light fluctuations are frequent and the speed with which gm can increase following shade to sun transitions affects crop carbon gain. Is there variability in gm within soybean germplasm? If so, indirect selection may have indirectly increased gm during domestication and subsequent breeding for sustainability and yield. A modern elite cultivar (LD11) was compared with four ancestor accessions of Glycine soja from the assumed area of domestication by concurrent measurements of gas exchange and carbon isotope discrimination (∆13C). gm was a significant limitation to soybean photosynthesis both at steady state and through light induction but was twice the value of the ancestors in LD11. This corresponded to a substantial increase in leaf photosynthetic CO2 uptake and water use efficiency.

Dual-Responsive Antibacterial Hydrogel Patch for Chronic-Infected Wound Healing.

Bacterial infections in chronic wounds, such as bedsores and diabetic ulcers, present significant healthcare challenges. Excessive antibiotic use leads to drug resistance and lacks precision for targeted wound treatment. Our study introduces an innovative solution: a near-infrared (NIR) and pH dual-responsive hydrogel patch incorporating regenerated silk fibroin (RSF) and molybdenum dioxide (MoO2) nanoparticles (NPs), offering enhanced mechanical properties, precise drug release, and superior antibacterial efficacy. The dual-responsive hydrogel patch allows for precise control over antibiotic release triggered by NIR light and pH fluctuations, enabling tailored treatment for infected wounds. First, the pH-responsive characteristic matches the alkaline environment of the infected wound, ensuring on-demand antibiotic release. Second, NIR exposure accelerates antibiotic release, enhancing wound healing and providing additional antibacterial effects. Additionally, the patch further blocks bacterial infection, promotes wound repair, and degrades in sync with the healing process, further bolstering the efficacy against wound infections.

Rapid wavelength interval selection technology for multi-channel intensity-interrogation surface plasmon resonance imaging sensing

Intensity-interrogation surface plasmon resonance (I-SPR) provides the fastest imaging speed among various SPR techniques, including wavelength, phase, and angle-based detection. It also integrates easily with other devices and has substantial practical application potential. However, I-SPR's reliance on direct detection of reflected light intensity renders it sensitive to light source fluctuations and detector dark noise, and its dynamic range is relatively limited. To address these challenges, we have developed a multi-channel I-SPR sensing platform featuring rapid wavelength interval selection technology. This system optimizes the sensing wavelength according to the refractive indices of different samples, mitigating issues related to inconsistent SPR signals from various biomolecules. Our improved I-SPR technology enables high-throughput biomolecular detection with a refractive index range size of 2.035 × 10−2 for dynamic monitoring, reaching a leading role among the existing I-SPR technologies. This represents the widest linear dynamic range for I-SPR to date. Experimental results on protein binding kinetic constants KD are consistent with those obtained from commercially available instruments. We believe that this study is expected to accelerate the development of SPR technology toward broader biological applications.

Resonant Cell-Based 1f Photoacoustic Gas Analyzer Immune to Light Power Fluctuation and Frequency Mismatch.

To overcome the light power fluctuation and frequency mismatch in photoacoustic spectroscopy (PAS), we proposed a self-corrected 1f-only resonant cell-based PAS (1f-RCPAS) gas analyzer. Based on the theoretical analysis of the 1f signal, a signal processing algorithm considering laser power-current nonlinearity is proposed. The 1f-only algorithm is well-tailored for the resonant systems, requiring no time-division multiplexing. The algorithm is further improved to extend the dynamic range. The T-type resonant cell incorporating a graphene sticker is utilized for effectively amplifying the acoustic signals from both the gas and solid to achieve normalization. No optical path alignment is needed. For the low resonance frequency, a digital orthogonal-vector lock-in amplifier is used, further simplifying the system setup. The gas analyzer is used to measure methane (CH4) with the near-infrared absorption peak at 1651 nm. The experiments demonstrated immunity to fiber coupling loss, laser power drift over time, and frequency mismatch caused by property differences between air and standard gases. The R2 value in the concentration calibration reaches 0.99995, and the minimum detection limit given by the Allan variance reaches 3.5 ppb at an average time of 105 s.

Industrial Product Surface Defect Detection Using CNN: A Deep Learning Approach

Defect identification on surfaces of industrial products is a thought-provoking issue that has garnered significant attention. Defect identification of industrial products plays a primordial role in ensuring quality and reliability of products at all levels of the manufacturing process. The traditional approach relies fundamentally on the human inspection, which was unreliable and inefficient. Moreover, human inspection cannot meet the high standards for real-time detection required in industrial production situations. While methods used in image processing can solve difficult modules, they are not meant to deal with complicated ambient textures, noise, or lighting fluctuations. Innovative techniques have so been used by researchers and practitioners to improve and expedite the defect diagnosis process. This study classifies the product as "defect or not defect." and provides an intelligent approach for surface defect identification using convolutional neural network (CNN). The model demonstrates strong performance in identifying many types of industrial product defects, such as "crack, patch, inclusion, rolled, pitting, and scratching," by utilizing deep learning approaches. This involved training the model with several datasets featuring surface textures. The results were indicative of better performance, where the proposed method achieved outstanding precision in fault detection.

Metabolomics of related C3 and C4 Flaveria species indicate differences in the operation of photorespiration under fluctuating light.

C3 photosynthesis can be complemented with a C4 carbon concentrating mechanism (CCM) to minimize photorespiratory losses. C4 photosynthesis is often more efficient than C3 under steady-state conditions. However, the C4 CCM depends on inter-cellular metabolite concentration gradients, which must increase following increases in light intensity and could decrease rates of C4 photosynthesis under fluctuating light. Additionally, incomplete flux through photorespiration could prove beneficial to C4 assimilation during light induction of the CCM. Here, we compare metabolic profiles in the closely related C3 Flaveria robusta and C4 Flaveria bidentis during a light transient from low to high light to determine if these non-steady state accumulation patterns provide insight to the induction of the metabolite gradients needed to drive C4 intermediate transport and if there is incomplete cycling of photorespiratory intermediates. In these C3 and C4 species, metabolite steady-state pool sizes suggest that C4 transport acids maintain concentration gradients across the bundle sheath and mesophyll cell types under these light fluctuations. However, there was incomplete flux through photorespiration in the C4 F. bidentis, which could reduce photorespiratory CO2 loss via glycine decarboxylation and help maintain higher rates of assimilation during following induction periods.

Current Progress of EUV Spectral Responsivity Calibration for EUV Lithography at CMS/ITRI

Abstract To support the development of advanced EUV lithography for the semiconductor industry in Taiwan, a calibration system for photodiodes’ spectral responsivity was established. The current system utilizes the synchrotron radiation light source and the method is traceable to PTB, Germany. The wavelength range is from 10 nm to 15 nm, including the most often used 13.5 nm. Several techniques were studied to compensate for light source fluctuation and to reduce the measurement uncertainty. The relative expanded uncertainty of the spectral responsivity calibration at 13.5 nm is 4.6 % (k=2). A wafer-type EUV radiant power meter designed to be used in EUV lithography chambers is being developed. Our goal is to develop simple and reliable methods for on-site EUV optical power measurement and dose estimation.

Pain assessment from facial expression images utilizing Statistical Frei-Chen Mask (SFCM)-based features and DenseNet

Estimating pain levels is crucial for patients with serious illnesses, those recovering from brain surgery, and those receiving intensive care etc. An automatic pain intensity estimator is proposed in this study that gathers information about pain and intensity from the user’s expressions. The faces in the database are first cropped using a ‘Chehra’ face detector, which performs well even in wildly uncontrolled environments with a wide range of lighting and position fluctuations. The suggested technique extracts the beneficial and distinct patterns from facial expressions using novel Statistical Frei-Chen Mask (SFCM)-based features and DenseNet-based features. As it offers quick as well as accurate pain identification and pain intensity estimation, the Radial Basis Function Based Extreme Learning Machine (RBF-ELM) is employed for pain recognition and pain intensity level estimation using the characteristics. All the data is kept, updated and protected in the cloud because availability and high-performance decision-making are so important for informing physicians and auxiliary IoT nodes (such as wearable sensors). In addition, cloud computing reduces the time complexity of the training phase of Machine Learning algorithms in situations where it is possible to build a complete cloud/edge architecture by allocating additional computational resources and memory in use. The facial expression images from the UNBC-McMaster Shoulder Pain Expression Archive and 2D face dataset are used to test the proposed method. The measurement of pain intensity uses four stages. When compared to the results from the literature, the proposed work attains enhanced performance.

The glow of axion quark nugget dark matter. Part I. Large scale structures

Axion quark nuggets (AQN) are hypothetical, macroscopically large objects with a mass greater than a few grams and sub-micrometer size, formed during the quark-hadrontransition. Originating from the axion field, they offer a possible resolution of the similarity between visible and dark components of the Universe, i.e. ΩDM ∼ Ωvisible and observed matter-antimatter asymmetry. These composite objects behave as cold dark matter, interacting with ordinary matter and resulting in pervasive electromagnetic radiation throughout the Universe. This work aims to predict the electromagnetic signature in large-scale structures from this AQN-baryon interaction, accounting for thermal and non-thermal radiations.We use Magneticum hydrodynamical simulations to describe the realistic distribution and dynamics of gas and dark matter at cosmological scales. We construct a light cone encompassing a 1.4 square degree area on the sky, extending up to redshift z = 5.4, and we calculate the electromagnetic signature across a wide range of frequencies from radio, starting at ν ∼ 1 GHz, up to a few keV X-ray energies. We find that the AQNs electromagnetic signature is characterized by global (monopole) and fluctuation signals. The amplitude of both signals strongly depends on the average nugget mass and the ionization level of the baryonic environment, allowing us to identify a most optimistic scenario and a minimal configuration. The signal of our most optimistic scenario is often near the sensitivity limit of existing instruments, such as FIRAS in the ν = [100-500] GHz range and the South Pole Telescope for high-resolution ℓ > 4000 at ν = 95 GHz. Fluctuations in the Extra-galactic Background Light caused by the axion quark nuggets in the most optimistic scenario can also be tested with space-based imagers Euclid and James Webb Space Telescope. In general, our minimal configuration is still out of reach of existing instruments, but future experiments might be able to pose some constraints.We conclude that the axion quark nuggets model represents a viable model for dark matter, which does not violate the canons of cosmology nor existing observations. A reanalysis of existing data sets could provide some evidence of axion quark nuggets if our most optimistic configuration is correct. The best chances for testing the model reside in 1) ultra-deep infrared and optical surveys, 2) future experiments to probe the frequency spectrum of the cosmic microwave background, and 3) low-frequency (1 GHz < ν < 100 GHz) and high-resolution (ℓ ≳ 104) observations.

Mathematical Modeling of Refraction Characteristics of Electromagnetic Radiation at Stochastic Gravitational Field

Numerical-analytical modeling is used for estimation of gravitational noise influence on propagation of electromagnetic radiation at gravitational field of a group of astrophysical objects. The method bases on numerical integrate of system of light differential equations in the Euler’s form, added differential equations for calculation of statistic moments of side fluctuations of light at a picture plane of observer. The influence of gravitation was taken into account by introducing of the effective refraction index of vacuum, expressed through a gravitational potential. Visions about spatial correlation function of inhomogeneities of effective refraction index of vacuum are used as model. Calculating of gravitation effects of group astrophysical objects are performed under assumption additive contribution of field of every objects in general gravitational field. Modeling of the refraction characteristics of electromagnetic radiation is performed for a various configurations of gravitation field. Results of calculating are shown at a picture plane of observer. The blurring effects of gravitational lensing are investigated at dependence from position of localized area of gravitational noise and astrophysical objects. It’s showed that for case of single-component gravitational field full coverage of object by area of gravitational noise match maximum blurring of lensing effect. The features of gravitational focusing are determined at dependence from position of area noise for asymmetrical twocomponent gravitational field, where one of objects coverage by area of gravitational noise. Offered tool of modeling can be used for both investigation of gravitational lensing of multi-component gravitational field in presence variety of limited area of gravitational noise and under conditions uniform spatial distribution of stochastic gravitational inhomogeneities. Also it can use for recovering of gravitational potentials of non-radiating (hidden) objects by characteristics of received electromagnetic radiation of remote space sources.

Photorespiratory glycine contributes to photosynthetic induction during low to high light transition

Leaves experience near-constant light fluctuations daily. Past studies have identified many limiting factors of slow photosynthetic induction when leaves transition from low light to high light. However, the contribution of photorespiration in influencing photosynthesis during transient light conditions is largely unknown. This study employs dynamic measurements of gas exchange and metabolic responses to examine the contribution of photorespiration in constraining net rates of carbon assimilation during light induction. This work indicates that photorespiratory glycine accumulation during the early light induction contributes 5–7% to the additional carbon fixed relative to the low light conditions. Mutants with large glycine pools under photorespiratory conditions (5-formyl THF cycloligase and hydroxypyruvate reductase 1) showed a transient spike in net CO2 assimilation during light induction, with glycine buildup accounting for 22–36% of the extra carbon assimilated. Interestingly, levels of many C3 cycle intermediates remained relatively constant in both mutants and wild-type throughout the light induction period where glycine accumulated, indicating that recycling of carbon into the C3 cycle via photorespiration is not needed to maintain C3 cycle activity under transient conditions. Furthermore, our data show that oxygen transient experiments can be used as a proxy to identify the photorespiratory component of light-induced photosynthetic changes.

Untangling light in noisy luminous environments

Electric light sources, including displays and luminaires, often exhibit rapid light fluctuations called temporal light modulation (TLM). TLM can be seen as their electrical footprint. This work demonstrates that TLM can be remotely detected and used to perform in situ photometric and spectroradiometric measurements of very low light levels superimposed to intense backgrounds. Illuminance levels about 500 000 times smaller than the background were reliably measured by lock-in detection, allowing photometric measurements to be performed during daytime, even with sunny conditions. Using a new type of optical lock-in spectrometer, it becomes possible to retrieve spectral quantities through the TLM of the light source. The spectral distribution of an LED lamp was measured against a daylit background more than 1500 times brighter. Lock-in detection is an excellent technique to perform in situ measurements of electric light, even at very low level, in the presence of daylight and other background lights, opening interesting opportunities to characterize anthropogenic light pollution.

Making the most of canopy light: Shade avoidance under a fluctuating spectrum and irradiance.

In the field, plants face constantly changing light conditions caused by both atmospheric effects and neighbouring vegetation. This interplay creates a complex, fluctuating light environment within plant canopies. Shade-intolerant species rely on light cues from competitors to trigger shade avoidance responses, ensuring access to light for photosynthesis. While research often uses controlled growth chambers with steady light to study shade avoidance responses, the influence of light fluctuations in real-world settings remains unclear. This review examines the dynamic light environments found in woodlands, grasslands, and crops. We explore how plants respond to some fluctuations but not others, analyse the potential reasons for these differences, and discuss the possible molecular mechanisms regulating this sensitivity. We propose that studying shade avoidance responses under fluctuating light conditions offers a valuable tool to explore the intricate regulatory network behind them.

In situ monitoring and unveiling of [formula omitted] ions transmembrane process via sensitive fiber-optic plasmonic spectral combs

Potassium ions (K+) transmembrane transport is essential for regulating various physiological processes. Herein, a label-free tilted fiber Bragg grating (TFBG)-assisted plasmonic sensor was proposed and experimentally demonstrated for measuring K+ transmembrane movement in situ and continuously. The sensor was coated with valinomycin-modified vesicular liposome, providing an exceptional capability for selectively capturing and transporting K+ through the liposome’s bilayer membrane. A differential demodulation method was proposed, by which great advancements were achieved in unveiling K+ transmembrane process showing three stages in real time. In particular, not only does the sensor sensitively detect low K+ level (detection limit down to 8.5 pM) but also successively decouple bulk and surface effects involved in transmembrane dynamics, along with additional advantage of resistance to temperature and light power fluctuations. These advancements represent great progress in revealing K+ transmembrane movement and highlight the potential for monitoring transmembrane-related cellular signaling pathways like cell proliferation and apoptosis.

Cyclic Electron Flow Alleviates the Stress of Light Fluctuation on Soybean Photosynthesis

Crops often face light intensity fluctuations in natural settings. Intercropping is widely used to improve crop yield and resource utilization worldwide, but crops suffer from high-frequency and high-intensity light fluctuations due to mutual crop influence. Soybean is an important legume crop and is often intercropped with other crops, but little is known about soybean’s response to light fluctuation environments. Herein, three fluctuation frequencies (1, 10, and 20 min/cycle) were used to analyze soybean photosynthesis responses by measuring leaf growth, chlorophyll content, gas exchange, and electron transfer. Our data revealed that faster fluctuation frequencies led to the stronger suppression of soybean morphology and photosynthesis, with significant reductions of 31.31% and 21.58%, respectively. Damage to photosystems II (PSII) and I (PSI) also intensified, with significant decreases of 18.52% and 18.38% in their effective quantum yields Y(II) and Y(I). Additionally, increased fluctuation frequency exacerbated the consumption of the plastoquinone pool and linear electron flow but enhanced the cyclic electron flow across the thylakoid membrane and, thus, increased heat dissipation in PSII. Our findings indicate that an increased fluctuation frequency inflicted more severe damage on the soybean photosynthesis system. However, PSI-enhanced CEF improved NPQ and coordinated photoprotection to some extent.

LRFE: A NOVEL LOCAL RESPONSE FEATURE ELIMINATION PROCESS FOR IDENTIFICATION OF LUNG CANCER CELLS

One of the main causes of cancer-related mortality across the globe is lung cancer. Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

Decoding Emotions: Intelligent visual perception for movie image classification using sustainable AI in entertainment computing

Affective computing researchers have been studying emotion recognition (ER) based on real-world facial photos and videos for a while now, and it’s a very popular area. With the sounds produced by head posture, facial deformation, and lighting fluctuation, ER is still difficult to execute in the wild. This research employs a machine learning model for sustainable artificial intelligence (AI) in entertainment computing to perform emotion decoding for movie picture categorization. Emotion labels are produced for every video sample by the suggested model, which takes video data as input. First, we employ a face identification and selection process based on the video data to identify the most consequential face areas. Here, facial expressions from films have been gathered as input pictures, which have then been processed for noise reduction and normalisation. Then, in order to analyse the facial expressions in this image, it was segmented using the fuzzy K-means equalisation clustering model. Convolutional adversarial U-net graph neural networks have been used to classify the studied pictures for emotion decoding. Several movie-based emotion datasets are subjected to experimental analysis in order to determine accuracy, precision, recall, F-1 score, RMSE, and AUC. With excellent classification accuracy, the suggested deep learning paradigm shows promise in identifying the emotional shifts in gamers. The suggested method achieved 97% accuracy, 96% precision, 92% recall, 85% F-1 score, 79% RMSE, and 86% AUC.