Search Results (788)

Objectives: Preterm infants confront multiple coexistent health risks and may not receive human milk (HM), which contains human milk oligosaccharides (HMOs), among other biologically active, beneficial components. 2′-Fucosyllactose (2′-FL) is the most abundant HMO in most HM. Interventional studies of formula with added HMOs have demonstrated multiple beneficial outcomes in healthy, term infants. Methods: This double-blind, randomized, placebo-controlled pilot study (NCT03306316) evaluated the effect of a 2′-FL supplement on growth, tolerance, and neurobehavior, as assessed by the Neonatal Intensive Care Unit (NICU) Network Neurobehavioral Scale (NNNS), in preterm infants. Eligible neonates born between 26 0/7 and 31 6/7 weeks gestational age (GA) were enrolled from three NICUs in the United States. Preterm infants consuming mother’s own milk or donor human milk were randomized to receive a supplement of 0.135 g/mL of 2′-FL or 0.05 g/mL dextrose (placebo) twice daily until 36 weeks corrected GA, NNNS assessment, or hospital discharge, whichever came first. Results: Of 42 participating preterm infants, 23 (55%) received 2′-FL and demonstrated no significant differences in growth in weight, length, or head circumference over 5 weeks. There were also no differences in stool frequency, adverse (AEs) or serious adverse events (SAEs), or neurobehavior (NNNS) between groups. Conclusions: This pilot study found that 2′-FL supplementation in preterm infants through 36 weeks corrected GA was safe and well-tolerated and supported adequate growth and neurobehavior. Full article

Dealloyed nanoporous metals have a unique bicontinuous solid/void structure that provides a sizable surface area and outstanding electrical conductivity, making them attractive candidates for use in a range of applications. But for many of these applications, the utilization of an engineered hierarchical porous network topology that promotes and optimizes mass transport would be quite advantageous. We present a soft template approach for the routine fabrication of hierarchical multimodal nanoporous gold monolith (hm-NPG). This self-supporting framework composed of multimodal porosity is produced employing a synergistic mix of metal reduction, templating, annealing, and chemical dealloying. This method provides for the simultaneous optimization of active surface area and mass transport in a porous metal electrode. It is reliable, simple, economical, accessible, and environmentally friendly. The procedure should be scalable and can produce hm-NPG for use in applications such as biosensing, energy systems, biofiltration, and catalysis. The material visually displays two visibly unique structural length scales that range from the macroporous network structure (average pore size of 0.58 ± 0.29 μm) to the mesoporous pore/ligament morphology (average pore size of 37 ± 10 nm) as determined by SEM analysis. Modification by self-assembly with lipoic acid (LA) gave a coverage of 5.12 × 10¹⁴ molecules/cm² of the hm-NPG surface, according to calculations made using thermogravimetric analysis (TGA) data. Following the dealloying procedure, a compositional study of the np-Au monolith using EDS revealed that it was almost 98.2 atomic % gold. The specific surface area of the hm-NPG was found to be 7.84 ± 0.01 m²/g (n = 3) through analysis utilizing the Brunauer–Emmett–Teller (BET) multi-point surface area method applied to krypton adsorption isotherms. BET analysis using N₂ adsorption isotherms and the Barrett–Joyner–Halenda (BJH) pore distribution analysis gives strong evidence for the additional presence of micropores of diameter 2–3 nm, thus making the material likely trimodal. Full article

Accurate assessment of the presence and severity of plant diseases is essential for effective crop monitoring and management. This study presents a deep learning-based pipeline for quantifying blackleg crown canker disease severity in canola stems by combining image segmentation and severity prediction tasks. Three architectures (ResUNet, UNet and SegFormer) were compared for the first step of stem segmentation to isolate relevant regions. The disease severity scores of four experts, and their aggregated median, were used to train models which were evaluated for label consistency, ambiguity, and model robustness. Among the three segmentation architectures, SegFormer achieved the best performance (mean IoU = 0.939, F1 score = 0.962), outperforming ResUNet and UNet. There was a high correlation in disease severity scores across expert labels, with the median-trained model achieving correlation coefficients of 0.924–0.963 against individual expert assessors on the evaluation dataset. Confusion matrix analysis further demonstrated reliable classification across severity levels. This work highlights the influence of segmentation quality, label aggregation strategies and data imbalances on downstream prediction tasks. This study uses controlled imaging conditions, but the proposed framework provides a strong foundation for future application in field environments. The framework enhances model interpretability by generating severity scores that align closely with expert assessments to support users such as agronomists and plant breeders for better decision-making and help track disease resistance by providing consistent, objective disease measurements over time. Future work will focus on exploring multi-task learning for greater efficiency, alongside validating the approach in field conditions to enable broader adoption. Full article

Background: The most accurate intraocular lens power calculation formula for highly myopic eyes remains uncertain. The objective of this study was to perform a systematic review and network meta-analysis to compare and rank IOL power calculation formulas in eyes with an axial length greater than 26 mm. Methods: A literature search was conducted in PubMed, the Cochrane Library, Scopus, and Web of Science. The main outcome for comparison was the percentage of eyes with a refractive prediction error <0.25 diopters, <0.50 diopters, and <1.00 diopter. A network meta-analysis was performed, and Surface Under the Cumulative Ranking Curve values were calculated to rank formulas across the three prediction error thresholds. Forest plots were generated comparing all formulas with Barrett Universal II as the reference. Results: Ten studies were finally included. Based on Surface Under the Cumulative Ranking Curve values, Kane and Holladay 1 (with the original Wang–Koch axial length adjustment) ranked among the top three formulas across all prediction error thresholds. Hill-RBF 3.0, EVO, and Barrett Universal II ranked among the top three for the <0.25 diopters, <0.50 diopters, and <1.00 diopter thresholds, respectively. The Kane and Holladay 1 (with the original Wang–Koch axial length adjustment) formulas were the only ones showing odds ratios favouring their accuracy over Barrett Universal II across all prediction error thresholds in the forest plots, although differences were not statistically significant. Conclusions: The Kane and Holladay 1 (with the original Wang–Koch axial length adjustment) formulas showed the most consistent performance in highly myopic eyes. Hill-RBF 3.0, EVO, and Barrett Universal II also demonstrated high precision and may be considered reliable alternatives. Full article

Background/Objectives: The choice of appropriate intraocular lens (IOL) power calculation formulas depends on the axial length of the eye. This study compares the accuracy of twenty formulas in medium-long eyes (24.50–25.99 mm). Methods: Data of patients with medium-long eyes, who underwent uneventful phacoemulsification between January 2018 and September 2023, were retrospectively reviewed. Preoperative IOL power was calculated using the IOLMaster 700 with six formulas: Barrett Universal II, Haigis, Hoffer Q, Holladay 1, Holladay 2, and SRK/T. Three months postoperatively, refraction was measured. Postoperative IOL power calculations were then performed using fourteen additional formulas: Castrop, EVO 2.0, Hoffer QST, K6, Kane, Karmona, Ladas Super Formula AI (LSF AI), Naeser 2, Olsen (OLCR), Olsen (standalone), PEARL-DGS, T2, VRF CMAL, and VRF-G. The main outcome measures included standard deviation (SD) of the prediction error (PE) and the percentage of eyes with PE within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 D. Results: Ninety-five eyes with axial lengths ranging from 24.52 mm to 25.97 mm were included. SD values among the twenty formulas ranged from 0.179 (SRK/T) to 0.468 (Olsen OLCR). The percentage of eyes with PE within ±0.50 D ranged from 75.79% (Olsen OLCR) to 98.95% (SRK/T). The SRK/T formula, followed by Holladay 1, demonstrated significantly higher accuracy than most other formulas, while Olsen (OLCR) and Castrop were the least accurate. Conclusions: SRK/T provided the highest accuracy in medium-long eyes, with Holladay 1 performing similarly well. All evaluated formulas achieved PE within ±0.50 D in over 74% of cases. Full article

The Caribbean is a recognized biodiversity hotspot; however, on islands such as Jamaica, many faunal groups of ecological and forensic importance, including flesh flies, remain poorly characterized, particularly at the genetic level. This study represents an initial step towards addressing that gap through DNA barcoding. Forty-four specimens from 16 species were analyzed through partial sequencing of the cytochrome c oxidase subunit I (COI) gene. Of the 44 sequences, 30% shared 97–100% similarity with reference sequences in GenBank and BOLD; however, taxonomic resolution varied between databases. The remaining sequences gave lower similarity matches (88–96%), with assignments restricted to the genus level or higher taxonomic ranks, or were discordant with morphology-based identifications. With one exception, pairwise intraspecific genetic distances were low (0.17%), whereas interspecific distances ranged from 5.68% to 12.83%. Neighbor-joining analysis showed that Jamaican taxa, including five endemic species and two new records, formed largely distinct monophyletic clades relative to sequences from North and South America, Europe and Oceania, with >70% bootstrap support. These findings demonstrate the utility of COI barcoding for species discrimination among Jamaican flesh flies and represent an important step towards establishing a regional DNA barcode library. Expanding Caribbean representation in global databases will be essential for improving the accuracy of molecular identification and resolving uncharacterized taxa. Full article

Number Theoretic Transform (NTT)-based polynomial multiplication is a computationally intensive operation in lattice-based post-quantum cryptography (PQC) schemes such as CRYSTALS-Dilithium. Existing hardware accelerators optimize area and timing performance, without focusing on evaluating trade-offs among hardware utilization, execution latency, operating frequency, and power consumption. This article investigates such trade-offs through two lightweight field-programmable gate array (FPGA) implementations of an iterative NTT-based polynomial multiplication accelerator, namely non-pipelined and 4-stage pipelined architectures. Both implementations employ a single butterfly unit based on Cooley–Tukey and Gentleman–Sande configurations to compute the forward NTT (FNTT), inverse NTT (INTT), and coefficient-wise multiplication (CWM). The 4-stage pipelined architecture employs pipeline registers in the modular multiplication and Barrett reduction datapaths to maximize the operating frequency. Both architectures are implemented on an Artix-7 FPGA and evaluated across operating frequencies ranging from 10 MHz to 280 MHz. The results show that the non-pipelined architecture provides reduced hardware overhead and lower power consumption, whereas the pipelined architecture improves timing scalability and successfully operates at 280 MHz. At the maximum operating frequency, the pipelined implementation utilizes 1115 slices and achieves execution times of 4.58 μs, $0.93\mathsf{\mu }$s, and $4.58\mathsf{\mu }$s for FNTT, CWM, and INTT computations, respectively, with an average power consumption of 133 mW. The Area–Time Product (ATP) and Energy–Delay Product (EDP) evaluations demonstrate that the pipelined architecture achieves improved overall efficiency within the proposed lightweight single-butterfly-based polynomial multiplication architecture at higher operating frequencies, obtaining an ATP of $7.74\times {10}^3$ Slice-$\mathsf{\mu }$s and EDP of 1501.52 nJ-$\mathsf{\mu }$s. Full article

Background/Objectives: Interpretation of high-dimensional molecular profiling data requires computational strategies that go beyond single-layer analyses to resolve coordinated biological programs. Although multi-omics integration has advanced latent structure discovery, translating these cross-omics signals into interpretable, functionally meaningful molecular states remains a central challenge. Methods: Here, we present an interpretable, pathway-centric multi-omics integration framework that combines expression, DNA methylation, and copy-number alteration data to capture nonlinear cross-omics interactions and enable biologically grounded representations of molecular states. We apply this framework towards oesophageal adenocarcinoma, a malignancy that remains relatively underexplored in integrative multi-omics and systems-level analyses, despite its rising incidence and poor prognosis. Results: Using samples spanning Barrett’s oesophagus and oesophageal adenocarcinoma from the Oesophageal Cancer Clinical and Molecular Stratification consortium, we demonstrate the framework’s ability to resolve coordinated oncogenic, metabolic, immune, and cell-cycle programs that evolve across disease states. Conclusions: Together, this work establishes a scalable and interpretable computational strategy for pathway-based multi-omics integration, along with providing a generalisable approach for molecular state assignment in complex biological systems. Full article

The imminent threat of large-scale quantum computers motivates the deployment of post-quantum cryptography (PQC). CRYSTALS-Kyber, a leading lattice-based Key Encapsulation Mechanism, relies heavily on Number Theoretic Transform (NTT) operations, which remain a major performance and resource bottleneck. This paper presents a cross-platform NTT evaluation framework for CRYSTALS-Kyber, centered on an adaptive FPGA-based mixed-radix accelerator supporting radix-2, radix-4, and radix-8 configurations, together with comparative classical implementations and exploratory quantum-circuit prototypes. Classical evaluations show that an iterative Cooley–Tukey implementation outperforms a matrix-based baseline (≈3.6× faster for the forward NTT, ≈6.3× faster for the inverse NTT). Quantum prototypes implemented in Qiskit demonstrate proof-of-concept QFT-based NTT constructions under classical simulation environments, highlighting circuit-depth growth and noise sensitivity rather than practical hardware acceleration. The proposed FPGA design, based on a Xilinx Virtex UltraScale+ platform, employs an adaptive radix controller, LUT-based twiddle management, and Montgomery/Barrett modular arithmetic. Montgomery reduction provides superior timing and area trade-offs, with an estimated $F_{\max }$ of up to 231.48 MHz and only 5 DSPs for radix-2. At the same time, radix-2 offers the best resource/performance balance with a latency of approximately 32,804 cycles. The hybrid approach strikes a balance between near-term FPGA practicality and long-term quantum potential while preserving Kyber’s MLWE-based security. Experimental results and comparative analysis indicate that the adaptive design substantially reduces resource usage and timing overhead compared to recent HLS-based NTT accelerators. Full article

Hexagonal close-packed (hcp) metals rely on twinning to accommodate plastic deformation at room temperature. These twins often impede dislocation mobility and can lead to brittle failure. Significant research has been done to identify methods to mitigate this brittle failure; however, a gap exists in the work on the mechanisms behind the brittle failure. All hcp twin modes require atomic shuffling to place sheared atoms in the correct lattice sites. While shuffling for the $\left\{10\overline{1}2\right\}$ twin is well defined, most of the shuffle processes for twinning in hcp are not defined, and the displacement field due to the introduction of a twin has not been detailed. This work serves to present new mechanical equations to compute atomic shuffles and introduce an algorithm to calculate the displacement map and insert an arbitrarily shaped twin into a defect-free lattice using those shuffle vectors. We substantiate the algorithm by placing $\left\{10\overline{1}2\right\}$, $\left\{10\overline{1}1\right\}$, and $\left\{2\overline{1}\overline{1}2\right\}$ twins in Mg and Ti and show that the twins undergo glissile transformation using molecular dynamics. Full article

Background: Immigrant communities and first-generation immigrants in the United States face persistent disparities in maternal health outcomes. These inequities are shaped by intersecting structural conditions, including socioeconomic exclusion, language barriers, cultural differences, and institutional constraints documented in prior research. Methods: This narrative review examined 28 peer-reviewed studies published between 2010 and 2024 that applied an intersectional framework to maternal health research focused on immigrant communities in the United States. Studies were identified through PubMed, Scopus, Web of Science, and Google Scholar. The review analyzed how each study conceptualized, designed, and interpreted maternal health in these populations. Results: Seven recurring themes were identified: barriers to and access to care; gaps in clinical guidance; limitations in health data and surveillance; immigration-related policy context; health system influences; intersectional vulnerability across subgroups; and the role of individual- and community-level supports. Conclusions: The literature highlights the importance of community-based strategies, Medicaid policy considerations, and culturally responsive care in addressing maternal health disparities among immigrant communities. Advancing birth equity will require coordinated efforts across healthcare systems, public health programs, and policy environments. Full article

This paper investigates the impact of modular reduction backends on the security–performance trade-offs of the CKKS approximate homomorphic encryption scheme. Specifically, we compare a standard Residue Number System–Chinese Remainder Theorem (RNS-CRT) implementation with a Barrett reduction–based backend across representative parameter sets $(N,logq)$, spanning approximately 80–256-bit security levels as recommended by the Homomorphic Encryption Standard. We evaluate typical CKKS workloads—including encoding, encryption, homomorphic multiplication with the Number Theoretic Transform (NTT), rescaling, relinearization, and decryption—by measuring execution time and peak memory usage on a uniform experimental platform. Our results indicate that the parameter pair $(N,logq)$ primarily determines both security level and computational cost, while the choice of backend significantly influences the trade-offs between performance and memory efficiency. In particular, the Barrett-based backend is competitive and slightly more memory-efficient at lower security levels, whereas the CRT-based approach achieves lower latency at higher security levels. However, Barrett reduction provides notable memory savings at the cost of a 2–4× increase in runtime. Based on these findings, we derive practical guidelines for selecting CKKS parameters and modular reduction backends under varying constraints on security, latency, and memory. Full article

Fibrosis in Crohn’s Disease (CD) occurs when there is continuous inflammation and repair mechanisms, which may lead to fibrotic strictures and ultimately intestinal surgical resection. There are limited non-invasive biomarkers for monitoring CD activity, particularly for detecting fibrosis. Thus, we set out to identify biomarkers that could be monitored for the detection and treatment of fibrosis. We used multiplex protein arrays to examine cytokines and pro-fibrotic factors in the serum of CD patients and analyzed their reliability to predict fibrosis. These markers were confirmed in tissues and the role of cytokines in upregulating fibrotic factors was examined. Collagen 1A1, Fibronectin, MMP9, and Timp-1 in patient serum were identified as strong predictors of fibrosis. IL-1β, MCP-1 and TNFα were identified as major regulators of fibrosis factors in human tissues. Overall, we identified four serum markers that are strong indicators of fibrosis and provided some novel insight into the impact of cytokines on fibrotic factors. Taken together, these findings may lead to earlier detection of fibrosis, and improved monitoring and treatment for CD patients. Full article

Burn injury induces profound immune dysregulation that extends beyond the acute phase of wound healing, contributing to complications such as delayed repair, infection, and long-term immune dysfunction. Importantly, these effects are not restricted to severe trauma, as similar immune alterations occur following small- to moderate-sized burns. Despite increasing recognition of post-burn immune dysregulation, targeted immunomodulatory therapies remain limited. In this review, we synthesize current insights into the mechanisms driving immune dysfunction after burn injury and outline therapeutic strategies aimed at restoring immune homeostasis. We examine approaches targeting inflammatory triggers and mediators, including acute clinical interventions, reduction in microbial burden, and inhibition of immune cell activation through systemic and local delivery. We also explore strategies to modulate dysregulated innate immune responses by targeting cell-specific functions, such as neutrophil activity and monocyte/macrophage polarization. Persistent activation and exhaustion of the adaptive immune system may be alleviated through interventions such as β-adrenergic blockade, while metabolic, endocrine, and oxidative stress pathways represent additional therapeutic targets. Finally, we highlight key challenges, including the need for improved diagnostics, early prognostic stratification, and personalized treatment approaches to improve outcomes following burn injury. Full article