Search Results (33,226)

A novel xylanase gene (RuXyn854) was identified from the rumen metagenome and was heterologously expressed in Escherichia coli to produce xylo-oligosaccharides (XOSs) as a prebiotic in this study. RuXyn854, a member of glycosyl hydrolase family 10, demonstrated peak enzymatic activity at pH 7.0 and 50 °C. RuXyn854 retains more than 50% of its activity after treatment at 100 °C for 10 min, highlighting the enzyme’s excellent heat resistance. RuXyn854 showed a preferential hydrolyzation of xylan, especially rice straw xylan. RuXyn854 activity was significantly increased in the presence of 15 mM Mn²⁺, 0.25% Tween-20, and 0.25% Triton X-100 (125%, 20%, and 26%, respectively). The reaction temperature (30, 40, and 50 °C), dosage (0.20, 0.27, and 0.34 U), and time (90, 120, and 150 min) of RuXyn854 affected the XOS yield and composition, with a higher yield at 0.27 U, 50 °C, and 120–150 min. Xylobiose, xylotriose, and xylotetraose were characterized as the predominant XOS products resulting from the enzymatic hydrolysis of wheat straw xylan by RuXyn854, with xylose present at a mere 0.49% of the total yield. The prebiotic potential of XOSs was assessed through in vitro fermentation with established probiotic strains of Bifidobacterium bifidum and Lactobacillus brevis. The results showed that, regardless of incubation time, XOSs stimulated the growth and xylanolytic enzyme secretion of the two probiotics compared to the controls. These results demonstrate that the feature of RuXyn854 to withstand temperatures up to 100 °C is impressive, and its ability to hydrolyze wheat xylan into XOSs promotes the growth of probiotics. Full article

The pursuit of efficient drying methods that preserve fruit quality remains a major challenge in food processing. Non-thermal pre-treatments such as ultrasonication (U), edible film coating (F), and osmotic dehydration (O) can improve drying performance but show limited effectiveness when applied individually. This study investigates a combined pre-treatment strategy for pear drying, evaluating a taro starch-based edible coating used alone and in combination with U and O. Pear slices received individual and combined pre-treatments (F, OF, UF, and UOF) prior to drying at temperatures of 60, 70, and 80 °C. The drying kinetics were modeled, and quality parameters such as effective moisture diffusivity (D_eff), rehydration capacity, microstructure, color, total phenolic content (TPC), antioxidant activity, and vitamin C, were assessed. The Page model fitted the drying data the best (R² > 0.9935). UF achieved the shortest drying time and a porous microstructure, thereby enhancing rehydration. OF showed the highest D_eff and best color retention, but the lowest rehydration. Conversely, UOF caused the greatest losses in bioactive compounds (TPC: 54.29 mg GAE/100 g; antioxidant activity: 15.39%; 0.48 mg vitamin C/100 g). Unlike single-technology studies, this sequential pre-treatment strategy for pears uniquely tailors the final quality, targeting efficiency, color, bioactivity, or structural properties. Full article

A widely used repository of violent death records is the U.S. Centers for Disease Control National Violent Death Reporting System (NVDRS). The NVDRS includes narrative data, which researchers frequently utilize to go beyond its structured variables. Prior work has shown that NVDRS narratives vary in length depending on decedent and incident characteristics, including race/ethnicity. Whether these length differences reflect differences in narrative information potential is unclear. We use the 2003–2021 NVDRS to investigate narrative length and complexity measures among 300,323 suicides varying in decedent and incident characteristics. To do so, we operationalized narrative complexity using three manifest measures: word count, sentence count, and dependency tree depth. We then employed regression methods to predict word counts and narrative complexity scores from decedent and incident characteristics. Both were consistently lower for black non-Hispanic decedents compared to white, non-Hispanic decedents. Although narrative complexity is just one aspect of narrative information potential, these findings suggest that the information in NVDRS narratives is more limited for some racial/ethnic minorities. Future studies, possibly leveraging large language models, are needed to develop robust measures to aid in determining whether narratives in the NVDRS have achieved their stated goal of fully describing the circumstances of suicide. Full article

Background: Cesarean section (CS) frequently results in abdominal scarring, affecting recovery, aesthetics, and quality of life. Platelet-rich plasma (PRP), an autologous concentrate rich in growth factors, may enhance wound healing. This pilot trial assessed the effect of intraoperative PRP on CS scar outcomes. Methods: In this single-center, single-blind randomized controlled trial (February 2023–December 2024), 100 women undergoing elective CS were randomized to PRP treatment (n = 50) or standard care (n = 50). PRP, prepared from 20 mL autologous blood, was infiltrated into uterine incision margins and subcutaneously before skin closure. Scar healing was evaluated at day 7 and day 40 postpartum using the Patient and Observer Scar Assessment Scale (POSAS; physician and patient), Vancouver Scar Scale, Manchester Scar Scale, REEDA (Redness, Edema, Ecchymosis, Discharge, Approximation) Scale, Visual Analog Scale (VAS), and Numeric Rating Scale (NRS). Mann–Whitney U tests and Cohen’s d effect sizes were calculated. Results: Follow-up was complete for all participants. On day 7, PRP-treated patients had lower mean scores across most scales (e.g., Vancouver: 1.74 ± 1.58 vs. 2.54 ± 2.30; p = 0.063). At day 40, improvements persisted, with POSAS Patient scores significantly lower in the PRP group (7.24 ± 1.81 vs. 8.00 ± 2.06; p = 0.029). Effect sizes were small-to-moderate (<0.5), suggesting underpowering. No adverse events occurred. Conclusions: PRP administration during CS showed favorable trends toward improved scar quality and reduced patient-reported discomfort, with statistical significance for POSAS Patient scores at 40 days. Larger, multicenter trials with extended follow-up are needed to confirm these findings. Full article

Methods, algorithms, and models for the creation and practical application of digital twins (3D models) of agricultural crops are presented, illustrating their condition under different levels of atmospheric CO₂ concentration, soil, and meteorological conditions. An algorithm for digital phenotyping using machine learning methods with the U²-Net architecture are proposed for segmenting plants into elements and assessing their condition. To obtain a dataset and conduct verification experiments, a prototype of a software and hardware complex has been developed that implements the process of cultivation and digital phenotyping without disturbing the microclimate inside the chamber and eliminating the subjectivity of measurements. In order to identify new data and confirm the data published in open scientific sources on the effects of CO₂ on crop growth and development, plants (ten species) were grown at different CO₂ concentrations (0.015–0.03% and 0.07–0.09%) with a 10-fold repetition. A model has been built and trained to distinguish between cases when plant segments need to be combined because they belong to the same leaf (p-value = 0.05), and when they belong to a separate leaf (p-value = 0.03). A knowledge base has been formed, including: 790 3D models of plants and data on their physiological characteristics. Full article

Background: Mental health conditions are a growing public health concern among U.S. adolescents, particularly high school students. Emerging data show a strong link between exposure to neighborhood violence and increased risk of poor mental health outcomes, such as depression, anxiety, and persistent sadness. Objective: This study examined the relationship between neighborhood violence exposure and mental health outcomes among high school students. Method: This is a cross-sectional study using the 2023 Youth Risk Behavior Survey. The sample included 19,910 students in grades 9–12 across gender and race. Mental health status and exposure to neighborhood violence were analyzed using chi-square tests and logistic regression models. Results: Students exposed to neighborhood violence had significantly higher odds of reporting poor mental health outcomes (AOR = 1.789, 95% CI: 1.573–2.035, p < 0.001) than the unexposed. Additionally, female, Hispanic/Latino, and multiracial students reported higher rates of mental health disorders than the male students. Conclusions: Neighborhood violence exposure was significantly associated with poor mental health outcomes among high school students. These findings signify the need for targeted interventions to support affected students and reduce neighborhood violence exposure, particularly in marginalized communities. The findings will inform public health professionals, educators, and policymakers to make targeted school-based mental health interventions and community-centered policies addressing neighborhood safety and adolescent mental health. Full article

The rapid development of unmanned aerial vehicles (UAVs) and deep learning has accelerated the application of semantic segmentation in precision agriculture (SSPA). A key driver of this progress lies in multimodal fusion, which leverages complementary structural, spectral, and physiological information to enhance the representation of complex agricultural scenes. Despite advancements, the efficacy of multimodal fusion in SSPA is limited by modality heterogeneity and the difficulty of simultaneously retaining fine details and capturing global context. To address these challenges, we propose AgriFusion, a dual-encoder framework based on convolutional and transformer architectures for SSPA tasks. Specifically, convolutional and transformer encoders are first used to extract crop-related local structural details and global contextual features from multimodal inputs. Then, an attention-based fusion module adaptively integrates these complementary features in a modality-aware manner. Finally, a MLP-based decoder aggregates multi-scale representations to generate accurate segmentation results efficiently. Experiments conducted on the Agriculture-Vision dataset demonstrate that AgriFusion achieves a mean Intersection over Union (mIoU) of 49.31%, Pixel Accuracy (PA) of 81.72%, and F1 score of 67.85%, outperforming competitive baselines including SegFormer, DeepLab, and AAFormer. Ablation studies further reveal that unimodal or shallow fusion strategies suffer from limited discriminative capacity, whereas AgriFusion adaptively integrates complementary multimodal features and balances fine-grained local detail with global contextual information, yielding consistent improvements in identifying planting anomalies and crop stresses. These findings validate our central claims that modality-aware spectral fusion and balanced multi-scale representation are critical to advancing agricultural semantic segmentation, and establish AgriFusion as a principled framework for enhancing remote sensing-based monitoring with practical implications for sustainable crop management and precision farming. Full article

Accurate subsurface velocity modeling is of fundamental scientific and practical significance for seismic data processing and interpretation. However, conventional depth-domain methods still face limitations in physical consistency and inversion accuracy. To overcome these challenges, this study proposes a deep learning-based seismic velocity modeling approach in the time domain. The method establishes an end-to-end mapping between seismic records and velocity models directly in the time domain, reducing the nonlinear complexity of mapping time-domain data to depth-domain models and improving prediction stability and accuracy. Synthetic aquifer velocity models were constructed from representative stratigraphic features, and multi-shot seismic records were generated through forward modeling. A U-Net network was employed, taking multi-shot seismic records as input and time-domain velocity fields as output, with training guided by a mean squared error (MSE) loss function. Experimental results show that the proposed strategy outperforms conventional depth-domain approaches in aquifer structure identification, velocity recovery, and interlayer contrast depiction. Quantitatively, significant improvements in MSE, peak signal-to-noise ratio, and structural similarity index indicate higher reconstruction reliability. Overall, the results confirm the effectiveness and potential of the proposed time-domain framework for aquifer velocity inversion and its promise for intelligent seismic velocity modeling. Full article

The satellite Internet of Things (SatIoT) enables real-time acquisition and large-scale coverage of hyperspectral imagery, providing essential data support for decision-making in domains such as geological exploration, environmental monitoring, and urban management. Hyperspectral remote sensing classification constitutes a critical component of intelligent applications driven by the SatIoT, yet it faces two major challenges: the massive data volume imposes heavy storage and processing burdens on conventional satellite systems, while dimensionality reduction often compromises classification accuracy; furthermore, mainstream neural network models are constrained by insufficient labeled data and spectral shifts, frequently leading to misclassification of unknown categories and degradation of cross-regional performance. To address these issues, this study proposes an open-set hyperspectral classification method with dual branches of reconstruction and prototype-based classification. Specifically, we build upon an autoencoder. We design a spectral–spatial attention module and an information residual connection module. These modules accurately capture spectral–spatial features. This improves the reconstruction accuracy of known classes. It also adapts to the high-dimensional characteristics of satellite data. Prototype representations of unknown classes are constructed by incorporating classification confidence, enabling effective separation in the feature space and targeted recognition of unknown categories in complex scenarios. By jointly leveraging prototype distance and reconstruction error, the proposed method achieves synergistic improvement in both accurate classification of known classes and reliable detection of unknown ones. Comparative experiments and visualization analyses on three publicly available datasets: Salinas-A, PaviaU, and Dioni-demonstrate that the proposed approach significantly outperforms baseline methods such as MDL4OW and IADMRN in terms of unknown detection rate (UDR), open-set overall accuracy (OpenOA), and open-set F1 score, while on the Salinas-A dataset, the performance gap between closed-set and open-set classification is as small as 1.82%, highlighting superior robustness. Full article

Translation of mRNAs is a tightly regulated process in gene expression. In mRNA, the 5′ untranslated region (5′ UTR) controls ribosome recruitment and frequently contains structured elements that modulate translation efficacy. This study investigates stable structural motifs within the 5′ UTR of DDX23 mRNA, encoding a protein relevant for anticancer therapy, as potential regulators and targets for antisense oligonucleotides (ASOs). Despite bioinformatic predictions and transcriptomic validations suggesting RNA G-quadruplex (rG4) formation, comprehensive structural analysis using a light-up assay and CD, UV, and NMR spectroscopy revealed that most putative rG4-forming sequences do not fold into stable rG4 structures, although one of them exists in an equilibrium between rG4 and an alternative, likely hairpin, conformation. Reporter assays using a robust G4 stabilizer also argue against the significant regulatory role of rG4s in DDX23 mRNA translation. Instead, we identified and characterized a stable hairpin structure with potential regulatory function. Based on these findings, we designed fully locked nucleic acid (LNA)-modified ASOs to target this hairpin and regions flanking the upstream open reading frame (uORF) and start codon of the coding sequence. A reporter assay demonstrated that cap-proximal targeting achieved robust translation inhibition up to 80%. In contrast, targeting the efficiently translated uORF was ineffective, presumably due to steric hindrances from the ribosomal complex. The study yields crucial design principles for translation-regulating ASOs: avoid targeting regions shielded by efficient uORF translation and carefully tune ASO-RNA duplex stability to surpass endogenous structures without disrupting regulatory mechanisms. These findings provide insights into the regulation of DDX23 expression and establish a framework for developing ASO-based therapeutics with broad implications for mRNA targeting in anticancer applications. Full article

The use of electronic cigarettes has increased in the U.S. with menthol and mint flavors showing notably higher sales. While research on the bacterial microbiome of traditional tobacco products is growing, particularly regarding menthol and nicotine effects, data regarding potential microbial contaminants within electronic liquids (e-liquids) remain limited. Additionally, the potential antibacterial properties of e-liquids remain sparse. To address these gaps, we evaluated the prevalence of viable bacteria in e-liquids; characterized their antimicrobial susceptibility patterns; and tested the antibacterial activity of the e-liquids. Two e-liquid flavors (menthol and non-menthol) across three different nicotine concentrations (0, 6 and 12 mg/mL) were tested using culture-based methods and Sanger sequencing. Antimicrobial susceptibility testing and e-liquid antibacterial activity assays were performed using the Kirby Bauer disc diffusion method. The majority of the isolates (63.15%) were identified as Pseudomonas aeruginosa and Bacillus spp. (B. pumilus, B. megaterium and B. cereus). Notably, P. aeruginosa and P. fluorescens isolates exhibited multidrug resistance against penicillin, tetracyclines, and phenicols. The e-liquids also demonstrated antimicrobial activity, inhibiting the growth of B. cereus, P. aeruginosa, and Staphylococcus aureus, with greater inhibition of P. aeruginosa growth at higher (12 mg/mL) compared to lower (0 mg/mL) nicotine concentrations across the menthol-flavored samples. These findings offer preliminary evidence of viable, multidrug-resistant bacteria and antibacterial properties in e-liquids, underscoring potential public health concerns regarding user exposure risks and microbial interactions, and emphasizing the need for continued surveillance of microbial safety in electronic cigarette products. Full article

Background: Argininosuccinate synthase 1 (ASS1), a key enzyme in arginine biosynthesis, is highly expressed in colorectal cancer (CRC) and promotes cancer progression, making it a potential therapeutic target. Evodiamine (EVO), a natural alkaloid from Evodia rutaecarpa acts as a novel Wnt signaling pathway inhibitor with strong anticancer activity against various cancers. However, its exact therapeutic mechanism in CRC remains unclear. Methods: To address this gap, experiments included enzyme-linked immunosorbent assay (ELISA) to test EVO’s effect on CRC arginine production; CCK-8, EdU, colony formation, and wound-healing assays to assess CRC cell proliferation and migration; RT-qPCR, Western blot, immunofluorescence (IF), and ShASS1 for mechanism exploration and target validation; and a syngeneic tumor allograft model to study EVO’s metabolic regulation and anticancer efficacy in CRC. Results: In vitro, EVO significantly inhibited arginine synthesis metabolism and reduced CRC cell proliferation/migration. In vivo, it suppressed tumor tissue arginine metabolism, slowed allograft tumor growth, and decreased ASS1 expression. Mechanistically, EVO concentration-dependently reduced ASS1 via the Wnt/β-catenin/c-MYC pathway; ShASS1 replicated EVO’s anticancer effects, confirming ASS1’s mediating role. Conclusions: EVO downregulates ASS1 via the Wnt/β-catenin/c-MYC pathway disrupts CRC arginine synthesis metabolism and inhibits CRC cell proliferation/migration. These results support the interaction between metabolic regulation and signaling pathways, highlighting EVO as a promising CRC therapeutic candidate. Full article

In recent years, the socio-legal concept of Emotional Support Animals (ESAs) has come under increasing scrutiny in the U.S. Widespread confusion about the legal status of ESAs versus service animals, and concerns about misrepresentation, led to a backlash and a cascade of new legislation addressing “fake service animals.” However, this backlash reveals a broader social problem rooted in policy lag: the integration of companion animals into society has not kept pace with the rise of multispecies families. This article examines law, policy, and public discourse about ESAs within two distinct social contexts: (1) the emergence of multispecies families alongside the systemic exclusion of companion animals from many private and public spaces, and (2) the medicalization of relational bonds and everyday life. Although well-intentioned, the ESA concept creates a two-tiered system where caregivers of companion animals must acquire a medical diagnosis in order to obtain equal access to basic necessities like housing. In addition to resting on weak evidence, it diverts attention from macro social issues by pathologizing the individual, creating a problematic medicalization of the human-animal bond that is ultimately detrimental to the well-being of companion animals, their caregivers, and people with disabilities. The legal and social construction of ESAs is also troublingly anthropocentric, centering what animals can do for humans without considering the emotional support needs and psychological well-being of the animals themselves. This article concludes by arguing that the ESA framework should be replaced with a multispecies social model that supports the human-animal bond without unnecessary medicalization. This approach advocates for equitable laws and policies, such as the expansion of pet-inclusive housing, that benefit families that include companion animals and the communities in which they are embedded. Full article

Background and Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of liver disease in the United States and frequently coexists with other liver diseases. Despite growing interest, the presence of MASLD in patients with primary sclerosing cholangitis (PSC) remains underexplored. This study aimed to assess the prevalence and characteristics of the MASLD-PSC overlap syndrome, with a specific focus on patient-reported outcomes such as pruritus and fatigue. Methods: A cross-sectional analysis was performed within a prospective cohort of patients with PSC enrolled in the Autoimmune Liver Diseases Registry at a United States tertiary medical center (2018–2024). MASLD overlap was established based on evidence of hepatic steatosis on liver imaging or biopsy, combined with at least one cardiometabolic risk factor. Fatigue and pruritus were assessed using the Chronic Liver Disease Questionnaire (CLDQ) and the 5D Itch Scale. Ordinal logistic regression models were used to explore the potential impact of MASLD overlap on fatigue and pruritus severity. Results: Among 103 PSC patients, 33% had MASLD overlap. These patients were older (55 vs. 46 years, p = 0.006), had a higher BMI (30 vs. 25 kg/m², p < 0.001), and were more likely to have small bile duct involvement (43% vs. 12%, p = 0.002). A history of liver transplantation (LT) was noted in 18% of PSC-only patients, compared to 3% of those with PSC/MASLD (p = 0.055). MASLD overlap was significantly associated with higher pruritus intensity (OR 3.09, 95% CI 1.02–9.28, p = 0.044), but was paradoxically linked to lower fatigue levels (OR 0.37, 95% CI 0.16–0.85, p = 0.020). Conclusions: Patients with PSC/MASLD exhibit distinct clinical features. MASLD overlap was found to significantly impact patient-reported outcomes, with lower fatigue intensity but increased pruritus severity, suggesting a role for metabolic or inflammatory pathways, warranting further investigation. Full article

Inspired by the bans on diversity, equity, and inclusion (DEI) efforts in the U.S., this article considers how DEI influences individualism. This study uses literature on Black racial identity development and the conceptual lens of individualism to examine how Black women define themselves. The data used for this study came from 22 college-educated Black women who discussed their paths towards wearing their natural hair, which included personalized descriptions of the ways that race, gender, White beauty norms, workplace expectations, and the perspectives of others influenced this process. The narratives provided by these Black women reveal that DEI helps to promote their individual identity development by helping them to sort through the various messages and opinions to arrive at both a hairstyle and an identity that corresponds to their character. Full article