Search Results (169)

Rubinstein–Taybi Syndrome type 1 (RSTS1) is an uncommon autosomal dominant genetic disorder associated with neurodevelopmental impairments and multiple congenital anomalies, with an incidence of 1:100,000–125,000 live births. The syndrome, caused by de novo mutations in the CREBBP gene, is characterized by phenotypic variability, including intellectual disability, facial dysmorphisms, and systemic abnormalities. The current case report describes a 15-year-old Brazilian female diagnosed with RSTS1 through whole-exome sequencing, which identified a de novo heterozygous missense mutation in the CREBBP gene (NM_004380.3; c.4393G > C; p.Gly1465Arg), classified as pathogenic. The patient’s clinical presentation included facial dysmorphisms, skeletal abnormalities, neurodevelopmental delay, psychiatric conditions, and other systemic manifestations. A comprehensive genetic counseling process facilitated the differential diagnosis and management strategies, emphasizing the importance of early and precise diagnosis for improving clinical outcomes. This report contributes to the growing knowledge of the genotype–phenotype correlations in RSTS1, aiding in the understanding and management of this uncommon condition. Full article

Background/Objectives: Developmental disabilities substantially affect the daily lives of children and their families. Although interest in examining the developmental profiles of children with various disabilities has grown, few studies have systematically compared them. This study aimed to characterize the developmental profiles of preschool-aged children with different disabilities. It was hypothesized that developmental profiles would differ depending on the type of developmental disability. Methods: A cross-sectional study was conducted. Scores on the Battelle^® Developmental Inventory, 2nd Edition (BDI−2) were retrieved for a non-probabilistic convenience sample of 46 children diagnosed with Down syndrome (DS) (n = 22), global developmental delay (GDD) (n = 17), and developmental language disorder (DLD) (n = 7) upon completion of an early intervention program. Developmental quotients (DQs) for the overall BDI−2 and for each domain were determined. Results: The children’s mean age was 42.39 ± 5.23 months (range: 30–57). Significant differences were observed among groups with regard to global DQ and all domain-specific DQs (p ≤ 0.01). The GDD group demonstrated the highest DQs across all domains and globally, in comparison to the other groups. Conversely, children with DS had substantially lower DQs across all domains and globally compared to those with GDD, and in the motor and communication domains compared to children with DLD. Conclusions: These findings underscore the importance of early intervention strategies to improve communication in children with DS and highlight the need for regular assessments to monitor progress and identify potential limitations, particularly during the preschool-to-school transition. Additionally, specialists should advise parents of children with DLD to adopt specific behaviors that support the development of their children’s social, adaptive, and language skills. Full article

Deep learning has demonstrated significant success in hyperspectral image (HSI) classification by effectively leveraging spatial–spectral feature learning. However, current approaches encounter three challenges: (1) high spectral redundancy and the presence of noisy bands, which impair the extraction of discriminative features; (2) limited spatial receptive fields inherent in convolutional operations; and (3) unidirectional context modeling that inadequately captures bidirectional dependencies in non-causal HSI data. To address these challenges, this paper proposes HG-Mamba, a novel hybrid geometry-aware bidirectional Mamba network for HSI classification. The proposed HG-Mamba synergistically integrates convolutional operations, geometry-aware filtering, and bidirectional state-space models (SSMs) to achieve robust spectral–spatial representation learning. The proposed framework comprises two stages. The first stage, termed spectral compression and discrimination enhancement, employs multi-scale spectral convolutions alongside a spectral bidirectional Mamba (SeBM) module to suppress redundant bands while modeling long-range spectral dependencies. The second stage, designated spatial structure perception and context modeling, incorporates a Gaussian Distance Decay (GDD) mechanism to adaptively reweight spatial neighbors based on geometric distances, coupled with a spatial bidirectional Mamba (SaBM) module for comprehensive global context modeling. The GDD mechanism facilitates boundary-aware feature extraction by prioritizing spatially proximate pixels, while the bidirectional SSMs mitigate unidirectional bias through parallel forward–backward state transitions. Extensiveexperiments on the Indian Pines, Houston2013, and WHU-Hi-LongKou datasets demonstrate the superior performance of HG-Mamba, achieving overall accuracies of 94.91%, 98.41%, and 98.67%, respectively. Full article

Given the growing impact of climate change, this study examines the flowering phenology of Rosa rugosa Thunb. in Kupinovo (Vojvodina, Serbia). Data collected over 18 years (2007–2024) were analyzed to assess changes in primary flowering, while secondary flowering was monitored from 2022 to 2025. Phenological stages were recorded every other day, and dates were converted into day-of-year (DOY) values. Heat accumulation (GDD) was calculated using daily max/min temperatures and thresholds. In 2024, R. rugosa exhibited a 37-day earlier onset and a 50.4-day later completion of primary flowering compared to previous years. The variability of key phenological events of primary flowering was observed in the interaction with climatic parameters, with regular fruiting. The species proved tolerant to heat and drought, suggesting potential range expansion. Optimal temperatures for secondary flowering were identified: abundant flowering occurred at 13.6 °C max and 4.9 °C min, while moderate flowering occurred at 9.0 °C max and 4.2 °C min. Regression analysis confirmed the positive effect of rising temperatures on flowering intensity. While freezing halted secondary flowering and damaged open buds, unopened buds remained unaffected. These findings highlight R. rugosa as a resilient, ornamental species, relevant to climate adaptation strategies, nature-based solutions, and the preservation of ecosystem services under global warming scenarios. Full article

The production of winter wines in Southeastern Brazil represents a relatively recent but expanding viticultural approach, with increasing adoption across diverse wine-growing regions. This system relies on the double-pruning technique, which allows for the harvest of grapes during the dry and cooler winter season, favoring a greater accumulation of sugars, acids, and phenolic compounds. This study aimed to characterize the phenological stages, thermal requirements, yield, and fruit quality of the fine wine grape cultivars ‘Sauvignon Blanc’, ‘Merlot’, ‘Tannat’, ‘Pinot Noir’, ‘Malbec’, and ‘Cabernet Sauvignon’ under double-pruning management in a subtropical climate. The vineyard was established in 2020, and two production cycles were evaluated (2022/2023 and 2023/2024). Significant differences in the duration of phenological stages were observed among cultivars, ranging from 146 to 172 days from pruning to harvest. The accumulated thermal demand was higher in the first cycle, with a mean of 1476.9 growing degree days (GDD) across cultivars. The results demonstrate the potential of Vitis vinifera L. cultivars managed with double pruning for high-quality wine production under subtropical conditions, supporting the viability of expanding viticulture in the state of São Paulo. ‘Cabernet Sauvignon’ and ‘Sauvignon Blanc’ showed the highest yields, reaching 3.03 and 2.75 kg per plant, respectively, with productivity values of up to 10.8 t ha⁻¹. ‘Tannat’ stood out for its high sugar accumulation (23.4 °Brix), while ‘Merlot’ exhibited the highest phenolic (234.9 mg 100 g⁻¹) and flavonoid (15.3 mg 100 g⁻¹) contents. These results highlight the enological potential of the evaluated cultivars and confirm the efficiency of the double-pruning system in improving grape composition and wine quality in non-traditional viticultural regions. Full article

Gnathodiaphyseal dysplasia (GDD) is a rare autosomal dominant genetic disease, mainly characterized by enlargement of the mandible, osteosclerosis, and frequent fracture of tubular bone. GDD is caused by heterozygous mutations in Anoctamin 5 (ANO5). We have previously generated an Ano5 knockout (KO) mice model and validated the phenotypes consistent with GDD patients, including enhanced bone formation and alkaline phosphatase (ALP) activity. Experiments have identified that Ano5 deficiency elevated the osteogenesis of calvaria-derived osteoblasts (mCOBs). In this study, we found that Ano5 deficiency notably inhibited miR-34c-5p expression. Krüppel-Like Factor 4 (Klf4), a target gene of miR-34c-5p confirmed by dual luciferase reporter assay, was up-regulated in Ano5^−/− mCOBs, accompanied by activated downstream canonical Wnt/β-catenin signaling and increased expression of β-catenin. Overexpression of miR-34c-5p in Ano5^−/− mCOBs inhibited osteogenic capacity by suppressing proliferative capacity, osteoblast-related factor levels, ALP activity, and matrix calcification through regulating KLF4/β-catenin signaling axis. Furthermore, miR-34c-5p adeno-associated virus (AAV) treatment in vivo rescued the abnormally thickened cortical bone and enhanced biomechanical properties in Ano5^−/− mice. Importantly, the serum level of P1NP, a marker of bone formation, was also significantly declined. We conclude that dysregulation of miR-34c-5p contributes to the enhanced osteogenesis in GDD by excessive activation of KLF4/β-catenin signaling axis under Ano5-deficient conditions. This study elucidates the pathogenesis of GDD and provides novel insights into the therapeutic strategies. Full article

Climate variability, intensified by climate change, poses significant challenges to agriculture, affecting crop development and productivity. Integrating seasonal weather forecasts (SWF) into crop growth modelling tools is therefore essential for improving agricultural decision-making. This study assessed the uncertainties of raw (non-bias-corrected) temperature forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 seasonal (seven-month forecasts) to estimate the spring–summer maize, melon, sunflower, and tomato crops cycle from 2013 to 2022 in the Caia Irrigation Scheme, southern Portugal. AgERA5 reanalysis data, after simple bias correction using local weather station data, was used as a reference. The growing degree-day (GDD) approach was applied to estimate the crop cycle duration, which was then validated against ground truth and satellite data. The results show that SWF tend to underestimate maximum temperatures and overestimate minimum temperatures, with these biases partially offsetting to improve mean temperature accuracy. Forecast skill decreased non-linearly with lead time, especially after the second month; however, in some cases, longer lead times outperformed earlier ones. Temperature forecast biases affected GDD-based crop cycle estimates, resulting in a slight underestimation of all crop cycle durations by around a week. Nevertheless, the forecasts captured the overall increasing temperature trend, interannual variability, and anomaly signals, but with marginal added value over climatological data. This study highlights the potential of integrating ground truth and Earth observation data, together with reanalysis data and SWF, into GDD tools to support agricultural decision-making, aiming at enhancing yield and resources management. Full article

Soybean, a globally important food and oil crop, requires accurate estimation of above-ground biomass (AGB) to optimize management and prevent yield loss. Despite the availability of various remote sensing methods, systematic research on effectively integrating canopy height (CH) and spectral information for improved AGB estimation remains insufficient. This study addresses this gap using drone data. Three CH utilization approaches were tested: (1) simple combination of CH and spectral vegetation indices (VIs), (2) fusion of CH and VI, and (3) integration of CH, VI, and growing-degree days (GDDs). The results indicate that adding CH always enhances AGB estimation which is based only on VIs, with the fusion approach outperforming simple combination. Incorporating GDD further improved AGB estimation for highly accurate CH data, with the best model achieving a root mean square error (RMSE) of 87.52 ± 5.88 g/m² and a mean relative error (MRE) of 28.59 ± 1.99%. However, for the multispectral data with low CH accuracy, the VIs + GDD fusion (RMSE = 92.94 ± 6.84 g/m², MRE = 30.08 ± 2.29%) surpassed CH + VIs + GDD (RMSE = 97.99 ± 6.71 g/m², MRE = 31.41 ± 2.56%). The findings highlight the role of CH accuracy in AGB estimation and validate the value of growth-stage information in robust modeling. Future research should prioritize the refining of CH prediction and the optimization of composite variable construction to promote the application of this approach in agricultural monitoring. Full article

In the context of climate change and the development of sustainable agricultural, crop yield prediction is key to ensuring food security. In this study, long-term vegetation and meteorological indices were obtained from the MOD09A1 product and daily weather data. Three types of time series data were constructed by aggregating data from an 8-day period (DP), 9-month period (MP), and six growth periods (GP). And we developed the yield prediction model by using random forest (RF) and long short-term memory (LSTM) networks. Results showed that the average root mean squared error (RMSE) of the RF model in each province was 0.5 Mg/ha lower than that of the LSTM model. Both the RF and LSTM prediction accuracies increased with the later growth stages data. Partial dependence plots showed that the influence degree of DVI on yield was above 2 Mg/ha. When the time length of the feature variables was shortened to MP or GP, the growing degree days (GDD), average minimum temperature (AveTmin), and effective precipitation (EP) showed stronger nonlinear relationships with the statistical yields. Full article

German chamomile (Matricaria chamomilla L.) is a suitable medicinal and aromatic crop to cultivate in diverse regions, but its relationship with weather is a major concern in evaluating the development and crop production in the Western Himalayan region. Thus, a field experiment was executed for two years (2018–2019 and 2019–2020) at CSIR-Institute of Himalayan Bioresource Technology, Palampur, India, to evaluate the crop weather relationship studies and different phenological phases of German chamomile under acidic soil conditions of mid hills of Western Himalaya. Agrometeorological indices were worked out for four different sowing times from 20 November to 20 January with foliar application of elicitor, i.e., salicylic acid at three levels (viz., SA0: 0 mg/L, SA1: 25 mg/L, SA2: 50 mg/L). The results revealed that the number of days required for attaining each phenological stage decreased with a delay in sowing time. Higher growing degree days (GDDs), photothermal units (PTUs) and heliothermal units (HTUs) were accumulated for early sowing of 20 November and showed a gradual decrease with delayed sowing. Salicylic acid application produced a significant effect on the accumulation of agrometeorological indices, irrespective of the applied doses, and showed irregularity. Higher accumulation of GDDs, PTUs, and HTUs is associated with higher flower and essential oil yield; thus, the results showed that agrometeorological indices are associated with the production of German chamomile. Full article

This article presents an innovative integration of Glow Discharge Detector Focal Plane Arrays (GDD FPA) with Chirped Frequency Modulated Continuous Wave (FMCW) Radar, enhancing millimeter-wave (MMW) imaging. The cost-effective FPA design using GDDs as pixel elements forms the foundation of the system. We investigate MMW effects on GDD discharge currents via basic data acquisition (DAQ) and implement a scanning mechanism with a step motor for sub-pixel imaging. The setup integrates an MMW source, optical components, a timer/counter, and an 8 × 8 FPA with 64 GDD, operating in electrical detection modes and processing signals using Fast Fourier Transform (FFT) algorithms. Recent advancements in millimeter-wave imaging have focused on improving image resolution and acquisition speed through various techniques, including lock-in amplifiers and electrical detection methods. However, these methods introduce complexity, cost, and extended acquisition times. Our approach mitigates these challenges by implementing a simplified FPA design that eliminates the need for external signal conditioning elements, providing faster and more efficient image acquisition. The primary contributions include significant improvements in the speed and automation of image acquisition achieved through a coordinated control mechanism for efficient row scanning. Compared to previous generations of GDD FPAs, this system achieves a notable reduction in image acquisition time by up to 75%, while maintaining high fidelity. These enhancements make the system particularly suitable for time-sensitive applications. Additionally, future research directions include the incorporation of 3D imaging using FMCW radar. Results from the FMCW measurements using the single GDD circuit demonstrate the system’s ability to accurately capture and process MMW radiation, even at low intensities. The combined strengths of GDD FPA and chirped FMCW radar underscore the system’s effectiveness in MMW detection, laying the groundwork for advanced MMW imaging capabilities across diverse applications. Full article

The cultivation of table grapes in Brazil is economically significant, with production influenced by edaphoclimatic factors and rootstock selection. The cultivar ‘BRS Núbia’ (Vitis vinifera L. x Vitis labrusca L.) is a promising alternative; however, its phenological behavior, thermal requirements, and compatibility with different rootstocks under subtropical conditions require further evaluation. This study aimed to assess the duration of phenological stages, thermal requirement, and ripening dynamics of ‘BRS Núbia’ grapevines grafted onto the rootstocks ‘IAC 572 Jales’, ‘IAC 766 Campinas’, and ‘Paulsen 1103’. The experiment was conducted in São Manuel, São Paulo, Brazil during the 2021 and 2022 production cycles using a split-plot experimental design (3 × 2). Evaluations included the duration of phenological stages from pruning to budburst, flowering, fruit set, onset of ripening, and harvest, as well as the ripening curve and thermal accumulation from pruning to harvest. Rootstocks did not significantly affect (p > 0.05) the duration of phenological stages; however, differences were observed between production cycles. The 2022 cycle was longer (167.7 days) compared to 2021 (142.6 days), with greater thermal accumulation (1871.7 GDDs vs. 1743.4 GDDs). The analysis of phenological stages revealed that, across both production cycles evaluated, the ‘BRS Núbia’ cultivar required an average accumulation of 1807.5 growing degree days from pruning to harvest. Soluble solids content ranged from 17.43 to 18.50°Brix, and titratable acidity decreased throughout maturation. The maturation index was highest in vines grafted onto ‘Paulsen 1103’, indicating its positive influence on fruit quality. The ‘BRS Núbia’ grapevine exhibited a mean thermal requirement of 1807.5 growing degree days (GDDs) to complete its phenological cycle, which lasted approximately 150 days under subtropical conditions. Full article

This paper presents a preliminary proof-of-concept study of a novel approach to 3D millimeter-wave (MMW) imaging, demonstrating the first implementation of Glow Discharge Detectors (GDDs) in this domain. GDDs offer significant advantages over conventional MMW detectors like Schottky diodes or bolometers due to their cost-effectiveness, robustness to high-power MMW signals, and reliable operation under diverse environmental conditions. Based on weakly ionized plasma (WIP) technology, GDDs detect changes in discharge current upon MMW exposure, providing an affordable and durable alternative to traditional MMW imaging systems. The system operates within a subset of the W-band (101–109 GHz), utilizing a customized transmitter (Tx 272 from VDI Technologies), which operates at a frequency range proportional to the VCO supply voltage level. The Frequency-Modulated Continuous Wave (FMCW) signal source is split into target and reference paths via a compact waveguide splitter, improving stability and reducing the complexity of the optical setup. Reflected signals are processed by the GDD, which functions as a heterodyne receiver, and Fast Fourier Transform (FFT) is used to extract range data. A 2D grid scanning mechanism, controlled by step motors, maps the surface of the object, while depth information is derived from FMCW frequency differentials to construct a complete 3D profile. This work demonstrates the potential of GDD-based 3D MMW imaging as a low-cost, efficient solution for security screening and industrial inspection. By addressing challenges in cost, scalability, and performance under high-power MMW signals, this approach represents a significant step forward in making MMW imaging technology more accessible, while highlighting the need for further development to achieve practical implementation. Full article

Hemp (Cannabis sativa L.), one of the earliest domesticated crops, has diverse applications in textiles, construction, nutrition, and medicine. Breeding advancements, including speed breeding, accelerate genetic improvements in crops by optimizing environmental conditions for reduced generation times. This study employed greenhouse and field experiments to develop a proprietary yellow-stemmed hemp germplasm with a unique stem trait. Initial crossbreeding between the late Eletta Campana (medium green stems) and the early Chamaeleon (yellow stems) demonstrated the recessive monogenic inheritance of the yellow-stem trait and fast and safe stabilization even in the case of parent varieties with different flowering times. Controlled flowering in the case of photoperiod-sensitive genotypes, manual pollination, and successive backcrossing stabilized the yellow-stem trait over six cycles, with 100% trait consistency achieved by the fifth cycle within just 12 months in total. Open-field trials validated greenhouse results, showing strong correlations between visual stem color assessments and visible atmospherically resistant index (VARI) obtained through remote sensing imagery. Cannabinoid analyses indicated significant reductions in tetrahydrocannabinol (THC) content while maintaining optimal cannabidiol (CBD) levels. Accumulated growing degree days (GDDs) optimized flowering and maturity, ensuring consistency in phenological traits. This research highlights the utility of speed breeding and chemical analysis to accelerate trait stabilization and improve industrial hemp’s agronomic potential for fiber and CBD production while adhering to regulatory THC limits. Full article

Climate change has led to significant changes in water and heat conditions in crop production areas, which have in turn affected the spring maize growth and yield. This study analyzed the spatiotemporal variation characteristics of the water and heat conditions, such as the growth degree-day (GDD), killing degree-day (KDD), sunshine hours (SD), effective precipitation (Pe), and irrigation water requirement (IR), of spring maize in Inner Mongolia based on data from 50 meteorological stations. The relationship between hydrothermal conditions and yield was revealed using methods that included stepwise regression analysis. The results showed that the GDD during the spring maize growth period ranged from 513 to 2011 °C, with high-value areas concentrated in western and southeastern regions of Inner Mongolia. The GDD showed an increasing trend during all the growth periods. High-KDD areas were mainly distributed in the Alxa League region in western Inner Mongolia, and the KDD showed an increasing trend during all periods except the rapid fertility period. The spatial distribution of the SD was consistent with that of the GDD, and the SD values for each reproductive period showed a decreasing trend. The average Pe and IR values in the last 60 years were 111 mm and 386 mm, respectively, and showed opposite spatial distribution trends. The overall Pe trend was decreasing and that of the IR was increasing, which will aggravate future water resource consumption in the region. Stepwise regression analysis showed that the Pe was the main factor affecting the spring maize yield. Overall, the spring maize fertility period in various regions of Inner Mongolia was extremely uneven in terms of the hydrothermal conditions. This study provides a basis for the regional spatial spring maize layout and the sustainable use of water and heat resources in the region. Full article