Inference Methods Research Articles

Automated single-cell omics end-to-end framework with data-driven batch inference.

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/ .

First Report of Exserohilum rostratum Causing Brown Spot on Spartina alterniflora in China.

Spartina alterniflora Loisel, a perennial grass, has become an invasive species in China's coastal wetlands (Zhang et al. 2018). In July 2021, brown spot symptoms were observed on S. alterniflora in a coastal wetland (21°45'48″N, 108°44'00″E) in Beihai City, Guangxi Province, China. The disease affected approximately 50% of the plants in the surveyed area (0.2 ha) and was also observed in other regions of Beihai. It caused brown lesions with a gray or whitish center on the leaves and stems of S. alterniflora. As the disease developed, it ultimately led to leaf shedding and plant death. To isolate the causal agent, 18 fragments (~ 5 mm) from six symptomatic plants (3 leaf pieces per plant) were surface sterilized with 1% NaOCl solution for 2 min and rinsed three times with sterilized water. Subsequently, the tissues were placed on potato dextrose agar (PDA) medium supplemented with chloramphenicol (0.1 g/liter) and incubated at 28°C for three days. The hyphal tips were transferred onto fresh PDA to obtain pure cultures. A total of 25 isolates were obtained, 20 of which shared similar morphologies, while the remaining five exhibited distinct morphological characteristics and were non-pathogenic to S. alterniflora. Three isolates (MC16.1.3, MC16.6.2, and MC16.8.3) were randomly selected from the 20 for further investigation. The colonies on PDA were flat with dense aerial mycelia. The colony margins were entire, light brown in the centre, white to grey at the margin; reverse dark brown in the centre, gray at the margin. Conidia were straight to slightly curved, light olive-brown to dark olive-brown, septate, measured 33.5 to 79.1 μm × 10.4 to 18.7 μm (average 52.9 × 14.4 μm, n = 100), with a distinctly protruding hilum swelled from the basal cell. For molecular identification, the genomic DNA was extracted from mycelium on PDA using the CTAB method (Guo et al. 2000). The internal transcribed spacer (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and translation elongation factor 1 alpha (TEF1-α) genes were amplified and sequenced with the primer pairs ITS1/ITS4 (White et al. 1990), GPD1/GPD2 (Berbee et al. 1999), and EF1-983/EF1-2218 (Rehner et al. 2005), respectively. A BLAST analysis revealed that the ITS (OR516787-9), GAPDH (OR523686-8), and TEF-α (OR523683-5) had 99.1 to 99.7% identity with those of E. rostratum strains BRIP 11417 (LT837836, LT882553, and LT896656) and CBS 128061 (KT265240, LT715900, and LT896658) (Hernández-Restrepo et al. 2018). Based on the concatenated sequences, a phylogenetic tree generated by PhyloSuite software (Zhang et al., 2020) through Bayesian inference (BI) and Maximum Likelihood (ML) methods placed the isolates within E. rostratum. These morphological characteristics and molecular analyses confirmed the pathogen as E. rostratum (Hernández-Restrepo et al. 2018; Kaboré et al. 2022). To confirm pathogenicity, a conidial water suspension (~ 1 × 106 conidia/ml) of each of the three strains was inoculated on nine healthy S. alterniflora plants that had been grown for six months. Control plants were treated with sterile water. All plants were then enclosed in plastic bags and incubated in a greenhouse at 28°C. Six days after inoculation, the plants exhibited symptoms similar to those observed in nature. The control plants developed no symptoms. These experiments were replicated three times with similar results. To fulfill Koch's postulates, E. rostratum was consistently re-isolated from symptomatic tissue and confirmed by morphology and sequencing, whereas no fungus was isolated from the control plants. In recent years, S. alterniflora has posed a serious threat to the indigenous biodiversity of wetland ecosystems (Zhang et al. 2018). To our knowledge, this is the first report of E. rostratum causing brown spot on S. alterniflora worldwide.

Safety and effectiveness of SGLT2 inhibitors in a UK population with type 2 diabetes and aged over 70 years: an instrumental variable approach.

Older adults are under-represented in trials, meaning the benefits and risks of glucose-lowering agents in this age group are unclear. The aim of this study was to assess the safety and effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in people with type 2 diabetes aged over 70 years using causal analysis. Hospital-linked UK primary care data (Clinical Practice Research Datalink, 2013-2020) were used to compare adverse events and effectiveness in individuals initiating SGLT2i compared with dipeptidyl peptidase-4 inhibitors (DPP4i). Analysis was age-stratified: <70 years (SGLT2i n=66,810, DPP4i n=76,172), ≥70 years (SGLT2i n=10,419, DPP4i n=33,434). Outcomes were assessed using the instrumental variable causal inference method and prescriber preference as the instrument. Risk of diabetic ketoacidosis was increased with SGLT2i in those aged ≥70 (incidence rate ratio compared with DPP4i: 3.82 [95% CI 1.12, 13.03]), but not in those aged <70 (1.12 [0.41, 3.04]). However, incidence rates with SGLT2i in those ≥70 was low (29.6 [29.5, 29.7]) per 10,000 person-years. SGLT2i were associated with similarly increased risk of genital infection in both age groups (incidence rate ratio in those <70: 2.27 [2.03, 2.53]; ≥70: 2.16 [1.77, 2.63]). There was no evidence of an increased risk of volume depletion, poor micturition control, urinary frequency, falls or amputation with SGLT2i in either age group. In those ≥70, HbA1c reduction was similar between SGLT2i and DPP4i (-0.3 mmol/mol [-1.6, 1.1], -0.02% [0.1, 0.1]), but in those <70, SGLT2i were more effective (-4 mmol/mol [4.8, -3.1], -0.4% [-0.4, -0.3]). Causal analysis suggests SGLT2i are effective in adults aged ≥70 years, but increase risk for genital infections and diabetic ketoacidosis. Our study extends RCT evidence to older adults with type 2 diabetes.

Circulating interleukin-27 is associated with the risk of chronic periodontitis and allergic rhinitis: A Mendelian randomization analysis

Background Chronic periodontitis (CP) and allergic rhinitis (AR) have attracted wide attention as global public health problems with high incidence. Recent studies have shown that circulating interleukin-27 (IL-27) is associated with the risk of CP and AR. The aim of this study is to analyze the causal effect between them using Mendelian randomization (MR) Methods Bidirectional MR analyses were performed with the use of publicly available genome-wide association study (GWAS) data. Summary data on circulating IL-27, CP, and AR published in genome-wide association studies were collected. Instrumental variables (IV) were extracted using assumptions of correlation, independence and exclusivity as criteria. Inverse variance weighting (IVW) was used as the main method, combined with weighted median method (WM) and MR-Egger and other MR Analysis methods for causal inference of exposure and outcome. Cochran’s Q and MR-Egger intercept were used for sensitivity analysis Results The IVW study showed a causal effect between increased circulating IL-27 levels and increased risk of CP (OR = 1.14, 95%CI = 1.02–1.26, p = .020). Similarly, the increase of circulating IL-27 level had a causal effect on the decreased risk of AR (OR = 0.88, 95%CI = 0.80–0.97, p = .012). In addition, IVW study found that there was a causal between the increased risk of CP and circulating IL-27 level (OR = 1.05, 95%CI = 1.01–1.10, p = .016). However, there was no significant causal relationship between the risk of AR and circulating IL-27 levels (OR = 0.97, 95%CI = 0.91–1.02, p = .209). no significant heterogeneity or horizontal pleiotropy was found in sensitivity analysis. Conclusions There is a causal effect between circulating IL-27 level and CP, AR, which will help to find new ideas and methods for the diagnosis and treatment of CP and AR.

How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China

BackgroundEvidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods. AimsWe aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods. MethodsWe used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality. ResultsWith an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025–1.041), 1.032 (95% CI: 1.027–1.038), and 1.020 (95% CI: 1.016–1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009–1.035) and 1.053 (1.038–1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031–1.053), 1.041 (1.032–1.050), and 1.0473 (1.025–1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results. ConclusionThis study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

Medication use evaluation of tocilizumab implementation in COVID-19 treatment guidelines: A causal inference approach.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Introduction of new medications to health-system formularies is often not accompanied by assessments of their clinical impact on the local patient population. The growing availability of electronic health record (EHR) data and advancements in pharmacoepidemiology methods offer institutions the opportunity to monitor the medication implementation process and assess clinical effectiveness in the local clinical context. In this study, we applied novel causal inference methods to evaluate the effects of a formulary policy introducing tocilizumab therapy for critically ill patients with coronavirus disease 2019 (COVID-19). We conducted a medication use evaluation utilizing EHR data from patients admitted to a large medical center during the 6 months before and after implementation of a formulary policy endorsing the use of tocilizumab for treatment of COVID-19. The impact of tocilizumab on 28-day all-cause mortality was assessed using a difference-in-differences analysis, with ineligible patients serving as a nonequivalent control group, and a matched analysis guided by a target trial emulation framework. Safety endpoints assessed included the incidence of secondary infections and liver enzyme elevations. Our findings were benchmarked against clinical trials, an observational study, and a meta-analysis. Following guideline modification, tocilizumab was administered to 69% of eligible patients. This implementation was associated with a 3.1% absolute risk reduction in 28-day mortality (odds ratio, 0.86; number needed to treat to prevent one death, 32) attributable to the inclusion of tocilizumab in the guidelines and an additional 8.6% absolute risk reduction (odds ratio, 0.65; number needed to treat to prevent one death, 12) linked to its administration. These findings were consistent with estimates from published literature, although the effect estimates from the difference-in-differences analysis exhibited imprecision. Evaluating formulary management decisions through novel causal inference approaches offers valuable estimates of clinical effectiveness and the potential to optimize the impact of new medications on population outcomes.

Modeling intra-individual inter-trial EEG response variability in autism.

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

Control of quadrotor robot via optimized nonlinear type-2 fuzzy fractional PID with fractional filter: Theory and experiment

This paper presents a novel approach for controlling quadrotor robots. It introduces an optimized nonlinear fractional order type-2 fuzzy system integrated with a Fractional Order Proportional-Integral-Derivative controller, supplemented by a fractional filter in the derivative section. The input scaling factors, which represent the scaling coefficients associated with the input variables of the fuzzy system, are adjusted using nonlinear gains. The proposed controller combines type-2 fuzzy logic, fractional calculus, and nonlinear gains to enhance the control performance of the system. Its purpose is to provide superior control accuracy, robustness, and disturbance rejection capabilities. In order to enhance the performance of the quadrotor, the Bat Optimization Algorithm is utilized to finely adjust the controller parameters and membership function parameters of the fuzzy system. The fuzzy system incorporates Gaussian membership functions and applies the Mamdani min-max method for inference, utilizing the centroid method for defuzzification. The performance of the controller is evaluated in two scenarios: tracking aerial maneuvers in the presence of wind disturbance, and achieving angular stabilization by tracking a pulse function in the Hardware-In-The-Loop real-time test. The findings demonstrate significant improvements compared to traditional PID controllers. These improvements surpass 34 % without a filter and 53 % with a filter, in all directions and scenarios.

VIP - Variational Inversion Package with example implementations of Bayesian tomographic imaging

Bayesian inference has become an important methodology to solve inverse problems and to quantify uncertainties in their solutions. Variational inference is a method that provides probabilistic, Bayesian solutions efficiently by using optimisation. In this study we present a Python Variational Inversion Package (VIP), to solve inverse problems using variational inference methods. The package includes automatic differential variational inference (ADVI), Stein variational gradient descent (SVGD) and stochastic SVGD (sSVGD), and provides implementations of 2D travel time tomography and 2D full waveform inversion including test examples and solutions. Users can solve their own problems by supplying an appropriate forward function and a gradient calculation code. In addition, the package provides a scalable implementation which can be deployed easily on a desktop machine or using modern high performance computational facilities. The examples demonstrate that VIP is an efficient, scalable, extensible and user-friendly package, and can be used to solve a wide range of low or high dimensional inverse problems in practice.

Multiple evidence reveals a new species of Neocucurbitaria (Cucurbitariaceae, Pleosporales) from China

Xanthoceras sorbifolium (yellowhorn) is an endemic edible oil crop in China. A novel Neocucurbitaria species associated with dead stems of yellowhorn in Jilin Province (northeast China) is described and illustrated. It is characterized by stromata with 1–5 locules and a subhyaline pycnidial wall under the inner layer. Full morphological details and a comparison with closely related species are also provided. The new species differs from the closely related species N. ribicola, N. rhamni, N. rhamnicola and N. rhamnioides by its distinct conidiogenous cells and conidia. Phylogenetic analysis (maximum likelihood and Bayesian inference method) of combined ITS, LSU, rpb2, tef1-α and tub2 sequence data reveal that two strains of the fungus differ from all other species of Neocucurbitaria (Cucurbitariaceae). Therefore, we introduce N. subribicola as a new species, and this is the first report of the asexual morph of Neocucurbitaria in China

DESI complete calibration of the colour–redshift relation (DC3R2): results from early DESI data

ABSTRACT We present initial results from the Dark Energy Spectroscopic Instrument (DESI) complete calibration of the colour–redshift relation (DC3R2) secondary target survey. Our analysis uses 230 k galaxies that overlap with KiDS-VIKING ugriZYJHKs photometry to calibrate the colour–redshift relation and to inform photometric redshift (photo-z) inference methods of future weak lensing surveys. Together with emission line galaxies (ELGs), luminous red galaxies (LRGs), and the Bright Galaxy Survey (BGS) that provide samples of complementary colour, the DC3R2 targets help DESI to span 56 per cent of the colour space visible to Euclid and LSST with high confidence spectroscopic redshifts. The effects of spectroscopic completeness and quality are explored, as well as systematic uncertainties introduced with the use of common Self-Organizing Maps trained on different photometry than the analysis sample. We further examine the dependence of redshift on magnitude at fixed colour, important for the use of bright galaxy spectra to calibrate redshifts in a fainter photometric galaxy sample. We find that noise in the KiDS-VIKING photometry introduces a dominant, apparent magnitude dependence of redshift at fixed colour, which indicates a need for carefully chosen deep drilling fields, and survey simulation to model this effect for future weak lensing surveys.

Estimating mutation rates under heterogeneous stress responses.

Exposure to environmental stressors, including certain antibiotics, induces stress responses in bacteria. Some of these responses increase mutagenesis and thus potentially accelerate resistance evolution. Many studies report increased mutation rates under stress, often using the standard experimental approach of fluctuation assays. However, single-cell studies have revealed that many stress responses are heterogeneously expressed in bacterial populations, which existing estimation methods have not yet addressed. We develop a population dynamic model that considers heterogeneous stress responses (subpopulations of cells with the response off or on) that impact both mutation rate and cell division rate, inspired by the DNA-damage response in Escherichia coli (SOS response). We derive the mutant count distribution arising in fluctuation assays under this model and then implement maximum likelihood estimation of the mutation-rate increase specifically associated with the expression of the stress response. Using simulated mutant count data, we show that our inference method allows for accurate and precise estimation of the mutation-rate increase, provided that this increase is sufficiently large and the induction of the response also reduces the division rate. Moreover, we find that in many cases, either heterogeneity in stress responses or mutant fitness costs could explain similar patterns in fluctuation assay data, suggesting that separate experiments would be required to identify the true underlying process. In cases where stress responses and mutation rates are heterogeneous, current methods still correctly infer the effective increase in population mean mutation rate, but we provide a novel method to infer distinct stress-induced mutation rates, which could be important for parameterising evolutionary models.

Probabilistic Nearest Neighbors Based Locality Preserving Projections for Unsupervised Metric Learning

Dimensionality reduction based unsupervised metric learning consists in finding meaningful compact data representations previously to clustering and classification problems. One of the major aspects of these algorithms is the approximation of the underlying manifold by a weighted graph. A limitation with most manifold learning algorithms is that edge weights in the proximity graph rely heavily on the Euclidean distance, which is known to be quite sensitive to the presence of outliers. In this paper, we propose to improve the Locality Preserving Projections (LPP) algorithm by incorporating a recently proposed graph inference method called Probabilistic Nearest Neighbors (PNN), an extension of the Clustering with Adaptive Neighbors (CAN) approach, used with success in graph-based semi-supervised learning. The proposed PNN-LPP algorithm is able to achieve better classification results than regular LPP, showing competitive performance against state-of-the-art approaches for dimensionality reduction, such as the UMAP algorithm, especially in datasets with a limited number of samples.

Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data

Understanding the response of a transportation system to disruptive events is significant for evaluating the resilience of the system. However, data collection during such events is always challenging, and the data volume is insufficient for building a robust model. Transfer learning provides an effective solution to this problem. In this study, we propose a floating car data (FCD) driven transfer learning framework for predicting the resilience of target transportation systems to similar disruptive events to those that have ever occurred in the source systems. The core of the framework is an unsupervised pattern extractor that combines the k-Shape clustering and Bayes inference methods for extracting resilience patterns from the FCD collected in the source systems during the disruption period. The extracted patterns can then be used to assist in the prediction of the resilience of the target systems. We examine the effectiveness of the proposed framework by conducting a case study under the context of the COVID-19 pandemic, in which the source domain cities include Antwerp and Bangkok, and the target domain city is Barcelona. Results show that the extracted resilience patterns can improve the prediction performance of transfer learning neural networks with less pre-event information and limited data volume.

Proposal and Verification of the Application of an Expert Inference Method to Present the Probability of Lithium-Ion Battery Thermal Runaway Risk

This study proposes a probabilistic quantification technique that applies an expert inference method to warn of the risk of a fire developing into a thermal runaway when a lithium-ion battery fire occurs. Existing methods have the shortcomings of low prediction accuracy and delayed responses because they determine a fire only by detecting the temperature rise and smoke in a lithium-ion battery to initiate extinguishing activities. To overcome such shortcomings, this study proposes a method to probabilistically calculate the risk of thermal runaway in advance by detecting the amount of off-gases generated in the venting stage before thermal runaway begins. This method has the advantage of quantifying the probability of a fire in advance by applying an expert inference method based on a combination of off-gas amounts, while maintaining high reliability even when the sensor fails. To verify the validity of the risk probability design, problems with the temperature and off-gas increase/decrease data were derived under four SOC conditions in actual lithium-ion batteries. Through the foregoing, it was confirmed that the risk probability can be accurately presented even in situations where the detection sensor malfunctions by applying an expert inference method to calculate the risk probability complexly. Additionally, it was confirmed that the proposed method is a method that can lead to quicker responses to thermal runaway fires.

RepFTI: Representation-Fused Function-Type Inference for Vehicular Secure Software Systems

To enhance the security of vehicular software systems, inversely identifying the underlying function types of binary files plays a key role in threat discovery. However, existing function-type inference (FTI) methods can only provide a suboptimal performance because of splitting binary files into multiple sub-blocks as inputs, which results in breaking the program context logic and complete data dependency. To solve this problem, we propose a novel representation-fused function-type inference (RepFTI) framework for secure vehicular software systems. First, the RepFTI learns semantic representations of assembly codes and then extracts node representations in the function call graph by the multi-head attention mechanism of Graph-Attention Transformer (GAT) models. Second, the RepFTI fuses these representations to accurately infer the function type. With RepFTI, the specific limits of in-vehicle software will be bypassed, which proposes a promising direction for other work that relies on reverse engineering to improve software security.

Generalized fiducial inference for the generalized logistic distribution: Censored and uncensored cases

. This article primarily considers the statistical inference of the scale parameter, shape parameter, and reliability of the generalized logistic distribution in both censored and uncensored cases. For the progressively Type-II censored and complete samples, the frequentist method is utilized to construct asymptotic confidence intervals, and the Bayesian inference method is employed for constructing the point estimators as well as posterior credible intervals. Then the generalized fiducial method is applied to construct the fiducial point estimators and the fiducial confidence intervals. Furthermore, the non parametric generalized fiducial method is introduced to estimate the survival function. Simulation results demonstrate that the generalized fiducial method consistently outperforms other methods in terms of mean square error, average length, and empirical coverage. Finally, two real datasets are used to illustrate the proposed methods.

CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data.

Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.

Genetically predicted asthma and the risk of abnormal spermatozoa

BackgroundSeveral previous animal and human studies have found a strong association between asthma and spermatozoa quality, but whether these associations are causal or due to bias remains to be elucidated.MethodsWe performed a two-sample Mendelian randomization (MR) analysis to assess the causal effect of genetically predicted asthma on the risk of abnormal spermatozoa. Asthma, childhood-onset asthma (COA), and adult-onset asthma (AOA) (sample sizes ranging from 327,670 to 408,442) were included as the exposures. Genetic information for abnormal spermatozoa was obtained from a genome-wide association study (GWAS) comprising 209,921 participants. In univariable MR (UVMR) analysis, the inverse variance weighted (IVW) method was conducted as the primary method, with the MR Egger and weighted median used as supplementary methods for causal inference. Sensitivity analyses, including the Cochran Q test, Egger intercept test, MR-PRESSO, and leave-one-out analysis, were performed to verify the robustness of the MR results. Multivariable MR (MVMR) was conducted to evaluate the direct causal effects of asthma on abnormal spermatozoa risk.ResultsUVMR detected causal associations between genetically predicted asthma and an increased risk of abnormal spermatozoa (OR: 1.270, 95% CI: 1.045–1.545, p = 0.017). Moreover, we found that AOA (OR: 1.46, 95% CI: 1.051, 2.018, p = 0.024) has positive causal effects on the risk of abnormal spermatozoa rather than COA (p = 0.558). Sensitivity analysis found little evidence of bias in the current study (p &amp;gt; 0.05). MVMR further confirmed that asthma directly affected the risk of abnormal spermatozoa.ConclusionOur MR study suggested that genetically predicted asthma could be associated with an increased risk of abnormal spermatozoa, and similar results were obtained in AOA. Further studies are warranted to explain the underlying mechanisms of this association and may provide new avenues for prevention and treatment.

First Report of Plumed Cockscomb (Celosia argentea var. plumosa) Stem Blight Caused by Phytophthora nicotianae in Taiwan.

Celosia spp. is a widely cultivated ornamental plant in gardens or parks in Taiwan. In September 2021, withering leaves and grayish-brown lesions were observed on the lower stem of plumed cockscombs (C. argentea var. plumosa) in Taichung City, with an incidence of about 22% in 136 plants after continuous precipitation, impacting the aesthetic value of the landscape. Symptomatic plants were collected, surface disinfected with 70% EtOH for ~20 sec., blotted dried, and excised diseased tissues (~ 3×3 mm2) were placed on 2% water agar. Four representative isolates were obtained after purification and the colonies were white with aerial and non-septated hyphae on V8 agar for 7 days. Sporangia were ovoid, ellipsoid or obpyriform, papillate, (26.3-55.9) 38.0 × 29.0 (20.1-40.6) µm (n = 200) (Ahonsi et al. 2007). Chlamydospores were spherical, terminal or intercalary, 26.0 (15.1-40.4) µm (n = 200). All isolates belong to A2 mating type with amphigynous antheridia and plerotic oospores, 21.0 (17.7-25.7) µm (n = 200), resembling the descriptions of Phytophthora (Erwin & Ribeiro 1996). For molecular identification, sequences of the ITS, β-tubulin (β-tub), and EF-1α regions of all isolates were amplified using ITS1/ITS4, TUBUF2/TUBUR1, and ELONGF1/ELONGR1 primers, respectively (White et al. 1990; Kroon et al. 2004). BLAST analyses of isolates cap1-2 (ITS: OQ581785; β-tub: OQ590022; EF-1α: OQ590026), cap1-3 (ITS: OQ581786; β-tub: OQ590023; EF-1α: OQ590027), cap2-1 (ITS: OQ581787; β-tub: OQ590024; EF-1α: OQ590028), and cap2-2 (ITS: OQ581788; β-tub: OQ590025; EF-1α: OQ590029) showed 100% of ITS identity, 99.5 to 99.9% of β-tub identity, and 99.4 to 99.6% of EF-1α identity with Phytophthora nicotianae (ITS: MG865551; β-tub: MH493987; EF-1α: MH359043). Phylogenetic trees were constructed using concatenated ITS, β-tub, and EF-1α sequences based on maximum likelihood with a GTR+G model in MEGA X and Bayesian inference method in Geneious Prime 2022.2. All isolates were clustered in P. nicotianae with similar topology, thereby were identified as P. nicotianae. To confirm pathogenicity, 7 to 10-day-old seedlings and 6-week-old plumed cockscomb plants were inoculated in separate trials and each experiment was conducted twice. For each seedling, the lower stem was inoculated with 50 μl of zoospore suspension (104 zoospores/ml), 3 plants per isolate, and then incubated at 30±2℃ with 12 h light. For adult plants, each was inoculated with mycelial plugs from one V8 plate of 10-day-old P. nicotianae, 5 plants per isolate, and incubated at 25±2℃ in a greenhouse. Control plants were inoculated with sterile water and V8 agar plugs, respectively. Stem and root rot were observed on seedlings 4 days after inoculation while wilting and lower stem browning were observed on adult plants 2 months after inoculation. All control plants remained healthy at the end of repeated trials and identical pathogens were re-isolated only from symptomatic plants, thus fulfilling Koch's rules. P. nicotianae has been reported causing root rot and stem necrosis not only on cockscomb (C. plumosa Hort. ex Burvenich) in Argentina (Frezzi 1950), but also infecting several ornamental plants recently in Taiwan (Ann et al. 2018). To our knowledge, this is the first report of stem blight caused by P. nicotianae on plumed cockscombs in Taiwan. This finding suggests limited options for landscaping and the host preference of the isolates obtained in this study should warrant further studies.