Biology subjects

Saringer, S.

Publications and source records attributed to Saringer, S..

3 recordsLinked to original sources

bioRxiv2025

Decoding the sex of faces using the power, phase, and the Fourier spectrum of the time-frequency representation

The EEG activity related to face processing has been studied extensively with machine learning techniques and most of these studies apply preprocessed data without further data transformation. Here we analyzed the data of two experiments and explored the potential in decoding the time-frequency representation in face processing, thus extracting not only temporal but also the frequency distribution. In the two experiments, participants were presented with faces and through changing the presentation number per face, we manipulated their familiarity. Then we determined the time window of facial sex related cortical activities with frequently employed decoding techniques on the preprocessed data. Subsequently, we performed Fourier transformation on the data and decoded time-frequency spectrum of the amplitude, the phase and also the complex Fourier spectrum. This analysis revealed a 500 ms long time window at the beginning of the stimulus presentation in the 2 to 17 Hz frequency range, which showed above-chance decoding accuracies in the case of more familiar faces. Less familiar faces showed similar, albeit more restricted time-frequency windows. By comparing the two experiments we also observed 350 ms long window in the low frequencies of 4-10 Hz, where familiar faces exhibited greater decoding accuracies. This method expanded on the generally observed time-window and complemented it with a frequency distribution related to facial sex processing. The current study demonstrates that machine learning applications can be applied to higher dimensional data, like the time-frequency representation in cognitive studies.

neuroscience↗

bioRxiv2025

Recognizability affects the processing of facial sex information

The different aspects of a face, like sex or identity, can be decoded from the cortical patterns related to its processing. Many studies have investigated this phenomenon with similar outcomes. These studies usually utilize a low number of facial identities and high repetition numbers, which affects the recognizability and familiarity of a face, thus altering the processing. We propose that this commonly employed paradigm influences cortical patterns associated with features seemingly unrelated to identity, such as the sex of a face. In the first experiment, we recreated the findings of previous studies using a few identities and a high presentation number for decoding facial sex. In the second experiment, the identity-presentation ratio was switched. This change resulted in a narrower time window where facial sex related cortical patterns were detected. Decoding accuracy was also diminished, yielding lower values and suggesting a reduced signal-to-noise ratio in the cortex. After expanding the sample size with balanced gender representation, we identified shared cortical patterns related to face-sex processing both within the population and across gender-based subpopulations. These results provide further evidence that familiarity impacts face processing and suggest that previous findings on sex information decoding were likely influenced by the experimental paradigms employed.

neuroscience↗

bioRxiv2024

The Neosartorya (Aspergillus) fischeri antifungal protein NFAP2 has low potential to trigger resistance development in Candida albicans in vitro

Due to the increase in the number of drug-resistant Candida albicans strains, new antifungal compounds with limited potential for development of resistance are urgently needed. NFAP2, an antifungal protein (AFP) secreted by Neosartorya (Aspergillus) fischeri, is a promising candidate. We investigated the ability of C. albicans to develop resistance to NFAP2 in a microevolution experiment compared with generic fluconazole (FLC). C. albicans adapted to only 1 x minimum inhibitory concentration (MIC) of NFAP2 compared with 32 x MIC of FLC. Genome analysis revealed non-silent mutations in only two genes in NFAP2-resistant strains and in several genes in FLC-resistant strains. Resistance development to NFAP2 did not influence cell morphology. The susceptibility of NFAP2-resistant strains did not change to FLC, amphotericin B, micafungin, terbinafine. These strains did not show altered susceptibility to AFPs from Penicillium chrysogenum, except one which had less susceptibility to P. chrysogenum antifungal protein B. FLC-resistant strains had decreased susceptibility to terbinafine and NFAP2, but not to other drugs and AFPs from P. chrysogenum. NFAP2- and FLC-resistant strains showed decreased and increased NFAP2 binding and uptake, respectively. The development of resistance to NFAP2 decreased tolerance to cell wall, heat, and UV stresses. The development of FLC resistance increased tolerance to cell wall stress and decreased tolerance to heat and UV stresses. Resistance to NFAP2 did not have significant metabolic fitness cost and could not increase virulence, compared with resistance to FLC. ImportanceDue to the increasing number of (multi)drug-resistant strains, only a few effective antifungal drugs are available to treat infections caused by opportunistic Candida species. Therefore, the incidence of hard-to-treat candidiasis has increased dramatically in the past decade, and the demand to identify antifungal compounds with minimal potential to trigger resistance is substantial. The features of NFAP2 make it a promising candidate for the topical treatment of Candida infection. Data on the development of resistance to AFPs in C. albicans are lacking. In this study, we provide evidence that NFAP2 has low potential to trigger resistance in C. albicans in vitro and the developed resistance mechanisms to NFAP2 are not associated with severe phenotypic changes compared with development of resistance to generic FLC. These results suggest the slow emergence of NFAP2-resistant Candida strains and that NFAP2 can reliably be used long-term in the clinic.

microbiology↗