Pyrenoid loss impairs carbon-concentrating mechanism induction and alters primary metabolism in Chlamydomonas reinhardtii

Carbon-concentrating mechanisms (CCMs) enable efficient photosynthesis and growth in CO2-limiting environments, and in eukaryotic microalgae localisation of Rubisco to a microcompartment called the pyrenoid is key. In the model green alga Chlamydomonas reinhardtii, Rubisco preferentially relocalises to the pyrenoid during CCM induction and pyrenoid-less mutants lack a functioning CCM and grow very poorly at low CO2. The aim of this study was to investigate the CO2 response of pyrenoid-positive (pyr+) and pyrenoid-negative (pyr–) mutant strains to determine the effect of pyrenoid absence on CCM induction and gene expression. Shotgun proteomic analysis of low-CO2-adapted strains showed reduced accumulation of some CCM-related proteins, suggesting that pyr– has limited capacity to respond to low-CO2 conditions. Comparisons between gene transcription and protein expression revealed potential regulatory interactions, since Rubisco protein linker (EPYC1) protein did not accumulate in pyr– despite increased transcription, while elements of the LCIB/LCIC complex were also differentially expressed. Furthermore, pyr− showed altered abundance of a number of proteins involved in primary metabolism, perhaps due to the failure to adapt to low CO2. This work highlights two-way regulation between CCM induction and pyrenoid formation, and provides novel candidates for future studies of pyrenoid assembly and CCM function

Surprise, curiosity, and confusion promote knowledge exploration: evidence for robust effects of epistemic emotions

Research has started to acknowledge the importance of emotions for complex learning and cognitive performance. However, research on epistemic emotions has only recently become more prominent. Research in educational psychology in particular has mostly focused on examining achievement emotions instead of epistemic emotions. Furthermore, only few studies have addressed functional mechanisms underlying multiple different epistemic emotions simultaneously, and only one study has systematically compared the origins and effects of epistemic emotions with other emotions relevant to knowledge generation (i.e., achievement emotions; Vogl et al., 2019). The present article aimed to replicate the findings from Vogl et al. (2019) exploring within-person interrelations, origins, and outcomes of the epistemic emotions surprise, curiosity, and confusion, and the achievement emotions pride and shame, as well as to analyze their robustness and generalizability across two different study settings (online; Study 1, n = 169 vs. lab; Study 2, n = 79). In addition, the previous findings by Vogl et al. (2019, Study 3) and the present two new studies were meta-analytically integrated to consolidate evidence on origins and outcomes of epistemic emotions. The results of the two new studies largely replicated the findings by Vogl et al. (2019). Combined with the meta-analytic results, the findings confirm distinct patterns of antecedents for epistemic vs. achievement emotions: Pride and shame were more strongly associated with the correctness of a person’s answer (i.e., accuracy), whereas surprise, curiosity, and confusion were more strongly related to incorrect responses a person was confident in (i.e., high-confidence errors) producing cognitive incongruity. Furthermore, in contrast to achievement emotions, epistemic emotions had positive effects on the exploration of knowledge. Implications for research and practice are discussed

Feature-Based Digital Modulation Recognition Using Compressive Sampling

Compressive sensing theory can be applied to reconstruct the signal with far fewer measurements than what is usually considered necessary, while in many scenarios, such as spectrum detection and modulation recognition, we only expect to acquire useful characteristics rather than the original signals, where selecting the feature with sparsity becomes the main challenge. With the aim of digital modulation recognition, the paper mainly constructs two features which can be recovered directly from compressive samples. The two features are the spectrum of received data and its nonlinear transformation and the compositional feature of multiple high-order moments of the received data; both of them have desired sparsity required for reconstruction from subsamples. Recognition of multiple frequency shift keying, multiple phase shift keying, and multiple quadrature amplitude modulation are considered in our paper and implemented in a unified procedure. Simulation shows that the two identification features can work effectively in the digital modulation recognition, even at a relatively low signal-to-noise ratio

Low Complexity Cyclic Feature Recovery Based on Compressed Sampling

To extract statistic features of communication signal from compressive samples, such as cyclostationary property, full-scale signal reconstruction is not actually necessary or somehow expensive. However, direct reconstruction of cyclic feature may not be practical due to the relative high processing complexity. In this paper, we propose a new cyclic feature recovery approach based on the reconstruction of autocorrelation sequence from sub-Nyquist samples, which can reduce the computation complexity and memory consumption significantly, while the recovery performance remains well in the same compressive ratio. Through theoretical analyses and simulations, we conducted to show and verify our statements and conclusions

A Density-ratio Framework for Statistical Data Processing

In statistical pattern recognition, it is important to avoid density estimation since density estimation is often more difficult than pattern recognition itself. Following this idea—known as Vapnik’s principle, a statistical data processing framework that employs the ratio of two probability density functions has been developed recently and is gathering a lot of attention in the machine learning and data mining communities. The purpose of this paper is to introduce to the computer vision community recent advances in density ratio estimation methods and their usage in various statistical data processing tasks such as non-stationarity adaptation, outlier detection, feature selection, and independent component analysis