Bayesian Inference Research Articles

Output related fault detection and diagnosis based on multi block modified orthogonal Broyden-Fletcher-Goldfarb-Shanno algorithm

In recent years, some progress has been made in fault diagnosis of multi-block regression model, but multi-block method uses the relationship between variables in Hilbert space to block, while the relationship between variables in complex industrial systems often exists in Banach space. In addition, Bayesian fusion method is used in fault monitoring, which can not accurately process the sub-block information and judge whether the fault information is related to the output. In this paper, a new multi-block method is proposed. Firstly, this method uses Ball Covariance (BCov) suitable for Banach space to describe the correlation between variables, and combines K-means clustering method to block variables to ensure that the blocking method is more reasonable. Secondly, an improved orthogonal decomposition Broyden-Fletcher -Goldfarb-Shanno (BFGS) algorithm is proposed. The MOBFGS algorithm performs orthogonal projection on potential structures, removing information orthogonal to the output from the input block. Furthermore, through complete orthogonal decomposition between the input block and the output block, information related to and unrelated to the output is completely separated, and used to detect output related and unrelated faults, respectively. Thirdly, at the block level, MOBFGS algorithm is used for fault detection and contribution graph method for fault diagnosis. Finally, numerical examples and Tennessee Eastman (TE) process simulation verify the effectiveness of this method.

Biomonitoring urinary organophosphorus flame retardant metabolites by liquid-liquid extraction and ultra-high performance liquid chromatography-tandem mass spectrometry and their association with oxidative stress.

Organophosphate flame retardants (OPFRs) are widely used as substitutes for traditional brominated flame retardants, necessitating a reliable and sensitive method for biomonitoring their urinary metabolites to assess human exposure. This study conducted biomonitoring of 10 metabolites of OPFRs in 152 adults and assessed their association with oxidative stress biomarkers 8-hydroxydeoxyguanosine and 8-hydroxyguanosine. Urinary metabolites of OPFRs were released via enzymatic deconjugation. The addition of sodium chloride to the urine samples increases the ionic strength, inducing a salting-out effect that reduces the solubility of these compounds, thereby facilitating their extraction with a mixture of ethyl acetate and acetonitrile. Then, the metabolites of OPFRs were quantified by ultra-high performance liquid chromatography-tandem mass spectrometry, and we validated the method for linear range, precision, matrix effect, and method detection limit. The detection limit of the metabolites of OPFRs ranged from 0.01 to 0.2μg/L, and these metabolites were detected with high frequencies ranging from 25.0 to 98.68% in the urine samples. The concentration of bis (2-chloroethyl) phosphate was significantly higher in males than in females, with the geometric mean concentration of 0.88μg/L for males and 0.53μg/L for females, respectively. Spearman correlation analysis revealed weak but statistically significant positive correlations among the urinary metabolites. Bayesian kernel machine regression analysis showed a significant positive association between elevated urinary concentrations of metabolites of OPFRs and increased oxidative stress levels. Di-n-butyl phosphate was identified as the metabolite that significantly contributed to the elevated level of 8-hydroxyguanosine.

Provider Adherence to the 2020 US Multi-Society Task Force Guidelines for Surveillance after Colonoscopy with Polypectomy

Background and AimsThe 2020 United States Multi-Society Task Force on Colorectal Cancer guidelines for surveillance after colonoscopy with polypectomy introduced significant changes in surveillance intervals. We sought to identify rates of adherence to these new guidelines at an academic medical center. MethodsAverage-risk screening colonoscopies where 1-4 polyps <10 mm were removed between 1/1/2020 and 6/30/21 were included. To determine predictors of non-adherence, a multivariable logistic regression analysis was conducted and included patient and procedure-related variables. Bayesian changepoint analysis was applied to identify timing of change in adherence. Multinomial logistic regression was used to identify predictors of variability within the guidelines. Results1,026 procedures were analyzed. Adherence to the guidelines was 85%. In procedures with 1-2 polyps, increasing size (OR 0.84 per mm; 95% CI: 0.75 - 0.93) and a mixture of tubular adenomas (TA) and hyperplastic polyps (HP) (OR 0.35; 95% CI: 0.21 - 0.59) were associated with non-adherence. Among procedures with 1-2 TAs, age (OR 0.86 per 5-year increase; 95% CI: 0.75 - 0.99), increasing size (OR 0.86 per mm; 95% CI: 0.75 - 0.99) and polyp number (OR 0.54; 95% CI: 0.33-0.90) were associated with non-adherence. Changepoint analysis identified a potential changepoint on 2/9/20 (95% credible interval 2/4/2020-2/22/2020). Larger TAs were less likely to receive a 10 year recommendation (OR 0.61, 95% CI 0.5 – 0.75). ConclusionAdherence rates to the 2020 surveillance guidelines were high with a potential changepoint identified on 2/9/20. Deviation from guidelines was associated with patient and procedure related variables.

Gender-Specific Pathways in Violent Crime: Investigating the Role of Demographic and Mental Health Factors Using Mixed Graphical Models and Bayesian Networks.

This research aims to uncover gender-specific relationships and pathways that contribute to the perpetration of violent crimes, using sophisticated analytical tools to analyze the complex interactions between various factors. Employing Mixed Graphical Models and Bayesian networks, the study analyzes a sample of 1,254 prisoners (61.64% males and 38.36% females) to investigate the relationships among demographic factors, mental health issues, and violent crime. The study utilizes comprehensive measures, including the Beck Depression Inventory, Beck Anxiety Inventory, and Childhood Trauma Questionnaire, to assess participants' mental health status.Key findings reveal significant gender differences in the pathways to violent crime. For males, incomplete parental marriages strongly correlate with criminal behavior severity, while marriage status emerges as a significant factor, with married males less likely to commit violent crimes. In contrast, these relationships are not significant for females. Bayesian network analysis indicates that living in urban areas differently influences education and emotional expression across genders, emphasizing the importance of contextual factors. The study highlights the need for gender-specific considerations in criminal justice policies and interventions. It underscores the complex interplay of demographic and mental health factors in influencing violent crime pathways, providing insights for developing more effective prevention strategies. Despite its cross-sectional design and reliance on self-reported data, the research significantly contributes to understanding the gendered dimensions of criminal behavior.

Quantitative fecal pollution assessment with bacterial, viral, and molecular methods in small stream tributaries

Stream water quality can be impacted by a myriad of fecal pollution sources and waste management practices. Identifying origins of fecal contamination can be challenging, especially in high order streams where water samples are influenced by pollution from large drainage areas. Strategic monitoring of tributaries can be an effective strategy to identify conditions that influence local water quality. Water quality is assessed using fecal indicator bacteria (FIB); however, FIB cannot differentiate sources of fecal contamination nor indicate the presence of disease-causing viruses. Under different land use scenarios, three small stream catchments were investigated under ‘wet’ and ‘dry’ conditions (Scenario 1: heavy residential; Scenario 2: rural residential; and Scenario 3: undeveloped/agricultural). To identify fecal pollution trends, host-associated genetic targets HF183/BacR287 (human), Rum2Bac (ruminant), GFD (avian), and DG3 (canine) were analyzed along with FIB (Escherichia coli and enterococci), viral indicators (somatic and F+ coliphage), six general water quality parameters, and local rainfall. Levels of E. coli exceeded single sample maximum limits (235 CFU/100 mL) in 70.7 % of samples, enterococci (70 CFU/100 mL) in 100 % of samples, and somatic coliphage exceeded advisory thresholds (600 PFU/L) in 34.1 % of samples. The detection frequency for the human-associated genetic marker was highest in Scenario 3 (50 % of samples) followed by Scenario 2 (46 %), while the ruminant-associated marker was most prevalent in Scenario 1 (64 %). Due to the high proportion of qPCR-based measurements below the limit of quantification, a Bayesian data analysis approach was applied to investigate links between host-associated genetic marker occurrence with that of rainfall and fecal indicator levels. Multiple trends associated with small stream monitoring were revealed, emphasizing the role of rainfall, the utility of fecal source information to improve water quality management. And furthermore, water quality monitoring with bacterial or viral methodologies can alter the interpretation of fecal pollution sources in impaired waters.

Vicariant speciation resulting from biogeographic barriers in the Australian tropics: The case of the red-cheeked dunnart (Sminthopsis virginiae).

Global biodiversity loss continues unabated, and in Australia, the rate of recent mammal extinctions is among the worst in the world. Meanwhile, the diversity among and within many endemic mammal species remains undescribed. This information is crucial to delineate species boundaries and thus inform decision-making for conservation. Sminthopsis virginiae (the red-cheeked dunnart) is a small, dasyurid marsupial found in four disjunct populations around the northern coast of Australia and New Guinea. There are three currently recognized subspecies, each occupying a distinct geographic location. Sminthopsis v. virginiae occurs in Queensland, S. v. rufigenis is distributed across New Guinea and the Aru Islands, and S. v. nitela has populations in the Top End of the Northern Territory and the Kimberley region of Western Australia. Previous molecular work has suggested the current subspecies definitions are not aligned with DNA sequence data, though the sampling was limited. We undertook a comprehensive genetic and morphological review of S. virginiae to clarify relationships within the species. This included mitochondrial (CR, 12S, and cytb) and nuclear (omega-globin, IRBP, and bfib7) loci, and morphometric analysis of skulls and whole wet-preserved specimens held in museums. Maximum Likelihood and Bayesian phylogenetic analyses resolved samples into two distinct clades, demarcated by the Gulf of Carpentaria in Australia's north. Sminthopsis. v. nitela was consistently separated from S. v. virginiae and S. v. rufigenis, based on the overall body and skull size and craniodental features, while S. v. virginiae and S. v. rufigenis were more difficult to distinguish from each other. Thus, we redescribed S. virginiae, recognizing two species, S. nitela (raised from subspecies) and S. virginiae (now comprising the subspecies S. v. virginiae and S. v. rufigenis). This study highlights the importance of recognizing cryptic mammal fauna to help address the gap in our knowledge about diagnosing diversity during a time of conservation crisis.

Split-and-merge model selection of mixtures of Gaussian processes with RJMCMC

The mixture of Gaussian processes is a powerful statistical learning model that can be effectively applied to curve clustering and prediction. However, the corresponding model selection problem, that is, selecting an appropriate number of components in the mixture, is rather difficult to solve. In our previous work, we established the split-and-merge automatic model selection algorithm for mixtures of Gaussian processes along the output space under the framework of Reversible Jump Markov Chain Monte Carlo (RJMCMC), which can not only determine the number of actual Gaussian processes but also dynamically adjust the Gaussian process components to avoid dependence on parameter initialization and initial partitioning of the dataset during the parameter learning on a given dataset. In this study, we propose two algorithms: Penalized Likelihood RJMCMC and Penalized Prior RJMCMC. The former integrates a penalized term into the likelihood, while the latter incorporates a penalized term into the prior and operates within the full Bayesian inference framework, both aiming to focus more sharply on determining the number of components in the convergence process. Furthermore, we prove the geometric ergodicity of the RJMCMC algorithm for the mixture of Gaussian processes model, ensuring convergence of the posterior distribution with sufficient iterations. The experimental results further demonstrate the robustness of our PP-RJMCMC algorithm in model selection, showing superior performance compared to traditional approaches in curve classification and clustering. Additionally, the prediction performance is comparable to the EM algorithm. Although not directly explored in this study, the RJMCMC results can be used to initialize the EM algorithm, which could potentially improve prediction accuracy and accelerate computation.

Lessons from Hubble and Spitzer: 1D Self-consistent Model Grids for 19 Hot Jupiter Emission Spectra

We present a population-level analysis of the dayside thermal emission spectra of 19 planets observed with Hubble WFC3 and Spitzer IRAC 3.6 and 4.5 μm, spanning equilibrium temperatures 1200–2700 K and 0.7–10.5 Jupiter masses. We use grids of planet-specific 1D, cloud-free, radiative–convective–thermochemical equilibrium models (1D-RCTE) combined with a Bayesian inference framework to estimate atmospheric metallicity, the carbon-to-oxygen ratio, and day-to-night heat redistribution. In general, we find that the secondary eclipse data cannot reject the physics encapsulated within the 1D-RCTE assumption parameterized with these three variables. We find a large degree of scatter in atmospheric metallicities, with no apparent trend, and carbon-to-oxygen ratios that are mainly consistent with solar or subsolar values but do not exhibit population agreement. Together, these indicate either (1) formation pathways vary over the hot and ultra-hot Jupiter population and/or (2) more accurate composition measurements are needed to identify trends. We also find a broad scatter in derived dayside temperatures that do not demonstrate a trend with equilibrium temperature. Like with composition estimates, this suggests either significant variability in climate drivers over the population and/or more precise dayside temperature measurements are needed to identify a trend. We anticipate that 1D-RCTE models will continue to provide valuable insights into the nature of exoplanet atmospheres in the era of JWST.

A Probability-Based Likelihood Function for Bayesian Updating of a Bridge Condition Deterioration Model

A Probability-Based Likelihood Function for Bayesian Updating of a Bridge Condition Deterioration Model

Efficient Sparse Bayesian Learning Model for Image Reconstruction Based on Laplacian Hierarchical Priors and GAMP

Efficient Sparse Bayesian Learning Model for Image Reconstruction Based on Laplacian Hierarchical Priors and GAMP

Using the Bayesian Sampling Method to Estimate Corporate Loss Given Default Distribution

We use Markov chain Monte Carlo (MCMC) sampling to draw model coefficients to generate LGD distributions. We find that applying this Bayesian method on a sophisticated model, such as the zero-one-inflated beta (ZOIB) model, that accounts for the bi-modal distribution of the LGDs can generate LGD distributions that mimic the observed distributions well. By contrast, applying this Bayesian sampling approach on a simple model such as Tobit cannot capture the bi-modal LGD distributions accurately. Finally, we argue that this Bayesian sampling approach to generate LGD distributions is better fit for the stress testing purpose than the typical approach to estimate LGD model coefficients and then stress the macro variables. The latter approach yields stressed LGDs that may not be conservative enough, even if the macro variables are stressed to their worst historical values.

The GAPS Programme at TNG LVIII. Two multi-planet systems with long-period substellar companions around metal-rich stars

Due to observational biases, a large fraction of known exoplanets are short-period objects. However, the search for planets began more than 20 years ago, and so it is already possible ---with the use of a suitable dataset--- to begin exploring a wider range of the parameter space, such as that encompassing long-period planets. The aim of this paper is to investigate the presence of long-period giant companions in two systems where one or more planets are already known and for which a long-term trend in the radial velocities (RVs) was noted in previous works. Over the last 11 years, we have collected 122 spectra of HD\,75898 and 72 spectra of HD\,11506 with the High Accuracy Radial velocity Planet Searcher for the Northern hemisphere (HARPS-N) in the framework of the Global Architecture of Planetary Systems (GAPS) project, from which we derived precise RV and activity indicator measurements. Additional RV data from the High-Resolution Echelle Spectrometer (HIRES) are also used here to increase the total time span. For our RV analysis, we used PyORBIT, an advanced Python tool for the simultaneous Bayesian analysis of RVs and stellar activity indicators. In addition, we used astrometric ( Gaia DR3) and imaging archive data to complete our analysis. In particular, we combined RVs and astrometry to better constrain the mass and period of the new long-period companions. We find evidence for one additional long-period companion (gas giant planet or brown dwarf) in both systems considered. The new candidate for HD\,75898 has a period of roughly 18 yr and a true mass of around 8.5 For HD\,11506, we confirm the new object (planet d) recently announced using HIRES data but we find that the period and true mass are both almost double the values based on HIRES results ($P_d 72$ yr, $M_d In addition, for HD\,75898, we also find evidence of an activity cycle affecting RVs with a period of one order of magnitude lower than found in the literature.

Genetic parameters for growth and reproductive traits in Santa Inês sheep

Although Santa Inês sheep demonstrate good adaptation to tropical environments, there is a need for effective breeding programs focused on growth and reproductive traits to harness the breed's full developmental potential. Thus, this study aimed to estimate (co)variance components and genetic parameters for weight at birth (WB), weight at 60 days (W60), age at first lambing (AFL), first lambing interval (FLI), and average lambing interval (AFI) in Santa Inês sheep. The data were obtained from the Sergipe Goat and Sheep Breeders' Association (ASCCO). (Co)variance components and genetic parameters were estimated by Bayesian analysis based on a bitrait model with Gibbs sampling using Multiple-Trait Gibbs Sampler for Animal Models (MTGSAM) software. Heritability estimates (h2) for WB, W60, AFL, ALI, and FLI were 0.11, 0.18, 0.028, 0.25, and 0.30, respectively. Positive correlations were observed between WB and W60, AFL and ALI, and AFL and FLI (0.31, 0.17, and 0.18, respectively). By contrast, negative correlations were obtained for WB and AFL (−0.55), WB and AFL (−0.42), WB and FLI (−0.39), W60 and ALI (−0.26), and W60 and FLI (−0.37), with magnitudes ranging from low to moderate. Correlation values for ALI and FLI and W60 and AFL were positive and strong (0.97 and 0.98, respectively). These results indicate that the studied traits can be used for indirect selection. Furthermore, the findings underscore the importance of determining genetic parameters when formulating strategies for the genetic improvement of Santa Inês sheep.

Bayesian Multi-line Intensity Mapping

Line intensity mapping (LIM) has emerged as a promising tool for probing the 3D large-scale structure through the aggregate emission of spectral lines. The presence of interloper lines poses a crucial challenge in extracting the signal from the target line in LIM. In this work, we introduce a novel method for LIM analysis that simultaneously extracts line signals from multiple spectral lines, utilizing the covariance of native LIM data elements defined in the spectral–angular space. We leverage correlated information from different lines to perform joint inference on all lines simultaneously, employing a Bayesian analysis framework. We present the formalism, demonstrate our technique with a mock survey setup resembling the SPHEREx deep-field observation, and consider four spectral lines within the SPHEREx spectral coverage in the near-infrared: Hα, [O iii], Hβ, and [O ii]. We demonstrate that our method can extract the power spectrum of all four lines at the ≳10σ level at z < 2. For the brightest line, Hα, the 10σ sensitivity can be achieved out to z ∼ 3. Our technique offers a flexible framework for LIM analysis, enabling simultaneous inference of signals from multiple line emissions while accommodating diverse modeling constraints and parameterizations.

Induced gravitational wave interpretation of PTA data: a complete study for general equation of state

We thoroughly study the induced gravitational wave interpretation of the possible gravitational wave background reported by PTA collaborations, considering the unknown equation of state w of the early universe. We perform a Bayesian analysis of the NANOGrav data using the publicly available PTArcade code together with SIGWfast for the numerical integration of the induced gravitational wave spectrum. We focus on two cases: a monochromatic and a log-normal primordial spectrum of fluctuations. For the log-normal spectrum, we show that, while the results are not very sensitive to w when the GW peak is close to the PTA window, radiation domination is out of the 2σ contours when only the infra-red power-law tail contributes. For the monochromatic spectrum, the 2σ bounds yield 0.1 ≲ w ≲ 0.9 so that radiation domination is close to the central value. We also investigate the primordial black hole (PBH) abundance for both monochromatic and log-normal power spectrum. We show that, in general terms, a larger width and stiffer equation of state alleviates the overproduction of PBHs. No PBH overproduction requires w ≲ 0.57 up to 2-σ level for the monochromatic spectrum. Furthermore, including bounds from the cosmic microwave background, we find in general that the mass range of the PBH counterpart is bounded by 10-5 M ⊙ ≲ M PBH ≲ 10-1 M ⊙. Lastly, we find that the PTA signal can explain the microlensing events reported by OGLE for w ~ 0.7. Our work showcases a complete treatment of induced gravitational waves and primordial black holes for general w for future data analysis.

Genetic structure of populations of Salvia ceratophylloides endemic to southern Calabria (southern Italy)

Assessing the degree of genetic diversity and differentiation of rare or endangered endemic species is essential to evaluate the conservation status of populations and successively implement appropriate conservation strategies. We investigated the population structure of Salvia ceratophylloides Ard., a scapose hemicryptophyte endemic to Calabria (southern Italy), both to answer questions about its genetic structure and to determine whether the actual population size has undergone significant demographic changes in the near past. The data obtained from the census showed that the populations are characterised by a greater number of adult individuals than juveniles and are on declining. The genetic analysis carried out on 99 individuals from four populations of the species under study, shows a mean expected heterozygosity value of 0.50 and an overall differentiation value of 0.083. The population structure shows that the four studied populations are distinct genetic units, genetically linked to four different ancestral gene pools. Bayesian analysis based on ABC models indicates that the present populations underwent a significant reduction in size in the past. This corresponds to the demographic decline at the end of the 19th century, which according to the literature, was due to the strong anthropic pressure (agriculture, grazing, fire and plantations) of Reggio Calabria suburbs. We can therefore conclude that populations are not affected by inbreeding and low genetic diversity and that there is no immediate danger of genetic erosion, and that the problems associated with population decline, past and present, are exclusively due to anthropogenic causes.

Bayesian Method for Quality Control with Weibull Distribution

Weibull distribution is one of the continous probability density function. This distribution is known as a flexible distribution. One of it’s flexibility is can be transformed into other distributions such as exponential distribution depends on parameter selected. Similar to other distribution, Weibull distribution also characterized by cumulative distribution function, mean, variance and moment generating function. One of the well-known application of this distribution is in the field of quality control utilizing reliability data and the well-known tool in quality control is control chart. Therefore, reliability data is not follow normal distribution then Shewhart control chart cannot be applied. To solve this problem, control chart can be formed with the control limit is obtained by Bayesian method. In applying Bayesian method, prior distribution of shape parameter is assumed to be uniform distribution and variables for reliability is assumed to be invers of Weibull distribution. The prior distribution is combine with likelihood function then posterior distribution is obtained which in then used as the control limits by find it’s mean.

Integrative taxonomy of the genus Coridius Illiger, 1807 (Hemiptera: Heteroptera: Dinidoridae) reveals hidden diversity and three new species from North-East India.

The genus Coridius Illiger, 1807 (Heteroptera: Dinidoridae) comprises a group of phytophagous terrestrial bugs consisting of 36 species distributed in the Afrotropical and Indo-Malayan regions. In several communities in northeastern India, insects are recognised as a delicacy, medicine, and a nutritional supplement, with Coridius being a popular delicacy. However, Coridius has received little taxonomic attention to date due to large intraspecific variations, inadequate taxonomic treatments, and the rarity of many species. To address this gap, an integrative taxonomy of the genus was performed. Two mitochondrial genes, viz., cytochrome oxidase subunit 1 (COI) and 16S rRNA, were sequenced to reconstruct the phylogenetic relationships within Coridius. We performed both maximum likelihood (ML) and Bayesian inference (BI) to develop a species tree, followed by the Bayesian implementation of the Poisson tree process (bPTP) and Assemble Species by Automatic Partitioning (ASAP) as an additional test to assess species boundaries and delimit operational taxonomic units. A linear discriminant analysis (LDA) of four key morphological characters was then performed to identify species groups. Overall, our analysis supported the establishment of three new species: Coridius adii sp. nov., Coridius esculentus sp. nov., and Coridius insperatus sp. nov., and revealed six distinct lineages within Coridius chinensis (Dallas, 1851). Linear discriminant analysis of morphological characters indicated the clustering of eight species. The species status of Coridius nigriventris (Westwood, 1837) stat. rev, formerly synonymized under Coridius nepalensis (Westwood, 1837), is reinstated in this study. Further, we revised the genus Coridius from India and rediscovered Coridius assamensis (Distant, 1902) and Coridius fuscus (Westwood, 1837) after 100 years.

First report of leaf spot disease caused by Pseudopithomyces chartarum on Lonicera caerulea L. in Heilongjiang Province, China.

Blue honeysuckle (Lonicera caerulea L.) cultivation has gradually expanded in China but continues to be limited by challenges such as leaf spot disease. Between September 2022 and September 2023, a leaf spot disease was observed on approximately 30% of 'Lanjingling' blue honeysuckles grown in a 2.66 ha field (a total of about 11,000 plants) in Jiamusi city (130.47°E, 46.16°N), Heilongjiang Province, China. Affected plants displayed brown necrotic lesions on their leaves that gradually expanded in area until the leaves fell off the plant entirely. Small, 3 to 4 mm segments of infected tissue from 50 randomly selected leaves were surface sterilized with 75% ethanol for 30 s and 5% sodium hypochlorite (NaOCl) for 3 min, rinsed three times with sterile distilled water, dried on paper towels, and plated in 9 cm Petri dishes containing potato dextrose agar (PDA) (Yan et al. 2022). Five pathogens (LD-232, LD-233, LD-234, LD-235, and LD-236) were isolated on PDA and displayed a conidia morphology consistent with Pseudopithomyces spp. (Perelló et al. 2017). The fungal colonies on PDA were villiform, white, and whorled and had sparse aerial mycelium on the surface with black conidiomata. The conidia were obpyriform and dark brown, had 0 to 3 transverse and 0 to 1 longitudinal septa, and measured 9.00 to 15.30 μm × 5.70 to 9.30 μm in size (n = 50). Genomic DNA was extracted from a representative isolate, LD-232, for molecular verification and PCR amplification was performed with ITS1/ITS4 (White et al. 1990), LROR/LR7 (Carbone and Kohn 1999), and RPB2-5F2/RPB2-7CR (Liu et al. 1999) primers. Sequences of LD-232 ITS (OR835654), LSU (OR835652), and RPB2 (OR859769) revealed 99.8% (530/531 nt), 98.8% (639/647 nt), and 99.8% (1015/1017 nt) shared identity with Pseudopithomyces chartarum sequences (OP269600, OP237014, and MK434892), respectively (Wu et al. 2023). Bayesian inference (BI) was used to construct the phylogenies using Mr. Bayes v. 3.2.7 to confirm the identity of the isolates (Ariyawansa et al. 2015). Phylogenetic trees cannot be constructed based on the genes' concatenated sequences because selective strains do not have complete rDNA-ITS, LSU, and RPB2 sequences. Therefore, based on the morphological characteristics and molecular phylogeny, LD-232 was identified as P. chartarum (Perelló et al. 2017; Wu et al. 2023). A pathogenicity test was performed with six healthy, two-year-old 'Lanjingling' blue honeysuckle plants. Three plants were inoculated by spraying the LD-232 conidial suspension (1 × 106 spores/ml) or clean water as an experimental control condition (Wu et al. 2023; Yan et al. 2023). All plants were cultured in a greenhouse at 28℃ under a 12-h light/dark cycle, and each experiment was replicated three times. Typical leaf spot symptoms were observed on inoculated leaves after 10 days. The same pathogens were reisolated from infected leaves, displayed the same morphological and molecular traits, and were again identified as P. chartarum, confirming Koch's postulate. P. chartarum previously caused leaf spot disease on Tetrapanax papyrifer in China (Wu et al. 2023). To our knowledge, this is the first report of blue honeysuckle leaf spot caused by P. chartarum in China. Identification of P. chartarum as a disease agent on blue honeysuckle will help guide future management of leaf diseases for this economically important small fruit tree.

Bayesian-Edge system for classification and segmentation of skin lesions in Internet of Medical Things.

Skin diseases are severe diseases. Identification of these severe diseases depends upon the abstraction of atypical skin regions. The segmentation of these skin diseases is essential to rheumatologists in risk impost and for valuable and vital decision-making. Skin lesion segmentation from images is a crucial step toward achieving this goal-timely exposure of malignancy in psoriasis expressively intensifies the persistence ratio. Defies occur when people presume skin diseases they have without accurately and precisely incepted. However, analyzing malignancy at runtime is a big challenge due to the truncated distinction of the visual similarity between malignance and non-malignance lesions. However, images' different shapes, contrast, and vibrations make skin lesion segmentation challenging. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. This paper introduces a skin lesions segmentation model that integrates two intelligent methodologies: Bayesian inference and edge intelligence. In the segmentation model, we deal with edge intelligence to utilize the texture features for the segmentation of skin lesions. In contrast, Bayesian inference enhances skin lesion segmentation's accuracy and efficiency. We analyze our work along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions from seminal works and a systematic viewpoint and examine how these dimensions have influenced current trends. We summarize our work with previously used techniques in a comprehensive table to facilitate comparisons. Our experimental results show that Bayesian-Edge networks can boost the diagnostic performance of skin lesions by up to 87.80% without incurring additional parameters of heavy computation.