Reconstruction Filters Improving the Spatial Resolution and Signal-to-Noise Ratio of Surface Plasmon Resonance Microscopy.

Benefitting from high sensitivity, real-time, and label-free imaging, surface plasmon resonance microscopy (SPRM) has become a powerful tool for dynamic detection of nanoparticles. However, the evanescent propagation of surface plasmon polaritons (SPPs) induces interference between scattered and launched SPPs, which deteriorates the spatial resolution and signal-to-noise ratio (SNR). Due to the simplicity and fast processing, image reconstruction based on deconvolution has shown the feasibility of improving the spatial resolution of SPRM imaging. Retrieving the particle scattering from SPRM interference imaging by filters is crucial for reconstruction. In this work, we illustrate the effect of filters extracting SPP scattering of nanoparticles with different sizes and shapes for reconstruction. The results indicate that the optimum filters are determined by the material of nanoparticles instead of particle sizes. The reconstruction of single Au and PS nanospheres as well as Ag nanowires with optimum filters is achieved. The reconstructed spatial resolution is improved to 254 nm, and the SNR is increased by 8.1 times. Our research improves the quality of SPRM imaging and provides a reliable method for fast detection of particles with diverse sizes and shapes.

Low expression of interleukin-1 receptor antagonist correlates with poor prognosis via promoting proliferation and migration and inhibiting apoptosis in oral squamous cell carcinoma.

BACKGROUND: Biomarkers are biochemical indicators that can identify changes in the structure or function of systems, organs, or cells and can be used to monitor a wide range of biological processes, including cancer. Interleukin-1 receptor antagonist (IL1RA) is an important inflammatory suppressor gene and tumor biomarker. The goal of this study was to investigate the expression of IL1RA, its probable carcinogenic activity, and its diagnostic targets in oral squamous cell carcinoma (OSCC). RESULTS: We discovered that IL1RA was expressed at a low level in OSCC tumor tissues compared to normal epithelial tissues and that the expression declined gradually from epithelial hyperplasia through dysplasia to carcinoma in situ and invasive OSCC. Low IL1RA expression was associated not only with poor survival but also with various clinicopathological markers such as increased infiltration, recurrence, and fatalities. Following cellular phenotyping investigations in OSCC cells overexpressing IL1RA, we discovered that recovering IL1RA expression decreased OSCC cell proliferation, migration, and increased apoptosis. CONCLUSIONS: In summary, our investigation highlighted the possible involvement of low-expression IL1RA in OSCC cells in promoting invasive as well as metastatic and inhibiting apoptosis, as well as the efficacy of IL1RA-focused monitoring in the early detection and treatment of OSCC.

First Report of Leaf Brown Spot Caused by Diaporthe phoenicicola on Pachira glabra in China.

Pachira glabra is an increasingly important ornamental landscape tree in southern China. In August 2022, brown spots were observed on P. glabra leaves in Xiangtan City, Hunan Province, China (27.932°N, 113.020°E), affecting up to 40% of the 792 trees surveyed. On each diseased tree, nearly 30% leaves had symptoms, with an average severity of 21.2 ± 5.8% (n=100). The disease initially started as small yellow lesions along leaf margins, which later progressed to pale brown to brown with dark brown borders, eventually coalescing into large necrotic areas. Thirty symptomatic leaf samples (2 × 2 mm) were surfaced-sterilized in 75% ethanol for 10 s, 2% NaOCl for 30 s, rinsed in sterile water three times, placed on potato dextrose agar (PDA), and incubated at 25°C for 5 to 7 days in dark. Eight morphologically similar isolates were obtained from diseased leaf samples through single-spore isolation. On PDA, colonies initially appeared white, turning gray, while the reverse developed a pale yellowish hue. Aerial mycelia were white, cottony, and developed visible black pycnidia with oil droplets at maturity. The α-conidia were unicellular, hyaline, aseptate, oval or fusiform, usually with 1 or 2 guttule(s) and rounded at each end. These conidia were 5.3-8.6 × 1.7-2.5 µm (avg. 6.7 × 2.2 µm, n = 100) and present more frequently than ß-conidia.The ß-conidia were unicellular, hyaline, aseptate, filiform, straight or hamate, eguttulate, 14.6-23.3 × 0.4-1.3 µm (avg. 18.4 × 0.9 µm, n = 30). Morphologically, the fungi were identified as Diaporthe sp. (Udayanga et al. 2014). For molecular identification, the internal transcribed spacer region (ITS), translation elongation factor 1α (EF1-α), calmodulin (CAL), tubulin 2 (TUB2), and histone H3 (HIS3) sequences of all isolates were amplified from genomic DNA, using primers ITS4/ITS5 (White et al. 1990), TEF-2/728F and CALD-38F/CALD-752R (Carbone and Kohn 1999), Bt2a/Bt2b and H3-1a/H3-1b (Glass and Donaldson 1995; Crous et al. 2004), respectively. The GenBank accession numbers for a representative isolate gpg2023-1 were OR533573 (ITS), OR570887 (EF1-α), OR570888 (TUB2), OR570890 (CAL), and OR570889 (HIS3). BLAST results showed that the ITS, EF1-α, TUB2, HIS, and CAL sequences were 99%, 99%, 99%, 99%, and 98% identity, respectively, with those of Diaporthe phoenicicola (GenBank: KC343032.1, KC343758.1, KC344000.1, KC343516.1, and KC343274.1). To confirm the pathogen's identity, phylogenetic analysis using MEGA7.0 based on Maximum Likelihood was constructed. Isolate gpg2023-1 clustered with D. phoenicicola. Based on morphological and molecular data, the fungus was identified as D. phoenicicola. Next, pathogenicity tests were performed three times on one-year-old potted P. glabra plants. For each isolate, twelve healthy leaves on each of three plants were either wounded by a sterile needle or left unwounded, and then sprayed with a conidial suspension (1×106 conidia/ml) for each isolate. Control plants received with sterile water only. Plants were kept in a greenhouse at 25°C, 80% relative humidity, with a 12-h photoperiod. All wounded, inoculated leaves developed brown spot symptoms similar to those observed in the field with six days, while unwounded leaves and control plants remained symptom-free. The fungus was reisolated from all diseased leaves, fulfilling Koch's postulates and proving D. phoenicicola as the causative agent of this brown spot disease on P. glabra. While D. pachirae has been reported to cause leaf spot on P. glabra in Brazil (Milagres et al. 2018), this study marks the first report of D. phoenicicola causing leaf brown spot on P. glabra in China. This finding can help develop control strategies for this disease.

Development of a TaqMan Real-Time PCR for Early and Accurate Detection of Anthracnose Pathogen Colletotrichum siamense in Pachira glabra.

Anthracnose, caused by Colletotrichum siamense, is a destructive disease of Pachira glabra in southern China. Early and proper monitoring and quantification of C. siamense is of importance for disease control. A calmodulin (CAL) gene-based TaqMan real-time PCR assay was developed for efficient detection and quantification of C. siamense, which reliably detected as low as 5 pg of genomic DNA and 12.8 fg (5800 copies) of target DNA. This method could specifically recognize all tested C. siamense isolates, while no amplification was observed in other closely related Colletotrichum species. The assay could still detect C. siamense in plant mixes, of which only 0.01% of the tissue was infected. A dynamic change in the amount of C. siamense population was observed during infection, suggesting that this real-time PCR assay can be used to monitor the fungal growth progression in the whole disease process. Moreover, the method enabled the detection of C. siamense in naturally infected and symptomless leaves of P. glabra trees in fields. Taken together, this specific TaqMan real-time PCR provides a rapid and accurate method for detection and quantification of C. siamense colonization in P. glabra, and will be useful for prediction of the disease to reduce the epidemic risk.

Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression.

Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of marginal empirical correlations between each dependent variable of potential interest and the response. This situation may arise due to privacy concerns, e.g., to avoid the release of sensitive genetic information. We extend GhostKnockoffs He et al. [2022] and introduce variable selection methods based on penalized regression achieving false discovery rate (FDR) control. We report empirical results in extensive simulation studies, demonstrating enhanced performance over previous work. We also apply our methods to genome-wide association studies of Alzheimer's disease, and evidence a significant improvement in power.

Second-order group knockoffs with applications to GWAS.

Conditional testing via the knockoff framework allows one to identify -- among large number of possible explanatory variables -- those that carry unique information about an outcome of interest, and also provides a false discovery rate guarantee on the selection. This approach is particularly well suited to the analysis of genome wide association studies (GWAS), which have the goal of identifying genetic variants which influence traits of medical relevance. While conditional testing can be both more powerful and precise than traditional GWAS analysis methods, its vanilla implementation encounters a difficulty common to all multivariate analysis methods: it is challenging to distinguish among multiple, highly correlated regressors. This impasse can be overcome by shifting the object of inference from single variables to groups of correlated variables. To achieve this, it is necessary to construct "group knockoffs." While successful examples are already documented in the literature, this paper substantially expands the set of algorithms and software for group knockoffs. We focus in particular on second-order knockoffs, for which we describe correlation matrix approximations that are appropriate for GWAS data and that result in considerable computational savings. We illustrate the effectiveness of the proposed methods with simulations and with the analysis of albuminuria data from the UK Biobank. The described algorithms are implemented in an open-source Julia package Knockoffs.jl, for which both R and Python wrappers are available.

Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics.

Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Here, we present a novel framework for genome-wide detection of sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. Crucially, our framework requires only summary statistics obtained from standard genome-wide marginal association testing. The described approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform usual two-stage marginal association testing and fine-mapping procedures in precision and recall. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of 67 large-scale GWAS summary statistics since 2013 for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.