Second-order group knockoffs with applications to genome-wide association studies.

MOTIVATION: Conditional testing via the knockoff framework allows one to identify-among a 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 that influence traits of medical relevance. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: The described algorithms are implemented in an open-source Julia package Knockoffs.jl. R and Python wrappers are available as knockoffsr and knockoffspy packages.

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.

Unit information Dirichlet process prior.

Prior distributions, which represent one's belief in the distributions of unknown parameters before observing the data, impact Bayesian inference in a critical and fundamental way. With the ability to incorporate external information from expert opinions or historical datasets, the priors, if specified appropriately, can improve the statistical efficiency of Bayesian inference. In survival analysis, based on the concept of unit information (UI) under parametric models, we propose the unit information Dirichlet process (UIDP) as a new class of nonparametric priors for the underlying distribution of time-to-event data. By deriving the Fisher information in terms of the differential of the cumulative hazard function, the UIDP prior is formulated to match its prior UI with the weighted average of UI in historical datasets and thus can utilize both parametric and nonparametric information provided by historical datasets. With a Markov chain Monte Carlo algorithm, simulations and real data analysis demonstrate that the UIDP prior can adaptively borrow historical information and improve statistical efficiency in survival analysis.

Summary statistics knockoffs inference with family-wise error rate control.

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.

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.

Enterovirus-71 utilizes small extracellular vesicles to cross the blood-brain barrier for infecting the central nervous system via transcytosis.

BACKGROUND: Central nervous system (CNS) infections caused by Enterovirus 71 (EV71) pose a serious threat to children, causing severe neurogenic complications and even fatality in some patients. However, the pathogenesis of EV71 infections in the CNS remains unclear. METHODS: An in vitro blood-brain barrier (BBB) model was constructed by coculturing brain microvascular endothelial cells (BMECs) and astrocytes in transwell inserts for simulating CNS infections. EV71 virions and small extracellular vesicles (sEVs) derived from EV71-infected cells (EV71-sEVs) were isolated from the cell culture supernatant by density gradient centrifugation. The BBB model was separately infected with EV71 virions and EV71-sEVs. The mechanism of crossing the BBB was determined by inhibiting the different endocytic modes. A murine model of EV71 infection was constructed for confirming the results of in vitro experiments. RESULTS: The EV71-sEVs containing viral components were endocytosed by BMECs and released on the abluminal side of the BBB model, where they infected the astrocytes without disrupting the BBB in the early stages of infection. The integrity of the tight junctions (TJs) between BMECs was breached via downregulation of PI3K/Akt signaling in the late stages of infection. CONCLUSIONS: EV71 utilized the circulating sEVs for infecting the CNS by crossing the BBB.

Structure and Function of Sodium Channel Nav1.3 in Neurological Disorders.

Nav1.3, encoded by the SCN3A gene, is a voltage-gated sodium channel on the cell membrane. It is expressed abundantly in the fetal brain but little in the normal adult brain. It is involved in the generation and conduction of action potentials in excitable cells. Nav1.3 plays an important role in many neurological diseases. The aim of this review is to summarize new findings about Nav1.3 in the field of neurology. Many mutations of SCN3A can lead to neuronal hyperexcitability and then cause epilepsy. The rapid recovery from inactivation and slow closed-state inactivation kinetics of Nav1.3 leads to a reduced activation threshold of the channel and a high frequency of firing of neurons. Hyperactivity of Nav1.3 also induces increased excitability of sensory neurons, a lower nociceptive threshold, and neuropathic pain. This review summarizes the structure and the function of Nav1.3 and focuses on its relationship with epilepsy and neuropathic pain.

Roles and mechanisms of exosomal microRNAs in viral infections.

Exosomes are small extracellular vesicles with a diameter of 30-150 nm that originate from endosomes and fuse with the plasma membrane. They are secreted by almost all kinds of cells and can stably transfer different kinds of cargo from donor to recipient cells, thereby altering cellular functions for assisting cell-to-cell communication. Exosomes derived from virus-infected cells during viral infections are likely to contain different microRNAs (miRNAs) that can be transferred to recipient cells. Exosomes can either promote or suppress viral infections and therefore play a dual role in viral infection. In this review, we summarize the current knowledge about the role of exosomal miRNAs during infection by six important viruses (hepatitis C virus, enterovirus A71, Epstein-Barr virus, human immunodeficiency virus, severe acute respiratory syndrome coronavirus 2, and Zika virus), each of which causes a significant global public health problem. We describe how these exosomal miRNAs, including both donor-cell-derived and virus-encoded miRNAs, modulate the functions of the recipient cell. Lastly, we briefly discuss their potential value for the diagnosis and treatment of viral infections.

A preoperative predictive model for stage IV endometriosis.

This was a retrospective study that evaluated a total of 280 patients who underwent surgery for complete removal of endometriosis to develop and validate the predictive model for stage IV endometriosis. The differences between stage I-III and stage IV endometriosis were performed by logistic regression. A model for the prediction of stage IV endometriosis was constructed, which was subsequently validated. The independent variables were visual analogue scale (VAS)≥4 [3.855, 95% confidence interval (CI): 1.675-8.871, p = 0.002], painful nodularity on uterosacral ligaments (13.954, 95% CI: 1.658-117.423, p = 0.015), and bilateral endometriosis (5.933, 95% CI: 1.931-18.225, p = 0.002). The AUC of the model was 0.777, with a sensitivity of 71.9% and specificity of 76.3% for stage IV endometriosis. Therefore, a complete collection of patient information prior to surgery, asking about pain and VAS scores, careful completion of pelvic examinations, and application of imaging techniques are conducive to better diagnosis and prediction of advanced endometriosis.IMPACT STATEMENTWhat is already known on this subject? Endometriosis, a chronic disease causing pain and infertility, is characterised by endometrial-like tissue outside the uterine cavity, which is often treated via surgery at present. Considering the risks of surgery, it is necessary to identify patients with stage IV endometriosis through non-invasive predictive models for adequate preparation for surgery. However, there is no reliable non-invasive predictive model now, despite utilisation of patient medical history, symptoms especially pain-related ones, pelvic examinations, laboratory examinations, and images in the preoperative diagnosis of endometriosis in the clinic.What do the results of this study add? A model developed based on three simple, accessible and non-invasive indicators displays good performance in predicting stage IV endometriosis.What are the implications of these findings for clinical practice and/or further research? It is conducive to diagnosing and predicting advanced endometriosis before surgery, so as to reduce the difficulty and improve the safety of surgery.

A high-performance SNP panel developed by machine-learning approaches for characterizing genetic differences of Southern and Northern Han Chinese, Korean, and Japanese individuals.

Population stratification analyses targeting genetically closely related East Asians have revealed that distinguishable differentiation exists between Han Chinese, Korean, and Japanese individuals, as well as between southern (S-) and northern (N-) Han Chinese. Previous studies offer a number of choices for ancestry informative single nucleotide polymorphisms (AISNPs) to discriminate East-Asian populations. In this study, we collected and examined the efficiency of 1185 AISNPs using frequency and genotype data from various publicly available databases. With the aim to perform fine-scale classification of S-Han, N-Han, Korean, and Japanese subjects, machine-learning methods (Softmax and Random Forest) were used to screen a panel of highly informative AISNPs and to develop a superior classification model. Stepwise classification was implemented to increase and balance the discrimination in the process of AISNP selection, first discriminating Han, Korean, and Japanese individuals, and then characterizing stratification between S-Han and N-Han. The final 272-AISNP panel is an alternative optimization of various previous works, which promises reliable and >90% accuracy in classification of the four East-Asian groups. This AISNP panel and the machine-learning model could be a useful and superior choice in medical genome-wide association studies and in forensic investigations for unknown suspect identity.

Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese.

Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are differences among the East Asian populations in genetic structure. To achieve fine-scale genetic classification of southern (S-) and northern (N-) Han Chinese, Korean and Japanese individuals in this study, we collected and analyzed 1185 ancestry informative SNPs (AISNPs) from previous literature reports and our laboratory findings. First, two machine learning algorithms, softmax and randomForest, were used to build genetic classification models. Then, phylogenetic tree, STRUCTURE and principal component analysis were used to evaluate the performance of classification for different AISNP panels. The 234-AISNP panel achieved a fine-scale differentiation among the target populations in four classification schemes. The accuracy of the softmax model was 92%, which realized the accurate classification of the S-Han, N-Han, Korean and Japanese individuals. The two machine learning models tested in this study provided important references for the high-resolution discrimination of close-range populations and will be useful tools to optimize marker panels for developing forensic DNA ancestry inference systems.

DNA aptamer against EV-A71 VP1 protein: selection and application.

BACKGROUND: Enterovirus 71 (EV-A71) is a highly infectious pathogen associated with hand, foot and mouth disease, herpangina, and various neurological complications, so it is important for the early detection and treatment of EV-A71. An aptamer is a nucleotide sequence that screened in vitro by the technology named systematic evolution of ligands by exponential enrichment technology (SELEX). Similar to antibodies, aptamers can bind to the targets with high specificity and affinity. Besides, emerging aptamers have many advantages comparing with antibodies, such as ease of synthesis and modification, having a wide variety of target materials, low manufacturing cost and easy flexibility in amending. Therefore, aptamers are promising in virus detection and anti-virus therapy. METHODS: Aptamers were selected by SELEX. Specificity, affinity and second structure were used to characterize the selected aptamers. Chemiluminescence was adopted to build an aptamer-based detection method for EV-A71. Cytopathogenic effects trial, the level of intracellular EV-A71 RNA and protein expression were used to evaluate the antiviral effect of the selected aptamers. RESULTS: Three DNA aptamers with high specificity and affinity for EV-A71structual protein VP1 were screened out. A rapid chemiluminutesescence aptamer biosensor for EV-A71 detection was designed out. The selected aptamers could inhibit the RNA replication and protein expression of EV-A71 in RD cells and ameliorate the cytopathogenic effects. CONCLUSIONS: The aptamers against EV-A71 have the potentiality to be applied as attractive candidates used for EV-A71 detection and treatment in the future.

Structural insights into the sequence-specific recognition of Piwi by Drosophila Papi.

The Tudor domain-containing (Tdrd) family proteins play a critical role in transposon silencing in animal gonads by recognizing the symmetrically dimethylated arginine (sDMA) on the (G/A)R motif of the N-terminal of PIWI family proteins via the eTud domains. Papi, also known as "Tdrd2," is involved in Zucchini-mediated PIWI-interacting RNA (piRNA) 3'-end maturation. Intriguingly, a recent study showed that, in papi mutant flies, only Piwi-bound piRNAs increased in length, and not Ago3-bound or Aub-bound piRNAs. However, the molecular and structural basis of the Papi-Piwi complex is still not fully understood, which limits mechanistic understanding of the function of Papi in piRNA biogenesis. In the present study, we determined the crystal structures of Papi-eTud in the apo form and in complex with a peptide containing unmethylated or dimethylated R10 residues. Structural and biochemical analysis showed that the Papi interaction region on the Drosophila Piwi contains an RGRRR motif (R7-R11) distinct from the consensus (G/A)R motif recognized by canonical eTud. Mass spectrometry results indicated that Piwi is the major binding partner of Papi in vivo. The papi mutant flies suffered from both fertility and transposon-silencing defects, supporting the important role conferred to Papi in piRNA 3' processing through direct interaction with Piwi proteins.

The Incidence Prognosis and Risk Factors of Cognitive Impairment in Maintenance Haemodialysis Patients.

OBJECTIVE: To investigate the incidence and the prognosis of cognitive impairment (CI) and to find out the risk factors associated with the outcome in maintenance haemodialysis (MHD) patients. METHODS: Enrolled the patients who met the criteria as below: MHD (≥3 months) patients before July 2014, ≥18 years old and could carry on the cognitive function test (Montreal Cognitive Assessment [MoCA]). All enrolled patients were divided into 2 groups: CI group (MoCA < 26) and non-CI group (MoCA ≥26). All patients were followed up for 36 months. The incidence, demography data, medical history, haemodialysis data, laboratory examination and prognosis of CI in haemodialysis patients were prospectively compared and analyzed. Multivariate logistic regression analysis was used to investigate the risk factors of CI. Kaplan-Meier survival curve was used for survival analysis. RESULTS: In the present study, 219 patients were enrolled. The ratio of male to female was 1.46: 1. Age was 60.07 ± 12.44 and dialysis vintage was 100.79 ± 70.23 months. One hundred thirteen patients' MoCA scores were lower than 26 were divided into CI group. Education status (OR 3.428), post-dialysis diastolic pressure (OR 2.234) and spKt/V (OR 1.982) were independent risk factors for CI in MHD patients. During the follow-up period, 15 patients died (13.2%) in the CI group and 5 died (4.72%) in the non-CI group (p < 0.05). The Kaplan-Meier survival curve analysis showed that the survival rate of patients with CI was lower than that of non-CI group in MHD patients during 3 years follow-up (p = 0.046). CONCLUSION: CI is one of the most common complications in MHD patients. The mortality is high in patients who had CI. Education status, post-dialysis diastolic pressure and spKt/V are independent risk factors for CI in MHD patients.

Maresin 1 attenuates mitochondrial dysfunction through the ALX/cAMP/ROS pathway in the cecal ligation and puncture mouse model and sepsis patients.

Inflammation always accompanies infection during sepsis. Mitochondrial dysfunction and the role of reactive oxygen species (ROS) produced by mitochondria have been proposed in the pathogenesis of sepsis. Maresins have protective and resolving effects in experimental models of infection. In the present study, we investigated the effects of maresin 1 (MaR1) on mitochondrial function in cecal ligation and puncture (CLP)-induced sepsis and sepsis patients to identify mechanisms underlying maresin 1-mediated stimulation of ROS in mitochondria. We found that treatment with MaR1 significantly inhibited production of cytokines, decreased bacterial load in the peritoneal lavage fluid, reduced the number of neutrophils, decreased lactic acid level and upregulated cyclic AMP (cAMP) concentration, with the outcome of decreased lung injury in CLP-induced sepsis in mice. The effects of MaR1 on downregulation nitric oxide (NOX) activity, improvement CAT and SOD activity to inhibit ROS production in mitochondria was dependent on lipoxin receptor (ALX) and cAMP. Survival rates were significantly increased after the treatment of mice with MaR1. In BMDM stimulated with LPS, MaR1 inhibited the ROS production, downregulated enzyme activity, reduced mtO2 production, increased mitochondrial membrane potential, improved adenosine triphosphate (ATP) content and mitochondrial DNA (mtDNA) copy number. Finally, the effects of MaR1 on ROS production in the blood of healthy volunteers stimulated with LPS or sepsis patients were associated with ALX and cAMP. Taken together, these data suggest that treatment with MaR1 could attenuate mitochondrial dysfunction during sepsis through regulating ROS production.

Lipoxin A4 activates alveolar epithelial sodium channel gamma via the microRNA-21/PTEN/AKT pathway in lipopolysaccharide-induced inflammatory lung injury.

Lipoxin A4 (LXA4), as an endogenously produced lipid mediator, promotes the resolution of inflammation. Previously, we demonstrated that LXA4 stimulated alveolar fluid clearance through alveolar epithelial sodium channel gamma (ENaC-γ). In this study, we sought to investigate the mechanisms of LXA4 in modulation of ENaC-γ in lipopolysaccharide (LPS)-induced inflammatory lung injury. miR-21 was upregulated during an LPS challenge and downregulated by LXA4 administration in vivo and in vitro. Serum miR-21 concentration was also elevated in acute respiratory distress syndrome patients as compared with healthy volunteers. LPS increased miR-21 expression by activation of activator protein 1 (AP-1). In A549 cells, miR-21 upregulated phosphorylation of AKT activation via inhibition of phosphatase and tensin homolog (PTEN), and therefore reduced the expression of ENaC-γ. In contrast, LXA4 reversed LPS-inhibited ENaC-γ expression through inhibition of AP-1 and activation of PTEN. In addition, an miR-21 inhibitor mimicked the effects of LXA4; overexpression of miR-21 abolished the protective effects of LXA4. Finally, both AKT and ERK inhibitors (LY294002 and UO126) blocked effects of LPS on the depression of ENaC-γ. However, LXA4 only inhibited LPS-induced phosphorylation of AKT. In summary, LXA4 activates ENaC-γ in part via the miR-21/PTEN/AKT signaling pathway.

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.