Search Results (1,117)

Chloroplasts, as the organelles primarily responsible for photosynthesis, require a substantial supply of iron ions. Conversely, due to Fe toxicity, the homeostasis of these ions is subject to tight regulation. Permease in chloroplast 1 (PIC1) has been identified as the primary iron importer into chloroplasts. However, previous studies suggested the existence of a distinct pathway for Fe transfer to chloroplasts, likely involving mitoferrin-like 1 (MFL1) protein. In this work, Arabidopsis MFL1 (AtMFL1) and its cucumber homolog (CsMFL1) were characterized using, among others, Arabidopsis protoplasts as well as both yeast and Arabidopsis mutants. Localization of both proteins in chloroplasts has been shown to be mediated via an N-terminal transit peptide. At the gene level, MFL1 expression profiles differed between the model plant and the crop plant under varying Fe availability. The expression of other genes involved in chloroplast Fe homeostasis, including iron acquisition, trafficking, and storage, was affected to some extent in both AtMFL1 knockout and overexpressing plants. Moreover, root growth and photosynthetic parameters changed unfavorably in the mutant lines. The obtained results imply that AtMFL1 and CsMFL1, as putative chloroplast iron transporters, play a role in both iron management and the proper functioning of the plant. Full article

Chagas disease, caused by Trypanosoma cruzi, poses a significant public health challenge due to its widespread prevalence, limited therapeutic options, and adverse effects associated with available medications. In this study, we developed 13 novel pyrazole-imidazoline derivatives, inspired by a previously identified cysteine protease inhibitor, and evaluated their antiparasitic activity. Our in silico analyses predicted favorable physicochemical profiles and promising oral bioavailability for these derivatives. Upon phenotypic screening, we observed that these new derivatives exhibited low cytotoxicity (CC₅₀ > 100 µM) and marked efficacy against intracellular amastigotes. Derivative 1k showed high activity (IC₅₀ = 3.3 ± 0.2 µM), selectivity (SI = 73.9), and potency (pIC₅₀ = 5.4). In a 3D cardiac microtissue model, 1k significantly reduced parasite load, matching the efficacy of benznidazole (Bz) even at lower concentrations. Both 1k and Bz effectively prevented parasite recrudescence; however, neither resulted in parasite sterility under the experimental conditions employed. The combination of 1k–Bz yielded an additive interaction, highlighting its potential for in vivo combination therapy. While structural changes abolished cysteine protease inhibition, incorporating a CF₃ substituent at the para position and excluding the amino group enhanced antiparasitic activity. These findings reinforce the promise of the pyrazole-imidazoline scaffold and support further structural optimizations to develop innovative candidates for treating Chagas disease. Full article

As diversity, equity, and inclusion (DEI) efforts in higher education face increasing political resistance, it is critical to understand how equity-centered institutional change is fostered, and who is transformed in the process. This study examines the intended and emergent outcomes of faculty professional development initiatives implemented through the Howard Hughes Medical Institute’s Inclusive Excellence (HHMI IE) program. We analyzed annual institutional reports and anonymous reflections from four public universities in a regional Peer Implementation Cluster (PIC), focusing on how change occurred at individual, community, and institutional levels. Guided by Kezar’s Shared Equity Leadership (SEL) framework, our thematic analysis revealed that while initiatives were designed to improve student outcomes through inclusive pedagogy, the most profound outcome was the development of equity consciousness among faculty. Defined as a growing awareness of systemic inequities and a sustained commitment to address them, equity consciousness emerged as the most frequently coded theme across all levels of change. These findings suggest that equity-centered faculty development can serve as a catalyst for institutional transformation, not only by shifting teaching practices but also by building distributed leadership and deeper organizational engagement with equity. This effort also emphasizes that documenting emergent outcomes is essential for recognizing the holistic impact of sustained institutional change. Full article

This in vitro study aims to compare the trueness of digital impressions obtained using two intraoral scanners (IOS) and one photogrammetry device for full-arch implant-supported rehabilitations. According to the Caramês Classification I, three models were produced with Straumann implants arranged in different spatial distributions: Option A with six implants and Options B and C with four implants each. The three models were scanned using a 12-megapixel scanner to create digital master casts. For each reference model, 30 digital impressions were acquired: 10 with the 3Shape Trios 3 intraoral scanner, 10 with the Medit i500 intraoral scanner, and 10 with the PIC Dental photogrammetry device. Trueness was assessed through best-fit superimpositions between the digital master casts and the corresponding virtual models. The Shapiro–Wilk test was applied to assess the normality of the data distribution, and Levene’s test was used to evaluate the homogeneity of variances. The non-parametric Kruskal–Wallis test was employed to compare group differences, with post hoc adjustments made using the Bonferroni correction. A significance threshold of p = 0.05 was adopted for all statistical tests. Statistically significant differences were observed in the root mean square values among the three devices. The Medit i500 demonstrated the highest trueness, with a median (interquartile range) deviation of 24.45 (18.18) µm, whereas the PIC Dental exhibited the lowest trueness, with a median deviation of 49.45 (9.17) µm. Among the implant distribution, the Option C showed the best trueness, with a median deviation of 19.00 (27.83). Considering the results of this in vitro study, intraoral scanners demonstrated comparable trueness, whereas the photogrammetry-based system exhibited lower trueness values. Additionally, a smaller number of implants and reduced inter-implant distances were associated with improved trueness in digital impressions for full-arch implant rehabilitation. Full article

Cancer remains one of the most formidable challenges to human health; hence, developing effective treatments is critical for saving lives. An important strategy involves reactivating tumor suppressor genes, particularly p53, by targeting their negative regulator MDM2, which is essential in promoting cell cycle arrest and apoptosis. Leveraging a drug repurposing approach, we screened over 24,000 clinically tested molecules to identify new MDM2 inhibitors. A key innovation of this work is the development and application of a selective cleaning algorithm that systematically filters assay data to mitigate noise and inconsistencies inherent in large-scale bioactivity datasets. This approach significantly improved the predictive accuracy of our machine learning model for pIC₅₀ values, reducing RMSE by 21.6% and achieving state-of-the-art performance (R² = 0.87)—a substantial improvement over standard data preprocessing pipelines. The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. We identified two clinical CB1 antagonists, MePPEP and otenabant, and the statin drug atorvastatin as promising repurposing candidates based on their high predicted potency and binding affinity toward MDM2. Interactions with the related proteins MDM4 and BCL2 suggest these compounds may enhance p53 restoration through multi-target mechanisms. Quantum mechanical (ONIOM) optimizations and molecular dynamics simulations confirmed the stability and favorable interaction profiles of the selected protein–ligand complexes, resembling that of navtemadlin, a known MDM2 inhibitor. This multiscale, accuracy-boosted workflow introduces a novel data-curation strategy that substantially enhances AI model performance and enables efficient drug repurposing against challenging cancer targets. Full article

Optical sensors have undergone significant evolution, transitioning from discrete optical microsystems toward sophisticated photonic integrated circuits (PICs) that leverage artificial intelligence (AI) for enhanced functionality. This review systematically explores the integration of optical sensing technologies with AI, charting the advancement from conventional optical microsystems to AI-driven smart devices. First, we examine classical optical sensing methodologies, including refractive index sensing, surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman spectroscopy (SERS), surface plasmon-enhanced chiral spectroscopy, and surface-enhanced fluorescence (SEF) spectroscopy, highlighting their principles, capabilities, and limitations. Subsequently, we analyze the architecture of PIC-based sensing platforms, emphasizing their miniaturization, scalability, and real-time detection performance. This review then introduces the emerging paradigm of in-sensor computing, where AI algorithms are integrated directly within photonic devices, enabling real-time data processing, decision making, and enhanced system autonomy. Finally, we offer a comprehensive outlook on current technological challenges and future research directions, addressing integration complexity, material compatibility, and data processing bottlenecks. This review provides timely insights into the transformative potential of AI-enhanced PIC sensors, setting the stage for future innovations in autonomous, intelligent sensing applications. Full article

The Luhua chicken is an outstanding local breed in China that has been placed under conservation due to the impact of specialized breeding and the widespread adoption of commercial varieties. As such, this study analyzed reproductive traits across three consecutive generations and utilized whole-genome resequencing data from 60 Luhua chickens to assess conservation efficacy through genetic diversity, run of homozygosity (ROH) distribution, kinship, and population structure so as to better conserve the breed. The results show that, across generations, the body weight at first egg increased, the age at first egg was delayed, and the egg weight at first laying increased. No significant variations were found in the body weight at 300 d or the total egg number. The key genetic parameters of the polymorphism information content (PIC), expected heterozygosity (HE), observed heterozygosity (HO), and mean identical-by-state (IBS) distance were 0.234, 0.351, 0.277, and 0.782, respectively. The majority of ROHs ranged from 0.5 to 1 Mb, and the inbreeding coefficient based on ROHs was calculated at 0.021. The findings reveal that these traits remained unchanged across the three generations. Our research suggests that optimizing the mating plan of Luhua chickens is essential to minimize inbreeding risk. Furthermore, the methodology applied in this study provides a valuable reference for the conservation monitoring of other indigenous chicken breeds. Full article

The application of fluidized bed reactors to biomass fast pyrolysis is regarded as a promising technology for enabling high-value utilization of biomass. This work established a three-dimensional numerical model of an industrial-scale fluidized bed reactor for biomass catalytic pyrolysis, employing the multiphase particle-in-cell method (MP-PIC) and coupling catalytic pyrolysis kinetics. Primary gas flow rate and biomass–catalyst injection modes were optimized to further improve the performance of the reactor. The model received additional validation from experimental data, primarily to ensure prediction accuracy. The results revealed that an optimal primary gas flow rate of 4 kg/s achieved a peak catalytic efficiency of 71.3%. Using maximum high-quality liquid fuels and adopting a relatively dispersed inlet mode with opposite jetting for biomass and catalyst promoted uniform particle distribution and thermal homogeneity in the dense phase zone, further increasing the catalytic efficiency to 75.6%. With the integration of the multiphase particle-in-cell (MP-PIC) method with catalytic pyrolysis kinetics at the industrial-scale, this work could provide theoretical guidance for designing fluidized bed catalytic pyrolysis reactors and optimizing biomass catalytic pyrolysis processes. Full article

Starting Active Materials for Synthesis (SAMS) represents a critical stage in drug manufacturing, directly influencing the microbiological quality and safety of the final product. The introduction of SAMS marks the point where Good Manufacturing Practices (GMP) begin to apply, which are essential for ensuring sterility and preventing microbial contamination during the synthesis process. However, defining the exact point in the process that qualifies as the SAMS is subject to uncertainties, as earlier stages are not always governed by stringent GMP standards. The regulatory differences between various countries further contribute to this issue. This study explores the implications of SAMS selection and use in relation to sterility and infection control, analyzing the guidelines of major Regulatory Authorities and comparing their approaches to GMP. Regulations from several international regulatory agencies were examined, with a particular focus on microbiological control measures and infection protection in the SAMS manufacturing process. The analysis focused on the microbiological control requirements and safety measures applicable to the stages preceding the introduction of SAMS into the production of the final Active Pharmaceutical Ingredients (APIs). Documents published between 2015 and 2025 were included based on predefined criteria regarding relevance, accessibility, and regulatory authority. The analysis revealed significant discrepancies between regulations regarding the definition and management of SAMS. In particular, the regulations in Mexico and India have notable gaps, failing to provide clear guidelines on SAMS sterility and protection against infectious contamination. Conversely, China has introduced risk-based approaches and early-stage microbiological controls, especially for sterile products, aligning with international standards. The European Medicines Agency (EMA), the U.S. Food and Drug Administration (FDA), the Pharmaceutical Inspection Co-operation Scheme (PIC/S), and the World Health Organization (WHO) have well-established systems for microbiological quality control of SAMS, including rigorous measures for the validation of suppliers and risk management to ensure that SAMS does not compromise the microbiological safety of the final product. The regulations in Brazil and Canada introduce additional measures to protect the microbiological quality of SAMS, with specifications for contamination control and certification of critical stages. The lack of a harmonized language for the definition of SAMS, coupled with a fragmented regulatory framework, presents a challenge for infection protection in pharmaceutical manufacturing. Key issues include the absence of specific regulations for stages prior to the introduction of SAMS and the lack of standards for inspections related to these stages. A desirable solution would be the mandatory extension of GMPs to the stages before SAMS introduction, with centralized control to ensure sterility and protection against infection throughout the entire manufacturing process. Full article

Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks. Full article

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO₂), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO₂ measurement is activated when the specified T and $RH$ change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and $RH$ signals, the number of CO₂ measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO₂ measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO₂ concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Full article

In this study, particle-in-cell (PIC) simulation was used to validate a conceptual design for a quasi-spherical, net power, hydrogen-plus-boron-11-fueled fusion reactor incorporating high-temperature superconducting (HTS) magnets. By burning a fully thermalized plasma, our proposed MET6 reactor uses the principles of the 1980 magneto-electrostatic trap design of Yushmanov to improve the classic Polywell design. Because the input power consumed by the reactor will barely balance the waste bremsstrahlung radiation, future research must focus on reducing the bremsstrahlung losses to reach practical net power levels. The first step to reducing bremsstrahlung, explored in this paper, is to tune the reactor parameters to reduce the energies of trapped electrons. We assume the quality factor Q can be approximated as the ratio of fusion power output divided by bremsstrahlung power loss. Thus, assuming the particles’ power loss is negligible compared to bremsstrahlung power loss, the resulting quality factor is estimated to be Q ≈ 1.3. Full article

Background/Objectives: Wild soybean could grow in different soil types and under diverse climate conditions, which provides rich genetic resources in the locality. It is important to understand the genetic diversity as well as phenotypic variation for soybean breeding. The objective of this study was to analyze the genetic and phenotypic characteristics of 96 wild soybean accessions collected in Zhejiang Province, and to explore the potential advantage of germplasm resources for further application. Methods: These 96 annual type soybean resources have been propagated, identified and evaluated in both 2022 and 2023. In addition, their agronomic, quality and genetic traits have been characterized. Results: Most of the accessions exhibited sooty seed coats with different stem and leaf shapes. The means of seed protein and oil contents were 45.4% and 10.0%, respectively. There were significant differences in both protein and oil contents based upon the seed size. The average number of alleles per loci was 3.96, and the average PIC value was 0.619. The 96 accessions were clustered into three different groups. Each group had a consistency with both the geographical sources and the seed quality traits. Conclusions: There were agronomic, quality and genetic variations of these wild soybean accessions by the comprehensive analyses. This study gave us a combined understanding of both phenotypic variation and genetic diversity of wild soybean accessions in Zhejiang. Therefore, both reasonable exchanging and crossing between different soybean types is highly recommended. Full article

Increasingly severe ocean acidification (OA) disrupts the balance of marine ecosystems. Seawater pH is a key indicator of OA but remains challenging to characterize due to sparse and limited in situ observations. In this study, we propose a spatiotemporal inversion method for surface pH based on interpretable machine learning. By applying carbonate system calculations, we construct an expanded pH observational dataset and obtain spatiotemporal distributions of pH and its influencing factors across the Pacific Ocean from 2003 to 2021. The interpretability analysis reveals that physical, biological, and optical factors contribute 53.9%, 23.9%, and 22.2%, respectively, to pH variability. Sea-surface temperature is the dominant driver, contributing 15.9% of all factors by regulating CO₂ solubility and biological activity. Particulate inorganic carbon (PIC) and particulate organic carbon (POC) show relative contributions of 12.6% and 9.4%, respectively, quantitatively reflecting the important roles of biogenic calcification and the biological carbon pump. Furthermore, the analysis focusing on the Niño 3.4 region reveals a potential pathway through which the ENSO disturbances may affect pH by influencing PIC and POC. Therefore, this study provides a data-driven approach to gain deeper insights into the spatiotemporal patterns of pH and its influencing factors. Full article

To identify effective strategies for preserving the genetic diversity of Strongylocentrotus intermedius populations, this study employed 15 SSR loci and SSR-seq technology to construct three parental mating groups based on different genetic distances: relatively distant (0.33640), relatively close (0.13051), and mixed (0.29916). These mating groups were used to produce three corresponding offspring populations: the distant group (D), the close group (C), and the mixed group (M). A total of 150 offspring from these populations were genotyped to analyze the effects of parental genetic distance on the genetic diversity of their offspring. The results showed that the observed allele number (Na) in the D and M groups was 4.200 and 4.733, respectively, both lower than the parental family population (FP) group (5.000) but higher than the C group (3.571). The effective allele number (Ne) in the D and M groups was 2.782 and 2.728, respectively, slightly below that of the parental FP group (2.816) but greater than the C group (2.211). Similarly, the observed heterozygosity (Ho) in the D and M groups was 0.496 and 0.488, respectively, both below that of the parental FP group (0.522) but above the C group (0.447). The expected heterozygosity (He) in the D and M groups was 0.586 and 0.579, respectively, slightly lower than the parental FP group (0.595) but higher than the C group (0.487). Additionally, the polymorphism information content (PIC) in the D and M groups was 0.530 and 0.531, respectively, indicating high polymorphism, although slightly lower than the parental FP group (0.546) and significantly higher than the C group (0.438). These findings indicate that the genetic diversity of all the three offspring populations declined to varying degrees compared to the parental population, with the C group experiencing the most severe reduction. In contrast, the D and M groups maintained comparably higher levels of genetic diversity, which were comparable to each other. This study underscores the importance of increasing the genetic distance between parents or adopting mixed mating strategies to sustain genetic diversity in breeding populations. These approaches are recommended for future breeding programs to ensure the long-term conservation and sustainability of genetic resources. Full article