Bayesian Inference Research Articles

Vortex of regret: How positive and negative coping strategies correlate with feelings of guilt

Bad coping behavior and guilt may reinforce each other as a negative feedback loop. Social contexts and expectations may also create cognitive dissonance in coping individuals and affect the effectiveness of coping styles. This study examines the associations between the feeling of guilt and specific coping styles belonging to both groups of positive and negative coping styles. We conducted Bayesian Multiple Regression analyses on secondary data from 3784 high school students in China. Positive coping is associated more with reduced feelings of guilt compared to negative coping. However, some positive coping styles were found to be positively associated with a sense of guilt, especially those involving confrontation against or conformity to social expectations. Most negative coping styles are positively associated with guilt, and substance use has the strongest influence among the examined negative coping styles. The findings suggest that the consideration of sociocultural contexts is very important in supporting those with guilt issues, especially adolescents in societies with dominant traditional East Asian values.

Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.

This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.

Quantitative Stylistic Analysis of Middle Chinese Texts Based on the Dissimilarity of Evolutive Core Word Usage

Stylistic analysis enables open-ended and exploratory observation of languages. To fill the gap in the quantitative analysis of the stylistic systems of Middle Chinese, we construct lexical features based on the evolutive core word usage and scheme a Bayesian method for feature parameters estimation. The lexical features are from the Swadesh list, each of which has different word forms along with the language evolution during the Middle Ages. We thus count the varied word of those entries along with the language evolution as the linguistic features. With the Bayesian formulation, the feature parameters are estimated to construct a high-dimensional random feature vector to obtain the pair-wise dissimilarity matrix of all the texts based on different distance measures. Finally, we perform the spectral embedding and clustering to visualize, categorize, and analyze the linguistic styles of Middle Chinese texts. The quantitative result agrees with the existing qualitative conclusions and, furthermore, betters our understanding of the linguistic styles of Middle Chinese from both the inter-category and intra-category aspects. It also helps unveil the special styles induced by the indirect language contact.

Inference of neutron-star properties with unified crust-core equations of state for parameter estimation

Context. Relating different global neutron-star (NS) properties, such as tidal deformability and radius, or mass and radius, requires an equation of state (EoS). Determining the NS EoS is therefore not only the science goal of a variety of observational projects, but it also enters in the analysis process; for example, to predict a NS radius from a measured tidal deformability via gravitational waves (GW) during the inspiral of a binary NS merger. To this aim, it is important to estimate the theoretical uncertainties on the EoS, one of which is the possible bias coming from an inconsistent treatment of the low-density region; that is, the use of a so called non-unified NS crust. Aims. We propose a numerical tool allowing the user to consistently match a nuclear-physics informed crust to an arbitrary high-density EoS describing the core of the star. Methods. We introduce an inversion procedure of the EoS close to saturation density that allows users to extract nuclear-matter parameters and extend the EoS to lower densities in a consistent way. For the treatment of inhomogeneous matter in the crust, a standard approach based on the compressible liquid-drop (CLD) model approach was used in our work. A Bayesian analysis using a parametric agnostic EoS representation in the high-density region is also presented in order to quantify the uncertainties induced by an inconsistent treatment of the crust. Results. We show that the use of a fixed, realistic-but-inconsistent model for the crust causes small but avoidable errors in the estimation of global NS properties and leads to an underestimation of the uncertainties in the inference of NS properties. Conclusions. Our results highlight the importance of employing a consistent EoS in inference schemes. The numerical tool that we developed to reconstruct such a thermodynamically consistent EoS, CUTER, has been tested and validated for use by the astrophysical community.

A Survey on Variational Autoencoders in Recommender Systems

Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based recommendation algorithms have been proposed. Among these, Variational AutoEncoder (VAE) -based recommendation methods stand out. VAEs are based on a flexible probabilistic framework, which is robust for data sparsity and compatible with other deep learning-based models for dealing with multimodal data. In addition, the deep generative structure of VAEs helps to perform Bayesian inference in an efficient manner. VAE-based recommendation algorithms have given rise to many novel graphical models, and they have achieved promising performance. In this article, we conduct a survey to systematically summarize recent VAE-based recommendation algorithms. Four frequently used characteristics of VAE-based recommendation algorithms are summarized, and a taxonomy of VAE-based recommendation algorithms is proposed. We also identify future research directions for, advanced perspectives on, and the application of VAEs in recommendation algorithms, to inspire future work on VAEs for recommender systems.

The power of binary pulsars in testing Gauss-Bonnet gravity

Context. Binary pulsars are a powerful tool for probing strong gravity that still outperform direct gravitational wave observations in a number of ways due to the remarkable accuracy of the pulsar timing. They can constrain the presence of additional charges of the orbiting neutron stars very precisely, leading to new channels of energy and angular momentum loss, such as scalar dipole radiation. Aims. In the present paper, we explore in detail the possibility of constraining different classes of scalar-Gauss-Bonnet gravity with binary pulsars. Additionally, we updated the existing constraints related to the observed maximum mass of neutron stars. Methods. Interestingly, depending on the equation of state, the resulting limits on the theory coupling parameters can outperform the constraints coming from binary merger observations by up to a factor of two, even for so-called Einstein-dilaton-Gauss-Bonnet gravity where neutron stars are often underestimated as relevant theory probes. As an additional merit, precise Bayesian methods are compared with approximate approaches, with the latter showing a very good performance despite their simplicity.

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.

Bayesian inference of multi-messenger astrophysical data: Joint and coherent inference of gravitational waves and kilonovae

Multi-messenger observations of binary neutron star mergers can provide information on the neutron star's equation of state (EOS) above the nuclear saturation density by directly constraining the mass-radius diagram. We present a Bayesian framework for joint and coherent analyses of multi-messenger binary neutron star signals. As a first application, we analyze the gravitational-wave GW170817 and the kilonova (kN) AT2017gfo data. These results are then combined with the most recent X-ray pulsar analyses of PSR J0030+0451 and PSR J0740+6620 to obtain new EOS constraints. We extend the infrastructure with a joint likelihood for multiple datasets, support for various semi-analytical kN models, and numerical-relativity (NR) -informed relations for the mass ejecta, as well as a technique to include and marginalize over modeling uncertainties. The analysis of GW170817 used the effective-one-body waveform template to model the gravitational-wave signal. The analysis of AT2017gfo used a baseline multicomponent spherically symmetric model for the kN light curves. Various constraints on the mass-radius diagram and neutron star properties were then obtained by resampling over a set of ten million parameterized EOSs, which was built under minimal assumptions (general relativity and causality). We find that a joint and coherent approach improves the inference of the extrinsic parameters (distance) and, among the intrinsic parameters, the mass ratio. The inclusion of NR-informed relations marks a strong improvement over the case in which an agnostic prior is used on the intrinsic parameters. Comparing Bayes factors, we find that the two observations are better explained by the common source hypothesis only by assuming NR-informed relations. These relations break some of the degeneracies in the employed kN models. The EOS inference folding-in PSR J0952-0607 minimum-maximum mass, PSR J0030+0451 and PSR J0740+6620 data constrains, among other quantities, the neutron star radius to $ km km $) and the maximum mass to $ Mo Mo $), where the ST+PDT (PDT-U) analysis of Vinciguerra et al (2023) for PSR J0030+0451 was employed. Hence, the systematics on the PSR J0030+0451 data reduction currently dominate the mass-radius diagram constraints. We conclude that delivers robust analyses in line with other state-of-the-art results in the literature. Strong EOS constraints are provided by pulsars observations, albeit with large systematics in some cases. Current gravitational-wave constraints are compatible with pulsar constraints and can further improve the latter.

Nonlinear impact of financial inclusion on tax revenue: evidence from the Monte-Carlo simulation algorithm under the Bayesian approach

PurposeThis article aims to study the nonlinear effect of financial inclusion on tax revenue of 21 low financial development countries (LFDCs) and 22 high financial development countries (HFDCs) from 2004 to 2020.Design/methodology/approachThe study calculates the world average financial development index (FD̅) for all countries using data from the IMF. The average FD of HFDCs is higher than (FD̅). On the other hand, the average FD of LFDCs is lower than (FD̅). Data of 21 LFDCs and 22 HFDCs cover the period 2004–2020. With the small sample problem, we applied the Bayesian method to examine the nonlinear effect of financial inclusion on the tax revenue of the two groups of countries.FindingsUsing the Bayesian method, the results show that financial inclusion negatively impacts tax revenue with an absolute probability of 100% in LFDCs and a lower probability of 92.45% in HFDCs. Additionally, the financial inclusion threshold at LFDCs is 18.90. Below this threshold, financial inclusion promotes tax revenue with a 100% probability. On the contrary, when financial inclusion exceeds the threshold, it will have a negative effect on tax revenue. Similarly, the financial inclusion threshold at HFDCs is 20.14, with a probability of 92.45%.Originality/valueTo the best of the authors’ knowledge, this is the first paper to examine the nonlinear impact of financial inclusion on tax revenue in high and low financial development countries.

A Bayesian method to detect drug-drug interaction using external information for spontaneous reporting system.

Due to the insufficiency of safety assessments of clinical trials for drugs, further assessments are required for post-marketed drugs. In addition to adverse drug reactions (ADRs) induced by one drug, drug-drug interaction (DDI)-induced ADR should also be investigated. The spontaneous reporting system (SRS) is a powerful tool for evaluating the safety of drugs continually. In this study, we propose a novel Bayesian method for detecting potential DDIs in a database collected by the SRS. By applying a power prior, the proposed method can borrow information from similar drugs for a drug assessed DDI to increase sensitivity of detection. The proposed method can also adjust the amount of the information borrowed by tuning the parameters in power prior. In the simulation study, we demonstrate the aforementioned increase in sensitivity. Depending on the scenarios, approximately 20 points of sensitivity of the proposed method increase from an existing method to a maximum. We also indicate the possibility of early detection of potential DDIs by the proposed method through analysis of the database shared by the Food and Drug Administration. In conclusion, the proposed method has a higher sensitivity and a novel criterion to detect potential DDIs early, provided similar drugs have similar observed-expected ratios to the drug under assessment.

A late-Ediacaran crown-group sponge animal.

Sponges are the most basal metazoan phylum1 and may have played important roles in modulating the redox architecture of Neoproterozoic oceans2. Although molecular clocks predict that sponges diverged in the Neoproterozoic era3,4, their fossils have not been unequivocally demonstrated before the Cambrian period5-8, possibly because Precambrian sponges were aspiculate and non-biomineralized9. Here we describe a late-Ediacaran fossil, Helicolocellus cantori gen. et sp. nov., from the Dengying Formation (around 551-539 million years ago) of South China. This fossil is reconstructed as a large, stemmed benthic organism with a goblet-shaped body more than 0.4 m in height, with a body wall consisting of at least three orders of nested grids defined by quadrate fields, resembling a Cantor dust fractal pattern. The resulting lattice is interpreted as an organic skeleton comprising orthogonally arranged cruciform elements, architecturally similar to some hexactinellid sponges, although the latter are built with biomineralized spicules. A Bayesian phylogenetic analysis resolves H. cantori as a crown-group sponge related to the Hexactinellida. H. cantori confirms that sponges diverged and existed in the Precambrian as non-biomineralizing animals with an organic skeleton. Considering that siliceous biomineralization may have evolved independently among sponge classes10-13, we question the validity of biomineralized spicules as a necessary criterion for the identification of Precambrian sponge fossils.

Bayesian network structure learning with a new ensemble weights and edge constraints setting mechanism

AbstractBayesian networks (BNs) are highly effective in handling uncertain problems, which can assist in decision-making by reasoning with limited and incomplete information. Learning a faithful directed acyclic graph (DAG) from a large number of complex samples of a joint distribution is currently a challenging combinatorial problem. Due to the growing volume and complexity of data, some Bayesian structure learning algorithms are ineffective and lack the necessary precision to meet the required needs. In this paper, we propose a new PCCL-CC algorithm. To ensure the accuracy of the network structure, we introduce the new ensemble weights and edge constraints setting mechanism. In this mechanism, we employ a method that estimates the interaction between network nodes from multiple perspectives and divides the learning process into multiple stages. We utilize an asymmetric weighted ensemble method and adaptively adjust the network structure. Additionally, we propose a causal discovery method that effectively utilizes the causal relationships among data samples to correct the network structure and mitigate the influence of Markov equivalence classes (MEC). Experimental results on real datasets demonstrate that our approach outperforms state-of-the-art methods.

Implementation a Different Segmentation Method as a Joint Prior Model to Reconstruction the Bayesian Image analysis

The basic concepts in the application of Bayesian approaches to image analysis have been introduced as advanced method. The Bayesian approach contains benefits in respect to image analysis and interpretation because it permits the use of prior knowledge concerning the situation under study. This paper use to investigate the application of some of the well-known procedures (determines number labels of image under several conditions like noise of image, resolution of image) for the Bayesian image analysis with segmentation as a joint prior model in order to estimate the Maximum Likelihood (ML). Markov random field with segmentation which resulted by mean of posterior (MP). This paper contains several sections. Firstly, includes introduction about the image analysis with, Bayes frame work and statistical background to Markov's random field and its relationship through Markov Chain Monte Carlo. Secondly, section, which directly addresses solutions by using a principle of segmentation, which is a representation in threshold, is simply type introduced. Thirdly, presents the description of Experiment of Segmentation by depend on histogram also study of the factor (one prior) and the same model by adding segmentation (joint prior) based on techniques presented in the previously section are discussed . Fourthly, this section contains on the second prior implementation and simulation by using phantom data (Castle from south East Asia) also steps of estimation. Finally, result of experiment as well as estimation and summary.

Efficient nanoscale characterization of wafer surfaces using intelligent sampling and Bayesian optimization

The geometrical attributes of a wafer, such as thickness, uniformity, and local curvature, serve as eminent determinants of its quality. Therefore, measurements that are both rapid and accurate are paramount as multi-stage fabrication processes hinge on them to ensure high product reliability, proper process control, and maximum efficiency. The manual wafer profiling measurement approach in use today is, unfortunately, a time-consuming process. This creates a need for fast and prompt analysis of wafer attributes. This research thus proposes an iterative sampling method that boasts reducing the number of samplings down to a minimum without the need to discard a sufficient level of accuracy for the purpose of reliable attribute estimation. Subsequently, we suggest a series of methods to improve the monitoring and evaluation of the surface topography, all within the context of ultra-precision/nanoscale manufacturing processes, such as ultra-precision machining (UPM) and chemical–mechanical planarization (CMP). For the purpose of both real-time and exception-based detection of anomalies in the UPM process, we constructed a process-machine interaction (PMI) model with Bayesian learning, which amalgamates the received data across heterogeneous sensors such as force, vibration, and acoustic emission in situ. Similarly, the CMP process is quantitatively characterized by a multi-scaled multilayer nonlinear decomposition model with predictive capabilities. This model helps understand the physio-mechanical phenomena underlying both CMP and the UPM processes and describe and predict the nonlinear interactions, as evidenced by the experimentally collected vibration signal patterns.

Deciphering the role of zinc homeostasis in the tumor microenvironment and prognosis of prostate cancer

BackgroundDysregulation of zinc homeostasis is widely recognized as a hallmark feature of prostate cancer (PCa) based on the compelling clinical and experimental evidence. Nevertheless, the implications of zinc dyshomeostasis in PCa remains largely unexplored.MethodsIn this research, the zinc homeostasis pattern subtype (ZHPS) was constructed according to the profile of zinc homeostasis genes. The identified subtypes were assessed for their immune functions, mutational landscapes, biological peculiarities and drug susceptibility. Subsequently, we developed the optimal signature, known as the zinc homeostasis-related risk score (ZHRRS), using the approach won out in multifariously machine learning algorithms. Eventually, clinical specimens, Bayesian network inference and single-cell sequencing were used to excavate the underlying mechanisms of MT1A in PCa.ResultsThe zinc dyshomeostasis subgroup, ZHPS2, possessed a markedly worse prognosis than ZHPS1. Moreover, ZHPS2 demonstrated a more conspicuous genomic instability and better therapeutic responses to docetaxel and olaparib than ZHPS1. Compared with traditional clinicopathological characteristics and 35 published signatures, ZHRRS displayed a significantly improved accuracy in prognosis prediction. The diagnostic value of MT1A in PCa was substantiated through analysis of clinical samples. Additionally, we inferred and established the regulatory network of MT1A to elucidate its biological mechanisms.ConclusionsThe ZHPS classifier and ZHRRS model hold great potential as clinical applications for improving outcomes of PCa patients.

A digital twin for advancing battery fast charging based on a Bayesian optimization-based method

The optimization of fast charging protocols is regarded as a key technology for promoting the use of electric vehicles because it can balance battery charging time and health. Optimizing such charging protocols through electrochemical models is a mainstream approach and can demonstrate high accuracy in simulating battery characteristics. However, the high complexity of the corresponding models makes the calculation and optimization processes difficult to perform in real time. To address this problem, this study presents an online closed-loop fast charging strategy optimization scheme that combines a battery digital twin model (BDTM) and Bayesian optimization (BO). Because the BO can quickly find a near-optimal solution in a few iterations, the optimization time is shortened, thereby reducing the computational burden incurred by highly complex models. To further improve the efficiency of the online optimization, a parallel multichannel optimization strategy is proposed, which further accelerates the process of finding the optimal protocol by simultaneously executing multiple optimization algorithms. Additionally, we analysed the effects of ageing parameters and ambient temperature on the optimization results. The results show that BO can obtain relatively stable optimization and has the highest efficiency when using four parallel channels. Specifically, the number of convergence evaluations for single-channel optimization is 2.5 times that of the four-channel optimization. Compared with the reference charging protocol, charging using the protocol optimized based on the Doyle-Fuller-Newman (DFN) model can effectively suppress the loss of lithium inventory (LLI) by up to 4.76 % within 90 cycles.

Identification of disulfidptosis-associated genes and characterization of immune cell infiltration in thyroid carcinoma.

The primary objective of this study is to conduct a comprehensive screening and analysis of differentially expressed genes related to disulfidoptosis (DEDRGs) in thyroid carcinoma (THCA). This entails delving into the intricate characterization of immune cell infiltration within the THCA context and subsequently formulating and validating a novel prognostic model. To achieve our objectives, we first delineated two distinct subtypes of disulfidoptosis-related genes (DRGs) via consensus clustering methodology. Subsequently, employing the limma R package, we identified the DEDRGs critical for our investigation. These DEDRGs underwent meticulous validation across various databases, alongside an in-depth analysis of gene regulation. Employing functional enrichment techniques, we explored the potential molecular mechanisms underlying disulfidoptosis in THCA. Furthermore, we scrutinized the immune landscape within the two identified subtypes utilizing CIBERSORT and ESTIMATE algorithms. The construction of the prognostic model for THCA entailed intricate methodologies including univariate, multivariate Cox regression, and LASSO regression algorithms. The validity and efficacy of our prognostic model were corroborated through Kaplan-Meier survival curves and ROC curves. Additionally, a nomogram was meticulously formulated to facilitate the prediction of patient prognosis. To fortify our findings, we conducted a comprehensive Bayesian co-localization analysis coupled with rigorous in vitro experimentation, aimed at unequivocally establishing the validity of the identified DEDRGs. Our analyses unveiled Cluster C1, characterized by elevated expression levels of DEDRGs, as harboring a favorable prognosis accompanied by abundant immune cell infiltration. Correlation analyses underscored predominantly positive associations among the DEDRGs, further affirming their significance in THCA. Differential expression patterns of DEDRGs between tumor samples and normal tissues were evident across the GEPIA and HPA databases. Insights from the TIMER database underscored a robust correlation between DEDRGs and immune cell infiltration. KEGG analysis elucidated the enrichment of DEDRGs primarily in pivotal pathways including MAPK, PPAR signaling pathway, and Proteoglycans in cancer. Furthermore, analyses using CIBERSORT and ESTIMATE algorithms shed light on the crucial role played by DEDRGs in shaping the immune microenvironment. The prognostic model, anchored by five genes intricately associated with THCA prognosis, exhibited commendable predictive accuracy and was intricately linked to the tumor immune microenvironment. Notably, patients categorized with low-risk scores stood to potentially benefit more from immunotherapy. The validation of DEDRGs unequivocally underscores the protective role of INF2 in THCA. In summary, our study delineates two discernible subtypes intricately associated with DRGs, revealing profound disparities in immune infiltration and survival prognosis within the THCA milieu. The implications of our findings extend to potential treatment strategies for THCA patients, which could entail targeted interventions directed towards DEDRGs and prognostic genes, thereby influencing disulfidptosis and the immune microenvironment. Moreover, the robust predictive capability demonstrated by our prognostic model, based on the five genes (ANGPTL7, FIRRE, ODAPH, PROKR1, SFRP5), underscores its potential clinical utility in guiding personalized therapeutic approaches for THCA patients.

Reliability of energy landscape analysis of resting-state functional MRI data.

Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.

Cognitive control training with domain-general response inhibition does not change children's brains or behavior.

Cognitive control is required to organize thoughts and actions and is critical for the pursuit of long-term goals. Childhood cognitive control relates to other domains of cognitive functioning and predicts later-life success and well-being. In this study, we used a randomized controlled trial to test whether cognitive control can be improved through a pre-registered 8-week intervention in 235 children aged 6-13 years targeting response inhibition and whether this leads to changes in multiple behavioral and neural outcomes compared to a response speed training. We show long-lasting improvements of closely related measures of cognitive control at the 1-year follow-up; however, training had no impact on any behavioral outcomes (decision-making, academic achievement, mental health, fluid reasoning and creativity) or neural outcomes (task-dependent and intrinsic brain function and gray and white matter structure). Bayesian analyses provide strong evidence of absent training effects. We conclude that targeted training of response inhibition does little to change children's brains or their behavior.

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.