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Data analysis is indeed quite popular and crucial in various fields such as meteorology, hydrology, epidemiology, economics, and biology. So, the purpose of this study is to introduce a new probability distribution to analyze rainfall data. A new extension of XLindley distribution is introduced using the alpha power transformation technique. The new distribution is named “alpha power transformed XLindley – (APTXL) distribution”. Mathematical properties of APTXL distribution are derived such as ordinary moments, moment generating function, quantile function, mean residual life function, and order statistics. The model parameters are estimated using five different estimation methods such as maximum likelihood, Anderson Darling, Cramer von Misses, Ordinary least squares, and weighted least squares. A comprehensive simulation study is utilized to check the behavior and efficiency of the derived estimators. It is found that the weighted least square approach efficiently estimates the model parameter than the other methods. This derivation of a new model not only contributes to theoretical advances in statistical methodology but also provides practical methods for rainfall data modeling. The APTXL distribution is used to evaluate rainfall datasets from Saudi Arabia. It is identified that the APTXL distribution provides efficient results as compared to considered competitive distributions.

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required — from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

This study introduces the New Exponential-Exponential Distribution (NEED) within the broader New Exponential-Generating (NE-G) family of distributions, targeting enhancements in rainfall data analysis. The significance of this research lies in addressing the need for sophisticated statistical models to accurately capture the complex variability of rainfall patterns, which are critical for effective environmental planning and disaster management. Aiming to refine rainfall data modeling, we employ the NEED model, emphasizing its application across diverse climatic conditions. Our methodology encompasses a comprehensive evaluation of seven distinct parameter estimation techniques, with a particular focus on the Anderson-Darling and maximum product spacing methods. These were selected based on their performance in minimizing bias and mean square error, assessed through a rigorous Monte-Carlo simulation study. Additionally, the study utilizes rainfall data from various geographical regions to validate the model's efficacy. The major conclusion of our investigation is the demonstrable superiority of NEED over traditional models in fitting rainfall data, as evidenced by its enhanced predictive accuracy. This outcome not only contributes to the theoretical advancements in statistical meteorology but also offers practical methodologies for improved weather forecasting. The integration of NEED with contemporary machine learning algorithms further suggests potential for groundbreaking applications in climate science and water resource management.

The primary goal of this research is to examine the impact of balancing data on the prediction quality and inference in multilevel logistic regression models. Logistic regression is a valuable approach for modeling binary outcomes expected in health applications. The class imbalance problem, where one of the two outcome categories occurs much more often than the other, is common in healthcare data, such as when modeling the risk factors for rare diseases. The issue is particularly relevant for medical data that contains individual measurements and other data sources measured at a geographic region level, such as environmental risk factors. For this work, both prediction and model interpretation are of interest. A simulation model is proposed to test the impact of balancing strategies on the logistic multilevel model's parameter estimation, inference, and predictive performance. The simulated information emulates characteristics of a Gestational Diabetes Mellitus (GDM) dataset from Indiana's Medicaid program. Several datasets were simulated with varying levels of complexity, involving the balance of the outcome variable and predictors. These datasets exhibited high- or low-frequency occurrences in specific intersections of variables, often called ‘cells.’ The impact of the balancing strategies on prediction and inference was assessed using different techniques, such as the Equivalence (TOST) Test, power analysis, and predictive measures. To the best of our knowledge, this is the first research that explores the impact of using balanced samples on coefficient estimation and prediction measures when using logistic multilevel modeling, finding evidence about the benefits of using balanced samples in this context.

Summary: This Personal View is intended for early-career researchers who are not yet experts in statistics. The Personal View focuses on common but usually avoidable flaws in the context of observational studies. I point out how study design, data collection, and statistical methods impact statistical results and research conclusions. With particular attention to study planning, sample selection, biases, lack of transparency and results misinterpretations.

Gene networks (GNs) use graphs to represent the interaction relationships between genes. Large-scale GNs are often sparse and contain hub genes that interact with many other genes. In this paper, we propose a novel method called NetARD, which utilizes Automatic Relevance Determination (ARD) to estimate partial correlations, to infer GNs with the hub genes from gene expression data. We test NetARD on simulated GNs and in silico GNs, and it outperforms existing methods. In our high-throughput gene expression data analysis, we integrate the NetARD into a method called GN Co-expression Extension (GNCE). This approach infers the GNs of co-expressed genes, with genes from a predefined GN serving as hub genes. We validate this approach by extending the core GN of transcription factor genes of E. coli using microarray data. In an application example, we identify biological process (BP) Gene Ontology (GO) terms that are significantly involved in cancer progression. This task is accomplished by analyzing the GN inferred through GNCE using the core GN associated with the colorectal cancer pathway and RNA-seq data.

This paper introduces a new three-parameter distribution called the modified Kies Gompertz (MKGO) distribution to identify an effective statistical distribution in a real-life modeling data set. The distribution’s probability density function may take numerous forms and graphs, and it could be negatively and positively skewed and uni-modal. We established several statistical properties of the MKGO distribution, such as moments, skewness, kurtosis, moment generating function, distribution of order statistic, quantile function, stress strength reliability and two cases of entropy measures. Further, we proposed six methods for estimating the unknown parameters of the suggested model. A brief Monter Carlo (MC) simulation study is illustrated in terms of average estimate (AE) and mean square error (MSE). to show the efficiency of these estimators. Finally, we analyzed four real data sets in the engineering sector to evaluate the potential of the proposed model with that of various existing distributions.

In this study, a new four-parameter Lomax distribution is proposed using a new alpha power transformation technique. The new distribution is named ''New Alpha Power Transformed Power Lomax Distribution.'' Mathematical properties, including moments, the moment-generating function, the mean residual life, order statistics, and the quantile function, are obtained. The maximum likelihood estimation approach is used to estimate the model parameters. A comprehensive simulation is used to evaluate the behavior of maximum likelihood estimators. Two real-world data sets were used to demonstrate the significance of the proposed model, and the results show that the new model performs better when interpreting lifetime data sets. In the end, for the data sets, Bayesian estimation and Metropolis-Hasting's approach were also utilized to construct the approximate Bayes estimates, and convergence diagnostic methods based on Markov Chain Monte Carlo techniques were applied.

In this paper, we propose exponentiated XLindley (EXL) distribution. The novel model is adaptable due to the mixt shapes of its density and failure rate functions. The following key statistical properties of EXL distribution are derived: quantile function, moments, hazard function, mean residual life, and Rényi entropy. The parameters are estimated using the maximum likelihood, Anderson Darling, Cramer von Misses, maximum product spacing, ordinary and weighted least square estimation procedures. To examine the behavior of the estimate, Monte Carlo simulation is used. Further Bayesian technique is also utilized to estimate the EXL parameters. The traceplot and Geweke diagnostics are used to track the convergence of simulated processes. The applicability of the EXL distribution is demonstrated by three datasets from different domains such as mortality rate due to COVID-19, precipitation in inches, and failure time for repairable items. The proposed distribution provides efficient results as compared to renowned competitive distributions.

Cognitive neuroscientists have been grappling with two related experimental design problems. First, the complexity of neuroimaging data (e.g. often hundreds of thousands of correlated measurements) and analysis pipelines demands bespoke, non-parametric statistical tests for valid inference, and these tests often lack an agreed-upon method for performing a priori power analyses. Thus, sample size determination for neuroimaging studies is often arbitrary or inferred from other putatively but questionably similar studies, which can result in underpowered designs – undermining the efficacy of neuroimaging research. Second, when meta-analyses estimate the sample sizes required to obtain reasonable statistical power, estimated sample sizes can be prohibitively large given the resource constraints of many labs. We propose the use of sequential analyses to partially address both of these problems. Sequential study designs – in which the data is analyzed at interim points during data collection and data collection can be stopped if the planned test statistic satisfies a stopping rule specified a priori – are common in the clinical trial literature, due to the efficiency gains they afford over fixed-sample designs. However, the corrections used to control false positive rates in existing approaches to sequential testing rely on parametric assumptions that are often violated in neuroimaging settings. We introduce a general permutation scheme that allows sequential designs to be used with arbitrary test statistics. By simulation, we show that this scheme controls the false positive rate across multiple interim analyses. Then, performing power analyses for seven evoked response effects seen in the EEG literature, we show that this sequential analysis approach can substantially outperform fixed-sample approaches (i.e. require fewer subjects, on average, to detect a true effect) when study designs are sufficiently well-powered. To facilitate the adoption of this methodology, we provide a Python package “niseq” with sequential implementations of common tests used for neuroimaging: cluster-based permutation tests, threshold-free cluster enhancement, t-max, F-max, and the network-based statistic with tutorial examples using EEG and fMRI data.

The rapid spread of African swine fever (ASF) in recent years has once again raised awareness of the need to improve our preparedness in preventing and managing outbreaks, for which modelling-based forecasts can play an important role. This is even more important in the case of a disease such as ASF, involving several types of hosts, characterised by a high case-fatality rate and for which there is currently no treatment or vaccine. Within the framework of the ASF challenge, we proposed a modelling approach based on a stochastic mechanistic model and an inference procedure to estimate key transmission parameters from provided data (incomplete and noisy) and generate forecasts for unobserved time horizons. The model is partly data driven and composed of two modules, corresponding to epidemic and demographic dynamics in domestic pig and wild boar (WB) populations, interconnected through the networks of animal trade and/or spatial proximity. The inference consists in an iterative procedure, alternating between the two models and based on a criterion optimisation. Estimates of transmission and detection parameters appeared to be of similar magnitude for each of the three periods of the challenge, except for the transmission rates in WB population through contact with infectious individuals and carcasses, higher during the first period. The predicted number of infected domestic pig farms was in overall agreement with the data. The proportion of positive tested WB was overestimated, but with a trend close to that observed in the data. Comparison of the spatial simulated and observed distributions of detected cases also showed an overestimation of the spread of the pathogen within WB metapopulation. Beyond the quantitative results and the inherent difficulties of real-time forecasting, we built a modelling framework that is flexible enough to accommodate changes in transmission processes and control measures that may occur during an epidemic emergency.

The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions were task-extensive. To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples. As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. Furthermore, to specify the object of interest we used a simple post-processing method. The proposed approach is compared over conventional methods using five-fold cross-validation and receiver operating characteristic (ROC) curve. The maximum accuracy of the MIL-net is 95%, which is 15% higher than the conventional methods. Furthermore, the ability to interpret the location of the iPSC colonies based on the image level label without using a pixel-wise ground truth image is more appealing and cost-effective in colony condition recognition.

Background and Objective: Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand., Methods: MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance., Results: MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner., Conclusions: MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: AC is an Editorial Board Member for Epidemics but was not involved in the editorial review or the decision to publish this article. AC has received payment from Pfizer for teaching of mathematical modelling of infectious diseases., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

In a rapidly changing and diverse world, the ability to reason about conflicting perspectives is critical for effective communication, collaboration, and critical thinking. The current pre-registered experiments with children ages 7 to 11 years investigated the developmental foundations of this ability through a novel social reasoning paradigm and a computational approach. In the inference task, children were asked to figure out what happened based on whether two speakers agreed or disagreed in their interpretation. In the prediction task, children were provided information about what happened and asked to predict whether two speakers will agree or disagree. Together, these experiments assessed children's understanding that disagreement often results from ambiguity about what happened, and that ambiguity about what happened is often predictive of disagreement. Experiment 1 (N = 52) showed that children are more likely to infer that an ambiguous utterance occurred after learning that people disagreed (versus agreed) about what happened and found that these inferences become stronger with age. Experiment 2 (N = 110) similarly found age-related change in children's inferences and also showed that children could reason in the forward direction, predicting that an ambiguous utterance would lead to disagreement. A computational model indicated that although children's ability to predict when disagreements might arise may be critical for making the reverse inferences, it did not fully account for age-related change., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Section 1 of this paper provides an introduction to this new “algebra of conditionals”, addresses various plausibility tests for such an algebra, provides a Venn diagram disproving a supposed counter-example, and answers various other objections raised in the literature about the efficacy of this algebraic extension of logic and conditional probability. Section 2 greatly simplifies the calculation of the implications of a set of conditional propositions or conditional events. These results depend on defining a deductive relation for conditionals (actually two have been found) with the property that the conjunction of two conditionals implies each of its components. That seemingly innocuous property assures that the deductively closed set implied by a finite set of n conditionals with respect to the deductive relation is implied by the single conditional formed by conjoining all n of them. The results are illustrated by solving several examples of deduction with several uncertain conditionals.

In brain imaging, decoding is widely used to infer relationships between brain and cognition, or to craft brain-imaging biomarkers of pathologies. Yet, standard decoding procedures do not come with statistical guarantees, and thus do not give confidence bounds to interpret the pattern maps that they produce. Indeed, in whole-brain decoding settings, the number of explanatory variables is much greater than the number of samples, hence classical statistical inference methodology cannot be applied. Specifically, the standard practice that consists in thresholding decoding maps is not a correct inference procedure. We contribute a new statistical-testing framework for this type of inference. To overcome the statistical inefficiency of voxel-level control, we generalize the Family Wise Error Rate (FWER) to account for a spatial tolerance δ, introducing the δ-Family Wise Error Rate (δ-FWER). Then, we present a decoding procedure that can control the δ-FWER: the Ensemble of Clustered Desparsified Lasso (EnCluDL), a procedure for multivariate statistical inference on high-dimensional structured data. We evaluate the statistical properties of EnCluDL with a thorough empirical study, along with three alternative procedures including decoder map thresholding. We show that EnCluDL exhibits the best recovery properties while ensuring the expected statistical control.

Respiratory Syncytial Virus (RSV) and Influenza cause a large burden of disease. Evidence of their interaction via temporary cross-protection implies that prevention of one could inadvertently lead to an increase in the burden of the other. However, evidence for the public health impact of such interaction is sparse and largely derives from ecological analyses of peak shifts in surveillance data. To test the robustness of estimates of interaction parameters between RSV and Influenza from surveillance data we conducted a simulation and back-inference study. We developed a two-pathogen interaction model, parameterised to simulate RSV and Influenza epidemiology in the UK. Using the infection model in combination with a surveillance-like stochastic observation process we generated a range of possible RSV and Influenza trajectories and then used Markov Chain Monte Carlo (MCMC) methods to back-infer parameters including those describing competition. We find that in most scenarios both the strength and duration of RSV and Influenza interaction could be estimated from the simulated surveillance data reasonably well. However, the robustness of inference declined towards the extremes of the plausible parameter ranges, with misleading results. It was for instance not possible to tell the difference between low/moderate interaction and no interaction. In conclusion, our results illustrate that in a plausible parameter range, the strength of RSV and Influenza interaction can be estimated from a single season of high-quality surveillance data but also highlights the importance to test parameter identifiability a priori in such situations.

How do embodied states influence the inferences people make about the meaning that is intended by communicators? We propose that embodied states encourage mental representation of certain meanings while inhibiting others, thereby facilitating or hindering comprehension in social interactions and potentially causing miscommunication. Four experiments demonstrate that bodily postures incompatible with the intended meaning of a sentence attenuated inferences of those meanings, especially when the intended meaning was not articulated directly and required more extensive inference-making effort. Participants were faster at responding to sentences containing verbs inferring a sitting position when they were sitting than when they were standing, and vice versa. Participants were also more likely to interpret the intended meaning of sentences as relevant to sitting when participants were themselves sitting, and relevant to standing when participants were standing. These outcomes were especially evident when the sentences required higher-interpretive effort (e.g., used indirect language) than lower-interpretive effort (e.g., used literal language). These results suggest that embodied states shape inference-making and can thereby influence comprehension and affect communication success, especially when inferences are more effortful to make.

This article adapts an existing experimental protocol for assessing individuals’ ability to transfer knowledge across instrumental and pavlovian learning stages. The protocol (Transfer of Control using differential outcomes learning) is adapted to fit social contexts wherein the pavlovian learning phase is modulated so that individuals are able to observe, and potentially learn from, the stimulus associated with reinforcing outcomes presented to another (observable) individual. Transfer of Control concerns participants combining knowledge of learned instrumental and pavlovian (stimulus, response, outcome) associations in order to ground the learning of new associations. The article describes the theoretical and procedural underpinnings of a novel Social Transfer of Control methodology. The use of such a methodology is two-fold: i) to serve as a guide to researchers interested in evaluating how individuals can learn from others in a partially observable setting, i.e. when behavioural and reinforcing outcome information is hidden, and bring to bear this knowledge on their own instrumental decision making; ii), to facilitate investigation of the routes of cognitive and emotional empathy, with potential applications for educational and clinical settings. • Three stage Transfer of Control behavioural methodology is adapted to include a social (pavlovian) learning stage. • Hypotheses can be tested that concern learning rewarding instrumental responses achieved by observation of others’ emotionally expressive reactions to differentially rewarding outcomes. • Methodological and validation considerations for evaluating the above are presented

Time series data about when heating is on and off in homes can be useful for research on building energy use and occupant behaviours, particularly data at room level and at a granularity of minutes. Direct methods which measure the temperature of radiators and other heaters can be effective at producing such data, but are expensive. Indirect methods, which infer heating on- and off-times from ambient room temperature data, can be cheaper but produce more error-prone data. Existing indirect methods have however utilised relatively simple prediction algorithms based on changes in ambient temperature between closely adjacent time points. In the method presented here we have implemented several refinements to this approach: • An Artificial Neural Network algorithm is applied to the prediction task: a deep, dilated convolutional network. • A wider range of input features is utilised to base predictions upon: ambient room temperature and humidity, and external temperature and humidity. • Predictions for each time point are based on data from a wider, 600-minute, time window. • We evaluate model performance on a dataset with 10 min granularity and achieve mean precision and recall during the heating season of >=0.74 for individual time points, and >=0.82 for full heating events, outperforming comparator methods.

This article contains performance data, questionnaire ratings, and EEG data from a differential outcomes learning task from two experiments. In both experiments, the standard differential outcomes learning task was extended to involve a social dimension, in order to capture how people can learn from others by observation. In Experiment 1 (N = 20), using a within-subjects design, participants learned pairings of image stimuli in four conditions: 1) individual-differential outcomes, 2) individual-non-differential outcomes, 3) social-differential outcomes, and 4) social-non-differential outcomes. The social condition had a screen-captured video recording of the outcomes (but not the actions themselves) of another person performing the task. During the task, the performance of the participants was measured. After the task, participants rated their experience in a questionnaire. The procedure for Experiment 2 (N = 33) was similar to Experiment 1, but with a stronger social manipulation using a video of another person's face showing facial expressions reflecting the outcomes. In addition, EEG was measured while performing the task. For more insight, please see Vicarious value learning: Knowledge transfer through affective processing on a social differential outcomes task (Rittmo et al., 2020).

The past decade has seen growing concern about research practices in cognitive neuroscience, and psychology more broadly, that shake our confidence in many inferences in these fields. We consider how these issues affect developmental cognitive neuroscience, with the goal of progressing our field to support strong and defensible inferences from our neurobiological data. This manuscript focuses on the importance of distinguishing between confirmatory versus exploratory data analysis approaches in developmental cognitive neuroscience. Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple comparisons. To counterbalance these concerns with confirmatory analyses, we present two complementary strategies. First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses. Second, we summarize defensible approaches for null hypothesis significance testing in confirmatory analyses, focusing on transparent and reproducible practices in our field. Specific recommendations are given, and templates, scripts, or other resources are hyperlinked, whenever possible.

The brain has been theorized to employ inferential processes to overcome the problem of uncertainty. Inference is thought to underlie neural processes, including in disparate domains such as value-based decision-making and perception. Value-based decision-making commonly involves deliberation, a time-consuming process that requires conscious consideration of decision variables. Perception, by contrast, is thought to be automatic and effortless. Both processes may call on a general neural system to resolve for uncertainty however. We addressed this question by directly comparing uncertainty signals in visual perception and an economic task using fMRI. We presented the same individuals with different versions of a bi-stable figure (Necker’s cube) and with a gambling task during fMRI acquisition. We experimentally varied uncertainty, either on perceptual state or financial outcome. We found that inferential errors indexed by a formal account of surprise in the gambling task yielded BOLD responses in the anterior insula, in line with earlier findings. Moreover, we found perceptual uncertainty and surprise in the Necker Cube task yielded similar responses in the anterior insula. These results suggest that uncertainty, irrespective of domain, correlates to a common brain region, the anterior insula. These findings provide empirical evidence that the brain interacts with its environment through inferential processes.

Hypomanic personality traits are present in the general population and represent a risk factor for developing bipolar disorder. This personality style, notably its social component, is linked to difficulties in theory of mind (i.e., ability to infer mental states). Exploring the neural correlates of mental states' inference in individuals with these personality traits can provide meaningful insights into the development of bipolar disorder. The aim of the present study was therefore to investigate the potential impact of hypomanic traits on brain activation and task-based connectivity strength during a dynamic theory of mind task in a nonclinical population. A total of 52 nonclinical participants were recruited, and hypomanic traits were assessed with the Hypomanic Personality Scale. The severity of hypomanic traits was positively associated with right middle and inferior frontal gyri activations (in high vs. low inference in nonemotional condition and emotion vs. no emotion in high inference, respectively). It was also associated with stronger connectivity between the salience network (i.e., bilateral putamen and pallidum) and bilateral superior temporal gyri (high inference in nonemotional condition), and between cerebellar and temporal areas (high inference in emotional condition). These changes may either reflect adaptations or differential processing, and further studies are therefore mandatory., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

We review and demonstrate how an empirical Bayes method, shrinking a protein's sample variance towards a pooled estimate, leads to far more powerful and stable inference to detect significant changes in protein abundance compared to ordinary t-tests. Using examples from isobaric mass labelled proteomic experiments we show how to analyze data from multiple experiments simultaneously, and discuss the effects of missing data on the inference. We also present easy to use open source software for normalization of mass spectrometry data and inference based on moderated test statistics.

The computer swine disease diagnosis is an important tool for swine farming industry, but the traditional expert system cannot meet the requirement of practical application. To improve the situation, a swine disease ontology is constructed, which can model the knowledge of swine disease diagnosis into a concept system, and a mechanism that can save the ontology into relational database is established, further more a computer system is developed to implement ontology-based swine disease diagnosis, so make the diagnosis results extended and more precise.