Search Results (921)

Background/Objectives: Bolting in lettuce (Lactuca sativa L.) is highly sensitive to elevated temperatures, leading to premature flowering and reduced crop quality and yield. Although SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) is a well-known floral integrator in Arabidopsis, its role in heat-induced bolting in lettuce remains unclear. Methods: In this study, we generated CRISPR/Cas9-mediated LsSOC1 knockout (KO) lines and evaluated their phenotypes under high-temperature conditions. Results: LsSOC1-KO lines exhibited delayed bolting up to 18.6 days, and stem elongation was reduced by approximately 3.8 cm, which is equivalent to a 36.1% decrease compared to wild-type (WT) plants. Transcriptome analysis of leaf and bud tissues identified 32 up-regulated and 10 down-regulated genes common to leaf tissue (|log₂FC| ≥ 1, adjusted p < 0.05). Among them, GA20-oxidase1 was significantly down-regulated in both tissues, which may have contributed to delayed floral transition and possibly to reduced stem elongation, although tissue-specific regulation of gibberellin metabolism warrants further investigation. In contrast, genes encoding heat shock proteins, ROS-detoxification enzymes, and flavonoid biosynthetic enzymes were up-regulated, suggesting a dual role of LsSOC1 in modulating thermotolerance and floral transition. qRT-PCR validated the sustained suppression of flowering-related genes in LsSOC1 KO plants under 37 °C heat stress. Conclusions: These findings demonstrate that LsSOC1 is a key integrator of developmental and thermal cues, orchestrating both bolting and stress-responsive transcriptional programs. Importantly, delayed bolting may extend the harvest window and improve postharvest quality in lettuce, highlighting LsSOC1 as a promising genetic target for breeding heat-resilient leafy vegetables. Full article

Background/Objectives: Chemotherapy is an aggressive form of oncological treatment often accompanied by numerous adverse effects. A patient’s baseline status significantly influences the course of therapy, its efficacy, quality of life, and overall survival. This review aims to analyze the published peer-reviewed studies in this area and to assess whether they permit the formulation of preliminary recommendations for future prehabilitation protocols. Methods: An integrative review was conducted due to the limited number of relevant studies. Four databases—MEDLINE/PubMed (Medical Literature Analysis and Retrieval System Online/National Library of Medicine), Scopus, Cochrane, and Web of Science—were systematically searched for English-language articles published between 2010 and 13 January 2025, using the terms “prehabilitation,” “chemotherapy,” “drug therapy,” and “neoadjuvant.” A total of 162 records were retrieved. After duplicate removal, titles and abstracts were screened. The remaining papers were subjected to detailed analysis, resulting in ten studies with diverse methodologies being included. Results: We reviewed ten (n = 10) studies, most of which were reviews focused on breast cancer, indicating variation in the state of knowledge across different cancer types. A protein intake of 1.4 g/kg body mass helps preserve fat-free mass, with whey being more effective than casein. Omega-3 fatty acid supplementation at a dose of 2.2 g/kg may prevent chemotherapy-related neurotoxicity and support appetite and weight maintenance. Physical activity, especially when it includes strength training, improves VO₂max, preserves fat-free mass, and may reduce stress and anxiety. We identified one randomized controlled trial in which a single exercise session before the first dose of doxorubicin resulted in a smaller reduction in cardiac function. Continuous psychological support should be available. A combined behavioural and pharmacological approach appears to be the most effective strategy for smoking cessation. Conclusions: No official guidelines exist for prehabilitation before chemotherapy, and the availability of studies on this topic is very limited. The pre-treatment period represents a critical window for interventions. Further research is needed to evaluate the effectiveness and applicability of particularly single-component interventions. Full article

Background/Objectives: Early diagnosis of Parkinson’s Disease (PD) is essential for initiating interventions that may slow its progression and enhance patient quality of life. Gait analysis provides a non-invasive means of capturing subtle motor disturbances, enabling the prediction of both disease presence and severity. This study evaluates and contrasts Bayesian-optimized convolutional neural network (CNN) and long short-term memory (LSTM) models applied directly to Vertical Ground Reaction Force (VGRF) signals for Parkinson’s disease detection and staging. Methods: VGRF recordings were segmented into fixed-length windows of 5, 10, 15, 20, and 25 s. Each segment was normalized and supplied as input to CNN and LSTM network. Hyperparameters for both architectures were optimized via Bayesian optimization using five-fold cross-validation. Results: The Bayesian-optimized LSTM achieved a peak binary classification accuracy of 99.42% with an AUC of 1.000 for PD versus control at the 10-s window, and 98.24% accuracy with an AUC of 0.999 for Hoehn–Yahr (HY) staging at the 5-s window. The CNN model reached up to 98.46% accuracy (AUC = 0.998) for binary classification and 96.62% accuracy (AUC = 0.998) for multi-class severity assessment. Conclusions: Bayesian-optimized CNN and LSTM models trained on VGRF data both achieved high accuracy in Parkinson’s disease detection and staging, with the LSTM exhibiting a slight edge in capturing temporal patterns while the CNN delivered comparable performance with reduced computational demands. These results underscore the promise of end-to-end deep learning for non-invasive, gait-based assessment in Parkinson’s disease. Full article

Background and Objectives: Borderline personality disorder (BPD) is a complex psychiatric condition characterized by emotional instability, impulsivity, a fluctuating self-image, and persistent difficulties in maintaining close interpersonal relationships. Among university students, these traits may be associated with social adjustment and academic functioning difficulties. The present study aimed to examine the prevalence of borderline traits within a Romanian student population and to investigate the associations between these traits and interpersonal difficulties encountered in family life, romantic relationships, and academic environments. Materials and Methods: This cross-sectional study included a total of 151 undergraduate students enrolled in higher education institutions across Romania. Data were gathered through an online questionnaire available between March and May 2025. The instrument comprised items addressing socio-demographic characteristics, diagnostic criteria for borderline personality traits according to the DSM, as well as self-reported social behaviour patterns. Statistical analysis was performed using GraphPad Prism 9, version 9.3.1 for Windows, employing Fisher’s exact test and the odds ratio (OR), with a significance threshold set at p < 0.05. Results: Most participants reported experiencing affective instability (71.5%) and distorted self-image (58.9%). Fear of abandonment was present in 29.4% of the respondents, while impulsivity was identified in 37.7%. Borderline personality traits were significantly associated with a range of social difficulties, including relational anxiety, outbursts of anger, peer conflicts, social withdrawal, and dissociative symptoms. Individuals who exhibited impulsivity, self-injurious behaviours, or dissociative episodes demonstrated a markedly increased risk of social dysfunction, with odds ratios ranging from 3 to 10 (p < 0.0001). Conclusions: The findings reveal a high prevalence of borderline traits within the analysed sample, along with statistically significant associations with social and emotional difficulties. These results underscore the importance of implementing psychological screening programs in universities, as well as early intervention strategies focused on the mental well-being of young adults. Establishing a supportive academic environment and fostering collaboration between faculty members and mental health professionals may play a key role in preventing symptom escalation and in promoting healthy personal and relational development. Full article

Background: The process of muscle protein synthesis (MPS) plays a pivotal role in the enhancement of muscle function. Following a bout of exercise, the rate of MPS experiences an elevation for a brief period, known as the “anabolic window.” Despite whey protein supplementation has been demonstrated to augment the post-exercise anabolic window, the optimal timing and dosage remain controversial. Therefore, the present systematic review and meta-analysis were conducted to evaluate the effects of whey protein supplementation on post-exercise MPS and its protein kinase B (AKT)/mammalian target of the rapamycin (mTOR) pathway in healthy adults. Methods: Following PRISMA guidelines, this review included 21 RCTs, with 15 studies subjected to meta-analysis and 6 studies to qualitative analysis. Eligible studies examined myofibrillar fractional synthetic rate (FSR) or the AKT/mTOR pathway-related protein phosphorylation levels in muscle biopsy samples. Results: The combination of whey protein supplementation and exercise has been shown to significantly enhance FSR (Hedge’s g = 1.24, 95% CI: 0.71–1.77; p < 0.001), with increases ranging from 1.3 to 1.6 folds when consumed immediately after exercise and up to 2.5 folds when given 45 min prior to multiple-set resistance exercise. A dose-dependent increase in FSR was observed in response to whey protein supplementation, ranging from 10 to 60 g. In comparison to the placebo group, whey protein supplementation enhanced the phosphorylation levels of AKT, mTOR, eukaryotic translation initiation factor 4E-binding protein-1 (4E-BP1), 70 kDa ribosomal protein S6 kinase (p70S6K), and ribosomal protein S6 (rpS6) at 1–2 h post-exercise. Phosphorylation levels of p70S6K and rpS6 decreased 4–5 h after exercise. Conclusions: The combination of whey protein supplementation and exercise improves MPS in a time- and dose-dependent manner. Consumption of 20–40g of whey protein before multiple sets of resistance exercise may enhance myofibrillar FSR and activate the AKT/mTOR pathway, thereby augmenting MPS and extending the anabolic window. Full article

Force-field Monte Carlo and Molecular Dynamics simulations are used to compare wetting behaviors of model carbon sheets mimicking neat graphene, its saturated derivative, graphane, and related planar allotropes penta-graphene, γ-graphyne, and ψ-graphene in contact with aqueous droplets or an aqueous film confined between parallel carbon sheets. Atomistic and area-integrated surface/water potentials are found to be essentially equivalent in capturing moderate differences between the wetting free energies of tested substrates. Despite notable differences in mechanical and electric properties of distinct allotropes, the predicted allotrope/water contact angles span a narrow window of weakly hydrophilic values. Contact angles in the range of 80 ± 10° indicate modest hydration repulsion incapable of competing with van der Waals attraction between carbon particles. Poor dispersibility in neat water is hence a common feature of studied materials. Full article

Accurate fetal R-peak detection from low-SNR fetal electrocardiogram (FECG) signals remains a critical challenge as current NI-FECG methods struggle to extract high SNR FECG signals and conventional algorithms fail when signal quality deteriorates. We proposed a U-Net-based method that enables robust R-peak detection directly from low-SNR FECG signals (0–12 dB), bypassing the need for high-SNR inputs that are clinically difficult to acquire. The method was evaluated on both real (A&D FECG) and synthetic (FECGSYN) databases, comparing against ten state-of-the-art detectors. The proposed method significantly reduces false predictions compared to commonly used detection algorithms, achieving a PPV of 99.81%, an SEN of 100.00%, and an F1-score of 99.91% on the A&D FECG database and a PPV of 99.96%, an SEN of 99.93%, and an F1-score of 99.94% on the FECGSYN database. Further investigation of robustness in low-SNR conditions (0 dB, 5 dB, and 10 dB) achieved 87.38% F1-score at 0 dB SNR on real signals, surpassing the best-performing algorithm implemented in Neurokit by 13.58%. In addition, the algorithm showed ≤2.65% performance variation across tolerance windows (50 reduced to 20 ms), further underscoring its detection accuracy. Overall, this work reduces the reliance on high-SNR FECG signals by reliably extracting R-peaks from suboptimal signals, providing implications for the reliability of fetal heart rate variability analysis in real-world noisy environments. Full article

Land use significantly influences mobility dynamics, affecting both travel behavior and mode choice. Traditional indicators such as the Floor Area Ratio, Land-Use Mix Index, and Built-up Area Ratio are widely used to describe settlement patterns; yet, they often fail to capture their functional impacts on road networks. This study introduces two complementary indicators—Settlement Ratio (SR) and Settled Area (SA)—developed through a spatial analysis framework integrating GIS data and MATLAB processing. SR offers a continuous typological profile of built-up functions along the road axis, while SA measures the percentage of anthropized land within fixed analysis windows. Applied to two Italian state roads, SS14 and SS309, in the Veneto Region, the dual-indicator approach reveals how the intensity (SR) and extent (SA) of settlement vary across different territorial contexts. In suburban segments, SR values exceeding 15–20, together with SA levels between 10% and 15%, highlight the significant spatial impact of isolated development clusters—often not evident from macro-scale observations. These findings demonstrate that the SR–SA framework provides a robust tool for analyzing land use in relation to road function. Although the study focuses on spatial structure and indicator design, future developments will explore correlations with traffic flow, speed, and crash data to support road safety analyses. Full article

This study investigates the influence of façade-design parameters on daylighting performance in hot arid climates, with a particular focus on Egypt. A total of nine façade configurations of a residential building were modeled and simulated using Autodesk Revit and Insight 360, varying three critical variables: glazing type (clear, blue, and dark), Window-to-Wall Ratio (WWR) of 15%, 50%, 75%, and indoor wall finish (light, moderate, dark) colors. These were compared to the Leadership in Energy and Environmental Design (LEED) daylighting quality thresholds. The results revealed that clear glazing paired with high WWR (75%) achieved the highest Spatial Daylight Autonomy (sDA), reaching up to 92% in living spaces. However, this also led to elevated Annual Sunlight Exposure (ASE), with peak values of 53%, exceeding the LEED discomfort threshold of 10%. Blue and dark glazing types successfully reduced ASE to as low as 0–13%, yet often resulted in underlit spaces, especially in private rooms such as bedrooms and bathrooms, with sDA values falling below 20%. A 50% WWR emerged as the optimal balance, providing consistent daylight distribution while maintaining ASE within acceptable limits (≤33%). Similarly, moderate color wall finishes delivered the most balanced lighting performance, enhancing sDA by up to 30% while controlling reflective glare. Statistical analysis using Pearson correlation revealed a strong positive relationship between sDA and ASE (r = 0.84) in highly glazed, clear glass scenarios. Sensitivity analysis further indicated that low WWR configurations of 15% were highly influenced by glazing and finishing types, leading to variability in daylight metrics reaching ±40%. The study concludes that moderate glazing (blue), medium WWR (50%), and moderate color indoor finishes provide the most robust daylighting performance across diverse room types. These findings support an evidence-based approach to façade design, promoting visual comfort, daylight quality, and sustainable building practices. Full article

Electrohydrodynamic (EHD) printing offers mask-free, high-resolution deposition across a broad range of ink viscosities, yet combining void-free filling of high-aspect-ratio through-glass vias (TGVs) with ultrafine drop-on-demand (DOD) line printing on the same platform requires balancing conflicting requirements: for example, high field strengths to drive ink into deep and narrow vias; sufficiently high ink viscosity to prevent gravity-induced leakage; and stable meniscus dynamics to avoid satellite droplets and charge accumulation on the glass surface. By coupling electrostatic field analysis with transient level-set simulations, we establish a dimensionless regime map that delineates stable cone-jetting regime; these predictions are validated by high-speed imaging and surface profilometry. Operating within this window, the platform achieves complete, void-free filling of 200 µm × 1.52 mm TGVs and continuous 10 µm-wide traces in a single print pass. Demonstrating its capabilities, we fabricate transparent Ku-band substrate-integrated waveguide antennas on borosilicate glass: the printed vias and arc feed elements exhibit a reflection coefficient minimum of −18 dB at 14.2 GHz, a −10 dB bandwidth of 12.8–16.2 GHz, and an 8 dBi peak gain with 37° beam tilt, closely matching full-wave predictions. This physics-driven, all-in-one EHD approach provides a scalable route to high-performance, glass-integrated RF devices and transparent electronics. Full article

Background/Objectives: Neck pain caused by myofascial pain syndrome (MPS) is a highly prevalent musculoskeletal condition. Repetitive peripheral magnetic stimulation (rPMS) is a promising treatment option; however, its therapeutic effect and optimal treatment frequency remain unclear. This study aimed to investigate the therapeutic effect and duration of effect of rPMS in patients with MPS of the neck. Methods: In this randomized, sham-controlled, crossover trial, 27 patients with neck MPS and baseline visual analog scale (VAS) scores ≥ 40 were enrolled. The mean age was 43.8 ± 9.1 years, and 63% were female. Participants were randomly assigned to receive either an initial rPMS treatment (a 10 min session delivering 3900 pulses at 5–10 Hz) or sham stimulation. After 7 days, groups crossed over. Pain intensity (VAS), disability (Neck Disability Index; NDI), and analgesic use were recorded daily for seven consecutive days. A linear mixed-effects model was used for analysis. Results: At baseline, the VAS and NDI scores were 61.8 ± 10.5 and 26.0 ± 6.3, respectively. rPMS produced a significantly greater reduction in both VAS and NDI scores, with the greatest differences observed on Day 4: the differences were −24.1 points in VAS and −8.5 points in NDI compared to the sham group. There was no significant difference in analgesic use between the two groups. Conclusions: A single rPMS session provides short-term improvement in pain and disability in neck MPS. Based on the observed therapeutic window, more frequent sessions (e.g., twice weekly) may provide sustained benefit and should be explored in future studies. Full article

We introduce a novel portfolio optimization framework—Distributionally Robust Multivariate Stochastic Cone Order (DR-MSCO)—which integrates partial orders on random vectors with Wasserstein-metric ambiguity sets and adaptive cone structures to model multivariate investor preferences under distributional uncertainty. Grounded in measure theory and convex analysis, DR-MSCO employs data-driven cone selection calibrated to market regimes, along with coherent tail-risk operators that generalize Conditional Value-at-Risk to the multivariate setting. We derive a tractable second-order cone programming reformulation and demonstrate statistical consistency under empirical ambiguity sets. Empirically, we apply DR-MSCO to 23 Borsa Istanbul equities from 2021–2024, using a rolling estimation window and realistic transaction costs. Compared to classical mean–variance and standard distributionally robust benchmarks, DR-MSCO achieves higher overall and crisis-period Sharpe ratios (2.18 vs. 2.09 full sample; 0.95 vs. 0.69 during crises), reduces maximum drawdown by 10%, and yields endogenous diversification without exogenous constraints. Our results underscore the practical benefits of combining multivariate preference modeling with distributional robustness, offering institutional investors a tractable tool for resilient portfolio construction in volatile emerging markets. Full article

Intermediate-band photovoltaics promise single-junction efficiencies that exceed the Shockley and Queisser limit, yet viable material platforms and device geometries remain under debate. Here, we perform comprehensive two-dimensional device-scale simulations using Silvaco Atlas TCAD to analyze p-i-n In_0.20Ga_0.80N solar cells in which the intermediate band is supplied by In_0.35Ga_0.65N quantum dots located inside the intrinsic layer. Quantum-dot diameters from 1 nm to 10 nm and areal densities up to 116 dots per period are evaluated under AM 1.5G, one-sun illumination at 300 K. The baseline pn junction achieves a simulated power-conversion efficiency of 33.9%. The incorporation of a single 1 nm quantum-dot layer dramatically increases efficiency to 48.1%, driven by a 35% enhancement in short-circuit current density while maintaining open-circuit voltage stability. Further increases in dot density continue to boost current but with diminishing benefit; the highest efficiency recorded, 49.4% at 116 dots, is only 1.4 percentage points above the 40-dot configuration. The improvements originate from two-step sub-band-gap absorption mediated by the quantum dots and from enhanced carrier collection in a widened depletion region. These results define a practical design window centred on approximately 1 nm dots and about 40 dots per period, balancing substantial efficiency gains with manageable structural complexity and providing concrete targets for epitaxial implementation. Full article

Background/Objectives: Traditional dietary screeners face significant limitations: they rely on subjective self-reporting, average intake estimates, and are influenced by a participant’s awareness of being observed—each of which can distort results. These factors reduce both accuracy and reproducibility. The Guide Against Age-Related Disease (GARD) addresses these issues by applying Assembly Theory to objectively quantify food and food behavior (FFB) complexity. This study aims to validate the GARD as a structured, bias-resistant tool for dietary assessment in clinical and research settings. Methods: The GARD survey was administered in an internal medicine clinic within a suburban hospital system in the southeastern U.S. The tool assessed six daily eating windows, scoring high-complexity FFBs (e.g., fresh plants, social eating, fasting) as +1 and low-complexity FFBs (e.g., ultra-processed foods, refined ingredients, distracted eating) as –1. To minimize bias, patients were unaware of scoring criteria and reported only what they ate the previous day, avoiding broad averages. A computer algorithm then scored responses based on complexity, independent of dietary guidelines. Internal (face, convergent, and discriminant) validity was assessed using Spearman rho correlations. Results: Face validation showed high inter-rater agreement using predefined Assembly Index (A_i) and Copy Number (N_i) thresholds. Positive correlations were found between high-complexity diets and behaviors (rho = 0.533–0.565, p < 0.001), while opposing constructs showed moderate negative correlations (rho = –0.363 to −0.425, p < 0.05). GARD scores aligned with established diet patterns: Mediterranean diets averaged +22; Standard American Diet averaged −10. Full article

The increasing complexity of modern control systems highlights the need for reliable and robust fault detection, isolation, and identification (FDII) methods, particularly in safety-critical and industrial applications. The study focuses on the FDII of multiplicative faults in a DC motor and its electronic amplifier. To simulate such scenarios, a complete laboratory platform was developed for real-time FDII, using relay-based switching and custom LabVIEW software 2009. This platform enables real-time experimentation and represents an important component of the study. Two estimation-based fault detection (FD) algorithms were implemented: the Sliding Window Algorithm (SWA) for discrete-time models and a modified Sliding Integral Algorithm (SIA) for continuous-time models. The modification introduced to the SIA limits the data length used in least squares estimation, thereby reducing the impact of transient effects on parameter accuracy. Both algorithms achieved high model output-to-measured signal agreement, up to 98.6% under nominal conditions and above 95% during almost all fault scenarios. Moreover, the proposed fault isolation and identification methods, including a decision algorithm and an indirect estimation approach, successfully isolated and identified faults in key components such as amplifier resistors (R₁, R₉, R₁₂), capacitor (C₈), and motor parameters, including armature resistance (R_a), inertia (J), and friction coefficient (B). The decision algorithm, based on continuous-time model coefficients, demonstrated reliable fault isolation and identification, while the reduced Jacobian-based approach in the discrete model enhanced fault magnitude estimation, with deviations typically below 10%. Additionally, the platform supports remote experimentation, offering a valuable resource for advancing model-based FDII research and engineering education. Full article