Search Results (24)

Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R²), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm² and 2.872 at the heading stage, and 0.968, 0.147 g/m² and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods. Full article

Biomolecular condensates are crucial compartments within cells, relying on their material properties for function. They form and persist through weak, transient interactions, often undetectable by classical biochemical approaches. Hence, microscopy-based techniques have been the most reliable methods to detail the molecular mechanisms controlling their formation, material properties, and alterations, including dissolution or phase transitions due to cellular manipulation and disease, and to search for novel therapeutic strategies targeting biomolecular condensates. However, technical challenges in microscopy-based analysis persist. This paper discusses imaging, data acquisition, and analytical methodologies’ advantages, challenges, and limitations in determining biophysical parameters explaining biomolecular condensate formation, dissolution, and phase transitions. In addition, we mention how machine learning is increasingly important for efficient image analysis, teaching programs what a condensate should resemble, aiding in the correlation and interpretation of information from diverse data sources. Influenza A virus forms liquid viral inclusions in the infected cell cytosol that serve as model biomolecular condensates for this study. Our previous work showcased the possibility of hardening these liquid inclusions, potentially leading to novel antiviral strategies. This was established using a framework involving live cell imaging to measure dynamics, internal rearrangement capacity, coalescence, and relaxation time. Additionally, we integrated thermodynamic characteristics by analysing fixed images through Z-projections. The aforementioned paper laid the foundation for this subsequent technical paper, which explores how different modalities in data acquisition and processing impact the robustness of results to detect bona fide phase transitions by measuring thermodynamic traits in fixed cells. Using solely this approach would greatly simplify screening pipelines. For this, we tested how single focal plane images, Z-projections, or volumetric analyses of images stained with antibodies or live tagged proteins altered the quantification of thermodynamic measurements. Customizing methodologies for different biomolecular condensates through advanced bioimaging significantly contributes to biological research and potential therapeutic advancements. Full article

The β-coronavirus family, encompassing Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Severe Acute Respiratory Syndrome Coronavirus (SARS), and Middle East Respiratory Syndrome Coronavirus (MERS), has triggered pandemics within the last two decades. With the possibility of future pandemics, studying the coronavirus family members is necessary to improve knowledge and treatment. These viruses possess 16 non-structural proteins, many of which play crucial roles in viral replication and in other vital functions. One such vital protein is non-structural protein 10 (nsp10), acting as a pivotal stimulator of nsp14 and nsp16, thereby influencing RNA proofreading and viral RNA cap formation. Studying nsp10 of pathogenic coronaviruses is central to unraveling its multifunctional roles. Our study involves the biochemical and biophysical characterisation of full-length nsp10 from MERS, SARS and SARS-CoV-2. To elucidate their oligomeric state, we employed a combination of Multi-detection Size exclusion chromatography (Multi-detection SEC) with multi-angle static light scattering (MALS) and small angle X-ray scattering (SAXS) techniques. Our findings reveal that full-length nsp10s primarily exist as monomers in solution, while truncated versions tend to oligomerise. SAXS experiments reveal a globular shape for nsp10, a trait conserved in all three coronaviruses, although MERS nsp10, diverges most from SARS and SARS-CoV-2 nsp10s. In summary, unbound nsp10 proteins from SARS, MERS, and SARS-CoV-2 exhibit a globular and predominantly monomeric state in solution. Full article

β barrels are ubiquitous proteins in the outer membranes of mitochondria, chloroplasts, and Gram-negative bacteria. These transmembrane proteins (TMPs) execute a wide variety of tasks. For example, they can serve as transporters, receptors, membrane-bound enzymes, as well as adhesion, structural, and signaling elements. In addition, multimeric β barrels are common structural scaffolds among many pore-forming toxins. Significant progress has been made in understanding the functional, structural, biochemical, and biophysical features of these robust and versatile proteins. One frequently encountered fundamental trait of all β barrels is their voltage-dependent gating. This process consists of reversible or permanent conformational transitions between a large-conductance, highly permeable open state and a low-conductance, solute-restrictive closed state. Several intrinsic molecular mechanisms and environmental factors modulate this universal property of β barrels. This review article outlines the typical signatures of voltage-dependent gating. Moreover, we discuss recent developments leading to a better qualitative understanding of the closure dynamics of these TMPs. Full article

Amid climate change, heatwave events are expected to increase in frequency and severity. As a result, yield losses in viticulture due to heatwave stress have increased over the years. As one of the most important crops in the world, an eco-friendly stress mitigation strategy is greatly needed. The present work aims to evaluate the physiological fitness improvement by two marine plant growth-promoting rhizobacteria consortia in Vitis vinifera cv. Antão Vaz under heatwave conditions. To assess the potential biophysical and biochemical thermal stress feedback amelioration, photochemical traits, pigment and fatty acid profiles, and osmotic and oxidative stress biomarkers were analysed. Bioaugmented grapevines exposed to heatwave stress presented a significantly enhanced photoprotection capability and higher thermo-stability, exhibiting a significantly lower dissipation energy flux than the non-inoculated plants. Additionally, one of the rhizobacterial consortia tested improved light-harvesting capabilities by increasing reaction centre availability and preserving photosynthetic efficiency. Rhizobacteria inoculation expressed an osmoprotectant promotion, revealed by the lower osmolyte concentration while maintaining leaf turgidity. Improved antioxidant mechanisms and membrane stability resulted in lowered lipid peroxidation product formation when compared to non-inoculated plants. Although the consortia were found to differ significantly in their effectiveness, these findings demonstrate that bioaugmentation induced significant heatwave stress tolerance and mitigation. This study revealed the promising usage of marine PGPR consortia to promote plant fitness and minimize heatwave impacts in grapevines. Full article

A biophysical model for calculating the effective parameters of low-frequency magnetic fields was developed by Lednev based on summarized empirical data. According to this model, calcium ions as enzyme cofactors can be the primary target of low-frequency magnetic fields with different parameters tuned to calcium resonance. However, the effects of calcium-resonant combinations of static and alternating magnetic fields that correspond to Lednev’s model and differ by order in frequency and intensity were not studied. It does not allow for confidently discussing the primary targets of low-frequency magnetic fields in terms of the magnetic influence on ions-enzyme cofactors. To clarify this issue, we examined the response of freshwater crustaceans Daphnia magna to the impact of combinations of magnetic fields targeted to calcium ions in enzymes according to Lednev’s model that differ in order of magnitude. Life-history traits and biochemical parameters were evaluated. Exposure of daphnids to both combinations of magnetic fields led to a long-term delay of the first brood release, an increase in the brood size, a decrease in the number of broods, and the period between broods. The amylolytic activity, proteolytic activity, and sucrase activity significantly decreased in whole-body homogenates of crustaceans in response to both combinations of magnetic fields. The similarity in the sets of revealed effects assumes that different magnetic fields tuned to calcium ions in biomolecules can affect the same primary molecular target. The results suggest that the low-frequency magnetic fields with parameters corresponding to Lednev’s model of interaction between biological molecules and ions can remain effective with a significant decrease in the static magnetic background. Full article

Colorectal cancer (CRC) has been addressed in the framework of molecular, cellular biology, and biochemical traits. A new approach to studying CRC is focused on the relationship between biochemical pathways and biophysical cues, which may contribute to disease understanding and therapy development. Herein, we investigated the mechanical properties of CRC cells, namely, HCT116, HCT15, and SW620, using static and dynamic methodologies by atomic force microscopy (AFM). The static method quantifies Young’s modulus; the dynamic method allows the determination of elasticity, viscosity, and fluidity. AFM results were correlated with confocal laser scanning microscopy and cell migration assay data. The SW620 metastatic cells presented the highest Young’s and storage moduli, with a defined cortical actin ring with distributed F-actin filaments, scarce vinculin expression, abundant total focal adhesions (FAK), and no filopodia formation, which could explain the lessened migratory behavior. In contrast, HCT15 cells presented lower Young’s and storage moduli, high cortical tubulin, less cortical F-actin and less FAK, and more filopodia formation, probably explaining the higher migratory behavior. HCT116 cells presented Young’s and storage moduli values in between the other cell lines, high cortical F-actin expression, intermediate levels of total FAK, and abundant filopodia formation, possibly explaining the highest migratory behavior. Full article

Remote sensing technology allows to provide information about biochemical and biophysical crop traits and monitor their spatiotemporal dynamics of agriculture ecosystems. Among multiple retrieval techniques, hybrid approaches have been found to provide outstanding accuracy, for instance, for the inference of leaf area index (LAI), fractional vegetation cover (fCover), and leaf and canopy chlorophyll content (LCC and CCC). The combination of radiative transfer models (RTMs) and data-driven models creates an advantage in the use of hybrid methods. Through this review paper, we aim to provide state-of-the-art hybrid retrieval schemes and theoretical frameworks. To achieve this, we reviewed and systematically analyzed publications over the past 22 years. We identified two hybrid-based parametric and hybrid-based nonparametric regression models and evaluated their performance for each variable of interest. From the results of our extensive literature survey, most research directions are now moving towards combining RTM and machine learning (ML) methods in a symbiotic manner. In particular, the development of ML will open up new ways to integrate innovative approaches such as integrating shallow or deep neural networks with RTM using remote sensing data to reduce errors in crop trait estimations and improve control of crop growth conditions in very large areas serving precision agriculture applications. Full article

In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. In this study, we analyzed two spectral dimensionality reduction methods: principal component analysis (PCA) and band ranking (BR), embedded in a hybrid workflow for the retrieval of specific leaf area (SLA), leaf area index (LAI), canopy water content (CWC), canopy chlorophyll content (CCC), the fraction of absorbed photosynthetic active radiation (FAPAR), and fractional vegetation cover (FVC). The SCOPE model was used to simulate training data sets, which were optimized with active learning. Gaussian process regression (GPR) algorithms were trained over the simulations to obtain trait-specific models. The inclusion of PCA and BR with 20 features led to the so-called GPR-20PCA and GPR-20BR models. The 20PCA models encompassed over 99.95% cumulative variance of the full spectral data, while the GPR-20BR models were based on the 20 most sensitive bands. Validation against in situ data obtained moderate to optimal results with normalized root mean squared error (NRMSE) from 13.9% (CWC) to 22.3% (CCC) for GPR-20PCA models, and NRMSE from 19.6% (CWC) to 29.1% (SLA) for GPR-20BR models. Overall, the GPR-20PCA slightly outperformed the GPR-20BR models for all six variables. To demonstrate mapping capabilities, both models were tested on a PRecursore IperSpettrale della Missione Applicativa (PRISMA) scene, spectrally resampled to Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), over an agricultural test site (Jolanda di Savoia, Italy). The two strategies obtained plausible spatial patterns, and consistency between the two models was highest for FVC and LAI ($R^2=0.91$, $R^2=0.86$) and lowest for SLA mapping ($R^2=0.53$). From these findings, we recommend implementing GPR-20PCA models as the most efficient strategy for the retrieval of multiple crop traits from hyperspectral data streams. Hence, this workflow will support and facilitate the preparations of traits retrieval models from the next-generation operational CHIME. Full article

Leaf biochemical traits indicating early symptoms of plant stress can be assessed using imaging spectroscopy combined with radiative transfer modelling (RTM). In this study, we assessed the potential applicability of the leaf radiative transfer model Fluspect-Cx to simulate optical properties and estimate leaf biochemical traits through inversion of two native Australian eucalypt species: Eucalyptus dalrympleana and E. delegetensis. The comparison of measured and simulated optical properties revealed the necessity to recalibrate the refractive index and specific absorption coefficients of the eucalypt leaves’ biochemical constituents. Subsequent validation of the modified Fluspect-Cx showed a closer agreement with the spectral measurements. The average root mean square error (RMSE) of reflectance, transmittance and absorptance values within the wavelength interval of 450–1600 nm was smaller than 1%. We compared the performance of both the original and recalibrated Fluspect-Cx versions through inversions aiming to simultaneously retrieve all model inputs from leaf optical properties with and without prior information. The inversion of recalibrated Fluspect-Cx constrained by laboratory-based measurements produced a superior accuracy in estimations of leaf water content (RMSE = 0.0013 cm, NRMSE = 6.55%) and dry matter content (RMSE = 0.0036 g·cm⁻², NRMSE = 21.28%). The estimation accuracies of chlorophyll content (RMSE = 8.46 µg·cm⁻², NRMSE = 24.73%), carotenoid content (RMSE = 3.83 µg·cm⁻², NRMSE = 30.82%) and anthocyanin content (RMSE = 1.69 µg·cm⁻², NRMSE = 37.12%) were only marginally better than for the inversion without any constraints. Additionally, we investigated the possibility to substitute the prior information derived in the laboratory by non-destructive reflectance-based spectral indices sensitive to the retrieved biochemical traits, resulting in the most accurate estimation of carotenoid content (RMSE = 3.65 µg·cm⁻², NRMSE = 29%). Future coupling of the recalibrated Fluspect with a forest canopy RTM is expected to facilitate retrieval of biophysical traits from spectral air/space-borne image data, allowing for assessing the actual physiological status and health of eucalypt forest canopies. Full article

It is an understatement that mating and DNA transfer are key events for living organisms. Among the traits needed to facilitate mating, cell adhesion between gametes is a universal requirement. Thus, there should be specific properties for the adhesion proteins involved in mating. Biochemical and biophysical studies have revealed structural information about mating adhesins, as well as their specificities and affinities, leading to some ideas about these specialized adhesion proteins. Recently, single-cell force spectroscopy (SCFS) has added important findings. In SCFS, mating cells are brought into contact in an atomic force microscope (AFM), and the adhesive forces are monitored through the course of mating. The results have shown some remarkable characteristics of mating adhesins and add knowledge about the design and evolution of mating adhesins. Full article

Low temperatures are a substantial limitation in the geographic distribution of warm-season crops such as cucumber (Cucumis sativus L.). Tolerance to low temperatures varies among different plant species and genotypes when changes in environmental cues occur. Therefore, biochemical and biophysical events should be coordinated to form a physiological response and cope with low temperatures. We examined how light intensity influences the effects of low temperature on photosynthesis and some biochemical traits. We used chlorophyll fluorescence imaging and polyphasic fluorescence transient to analyze cold stress damage by 4 °C. Photosynthetic Photon Flux Densities (PPFDs) of 0, 300, and 600 μmol m⁻² s⁻¹, in four accessions of cucumber, were investigated. The results show that the negative effects of cold stress are PPFD-dependent. The adverse effect of cold stress on the electron transport chain is more pronounced in plants exposed to 600 μmol m⁻² s⁻¹ than the control and dark-exposed plants, indicated by a disturbance in the electron transport chain and higher energy dissipation. Moreover, biochemical traits, including the H₂O₂ content, ascorbate peroxidase activity, electrolyte leakage, and water-soluble carbohydrate, increased under low temperature by increasing the PPFD. In contrast, chlorophyll and carotenoid contents decreased under low temperature through PPFD elevation. Low temperature induced a H₂O₂ accumulation via suppressing ascorbate peroxidase activity in a PPFD-dependent manner. In conclusion, high PPFDs exacerbate the adverse effects of low temperature on the cucumber seedlings. Full article

Accurate knowledge of photosynthetic capacity is critical for understanding the carbon cycle under climate change. Despite the fact that deep neural network (DNN) models are increasingly applied across a wide range of fields, there are very few attempts to predict leaf photosynthetic capacity (indicated by maximum carboxylation rate, Vcmax, and maximum electron transport rate, Jmax) from reflected information. In this study, we have built a DNN model that uses leaf reflected spectra, alone or together with other leaf traits, for the reliable estimation of photosynthetic capacity, accounting for leaf types and growing periods in cool–temperate deciduous forests. Our results demonstrate that even though DNN models using only the reflectance spectra are capable of estimating both Vcmax and Jmax acceptably, their performance could nevertheless be improved by including information about other leaf biophysical/biochemical traits. The results highlight the fact that leaf spectra and leaf biophysical/biochemical traits are closely linked with leaf photosynthetic capacity, providing a practical and feasible approach to tracing functional traits. However, the DNN models developed in this study should undergo more extensive validation and training before being applied in other regions, and further refinements in future studies using larger datasets from a wide range of ecosystems are also necessary. Full article

Natural genetic variation in photosynthesis is strictly associated with the remarkable adaptive plasticity observed amongst Arabidopsis thaliana accessions derived from environmentally distinct regions. Exploration of the characteristic features of the photosynthetic machinery could reveal the regulatory mechanisms underlying those traits. In this study, we performed a detailed characterisation and comparison of photosynthesis performance and spectral properties of the photosynthetic apparatus in the following selected Arabidopsis thaliana accessions commonly used in laboratories as background lines: Col-0, Col-1, Col-2, Col-8, Ler-0, and Ws-2. The main focus was to distinguish the characteristic disparities for every accession in photosynthetic efficiency that could be accountable for their remarkable plasticity to adapt. The biophysical and biochemical analysis of the thylakoid membranes in control conditions revealed differences in lipid-to-protein contribution, Chlorophyll-to-Carotenoid ratio (Chl/Car), and xanthophyll cycle pigment distribution among accessions. We presented that such changes led to disparities in the arrangement of the Chlorophyll-Protein complexes, the PSI/PSII ratio, and the lateral mobility of the thylakoid membrane, with the most significant aberrations detected in the Ler-0 and Ws-2 accessions. We concluded that selecting an accession suitable for specific research on the photosynthetic process is essential for optimising the experiment. Full article

Stabilization of the HIV-1 Envelope glycoprotein trimer (Env) in its native pre-fusion closed conformation is regarded as one of several requirements for the induction of neutralizing antibody (nAb) responses, which, in turn, will most likely be a prerequisite for the development of an efficacious preventive vaccine. Here, we systematically analyzed how the stepwise stabilization of a clade C consensus (ConC) Env immunogen impacts biochemical and biophysical protein traits such as antigenicity, thermal stability, structural integrity, and particle size distribution. The increasing degree of conformational rigidification positively correlates with favorable protein characteristics, leading to optimized homogeneity of the protein preparations, increased thermal stability, and an overall favorable binding profile of structure-dependent broadly neutralizing antibodies (bnAbs) and non-neutralizing antibodies (non-nAbs). We confirmed that increasing the structural integrity and stability of the Env trimers positively correlates with the quality of induced antibody responses by the immunogens. These and other data contribute to the selection of ConCv5 KIKO as novel Env immunogens for use within the European Union’s H2020 Research Consortium EHVA (European HIV Alliance) for further preclinical analysis and phase 1 clinical development. Full article