Changing Light Conditions Research Articles

A smartphone-based innovative approach to discriminate red pigments in roman frescoes mock-ups

• Frescoes replicas using red pigments were prepared to develop a smartphone-based methodological approach. • Colour properties were measured by spectrocolorimeter and smartphone. • Chemometric classificatory methods were used to identify red pigments colors. • Low classification errors were obtained employing LDA and SVC classifying methods. • A new and easy approach employing mobile phones to discriminate red pigments is presented. The characterization of red pigments in frescoes wall paintings has been of great interest for researchers to better understand raw material procurement dynamics, pigment receipts, stylistic evolution and to assess their conservation state. In this study a non-destructive colorimetric approach implementing a smartphone-based method was developed in order to be able to distinguish between three pigments made from minium, haematite and cinnabar minerals, and also mixed pigments, preparing frescoes mock-ups following the roman receipt described by Vitruvius. Portable FT-IR, Raman spectroscopy, portable XRF and visible reflectance spectra analyses were carried out as reference methods for smartphone colorimetry results validation. Employing a reference colour sheet to control changing lighting conditions, different chemometric approaches have been developed and tested, cross-referencing standard analytical results with the data obtained by smartphone. Overall, using only colour parameters from the smartphone, a Linear Discriminant Analysis and a Support Vector Classifier were tested to efficiently classify each sample based on the red pigment used, with low prediction errors. This work shows the potential of smartphones as cheap, fast and user-friendly analytical devices for the screening of frescoes, and as a prior selective step before carrying out further more expensive and specialized analyses.

Planetary sleep medicine: Studying sleep at the individual, population, and planetary level.

Circadian rhythms are a series of endogenous autonomous oscillators that are generated by the molecular circadian clock which coordinates and synchronizes internal time with the external environment in a 24-h daily cycle (that can also be shorter or longer than 24 h). Besides daily rhythms, there exist as well other biological rhythms that have different time scales, including seasonal and annual rhythms. Circadian and other biological rhythms deeply permeate human life, at any level, spanning from the molecular, subcellular, cellular, tissue, and organismal level to environmental exposures, and behavioral lifestyles. Humans are immersed in what has been called the "circadian landscape," with circadian rhythms being highly pervasive and ubiquitous, and affecting every ecosystem on the planet, from plants to insects, fishes, birds, mammals, and other animals. Anthropogenic behaviors have been producing a cascading and compounding series of effects, including detrimental impacts on human health. However, the effects of climate change on sleep have been relatively overlooked. In the present narrative review paper, we wanted to offer a way to re-read/re-think sleep medicine from a planetary health perspective. Climate change, through a complex series of either direct or indirect mechanisms, including (i) pollution- and poor air quality-induced oxygen saturation variability/hypoxia, (ii) changes in light conditions and increases in the nighttime, (iii) fluctuating temperatures, warmer values, and heat due to extreme weather, and (iv) psychological distress imposed by disasters (like floods, wildfires, droughts, hurricanes, and infectious outbreaks by emerging and reemerging pathogens) may contribute to inducing mismatches between internal time and external environment, and disrupting sleep, causing poor sleep quantity and quality and sleep disorders, such as insomnia, and sleep-related breathing issues, among others. Climate change will generate relevant costs and impact more vulnerable populations in underserved areas, thus widening already existing global geographic, age-, sex-, and gender-related inequalities.

COMPARISON OF METHODS FOR AUTOMATIC LICENSE NUMBER RECOGNITION

The paper is devoted to the problem of automatic detection and recognition of license plates, the solution of which has many potential applications, from security to traffic management. The purpose of this work was to compare the methods of finding and recognizing car number plates, based on the application of deep learning algorithms, which takes into account different regional standards of car number plates, video quality, different speeds of vehicles, the location of the camera in relation to the vehicle license plate, defects of the car number plate (pollution , deformation), as well as changes in external lighting conditions. The advantages and disadvantages of localization and segmentation of car number plates on cars using image binarization, Viola–Jones and Harr methods are given. It was determined that adaptive approaches are better due to the possibility of compensating the impact of obstacles on different areas of the image, for example, the distribution of shadows due to the heterogeneity of illumination. It was determined that many methods in real algorithms rely directly or indirectly on the presence of number limits. Even if the limits are not used when the number is determined, they have the possibility to be used for further analysis. The methods of templates, image histograms, and contour analysis were compared to identify familiar features in the image (segmentation). It is shown that an effective approach for recognition of car license plates can be based on the application of the methods of Viola-Jones, Harr, the analysis of brightness histograms and the SVM method. Formulated conclusions on the effectiveness of the implementation of each of the procedures were confirmed as a result of conducting experiments with the developed software in the python 3 language using the cv2 computer vision library. The described approach makes it possible to obtain a fairly high recognition accuracy at different angles of rotation of the license plate relative to the camera. Keywords: automatic recognition, license plates, localization, normalization, segmentation, character recognition.

Improved Combined Metric for Automatic Quality Assessment of Stitched Images

An automatic quality assessment of stitched images is an essential task in image analysis and is particularly useful not only in the creation of general-purpose panoramic images but also in terrain exploration and mapping made by mobile robots and drones. In Visual Simultaneous Localization and Mapping (VSLAM) solutions, the environment maps acquired by cameras mounted on the mobile robots may be captured in dynamically changing lighting conditions and subject to some other distortions influencing the final quality of the panoramic images representing the robot’s surroundings. Such images may also be used for motion planning and visual navigation for other robots, e.g., in follow-the-leader scenarios. Another relevant application area of panoramic imaging is Virtual Reality (VR), particularly head-mounted displays, where perceived image quality is even more important. Hence, automatic quality evaluations of stitched images should be made using algorithms that are both sensitive to various types of distortions and strongly consistent with subjective quality impression. The approach presented in this paper extends the state-of-the-art metric known as the Stitched Image Quality Evaluator (SIQE) by embedding it with some additional features using the proposed new combination scheme. The developed combined metric based on a nonlinear combination of the SIQE and additional features led to a substantially higher correlation with the subjective quality scores.

Understanding the depth limit of the seagrass Cymodocea nodosa as a critical transition: Field and modeling evidence

Changes in light and sediment conditions can sometimes trigger abrupt regime shifts in seagrass meadows resulting in dramatic and unexpected die-offs of seagrass. Light attenuates rapidly with depth, and in seagrass systems with non-linear behaviours, can serve as a sharp boundary beyond which the meadow transitions to bare sand. Determining system behaviour is therefore essential to ensuring resilience is maintained and to prevent stubborn critical ecosystem transitions caused by declines in water quality. Here we combined field and modelling studies to explore the transition from meadow to bare sand in the seagrass Cymodocea nodosa at the limit of its depth distribution in a shallow, light-limited bay. We first describe the relationship between light availability and seagrass density along a depth gradient in an extensive unfragmented meadow (Alfacs bay, NE Spain). We then develop a simple mechanistic model to characterise system behaviour. In the field, we identified sharp decline in shoot density beyond a threshold of ∼1.9 m depth, shifting from a vegetated state to bare sand. The dynamic population model we developed assumes light-dependent growth and an inverse density-dependent mortality due to facilitation between shoots (mortality rate decreases as shoot density increases). The model closely tracked our empirical observations, and both the model and the field data showed signs of bistability. This strongly suggests that the depth limit of C. nodosa is a critical transition driven by photosynthetic light requirements. While the mechanisms still need to be confirmed with experimental evidence, recognizing the non-linear behaviour of C. nodosa meadows is vital not only in improving our understanding of light effects on seagrass dynamics, but also in managing shallow-water meadows. Given the shallow threshold (<2m), light-limited systems may experience significant and recalcitrant meadow retractions with even small changes in sediment and light conditions. Understanding the processes underlying meadow resilience can inform the maintenance and restoration of meadows worldwide.

Object-Plane Co-Represented and Graph Propagation-Based Semantic Descriptor for Relocalization

Relocalization is a critical component of robotics applications, it poses challenges due to changes in lighting conditions, weather, and viewing point. Image feature-based approaches are appearance-sensitive, high-level semantic landmark-based methods are ambiguous, and topological map matching-based methods are not robust enough among available solutions. We propose a highly robust and highly expressive semantic descriptor for graph matching. Specifically, we begin by introducing an object-plane co-represented topological graph and a graph propagation algorithm to formulate descriptors for high-level landmarks; we then solve graph matching using the sKM algorithm. Finally, we develop a relocalization system that combines semantic objects and geometric planes for pose optimization and conducts experimental validation on various datasets. Experimental results demonstrate that the suggested method remains effective even when the viewpoint changes by more than 80 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> on the 2D-3D-S dataset, and the mean orientation error is less than 5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> on the sceneNN dataset.

Classifying Sparse Vegetation in a Proglacial Valley Using UAV Imagery and Random Forest Algorithm

Extreme hydro-meteorological events become an increasing risk in high mountain environments, resulting in erosion events that endanger human infrastructure and life. Vegetation is known to be an important stabilizing factor; however, little is known about the spatial patterns of species composition in glacial forelands. This investigation aims to differentiate sparse vegetation in a steep alpine environment in the Austrian part of the Central Eastern Alps using low-cost multispectral cameras on an unmanned aerial vehicle (UAV). Highly resolved imagery from a consumer-grade UAV proved an appropriate basis for the SfM-based modeling of the research area as well as for vegetation mapping. Consideration must be paid to changing light conditions during data acquisition, especially with multispectral sensors. Different approaches were tested, and the best results were obtained using the Random Forest (RF) algorithm with the target class discrimination based on the RGB orthomosaic and the DEM as supplementary dataset. Our work contributes to the field of biogeomorphic research in proglacial areas as well as to the field of small-scale remote sensing and vegetation measuring. Our findings show that the occurrence of vegetation patches differs in terms of density and diversity within this relatively recent deglaciated environment.

Influence of Different Illuminants on Spectrophotometric Tooth Color Determination

Statement of problem: The vast majority of studies address the comparison of visual and instrumental tooth color determination. Although, the lighting conditions of the environment may affect the clinical shade matching performance. This study evaluated if and how different light sources can influence the spectrophotometric determination of natural tooth color. Purpose: The purpose of this study was to evaluate the precision of the spectrophotometer in tooth shade determination under the influence of different illuminants. Material and method: Three examiners, determined the shade of an upper central incisor with Vitapan 3D-Master (VITA Zahnfabrik, Bad Säckingen, Germany) and with the spectrophotometer (Vita Easyshade V, Vita Zahnfabrik, Bad Säckingen, Germany). A special fabricated device with eight light sources, designed to offer reproductible lighting conditions, was used to determine the efficiency of instrumental color determination under different illuminants. Results: Natural light (300 lux) and dental unit light at full intensity (32500 lux) produced the less ∆Eab modification. Color coordinates determined in the presence of halogen light (1800 lux), neon ambient light (3000 lux), LED with cold light (21200 lux), halogen light with neon ambient light (23400 lux), both warm and cold LED simultaneously (36000 lux) and all eight light sources of the device (44000 lux), suffered a slight modification of ∆E ab, which does not exceed the acceptability and the perceptibility threshold. The illuminant that caused the biggest modifications in color coordinates was LED warm light (1940 lux) with a ∆Eab 5.804 ± 0.271, that exceeds the acceptability and perceptability threshold. Conclusions: LED warm light influences the instrumental tooth color determination and when used, can lead to errors in communication of the color to the dental laboratory, which can cause esthetic failure of a prosthetic restoration. The best illuminant for color determination is natural light, or illuminants that simulate lighting in daily living conditions. Clinical implications: Based on the spectrophotometric readings, it was demonstrated that tooth color shifts with the change in ambient lighting conditions. Therefore, color determination should be performed with natural daylight illumination.

Decrease in the rewarding value of spatial novelty due to the contamination of the stimulus field with light – Evidence from a free exploration test involving rats

It has been shown that rearranging the spatial properties of a familiar environment consistently elicits a positive response in rats directed toward the source of novelty. Previous studies have been conducted under red light or darkness. The purpose of this study was to test the effect of rearranging the spatial properties of a familiar environment in conjunction with a change in lighting conditions. The results have shown specific effects of the light presence and its intensity on different behavioral measures. We propose that this study provides a basis for hypothesizing a two-way mechanism of the behavioral response to light regulation in rats. The first is based on ON/OFF states. This level may be related to fundamental, evolutionarily early, emergent components of behavioral antipredator adaptations. Another level of behavioral regulation involves mechanisms sensitive to light intensity. These appear to be involved in the regulation of more advanced behavioral acts, such as exploratory responses. This may suggest that light intensity analysis may require the involvement of more advanced cognitive components in the behavioral regulation system.

Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure

Research has been continually growing toward the development of image-based structural health monitoring tools that can leverage deep learning models to automate damage detection in civil infrastructure. However, these tools are typically based on RGB images, which work well under ideal lighting conditions, but often have degrading performance in poor and low-light scenes. On the other hand, thermal images, while lacking in crispness of details, do not show the same degradation of performance in changing lighting conditions. The potential to enhance automated damage detection by fusing RGB and thermal images together within a deep learning network has yet to be explored. In this paper, RGB and thermal images are fused in a ResNET-based semantic segmentation model for vision-based inspections. A convolutional neural network is then employed to automatically identify damage defects in concrete. The model uses a thermal and RGB encoder to combine the features detected from both spectrums to improve its performance of the model, and a single decoder to predict the classes. The results suggest that this RGB-thermal fusion network outperforms the RGB-only network in the detection of cracks using the Intersection Over Union (IOU) performance metric. The RGB-thermal fusion model not only detected damage at a higher performance rate, but it also performed much better in differentiating the types of damage.

Chromatic Acclimation Processes and Their Relationships with Phycobiliprotein Complexes.

Chromatic acclimation (CA) is a widespread mechanism for optimizing the composition of phycobiliprotein complexes to maximize the cyanobacterial light capture efficiency. There are seven CA types, CA1-CA7, classified according to various photoregulatory pathways. Here, we use sequence analyses and bioinformatics to predict the presence of CA types according to three GAF (cGMP phosphodiesterase/adenylyl cyclase/FhlA)-containing photoreceptors, CcaS (cyanobacterial chromatic acclimation sensor), RcaE (regulator of chromatic adaptation), and RfpA (regulator for far-red photoacclimation). These photoreceptors were classified into three different phylogenetic groups leading different CA types in a diverse range of cyanobacteria. Combining with genomic information of phycobilisome compositions, the CA capabilities of various cyanobacteria were conjectured. Screening 65 accessible cyanobacterial genomes, we defined 19 cyanobacteria that have the capability to perform far-red light photoacclimation (FaRLiP) under the control of RfpA. Forty out of sixty-five cyanobacteria have the capability to perform green/red light photoacclimation, although they use different photoreceptors (RcaE and/or CcaS) and photoregulatory pathways. The reversible response of photoreceptors in CA regulation pathways trigged by changed light conditions reflects the flexibility of photoregulatory mechanisms in cyanobacteria and the putative independent evolutionary origin of photoacclimation types.

Chloroplast Acetyltransferase GNAT2 is Involved in the Organization and Dynamics of Thylakoid Structure.

Higher plants acclimate to changes in light conditions by adjusting the thylakoid membrane ultrastructure. Additionally, excitation energy transfer between photosystem II (PSII) and photosystem I (PSI) is balanced in a process known as state transition. These modifications are mediated by reversible phosphorylation of Lhcb1 and Lhcb2 proteins in different pools of light-harvesting complex (LHCII) trimers. Our recent study demonstrated that chloroplast acetyltransferase NUCLEAR SHUTTLE INTERACTING (NSI)/GNAT2 (general control non-repressible 5 (GCN5)-related N-acetyltransferase 2) is also needed for the regulation of light harvesting, evidenced by the inability of the gnat2 mutant to perform state transitions although there are no defects in LHCII phosphorylation. Here, we show that despite contrasting phosphorylation states of LHCII, grana packing in the gnat2 and state transition 7 (stn7) mutants possesses similar features, as the thylakoid structure of the mutants does not respond to the shift from darkness to light, which is in striking contrast to wild type (Wt). Circular dichroism and native polyacrylamide gel electrophoresis analyses further revealed that the thylakoid protein complex organization of gnat2 and stn7 resembles each other, but differ from that of Wt. Also, the location of the phosphorylated Lhcb2 as well as the LHCII antenna within the thylakoid network in gnat2 mutant is different from that of Wt. In gnat2, the LHCII antenna remains largely in grana stacks, where the phosphorylated Lhcb2 is found in all LHCII trimer pools, including those associated with PSII. These results indicate that in addition to phosphorylation-mediated regulation through STN7, the GNAT2 enzyme is involved in the organization and dynamics of thylakoid structure, probably through the regulation of chloroplast protein acetylation.

Deep Learning-Based Picture Object Identification System Design and Implementation

The computer plays a significant role in computer vision to help with everyday tasks. Defense, biometrics, visual surveillance, robotics, and driver assistance are just a few of the many uses for object recognition. Lane/railroad track detection, the detection of obstacles before the vehicle/train, is part of the driver assistance system. Driver assistance systems for various modes of transportation can be improved by employing an efficient object-recognition approach (road, rail, etc.). One of the major problems with the driver assistance system is its inability to detect railroad obstacles. There has been an increase in the amount of research and development in obstacle detection for road transportation in the last few years. Despite the fact that railroads are the other primary land transportation mode, much less effort has been put into developing technologies for detecting obstacles on the rails than on the roads. Efforts to improve the recognition performance of safety inferences are ongoing. Real-time object recognition in driving situations despite the rapid development in the field of object recognition on datasets with a tremendous number of different types of objects remains extremely challenging. Autonomous driving systems (ADAS) and advanced driver assistance systems (ADAS) face a number of key difficulties in visual object recognition. Object recognition is complicated by a variety of factors, including changes in lighting conditions, the presence of shadows, and partial occlusion, for example. Changing light conditions are the most common triggers for variation. These critical realities are taken into account in this study, which aims to offer a solution to these problems.

ViViD++ : Vision for Visibility Dataset

In this letter, we present a dataset capturing diverse visual data formats that target varying luminance conditions. While RGB cameras provide nourishing and intuitive information, changes in lighting conditions potentially result in catastrophic failure for robotic applications based on vision sensors. Approaches overcoming illumination problems have included developing more robust algorithms or other types of visual sensors, such as thermal and event cameras. Despite the alternative sensors’ potential, there still are few datasets with alternative vision sensors. Thus, we provided a dataset recorded from alternative vision sensors, by handheld or mounted on a car, repeatedly in the same space but in different conditions. We aim to acquire visible information from co-aligned alternative vision sensors. Our sensor system collects data more independently from visible light intensity by measuring the amount of infrared dissipation, depth by structured reflection, and instantaneous temporal changes in luminance. We provide these measurements along with inertial sensors and ground-truth for developing robust visual SLAM under poor illumination.

Light Intensity- and Spectrum-Dependent Redox Regulation of Plant Metabolism.

Both light intensity and spectrum (280–800 nm) affect photosynthesis and, consequently, the formation of reactive oxygen species (ROS) during photosynthetic electron transport. ROS, together with antioxidants, determine the redox environment in tissues and cells, which in turn has a major role in the adjustment of metabolism to changes in environmental conditions. This process is very important since there are great spatial (latitude, altitude) and temporal (daily, seasonal) changes in light conditions which are accompanied by fluctuations in temperature, water supply, and biotic stresses. The blue and red spectral regimens are decisive in the regulation of metabolism because of the absorption maximums of chlorophylls and the sensitivity of photoreceptors. Based on recent publications, photoreceptor-controlled transcription factors such as ELONGATED HYPOCOTYL5 (HY5) and changes in the cellular redox environment may have a major role in the coordinated fine-tuning of metabolic processes during changes in light conditions. This review gives an overview of the current knowledge of the light-associated redox control of basic metabolic pathways (carbon, nitrogen, amino acid, sulphur, lipid, and nucleic acid metabolism), secondary metabolism (terpenoids, flavonoids, and alkaloids), and related molecular mechanisms. Light condition-related reprogramming of metabolism is the basis for proper growth and development of plants; therefore, its better understanding can contribute to more efficient crop production in the future.

Role of serine/threonine protein kinase STN7 in the formation of two distinct photosystem I supercomplexes in Physcomitrium patens.

Reversible thylakoid protein phosphorylation provides most flowering plants with dynamic acclimation to short-term changes in environmental light conditions. Here, through generating Serine/Threonine protein kinase 7 (STN7)-depleted mutants in the moss Physcomitrella (Physcomitrium patens), we identified phosphorylation targets of STN7 kinase and their roles in short- and long-term acclimation of the moss to changing light conditions. Biochemical and mass spectrometry analyses revealed STN7-dependent phosphorylation of N-terminal Thr in specific Light-Harvesting Complex II (LHCII) trimer subunits (LHCBM2 and LHCBM4/8) and provided evidence that phospho-LHCBM accumulation is responsible for the assembly of two distinct Photosystem I (PSI) supercomplexes (SCs), both of which are largely absent in STN7-depleted mutants. Besides the canonical state transition complex (PSI-LHCI-LHCII), we isolated the larger moss-specific PSI-Large (PSI-LHCI-LHCB9-LHCII) from stroma-exposed thylakoids. Unlike PSI-LHCI-LHCII, PSI-Large did not demonstrate short-term dynamics for balancing the distribution of excitation energy between PSII and PSI. Instead, PSI-Large contributed to a more stable increase in PSI antenna size in Physcomitrella, except under prolonged high irradiance. Additionally, the STN7-depleted mutants revealed altered light-dependent phosphorylation of a monomeric antenna protein, LHCB6, whose phosphorylation displayed a complex regulation by multiple kinases. Collectively, the unique phosphorylation plasticity and dynamics of Physcomitrella monomeric LHCB6 and trimeric LHCBM isoforms, together with the presence of PSI SCs with different antenna sizes and responsiveness to light changes, reflect the evolutionary position of mosses between green algae and vascular plants, yet with clear moss-specific features emphasizing their adaptation to terrestrial low-light environments.

Photoreceptors' gene expression of Arabidopsis thaliana grown with biophilic LED-sourced lighting systems.

Using specific photoreceptors, plants can sense light signals fundamental to their growth and development under changing light conditions. Phytochromes sense red and far-red light, cryptochromes and phototropins sense UV-A and blue light, while the UVR8 gene senses UV-B signals. The study of the molecular mechanisms used by plants to respond to artificial biophilic lighting is of pivotal importance for the implementation of biophilic approaches in indoor environments. CoeLux® is a new lighting system that reproduces the effect of natural sunlight entering through an opening in the ceiling, with a realistic sun perceived at an infinite distance surrounded by a clear blue sky. We used the model plant Arabidopsis thaliana to assess the gene expression of the main plant photoreceptors at different light intensities and at different times after exposure to the CoeLux® light type, using high-pressure sodium (HPS) lamps as control light type. Genes belonging to different families of photoreceptors showed a similar expression pattern, suggesting the existence of a common upstream regulation of mRNA transcription. In particular, PHYA, PHYC, PHYD, CRY1, CRY2, PHOT1, and UVR8, showed a common expression pattern with marked differences between the two light types applied; under the HPS light type, the expression levels are raising with the decrease of light intensity, while under the CoeLux® light type, the expression levels remain nearly constant at a high fold. Moreover, we showed that under biophilic illumination the light spectrum plays a crucial role in the response of plants to light intensity, both at the molecular and morphological levels.

Illumination invariant face recognition using contourlet transform and convolutional neural network

The face is a dominant biometric for recognizing a person. However, face recognition becomes challenging when there are severe changes in lighting conditions, i.e., illumination variations, which have been shown to have a more severe effect on recognition performance than the inherent differences between individuals. Most of the existing methods for tackling the problem of illumination variation assume that illumination lies in the large-scale component of a facial image; as such, the large-scale component is discarded, and features are extracted from small-scale components. Recently, it has been shown that large-scale component is also important; in addition, small-scale component contains detrimental noise features. Keeping this in view, we introduce a method for illumination invariant face recognition that exploits large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using ContourletDS. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model; we examined three widely employed CNN models: VGG-16, GoogLeNet, and ResNet152. To reduce the dimensions of local and global features and fuse them, we employ linear discriminant analysis (LDA). Finally, ridge regression is used for recognition. The method was evaluated on three benchmark datasets; it achieved accuracies of 99.7%, 100%, and 79.76% on Extended Yale B, AR, and M-PIE, respectively. The comparison reveals that it outperforms the state-of-the-art methods.

Systematic monitoring of 2-Cys peroxiredoxin-derived redox signals unveiled its role in attenuating carbon assimilation rate

Transmission of reductive and oxidative cues from the photosynthetic electron transport chain to redox regulatory protein networks plays a crucial role in coordinating photosynthetic activities. The tight balance between these two signals dictates the cellular response to changing light conditions. While the role of reductive signals in activating chloroplast metabolism is well established, the role of their counterbalanced oxidative signals is still unclear, mainly due to monitoring difficulties. Here, we introduced chl-roGFP2-PrxΔCR, a 2-Cys peroxiredoxin-based biosensor, into Arabidopsis thaliana chloroplasts to monitor the dynamic changes in photosynthetically derived oxidative signaling. We showed that chl-roGFP2-PrxΔCR oxidation states reflected oxidation patterns similar to those of endogenous 2-Cys peroxiredoxin under varying light conditions. By employing a set of genetically encoded biosensors, we showed the induction of 2-Cys peroxiredoxin-dependent oxidative signals, throughout the day, under varying light intensities and their inverse relationship with NADPH levels, unraveling the combined activity of reducing and oxidizing signals. Furthermore, we demonstrated the induction of 2-Cys peroxiredoxin-derived oxidative signals during a dark–to–low-light transition and uncovered a faster increase in carbon assimilation rates during the photosynthesis induction phase in plants deficient in 2-Cys peroxiredoxins compared with wild type, suggesting the involvement of oxidative signals in attenuating photosynthesis. The presented data highlight the role of oxidative signals under nonstress conditions and suggest that oxidative signals measured by peroxiredoxin-based biosensors reflect the limitation to photosynthesis imposed by the redox regulatory system.

Comparative proteomics analysis of Arabidopsis thaliana response to light-emitting diode of narrow wavelength 450 nm, 595 nm, and 650 nm

Incident light is a central modulator of plant growth and development. However, there are still open questions surrounding wavelength-specific plant proteomic responses. Here we applied tandem mass tag based quantitative proteomics technology to acquire an in-depth view of proteome changes in Arabidopsis thaliana response to narrow wavelength blue (B; 450 nm), amber (A; 595 nm), and red (R; 650 nm) light treatments. A total of 16,707 proteins were identified with 9120 proteins quantified across all three light treatments in three biological replicates. This enabled examination of changes in the abundance for proteins with low abundance and important regulatory roles including transcription factors and hormone signaling. Importantly, 18% (1631 proteins) of the A. thaliana proteome is differentially abundant in response to narrow wavelength lights, and changes in proteome correlate well with different morphologies exhibited by plants. To showcase the usefulness of this resource, data were placed in the context of more than thirty published datasets, providing orthogonal validation and further insights into light-specific biological pathways, including Systemic Acquired Resistance and Shade Avoidance Syndrome. This high-resolution resource for A. thaliana provides baseline data and a tool for defining molecular mechanisms that control fundamental aspects of plant response to changing light conditions, with implications in plant development and adaptation. SignificanceUnderstanding of molecular mechanisms involved in wavelength-specific response of plant is question of widespread interest both to basic researchers and to those interested in applying such knowledge to the engineering of novel proteins, as well as targeted lighting systems. Here we sought to generate a high-resolution proteomic profile of plant leaves, based on exposure to specific narrow-wavelength lights. Although changes in plant physiology in response to light spectral composition is well documented, there is limited knowledge on the roles of specific light wavelengths and their impact. Most previous studies have utilized relatively broad wavebands in their experiments. Such multi-wavelengths lights trigger diverse and complex signaling networks that pose major challenges in inference of wavelength-specific molecular processes that underly the plant response. Moreover, most studies have compared the effect of blue and red wavelengths comparing with FL, as control. As FL light consists the mixed spectra composition of both red and blue as well as numerous other wavelengths, comparing undeniably results in inconsistent and overlapping responses that will hamper effects to elucidate the plant response to specific wavelengths [1, 2]. Monitoring plant proteome response to specific wavelengths and further contrasting the changes with one another, rather than comparing plants proteome to FL, is thus necessary to gain detailed insights on underlying biological pathways and their consequences in plant physiology. Here, we employed narrow wavelength LED lights in our design to eliminate a potential overlap in molecular responses by ensuring non-overlapping wavelengths in the light treatments. We further applied TMT-labeling technology to gain a high-resolution view on the proteome changes. Our proteomics data provides an in-depth coverage suitable for system-wide analyses, providing deep insights on plant molecular response particularly because of the tremendous increase in the coverage of identified proteins which outreach the other biological data.