Episodic Memory Precision and Reality Monitoring Following Stimulation of Angular Gyrus

The qualities of remembered experiences are often used to inform “reality monitoring” judgments, our ability to distinguish real and imagined events [Johnson, M. K., & Raye, C. L. Reality monitoring. Psychological Review, 88, 67–85, 1981]. Previous experiments have tended to investigate only whether reality monitoring decisions are accurate or not, providing little insight into the extent to which reality monitoring may be affected by qualities of the underlying mnemonic representations. We used a continuous-response memory precision task to measure the quality of remembered experiences that underlie two different types of reality monitoring decisions: self/experimenter decisions that distinguish actions performed by participants and the experimenter and imagined/perceived decisions that distinguish imagined and perceived experiences. The data revealed memory precision to be associated with higher accuracy in both self/experimenter and imagined/perceived reality monitoring decisions, with lower precision linked with a tendency to misattribute self-generated experiences to external sources. We then sought to investigate the possible neurocognitive basis of these observed associations by applying brain stimulation to a region that has been implicated in precise recollection of personal events, the left angular gyrus. Stimulation of angular gyrus selectively reduced the association between memory precision and self-referential reality monitoring decisions, relative to control site stimulation. The angular gyrus may, therefore, be important for the mnemonic processes involved in representing remembered experiences that give rise to a sense of self-agency, a key component of “autonoetic consciousness” that characterizes episodic memory [Tulving, E. Elements of episodic memory. Oxford, United Kingdom: Oxford University Press, 1985].

Bayesian Genomic Linear Regression

The Bayesian paradigm for parameter estimation is introduced and linked to the main problem of genomic-enabled prediction to predict the trait of interest of the non-phenotyped individuals from genotypic information, environment variables, or other information (covariates). In this situation, a convenient practice is to include the individuals to be predicted in the posterior distribution to be sampled. We explained how the Bayesian Ridge regression method is derived and exemplified with data from plant breeding genomic selection. Other Bayesian methods (Bayes A, Bayes B, Bayes C, and Bayesian Lasso) were also described and exemplified for genome-based prediction. The chapter presented several examples that were implemented in the Bayesian generalized linear regression (BGLR) library for continuous response variables. The predictor under all these Bayesian methods includes main effects (of environments and genotypes) as well as interaction terms related to genotype × environment interaction.

Support Vector Machines and Support Vector Regression

In this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give examples of svm for binary and categorical response variables with plant breeding data for genomic selection. Finally, general issues for adopting the svm methodology for continuous response variables are provided, and some examples of svm for continuous response variables for genomic prediction are described.

Reproducing Kernel Hilbert Spaces Regression and Classification Methods

The fundamentals for Reproducing Kernel Hilbert Spaces (RKHS) regression methods are described in this chapter. We first point out the virtues of RKHS regression methods and why these methods are gaining a lot of acceptance in statistical machine learning. Key elements for the construction of RKHS regression methods are provided, the kernel trick is explained in some detail, and the main kernel functions for building kernels are provided. This chapter explains some loss functions under a fixed model framework with examples of Gaussian, binary, and categorical response variables. We illustrate the use of mixed models with kernels by providing examples for continuous response variables. Practical issues for tuning the kernels are illustrated. We expand the RKHS regression methods under a Bayesian framework with practical examples applied to continuous and categorical response variables and by including in the predictor the main effects of environments, genotypes, and the genotype ×environment interaction. We show examples of multi-trait RKHS regression methods for continuous response variables. Finally, some practical issues of kernel compression methods are provided which are important for reducing the computation cost of implementing conventional RKHS methods.

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

Long-Term Survival of PD-1 Inhibitor Combined with Antiangiogenic Agents in a Patient with Pulmonary Sarcomatoid Carcinoma Complicated with Esophageal Cancer: A Case Resport and Literature Review

Pulmonary sarcomatoid carcinoma (PSC) is a highly aggressive rare subtype of non-small cell lung cancer (NSCLC). PSC is known for its poor prognosis and low sensitivity to conventional treatments such as chemotherapy, radiation, and adjuvant therapies. In recent years, the application of targeted therapy and immunotherapy in this field has made progress. Although programmed cell death 1 (PD-1) inhibitors have been reported to show favorable antitumor effects in PSC patients with high programmed death-ligand 1 (PD-L1) expression, the efficacy of PD-1 inhibitors in combination with antiangiogenic drugs has not been investigated. Here, we report for the first time a case of dual-source cancer with low expression of PD-L1 and microsatellite stability (MSS) which showed continuous response to sintilimab combined with anlotinib as first-line treatment and achieved a long progression free survival (PFS) of 24 months with no serious adverse reactions. This case presents a new therapeutic prospect for PSC and a potential to enhance its prognosis and treatment strategies.

Current trends in welding flux development

Welding flux makes significant contribution to weld-metal quality, productivity of welding process and rapid deployment of new materials. Deployment of new materials has been hampered because of lengthy trial-and-test experiments and paucity of methodology for modelling and optimisation in the traditional welding flux development. This paper discussed the contributions made to mitigate the drawbacks of traditional welding flux development in areas of experimentations, prediction modelling and optimisation. Limitations of current efforts were identified and suggested for future research, namely (i) current response models are limited to well-behaved flux systems and do not account for edge and additive effects of flux ingredients (ii) non-incorporation of stakeholder’s preferences concerning the relative importance of quality attributes (iii) lack of prediction and optimisation tools for determining optimal coating factor and flux heights for Shielded Metal Arc Welding and Submerge Arc Welding respectively and (iv) non-continuous response functions and concave regions of the trade-off surface are not considered.

The number of response categories in ordered response models

The choice of the number m of response categories is a crucial issue in categorization of a continuous response. The paper exploits the Proportional Odds Models’ property which allows to generate ordinal responses with a different number of categories from the same underlying variable. It investigates the asymptotic efficiency of the estimators of the regression coefficients and the accuracy of the derived inferential procedures when m varies. The analysis is based on models with closed-form information matrices so that the asymptotic efficiency can be analytically evaluated without need of simulations. The paper proves that a finer categorization augments the information content of the data and consequently shows that the asymptotic efficiency and the power of the tests on the regression coefficients increase with m. The impact of the loss of information produced by merging categories on the efficiency of the estimators is also considered, highlighting its risks especially when performed in its extreme form of dichotomization. Furthermore, the appropriate value of m for various sample sizes is explored, pointing out that a large number of categories can offset the limited amount of information of a small sample by a better quality of the data. Finally, two case studies on the quality of life of chemotherapy patients and on the perception of pain, based on discretized continuous scales, illustrate the main findings of the paper.

Episodic memory precision and reality monitoring following stimulation of angular gyrus

The qualities of remembered experiences are often used to inform ‘reality monitoring’ judgments, our ability to distinguish real and imagined events (Johnson & Raye, 1981). Previous experiments have tended to investigate only whether reality monitoring decisions are accurate or not, providing little insight into the extent to which reality monitoring may be affected by qualities of the underlying mnemonic representations. We used a continuous-response memory precision task to measure the quality of remembered experiences that underlie two different types of reality monitoring decisions: agency decisions that distinguish actions performed by participants and the experimenter, and perceptual decisions that distinguish perceived and imagined experiences. The data revealed memory precision to be associated with higher accuracy in both agency and perceptual reality monitoring decisions, with reduced precision linked with a tendency to misattribute self-generated experiences to external sources. We then sought to investigate the possible neurocognitive basis of these observed associations by applying brain stimulation to a region that has been implicated in precise recollection of personal events, left angular gyrus. Stimulation of angular gyrus selectively reduced the association between memory precision and self-referential reality monitoring decisions, relative to control site stimulation. Angular gyrus may, therefore, be important for the ability to imbue remembered experiences with a sense of self-agency, a key component of ‘autonoetic consciousness’ that characterises episodic memory (Tulving, 1985).