Browse Articles

Entanglement distribution in lossy quantum networks

Abstract Entanglement distribution is essential for unlocking the potential of distributed quantum information processing. We consider an N-partite network where entanglement is distributed via a central source over lossy channels, and network participants cooperate to establish entanglement between any two chosen parties under local operations and classical communication (LOCC). We develop a general mathematical framework to assess the average bipartite entanglement shared in a lossy distribution, and introduce a tractable lower bound by optimizing over a subset of single-parameter LOCC transformations. Our results show that probabilistically extracting Bell pairs from W states is more advantageous than deterministically extracting them from GHZ-like states in lossy networks, with this advantage increasing with network size. We further extend our analysis analytically, proving that W states remain more effective in large-scale networks. These findings offer valuable insights into the practical deployment of near-term networks, and corroborate a trade-off relationship between the success conversion probability of entanglement distribution protocols and their robustness to loss.

Prevalence of glaucoma in Africa: A systematic review and Bayesian meta-analysis

Purpose This study sought to establish the prevalence of glaucoma and the associated factors in Africa. Methods A systematic review of studies reporting the prevalence of glaucoma was performed using three electronic databases: PubMed, Scopus, and Web of Science. Data were extracted and study-specific estimates of the prevalence of glaucoma were combined using meta-analysis to obtain pooled proportions. Results A total of 9 studies that evaluated the prevalence of glaucoma in 29,606 individuals, comprising 14,487 males and 15,119 females were included in this study. The prevalence of glaucoma (unclassified glaucoma) in Africa was found to be 5.59% (95% Credible Interval (CrI) 4.32% to 7.74%). The prevalence of Primary Open-Angle Glaucoma (POAG), Primary Angle-Closure Glaucoma (PACG), and secondary glaucoma (SG) in Africa are 5.07% (95% (CrI) 3.51% to 8.52%), 0.98% (95% CrI 0.29% to 5.38%), and 2.19% (95% CrI 0.64% to 10.00%), respectively. The prevalence of glaucoma is highest in Southern Africa (6.47%, 95% CrI 3.10% to 12.10%) and lowest in East Africa (4.80%, 95% CrI 2.37% to 9.27%). The prevalence of POAG is highest in West Africa (6.48% 95% CrI 5.23% to 9.89%) and lowest in East Africa (3.23% 95% CrI 2.21% to 5.07%). Conclusion The prevalence rates of glaucoma and POAG are high, with geographical regional variations worthy of note. Continued efforts are necessary to implement population-based screening and public health education initiatives to foster early diagnosis and management.

Y. Wang et al. reply

Mineralogical controls of the oceanic nickel cycle

Abstract Transition metals and their isotopes are promising paleo-productivity proxies, but their utility depends on understanding their cycling between sediment and seawater. Using nickel (Ni) as an example, we show how manganese (Mn) minerals control its isotopic composition in oxic marine sediments. By analysing synthetic and natural samples, and simulating sediment diagenesis, we find that most Ni isotope variability in modern Mn-rich sediments is driven by the relative contribution of two bonding mechanisms – adsorption to and structural incorporation into Mn oxides – which evolve during Mn mineral aging and transformation. We also find that isotopically heavy Ni is preferentially released during transformation. This supports a conceptual model where Mn mineral aging and transformation co-modify sediment and seawater Ni isotopes. Using isotope mass-balance we explore the sensitivity of seawater Ni isotope archives to redox change. We suggest that Mn mineral processes are important for any metal isotope proxy whose cycling is coupled to Mn mineral formation.

Sustainable UV approaches backed by one step extraction procedure for quantifying the newly released mirabegron and silodosin mixture in urine

Abstract Mirabegron (MIR) and silodosin (SIL) have recently been combined in a single pill to significantly enhance the effectiveness of treating detrusor hyperactivity with impaired contractility (DHIC), leading to appreciable improvements in symptoms associated with overactive bladder. Additionally, this combination effectively manages lower ureteric stones and improves patient outcomes with no significant side effects, especially in elderly patients. Accordingly, this study introduces two UV techniques for analyzing MIR and SIL in their mixtures (pure and commercial mixtures). These techniques were backed by a one-step salting-out liquid/liquid extraction (SALLE) procedure for quantifying MIR and SIL in urine samples without matrix interference. The proposed UV techniques succeeded in resolving the superimposed MIR’s and SIL’s UV spectra by employing straightforward mathematical filtration. The UV techniques were Fourier self-deconvolution (FSD) and induced dual-wavelength (IDW) techniques, with linearities of (50–350) µg/mL for MIR and (5–100) µg/mL for SIL. The applied techniques were verified following the International Council for Harmonisation (ICH) directives and were statistically evaluated against the published technique, with no noteworthy differences found. The applied techniques’ practicality (blueness), whiteness, and greenness were appraised utilizing various metrics. Per the preceding, the applied approaches have been proven to be sustainable, delicate, and appropriate for quality control (QC) testing. Also, backing the applied approaches with the SALLE procedure enables precise monitoring of MIR and SIL in miscellaneous biological fluids with excellent recoveries, presenting an inventive approach for further bioanalytical applications.

Application of PSO-integrated K-means algorithm in resident digital portrait classification

As digital governance progresses rapidly, constructing digital portraits of residents has become instrumental in enhancing local-level administrative capabilities. Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. To address this issue, this paper proposes a novel hybrid algorithm—PSO-KM—that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. Drawing on comprehensive resident information collected in 2023 from a community management system, the method leverages PSO’s global optimization abilities alongside K-means’ iterative refinement to dynamically update cluster centroids. Performance evaluation shows a significant uplift in clustering metrics, with a silhouette score of 0.752 ± 0.021 and inter-cluster distance of 1.493 ± 0.036. Comparative analysis against conventional and advanced methods (e.g., GA-K-means, DBSCAN) reveals that PSO-KM delivers superior outcomes. Among different feature categories, behavioral data yield the best classification performance, with a silhouette value of 0.184, highlighting the discriminatory power of dynamic behavioral traits. Furthermore, segmentation results disclose varying dominant features across income brackets: demographic factors are primary for low-income groups, behavioral metrics dominate middle-income segments, while social network indicators are key for high-income populations. These insights confirm PSO-KM’s potential in refining digital profiling processes and fostering the advancement of grassroots digital governance practices.

Plants monitor the integrity of their barrier by sensing gas diffusion

Abstract Barrier tissues isolate organisms from their surrounding environment. Maintaining the integrity of the tissues is essential for this function. In many seed plants, periderm forms as the outer barrier during secondary growth to prevent water loss and pathogen infection1. The periderm is regenerated when its integrity is lost following injury; however, the underlying mechanism remains largely unknown, despite its importance for plant survival. Here we report that periderm integrity in Arabidopsis roots is sensed by diffusion of the gases ethylene and oxygen. Following injury of the periderm, ethylene leaks out through the wound and oxygen enters, resulting in attenuation of ethylene signalling and hypoxia signalling. This condition promotes periderm regeneration in the root. When regeneration is complete and barrier integrity is re-established, pre-injury levels of ethylene and hypoxia signalling are regained. Gas diffusion monitoring is also used to re-establish the barrier in inflorescence stems after the epidermis is injured. We thus propose that gas diffusion is used by plants as a general principle to monitor and re-establish barrier integrity.

Collagen VI microfibril structure reveals mechanism for molecular assembly and clustering of inherited pathogenic mutations

Abstract Collagen VI links the cell surface to the extracellular matrix to provide mechanical strength to most mammalian tissues, and is linked to human diseases including muscular dystrophy, fibrosis, cardiovascular disease and osteoarthritis. Collagen VI assembles from heterotrimers of three different α-chains into microfibrils, but there are many gaps in our knowledge of the molecular assembly process. Here, we determine the structures of both heterotrimeric mini-collagen VI constructs and collagen VI microfibrils, from mammalian tissue, using cryogenic-electron microscopy. These structures reveal a cysteine-rich coiled coil region involved in trimerisation as well as microfibril assembly. Furthermore, our structures show that pathogenic mutations are located at interaction sites involved in different steps of collagen VI assembly, from the trimeric-coiled coil region that mediates heterotrimerisation, to clusters of mutations in the triple-helical region involved in microfibril formation. Our microfibril structure provides a template for understanding supramolecular assembly, and offers a platform for rationale design of therapeutics for collagen VI pathologies.

Determination of the main phenolic compounds of olive (Olea europaea L.) leaves by near infrared spectroscopy (NIR)

Observing the dynamics of quantum states generated inside nonlinear optical cavities

A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection

Abstract Segmenting liver tumors in medical imaging is pivotal for precise diagnosis, treatment, and evaluating therapy outcomes. Even with modern imaging technologies, fully automated segmentation systems have not overcome the challenge posed by the diversity in the shape, size, and texture of liver tumors. Such delays often hinder clinicians from making timely and accurate decisions. This study tries to resolve these issues with the development of UIGO. This new deep learning model merges U-Net and Inception networks, incorporating advanced feature selection and optimization strategies. The goals of UIGO include achieving high precision segmented results while maintaining optimal computational requirements for efficiency in real-world clinical use. Publicly available liver tumor segmentation datasets were used for testing the model: LiTS (Liver Tumor Segmentation Challenge), CHAOS (Combined Healthy Abdominal Organ Segmentation), and 3D-IRCADb1 (3D-IRCAD liver dataset). With various tumor shapes and sizes ranging across different imaging modalities such as CT and MRI, these datasets ensured comprehensive testing of UIGO’s performance in diverse clinical scenarios. The experimental outcomes show the effectiveness of UIGO with a segmentation accuracy of 99.93%, an AUC score of 99.89%, a Dice Coefficient of 0.997, and an IoU of 0.998. UIGO demonstrated higher performance than other contemporary liver tumor segmentation techniques, indicating the system’s ability to enhance clinician’s ability to deliver precise and prompt evaluations at a lower computational expense. This study underscores the effort towards advanced streamlined, dependable, and clinically useful devices for liver tumor segmentation in medical imaging.

Snhg18 regulates Yap subcellular localization to maintain bone homeostasis

The self supervised multimodal semantic transmission mechanism for complex network environments

Publisher Correction: A sodium superionic chloride electrolyte driven by paddle wheel mechanism for solid state batteries

Analysis of deep learning-based technological innovation governance on the intelligent allocation of innovation resources in the high-technology industry

Humoral determinants of checkpoint immunotherapy

Single-cell eQTL mapping of human endogenous retroviruses reveals cell type-specific genetic regulation in autoimmune diseases

Abstract Human endogenous retroviruses constitute a significant portion of the human genome and play complex roles in gene regulation and disease processes. However, the expression pattern and disease associations of specific retroviral loci remain pooly understood. This study examines the expression and regulatory mechanisms of these retroviral elements in immune cells. Utilizing single-cell RNA sequencing data from peripheral blood mononuclear cells, we identify 41,460 expressed retroviral loci, with 1936 showing cell type-specific expression. We further detect 3463 conditionally independent expression quantitative trait loci linked to retroviral elements, highlighting their potential role in mediating genetic variants and disease associations. Notably, these retroviral sequences associate significantly with autoimmune diseases, with specific loci demonstrating pleiotropic associations with disease-related genes. Our findings suggest that these elements are not merely genomic remnants but active participants in cellular regulation and disease progression. This work advances understanding of human-retroviral coevolution and highlights potential therapeutic targets in immune disorders.

Ecological impacts of human thioredoxin expression and interspecific hybridization on the soybean rhizosphere microbiome: insights from ASV-level niche analysis

Complex genetic variation in nearly complete human genomes

Abstract Diverse sets of complete human genomes are required to construct a pangenome reference and to understand the extent of complex structural variation. Here we sequence 65 diverse human genomes and build 130 haplotype-resolved assemblies (median continuity of 130 Mb), closing 92% of all previous assembly gaps 1,2 and reaching telomere-to-telomere status for 39% of the chromosomes. We highlight complete sequence continuity of complex loci, including the major histocompatibility complex (MHC), SMN1 / SMN2 , NBPF8 and AMY1/AMY2 , and fully resolve 1,852 complex structural variants. In addition, we completely assemble and validate 1,246 human centromeres. We find up to 30-fold variation in α-satellite higher-order repeat array length and characterize the pattern of mobile element insertions into α-satellite higher-order repeat arrays. Although most centromeres predict a single site of kinetochore attachment, epigenetic analysis suggests the presence of two hypomethylated regions for 7% of centromeres. Combining our data with the draft pangenome reference 1 significantly enhances genotyping accuracy from short-read data, enabling whole-genome inference 3 to a median quality value of 45. Using this approach, 26,115 structural variants per individual are detected, substantially increasing the number of structural variants now amenable to downstream disease association studies.

Sub-angstrom strain in high-entropy intermetallic boosts the oxygen reduction reaction in fuel cell cathodes

Abstract The strain effect of high-entropy intermetallic (HEI) catalysts on oxygen reduction reaction (ORR) performance remains largely unexplored, primarily due to the significant challenges associated with characterizing and calculating the intricate local coordination environments. Here, we design a nitrogen (N)-doped L10-ordered PtCoNiFeCu intermetallic catalyst supported on Ketjenblack carbon (N-HEI/KB), and reveal the origin of the sub-angstrom strain in N-HEI and its impact on ORR performance by combining atomic-scale characterization and theoretical calculations. The synergistic interplay of the sub-angstrom strain, the pinning effect of metal-N bonds, and the high-entropy effect contribute to the competitive stability of N-HEI/KB catalysts, providing high current density of 1388 mA cm-2 at 0.7 V after 90,000 cycles even under harsh heavy-duty vehicle conditions. These findings broaden the avenues for designing high-performance high-entropy intermetallic cathode electrocatalysts.