<i>Luteodorsum huanglongense</i> (Gomphaceae, Gomphales), a New Genus and Species of Gomphoid Fungus from the Loess Plateau, Northwest China by Zijia Peng, Yiming Wu, Zeyu Luo, Chaowei Xiong, Xiaoyong Liu, Bin Wang, Baoyou Ma, Jianxian Wei, Zhongdong Yu

“…The results confirmed that <i>L. huanglongense</i> forms an independent clade within Gomphales, with full maximum likelihood bootstrap support (MLBS), maximum parsimony bootstrap support (MPBS), and Bayesian posterior probability (BPP). <i>L. huanglongense</i> is characterized by its sandy-brown, orange-brown, or coffee-brown color; clavate to infundibuliform shape; wrinkled and ridged hymenophore; ellipsoid to obovoid warted basidiospores; cylindrical to clavate flexuous pleurocystidia; and crystal basal mycelium. …”
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3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys by Xianghao Hou, Jianbo Zhou, Yixin Yang, Long Yang, Gang Qiao

“…Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. …”
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Different Approaches for Face Authentication as Part of a Multimodal Biometrics System by Jaromir Tovarek, Miroslav Voznak, Jan Rozhon, Filip Rezac, Jakub Safarik, Pavol Partila

“…Obtained parameters are evaluated by classifiers and for each detected face, authors get posterior probability as the output of the classifier. Different approaches for face authentication are compared with each other using False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) curves. …”
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Research on the Derated Power Data Identification Method of a Wind Turbine Based on a Multi-Gaussian–Discrete Joint Probability Model by Yuanchi Ma, Yongqian Liu, Zhiling Yang, Jie Yan, Tao Tao, David Infield

“…According to the posterior probability of the wind-power scatter points, the normal, derated power and abnormal data in the wind turbine SCADA data were identified. …”
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Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension by Ta-Wen Kuan, Shih-Pang Tseng, Che-Wen Chen, Jhing-Fa Wang, Chieh-An Sun

“…Features of the chosen ten skeleton joints are sequentially extracted in terms of pose sequences for a specific human activity, and then, processed through coordination transformation and vectorization into a codebook prior to the D-HMM for estimating the maximal posterior probability to predict the target. In the experiments, the confusion matrix is evaluated based on eleven human activities; furthermore, the extension criterion of the confusion matrix is also examined to verify the robustness of the proposed work. …”
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A novel decoder based on Bayesian rules for task‐driven object segmentation by Yuxiang Cai, Yuanlong Yu, Weijie Jiang, Rong Chen, Weitao Zheng, Xi Wu, Renjie Su

“…What's more, a Bayesian rule is established in the decoder, in which the control signal is set as the prior, and the latent features learned in encoder is transferred to the corresponding layer of decoder as observation, thus the posterior probability of each object with respect to the specific‐class can be calculated, and the objects belonging to this class can be segmented. …”
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Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps by Mahajan, Anubha, Taliun, Daniel, Thurner, Matthias, Robertson, Neil R., Torres, Jason M., Rayner, N. William, Payne, Anthony J., Steinthorsdottir, Valgerdur, Scott, Robert A., Grarup, Niels, Cook, James P., Schmidt, Ellen M., Wuttke, Matthias, Sarnowski, Chloé, Mägi, Reedik, Nano, Jana, Gieger, Christian, Trompet, Stella, Lecoeur, Cécile, Preuss, Michael H., Prins, Bram Peter, Guo, Xiuqing, Bielak, Lawrence F., Below, Jennifer E., Bowden, Donald W., Chambers, John Campbell, Kim, Young Jin, Ng, Maggie Chor Yin, Petty, Lauren E., Sim, Xueling, Zhang, Weihua, Bennett, Amanda J., Bork-Jensen, Jette, Brummett, Chad M., Canouil, Mickaël, Ec Kardt, Kai-Uwe, Fischer, Krista, Kardia, Sharon L. R., Kronenberg, Florian, Läll, Kristi, Liu, Ching-Ti, Locke, Adam E., Luan, Jian'an, Ntalla, Ioanna, Nylander, Vibe, Schönherr, Sebastian, Schurmann, Claudia, Yengo, Loïc, Bottinger, Erwin P., Brandslund, Ivan, Christensen, Cramer, Dedoussis, George, Florez, Jose C., Ford, Ian, Franco, Oscar H., Frayling, Timothy M., Giedraitis, Vilmantas, Hackinger, Sophie, Hattersley, Andrew T., Herder, Christian, Ikram, M. Arfan, Ingelsson, Martin, Jørgensen, Marit E., Jørgensen, Torben, Kriebel, Jennifer, Kuusisto, Johanna, Ligthart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Lyssenko, Valeriya, Mamakou, Vasiliki, Meitinger, Thomas, Mohlke, Karen L., Morris, Andrew D., Nadkarni, Girish, Pankow, James S., Peters, Annette, Sattar, Naveed, Stančáková, Alena, Strauch, Konstantin, Taylor, Kent D., Thorand, Barbara, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, Tuomilehto, Jaakko, Witte, Daniel R., Dupuis, Josée, Peyser, Patricia A., Zeggini, Eleftheria, Loos, Ruth J. F., Froguel, Philippe, Ingelsson, Erik, Lind, Lars, Groop, Leif, Laakso, Markku, Collins, Francis S., Jukema, J. Wouter, Palmer, Colin N. A., Grallert, Harald, Metspalu, Andres, Dehghan, Abbas, Köttgen, Anna, Abecasis, Goncalo R., Meigs, James B., Rotter, Jerome I., Marchini, Jonathan, Pedersen, Oluf, Hansen, Torben, Langenberg, Claudia, Wareham, Nicholas J., Stefansson, Kari, Gloyn, Anna L., Morris, Andrew P., Boehnke, Michael, McCarthy, Mark I.

“…With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).…”
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Bayesian updating of model parameters using adaptive Gaussian process regression and particle filter by Yoshida, Ikumasa, Nakamura, Tomoka, Au, Siu-Kui

“…This study proposes an efficient method to estimate the posterior probability density function (PDF) of model parameters by using a surrogate model constructed using adaptive Gaussian Process Regression and PF. …”
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Molnupiravir plus usual care versus usual care alone as early treatment for adults with COVID-19 at increased risk of adverse outcomes (PANORAMIC): preliminary analysis from the un... by Butler, C, Hobbs, FDR, Gbinigie, O, Rahman, NM, Hayward, G, Richards, D, Dorward, J, Lowe, D, Standing, JF, Breuer, J, Khoo, S, Petrou, S, Hood, K, Nguyen-Van-Tam, JS, Patel, M, Saville, BR, Marion, J, Ogburn, E, Allen, J, Rutter, H, Francis, N, Thomas, N, Evans, P, Dobson, M, Madden, T-A, Holmes, J, Harris, V, Png, ME, Lown, M, van Hecke, O, Detry, M, Saunders, C, Fitzgerald, M, Berry, N, Mwandigha, L, Galal, U, Jani, B, Hart, N, Butler, D, Chalk, J, Lavallee, L, Hadley, E, Cureton, L, Benysek, M, Andersson, M, Coates, M, Barrett, S, Bateman, C, Davies, J, Raymundo-Wood, I

“…There was an estimated benefit of 4·2 (95% BCI: 3·8 – 4·6) days in time-to-first-recovery (TTR) giving a posterior probability of superiority of >0·999 (estimated median TTR 10·3 [10·2 – 10·6] days vs 14·5 [14·2 – 14·9] days respectively; hazard ratio [95% BCI], 1·36 [1·3–1·4] days), which met the pre-specified superiority threshold. …”

Characterization of Ralstonia solanacearum race 2 biovar 1 associated with moko disease of banana in Peninsular Malaysia by Mohamed Zulperi, Dzarifah

“…Meanwhile, phylogenetic analyses further demonstrated that all strains were grouped with 100% posterior probability support to the published R. solanacearum race 2 insertion sequence gene, ISRso19 (AF450275). …”
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DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment by Sun-yong Wu, Jun Zhao, Xu-dong Dong, Qiu-tiao Xue, Ru-hua Cai

“…In order to overcome the problem of particle decay in particle filtering, UT is adopted to select a group of sigma points with different weights to make them close to the posterior probability density of the state. Since the <i>&#945;</i> stable distribution does not have finite covariance, the Fractional Lower Order Moment (FLOM) matrix of the received array data is employed to replace the covariance matrix to formulate a MUSIC spatial spectra in the MeMBer filter. …”
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The complete mitochondrial genome of leafhopper Atkinsoniella nigrita (Hemiptera: Cicadellidae) with the shortest 12S rRNA and longest tRNA-Lys of the Atkinsoniella genus by Hu Li, Kai Yu, Rui Zhao, Gang Wu, Chuan-Feng Xiong

“…A phylogenetic analysis of 31 Cicadellinae and two Ledrinae concatenated sequences of 13 PCGs of their mitogenomes using Bayesian inference (BI) revealed that A. nigrita belongs to the genus Atkinsoniella with strong nodal support (BI posterior probability = 1).…”
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Complete chloroplast genomes of six neotropical palm species, structural comparison, and evolutionary dynamic patterns by Ana Flávia Francisconi, Jonathan Andre Morales Marroquín, Luiz Augusto Cauz-Santos, Cássio van den Berg, Kauanne Karolline Moreno Martins, Marcones Ferreira Costa, Doriane Picanço-Rodrigues, Luciano Delmodes de Alencar, Cesar Augusto Zanello, Carlos Augusto Colombo, Brenda Gabriela Díaz Hernández, Danilo Trabuco Amaral, Maria Teresa Gomes Lopes, Elizabeth Ann Veasey, Maria Imaculada Zucchi

“…Almost all nodes of the phylogenetic analysis showed a posterior probability (PP) of 1.0, reaffirming the close relationship between Acrocomia species. …”
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DNA barcoding reveals distinct population of Dopasia gracilis (Squamata: Anguidae) in Mizoram, Northeast India by Shantanu Kundu, Hmar Tlawmte Lalremsanga, Lal Biakzuala, Kailash Chandra, Vikas Kumar

“…The studied species showed monophyletic clustering in the Bayesian analysis (BA) phylogeny with strong posterior probability support and also discriminated sufficient Kimura 2 parameter genetic distances. …”
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In silico structural and functional prediction of African swine fever virus protein-B263R reveals features of a TATA-binding protein by Dickson Kinyanyi, George Obiero, George F.O. Obiero, Peris Amwayi, Stephen Mwaniki, Mark Wamalwa

“…Iterative Threading ASSEmbly Refinement (I-TASSER) was used to model the three-dimensional structure of pB263R. The posterior probability of fold family assignment was calculated using TM-fold, and biological function was assigned using TM-site, RaptorXBinding, Gene Ontology, and TM-align. …”
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Bayesian Inference for COVID-19 Transmission Dynamics in India Using a Modified SEIR Model by Kai Yin, Anirban Mondal, Martial Ndeffo-Mbah, Paromita Banerjee, Qimin Huang, David Gurarie

“…The uncertainty of the parameters is naturally expressed as the posterior probability distribution. The calibrated model is used to estimate undetected cases and study different initial intervention policies, screening rates, and public behavior factors, that can potentially strike a balance between disease control and the humanitarian crisis caused by a sudden strict lockdown.…”
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Delta Neutrophil Index for the Prediction of Prognosis in Acute Gastrointestinal Diseases; Diagnostic Test Accuracy Meta-Analysis by Hae Min Jeong, Chang Seok Bang, Jae Jun Lee, Gwang Ho Baik

“…Fagan’s nomogram indicated that the posterior probability of ‘poor prognosis’ was 76% if the test was positive, and ‘no poor prognosis’ was 25% if the test was negative. …”
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Joint Application of Fractal Analysis and Weights-of-Evidence Method for Revealing the Geological Controls on Regional-Scale Tungsten Mineralization in Southern Jiangxi Province, C... by Tao Sun, Kaixing Wu, Lingkang Chen, Weiming Liu, Yun Wang, Cisheng Zhang

“…Regions identified by high posterior probability in conjunction with high fractal dimensions of both faults and fault intersections are evaluated as the most favorable targets.…”
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Integrating Hydrological and Machine Learning Models for Enhanced Streamflow Forecasting via Bayesian Model Averaging in a Hydro-Dominant Power System by Francisca Lanai Ribeiro Torres, Luana Medeiros Marangon Lima, Michelle Simões Reboita, Anderson Rodrigo de Queiroz, José Wanderley Marangon Lima

“…BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. …”
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Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling by Jiabei Tang, Minpeng Xu, Jin Han, Miao Liu, Tingfei Dai, Shanguang Chen, Dong Ming

“…In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. …”
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