Search Results (337)

Background and Objectives: Autosomal recessive cutis laxa (ARCL) is a genetically heterogeneous group of connective-tissue disorders characterized by loose, inelastic skin and variable systemic involvement. Biallelic variants in PYCR1 are associated with PYCR1-related ARCL, including ARCL type IIB (OMIM #612940) and type IIIB (OMIM #614438). We aimed to characterize the clinical and molecular findings in one affected family and provide segregation and variant-specific functional evidence for two previously unreported PYCR1 variants. Methods: The proband and family underwent whole-exome sequencing with copy-number analysis and Sanger confirmation. The splice-site variant was evaluated by qualitative RT-PCR and Sanger sequencing of endogenous peripheral-blood PYCR1 transcripts, whereas the frameshift variant was studied in a heterologous expression system by qPCR and Western blotting. Neuroimaging, exploratory EEG spectral analysis, and public developmental transcriptomic/single-cell resources were used for complementary phenotypic context. Results: The 16-year-old proband had thin lax skin with prominent superficial veins, generalized joint hypermobility, facial dysmorphism, reduced muscle bulk, and motor developmental delay. Chinese WAIS-IV showed a markedly uneven profile (VCI 52; PRI 94; FSIQ 75). Two previously unreported PYCR1 variants were identified in trans: maternally inherited NM_006907.4:c.139-1G>A and paternally inherited NM_006907.4:c.724_725del, p.(Leu242AlafsTer31). Endogenous RNA analysis identified exon 3 skipping associated with c.139-1G>A (r.139_318del), predicting p.(Met48_Lys107del). The c.724_725del construct showed consistently reduced steady-state abundance of the truncated PYCR1 protein relative to WT across three independent experiments. Both variants were classified as likely pathogenic using the ACMG/AMP framework with relevant ClinGen refinements. Brain MRI revealed irregular morphology of the anterior corpus callosum. Discussion: These two previously unreported variants extend the PYCR1 variant spectrum and are supported by complementary segregation, RNA-level, and protein-level evidence. The neurodevelopmental observations provide additional phenotypic context and are interpreted descriptively. Full article

The accurate and non-destructive estimation of plant physiological and biochemical indicators (biomarkers) is crucial for crop stress assessment. However, conventional spectral preprocessing methods may not fully exploit the diverse spectral components associated with biomarkers with different spectral response characteristics. To address this issue, we proposed the multi-granularity spectral segmentation (MGSS)-based retrieval framework for the estimation of multiple biomarkers of Medicago truncatula under salt stress. Multi-granularity segmentation (MGS), as one part of the MGSS framework, was used to extract spectral features at different granularities. In this study, we obtained a dataset of leaf spectra and corresponding biomarkers (including relative chlorophyll content (SPAD), soluble sugar (SS), and malondialdehyde (MDA)) for 720 pots of Medicago truncatula. These plants were subjected to different levels of salt stress (including 100, 200, and 250 mmol L⁻¹ NaCl and untreated (CK)). Using PLSR, we compared MGS features with conventional spectral features for biomarker estimation. The results showed that MGS achieved competitive estimation performance on SPAD, SS, and MDA, with the optimal validation R² values of 0.98, 0.69, and 0.36, respectively, compared to the optimal conventional features. More importantly, the optimal granularities were different among the three biomarkers. SPAD, SS, and MDA reached the optimal performance at G2, G5, and G18, respectively, which showed different spectral responses to different frequency levels. This study provides a promising framework for accurately estimating multiple biomarkers in crops under abiotic stress. This method may provide a new interpretable perspective on the differences in spectral retrieval performance among various biomarkers. Full article

Autistic speech is highly heterogeneous, yet group-level comparisons may obscure meaningful individual acoustic patterns. This study used unsupervised machine learning to identify data-driven acoustic profiles in native speakers of Cypriot Greek, including autistic and neurotypical adults. Participants produced disyllabic pseudowords across controlled phonetic and stress conditions. Sixteen acoustic measures, including fundamental frequency, formants, duration, cepstral peak prominence, Mel-frequency cepstral coefficients, jitter, shimmer, harmonics-to-noise ratio, and intensity, were summarized at the participant level and normalized appropriately. Principal component analysis retained eight components explaining 81.4% of total variance, followed by k-means clustering. A three-cluster solution provided the best silhouette coefficient among tested solutions and showed good bootstrap stability. Cluster membership was significantly associated with diagnostic group: one profile was exclusively autistic, one was relatively balanced, and one was predominantly neurotypical. The dominant acoustic dimension was driven primarily by voice-quality and spectral measures, particularly cepstral peak prominence, intensity, shimmer, harmonics-to-noise ratio, and jitter, whereas pitch and formant measures contributed comparatively little. These findings demonstrate that unsupervised acoustic profiling can reveal stable, diagnostically relevant speech phenotypes that are not captured by conventional binary group comparisons, highlighting substantial within-group heterogeneity in autistic speech and supporting more individualized approaches to characterizing vocal variation. Full article

Nitrogen (N) availability strongly influences tea growth and the accumulation of quality-related metabolites, but long-term responses to withdrawal of external N can vary among cultivars. In this study, we characterized phenotypic, spectral, biochemical, and transcriptomic responses of four Camellia sinensis accessions—‘Kolkhida’, ‘Karatum’, and γ-irradiation-derived mutant forms #582 and #619—during two, four, or six months of complete withdrawal of exogenous NH₄NO₃ in greenhouse sand culture. Phenotypic, spectral, and biochemical measurements included all four accessions at all three time points; RNA-seq data were available for all four accessions were represented at four and six months, whereas two-month point were available only for ‘Kolkhida’ and ‘Karatum’. Nitrogen withdrawal decreased L-theanine and caffeine and generally increased simple and gallated catechins, consistent with a change in carbon/nitrogen balance towards phenolic metabolism. The four cultivars exhibited markedly distinct responses. ‘Karatum’ showed the optimum resilience, with minimal phenotypic alteration and a neutral transcriptional response, while #582 demonstrated a stronger stress-associated phenotype and metabolic response. Within the available RNA-seq comparisons, DEG counts were lower at four months than at six months, while the two-month datasets for ‘Kolkhida’ and ‘Karatum’ contained the largest DEG sets for those two cultivars, suggesting a phased acclimation process. CsELIP1, CsSRG1, CsHHO2/4, CsNRT2.4, and CsTAT2 were identified as potential candidate genes associated with nitrogen limitation, flavonoid regulation, and amino acid metabolism. Our findings show that N-deficiency reactions in tea are highly cultivar- and time-dependent, making single-time-point evaluations insufficient. We propose ‘Karatum’ as a promising candidate for further evaluation in low-nitrogen dose–response and field trials based on our combined biochemical and transcriptional findings. Full article

Protected horticultural crops are commonly produced at high planting densities and have short production cycles; imbalances in water and fertilizer supply can rapidly affect plant vigor, yield, and quality. Non-destructive diagnostic methods are therefore needed to characterize plant nutritional status under greenhouse conditions. Spectral imaging can simultaneously capture spatial and spectral information associated with pigments, water status, tissue structure, and canopy phenotype. It does not directly detect nutrient ions; rather, it captures physiological and structural responses that may be associated with nutrient status and may also be influenced by water deficit, disease, temperature, salinity, phenology, and genotype. This review focuses on crops grown in soil, substrate, and hydroponic systems under greenhouse conditions. Studies conducted in vertical farms, growth chambers, and open fields are included only as supplementary references for sensor selection, model calibration, and inspection methods. This article synthesizes diagnostic indicators for nitrogen, phosphorus, and potassium, together with their associated physiological responses and spectral characteristics; compares the performance of hyperspectral, multispectral, and machine learning methods at the leaf, plant, and canopy scales; and examines fixed measurement, stop-and-go mobile inspection, continuous motion imaging, and autonomous plant revisitation. Existing studies have established a solid foundation for nutrient content retrieval, deficiency identification, and mobile monitoring. However, several challenges remain inadequately addressed under continuous inspection conditions, including radiometric–geometric joint calibration, plant identity preservation, acquisition of multi-element chemical truth values, model generalization across growth stages and greenhouse types, and long-term performance evaluation. Future work should refine standardized protocols for dynamic data collection and water–fertilizer environmental control, integrate mechanistic constraints with data driven approaches, and incorporate plant re-identification, spatiotemporal registration, uncertainty quantification, and online calibration. These efforts will contribute to constructing a long-term stable and comparable nutritional diagnostic system, thereby advancing the transition of facility vegetable nutritional monitoring from single-time static measurements toward continuous, traceable, and autonomously patrolled systems that may ultimately support precision irrigation and fertilization management after appropriate independent validation. Full article

High-intensity blue light is a powerful regulatory signal for plants, but its excess induces oxidative stress requiring rapid diagnosis. This study evaluated the potential for early detection of changes in the spectral characteristics of microgreen canopy cover caused by high-intensity blue light (PPFD 2500 µmol·m⁻²·s⁻¹, 12 h) in five species of microgreens (Helianthus annuus, Pisum sativum, Eruca sativa, Hordeum vulgare, Raphanus sativus ‘Sango Purple’) using hyperspectral imaging (450–950 nm) and machine learning. A Random Forest model trained on 85 vegetation indices classified light stress with high accuracy (Accuracy > 93%, Kappa > 0.87, F1-score > 93%) and detected spectral changes characteristic of light stress as early as 1–3 h of exposure. SHAP analysis identified carotenoid-sensitive indices (PRI, CCI, PRICI2) and chloroplast movement and stress indices (CMI, LSI_Red, LSI_Norm, Carter5) as the most informative predictors. It is assumed that the primary mechanism underlying early spectral changes was chloroplast avoidance rather than pigment degradation, as confirmed by the reversibility of canopy bleaching, rapid recovery of maximum quantum yield of photosystem II, and unchanged chlorophyll and carotenoid contents. Sunflower and radish were the most sensitive species to high-dose blue light, while barley was the least sensitive. LSI_Red was identified as a reliable qualitative marker of light stress that does not require a control sample, simplifying its use in automated monitoring systems. These findings demonstrate the effectiveness of hyperspectral phenotyping for non-invasive, rapid diagnosis of light stress in microgreens, providing a tool for optimising lighting regimes in controlled environment agriculture. Full article

We analyse whether Darwinian natural selection acts as a metabolic-entropy-production-increasing process in a model of an open chemostat ecosystem, where Michaelis–Menten uptake kinetics are grounded in a thermodynamically consistent mesoscopic chemical-reaction-network model, and the continuum limit of the discrete replicator equation is derived as a Crow–Kimura reaction–diffusion process. On the quasi-static ecological manifold, an exact Fisher-type identity yields a monotonic increase in the metabolic entropy-production rate, proportional to the uptake-rate variance. Solving the reduced moment equations in closed form, under an explicit quasi-static and zero-skewness closure, identifies a thermodynamic ceiling imposed by mass conservation, while phenotypic trade-offs produce a finite sub-ceiling. When ecological and evolutionary timescales become comparable, a frozen-parameter Routh–Hurwitz analysis identifies an instantaneous spectral instability boundary whose scope and self-consistency are assessed for both unbounded and bounded trait models. The results of this analysis delineate the precise dynamical and biochemical conditions under which selection increases metabolic entropy production and the regimes in which that tendency is reshaped. Full article

Korean ginseng (Panax ginseng C. A. Meyer) is a perennial medicinal crop highly sensitive to waterlogging stress. Although excess soil moisture is known to induce oxygen limitation in the rhizosphere, the resulting metabolic constraints may precede visible shoot symptoms such as wilting, chlorosis, or necrosis. To examine the molecular responses under sustained high soil moisture before overt shoot necrosis, one-year-old ‘Yunpoong’ plants were exposed to ambient waterlogging at 45–55% volumetric soil water content (VSWC), while control plants were maintained at 25% VSWC. Phenotypic observations and hyperspectral imaging were conducted immediately after treatment initiation (week 0) and at weeks 1, 2, and 3, whereas whole-plant samples for RNA-seq were collected at weeks 1–3. Hyperspectral imaging and normalized difference vegetation index (NDVI) analysis indicated limited temporal changes in canopy-level spectral traits during the treatment period. In contrast, transcriptomic analyses revealed time-dependent changes in whole-plant gene expression. A total of 6448 unique differentially expressed genes (DEGs) were identified, of which 4369 were specific to week 3. Gene Ontology (GO) enrichment analysis suggested a stepwise adaptive pattern consisting of early priming, transient stabilization, and long-term remodeling. Hypoxia-, jasmonic acid-, and lignin-related processes were enriched at week 1, water deprivation and protein quality control responses were adjusted at week 2, and glycolysis, phosphate starvation, and repression of growth-related processes became prominent at week 3. Weighted gene co-expression network analysis (WGCNA) and STRING-based protein–protein interaction (PPI) analysis further identified functional modules associated with RNA processing, photosystem regulation, proteostasis, and central carbon, energy, and phosphate metabolism. Integrated transcriptome and network analyses prioritized six candidate genes, GAPC2, MDH, PGI1, PPC1, Lhb1B1, and RD21A, for further functional validation in relation to prolonged ambient waterlogging responses. Full article

Sorghum (Sorghum bicolor L. Moench) is a major cereal in water-limited environments. Its C₄ carbon-concentrating pathway suppresses photorespiration and supports comparatively high photosynthetic and water-use efficiency at high temperature, although yield remains sensitive to the timing and intensity of drought. This systematic review critically evaluates how coordinated variation in phenology, canopy development, transpiration regulation, photosynthetic resilience and root-mediated water capture can be phenotyped for sorghum improvement. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Eligible primary studies examined sorghum drought physiology, sensing-based phenotyping, trait retrieval, root-associated water capture, or breeding applications. Following duplicate removal and title, abstract and full-text screening, 45 sorghum-specific studies were included. Owing to substantial heterogeneity in experimental design, drought treatment, sensing platform, target trait, and validation metric, evidence was synthesised narratively rather than by meta-analysis. We compare sorghum studies across Light Detection and Ranging (LiDAR), multi-spectral, hyperspectral, thermal, structural, and fluorescence sensing, with emphasis on reported accuracy, transferability and physiological interpretation. We then examine how PROSAIL (PROSPECT coupled with Scattering by Arbitrarily Inclined Leaves) and SCOPE (Soil Canopy Observation, Photochemistry and Energy Fluxes) can be constrained for sorghum canopies and combined with machine learning. The central contribution is a sorghum-specific framework that distinguishes directly observed or model-retrieved canopy traits from indirect root-function predictions requiring ground validation. The synthesis identifies practical routes for measuring functional stay-green, high-vapour-pressure-deficit responses and post-anthesis water capture, while defining priorities for cross-environment validation and breeding deployment. Full article

Plant breeding trials often involve a large number of genotypes, making field-based evaluation of agronomic traits labor-intensive, expensive, and time-consuming. Pre-harvest identification of superior genotypes using remote sensing could substantially improve breeding efficiency. This study evaluated the potential of unsupervised (clustering) and supervised (regression) methods based on unmanned aerial vehicle (UAV) multispectral imagery to differentiate winter durum wheat genotypes according to yield, grain protein content (GPC), and protein yield (PY). A three-year field experiment involving 26 genotypes was conducted at the Institute of Field Crops (Chirpan, Bulgaria). UAV data acquired at the end of flowering (BBCH 69) and the beginning of grain filling (BBCH 71) with a DJI Phantom 4 Multispectral were used to derive spectral vegetation indices (SVIs) and texture features (TFs). In the supervised approach, machine learning regression models were used to predict the target traits before grouping genotypes into low-, medium-, and high-performance classes, whereas Ward’s hierarchical clustering was applied directly to the UAV-derived features in the unsupervised approach. The resulting genotype groups were compared with genotype groupings based on the means and standard deviations derived from the field measurements. Both approaches successfully identified high- and low-performing genotypes for yield and PY, achieving accuracies of 50–100%. In contrast, both methods showed limited performance for GPC. ANOVA revealed that agronomic traits and the best-performing SVI, the Normalized Difference Red-Edge Index (NDRE), were strongly influenced by environmental variability, whereas TFs appeared to capture more genotype-specific structural characteristics. These findings demonstrate that both supervised and unsupervised UAV-based approaches can support early identification of superior wheat genotypes, with texture features showing particular promise for genotype discrimination across breeding trials. Full article

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions. Full article

Unmanned aerial vehicle (UAV) imagery can support plot-scale crop phenotyping, but spectral, RGB and structural predictors may contribute differently to different traits. We compared six predefined feature groups for predicting soybean SPAD and plant height (PH) in a 1.3 ha field experiment in Sanya, China. The field contained 6197 soybean planting plots, of which 234 had paired SPAD and PH measurements. Multispectral bands, vegetation indices (VIs), RGB descriptors and digital surface model (DSM) metrics were extracted from DJI Mavic 3 Multispectral imagery. Six regression algorithms were evaluated using random fivefold cross-validation, spatial block cross-validation and nested spatial cross-validation. Under random cross-validation, ExtraTrees with multispectral bands, VIs and RGB descriptors produced the numerically highest SPAD performance (R² = 0.589; RMSE = 6.66), while BayesianRidge with multispectral bands, VIs and DSM metrics produced the highest PH performance (R² = 0.760; RMSE = 7.14 cm). Nested spatial cross-validation yielded R² = 0.473 and RMSE = 7.56 for SPAD and R² = 0.690 and RMSE = 8.13 cm for PH. G4 was selected in four of the five outer folds for SPAD, although the selected algorithm varied, and G5 was selected in all five outer folds for PH. VIs improved prediction of both traits relative to the original bands. Adding RGB descriptors produced only a small and model-dependent improvement for SPAD, whereas adding DSM metrics produced a larger and more consistent improvement for PH. The complete feature set did not outperform G4 for SPAD or G5 for PH. The retained models were applied to all 6197 plots to map SPAD, PH and their field relative combinations. Because all of the validations used one field and one UAV acquisition date, the results describe performance within this experiment and do not establish transferability to other sites, years or growth stages. Full article

Obstructive sleep apnea syndrome (OSAS) is heavily implicated in subclinical cardiovascular disease; however, traditional polysomnographic metrics fail to capture the localized mechanical trauma exerted on the carotid artery. To address this methodological gap, this study evaluated exploratory associations between frequency-domain snoring characteristics and right carotid artery alterations in 50 patients with OSAS. Snoring was quantified using a dual-modality bioelectronic approach: an ambient microphone captured airborne snoring sound energy (SSE), while a portable neck-surface piezoelectric sensor recorded tissue-conducted snoring vibratory energy (SVE). Subclinical vascular changes, including carotid intima-media thickness (CIMT) and atherosclerosis, were assessed via ultrasonography. Hierarchical multivariable regression demonstrated that acoustic SSE%-404–500 Hz and mechanical SVE%-112–144 Hz independently correlated with preliminary CIMT increases (adjusted β = 0.033 and 0.021, respectively; both p < 0.05), alongside neck circumference. Conversely, SSE%-404–500 Hz emerged as an exploratory marker for focal carotid atherosclerosis (adjusted odds ratio = 1.828; p = 0.009). Integrating this metric with baseline parameters yielded exploratory diagnostic capacity (area under the curve = 0.833; p < 0.001), achieving 89% sensitivity and 69% specificity. These findings suggest that dual-modality spectral analysis provides a non-invasive exploratory framework for cardiovascular risk stratification, isolating localized mechanotransduction phenotypes independently of systemic hypoxia. Full article

Background: The accurate enumeration and identification of probiotic strains are essential for product quality. The plate count (PC) gold standard enumerates viable, culturable cells but does not by itself resolve individual strains within multi-strain consortia, and molecular methods (qPCR, ddPCR) are costly and face recognised challenges in quantifying relative strain abundance. Fourier transform infrared (FTIR) spectroscopy is a promising phenotypic alternative. Methods: We developed an FTIR-based artificial neural network classifier to identify and quantify a four-strain probiotic blend comprising Lactobacillus acidophilus LA02, Lacticaseibacillus rhamnosus LR04, Limosilactobacillus fermentum LF08, and Bifidobacterium animalis subsp. lactis BS01 compared against selective plate counting and species-specific PCR. Results: The classifier correctly identified all 36 test colonies (100%; 95% Clopper–Pearson CI: 90.3–100%); descriptive cluster analysis indicated spectral distinctiveness (silhouette = 0.908; Davies–Bouldin = 0.127; cophenetic correlation = 0.955). Enumeration agreement with selective plate counting was assessed descriptively (Pearson r = 0.78, 95% CI [−0.72, 1.00], n = 4 strain means; mean difference −0.028 log₁₀ CFU/mL; all strain-level differences < 0.1 log₁₀ CFU/mL). PCR confirmed all FTIR classifications. Conclusions: This proof-of-concept study demonstrates the feasibility of coupling cultivation with spectroscopic identification in a hybrid PC + FTIR workflow for multi-strain probiotic quality control. Because the findings derive from a single blend preparation analysed in technical replicates, they characterise this dataset and require confirmation on independently prepared batches before the approach can be regarded as a validated method. Full article

Photosynthetic phenotypic parameters of winter wheat are important indicators of canopy physiological status, photosynthetic function, and crop growth. However, canopy-scale hyperspectral estimation of these parameters remains affected by canopy structural heterogeneity, environmental variation, and mixed spectral signals. This study evaluated the contribution of visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) regions and their combinations to estimating photosynthetic phenotypic parameters of winter wheat. Field experiments were conducted under three nitrogen application levels and 65 winter wheat genotypes, and a total of 507 valid canopy-level samples were used for model development and validation. Competitive adaptive reweighted sampling (CARS) was used to select characteristic wavelengths, and partial least squares regression (PLSR), Bayesian ridge regression (BR), and backpropagation neural network (BPNN) were applied to construct estimation models. Model performance was assessed using R², RMSE, and RPD. Results showed that NIR-based models achieved the best overall performance, with the highest validation R² of 0.828 for photosynthetic rate. The VIS + NIR combination showed stable predictive ability across multiple parameters, whereas SWIR-only models showed limited performance, with R² values below 0.5 for most parameters. Photosynthetic rate, intercellular CO₂ concentration, performance index on an absorption basis, and chlorophyll a content were predicted more accurately than the other traits. These findings indicate that canopy hyperspectral data can support quantitative monitoring of photosynthetic phenotypic parameters, and that NIR-related structural and scattering information plays a key role in winter wheat canopy phenotyping. Full article