RESUMEN

Generations of researchers observed a mismatch between headphone and loudspeaker presentation: the sound pressure level at the eardrum generated by a headphone has to be about 6 dB higher compared to the level created by a loudspeaker that elicits the same loudness. While it has been shown that this effect vanishes if the same waveforms are generated at the eardrum in a blind comparison, the origin of the mismatch is still unclear. We present new data on the issue that systematically characterize this mismatch under variation of the stimulus frequency, presentation room, and binaural parameters of the headphone presentation. Subjects adjusted the playback level of a headphone presentation to equal loudness as loudspeaker presentation, and the levels at the eardrum were determined through appropriate transfer function measurements. Identical experiments were conducted at Oldenburg and Aachen with 40 normal-hearing subjects including 14 that passed through both sites. Our data verify a mismatch between loudspeaker and binaural headphone presentation, especially at low frequencies. This mismatch depends on the room acoustics, and on the interaural coherence in both presentation modes. It vanishes for high frequencies and broadband signals if individual differences in the sound transfer to the eardrums are accounted for. Moreover, small acoustic and non-acoustic differences in an anechoic reference environment (Oldenburg vs. Aachen) exert a large effect on the recorded loudness mismatch, whereas not such a large effect of the respective room is observed across moderately reverberant rooms at both sites. Hence, the non-conclusive findings from the literature appear to be related to the experienced disparity between headphone and loudspeaker presentation, where even small differences in (anechoic) room acoustics significantly change the response behavior of the subjects. Moreover, individual factors like loudness summation appear to be only loosely connected to the observed mismatch, i.e., no direct prediction is possible from individual binaural loudness summation to the observed mismatch. These findings - even though not completely explainable by the yet limited amount of parameter variations performed in this study - have consequences for the comparability of experiments using loudspeakers with conditions employing headphones or other ear-level hearing devices.

RESUMEN

In order to assess safety and efficacy of small molecule drugs as well as agrochemicals, it is key to understanding the nature of protein-ligand interaction on an atomistic level. Prothioconazole (PTZ), although commonly considered to be an azole-like inhibitor of sterol 14-α demethylase (CYP51), differs from classical azoles with respect to how it binds its target. The available evidence is only indirect, as crystallographic elucidation of CYP51 complexed with PTZ have not yet been successful. We derive a binding mode hypothesis for PTZ binding its target, compare to DPZ, a triazole-type metabolite of PTZ, and set our findings into context of its biochemistry and spectroscopy. Quantum Theory of Atoms in Molecules (QTAIM) analysis of computed DFT electron densities is used to qualitatively understand the topology of binding, revealing significant differences of how R- and S-enantiomers are binding and, in particular, how the thiozolinthione head of PTZ binds to heme compared to DPZ's triazole head. The difference of binding enthalpy is calculated at coupled cluster (DLPNO-CCSD(T)) level of theory, and we find that DPZ binds stronger to CYP51 than PTZ by more than ΔH ~ 11 kcal/mol.

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Automatic identification of frontal (posteroanterior/anteroposterior) vs. lateral chest radiographs is an important preprocessing step in computer-assisted diagnosis, content-based image retrieval, as well as picture archiving and communication systems. Here, a new approach is presented. After the radiographs are reduced substantially in size, several distance measures are applied for nearest-neighbor classification. Leaving-one-out experiments were performed based on 1,867 radiographs from clinical routine. For comparison to existing approaches, subsets of 430 and 5 training images are also considered. The overall best correctness of 99.7% is obtained for feature images of 32 x 32 pixels, the tangent distance, and a 5-nearest-neighbor classification scheme. Applying the normalized cross correlation function, correctness yields still 99.6% and 99.3% for feature images of 32 x 32 and 8 x 8 pixel, respectively. Remaining errors are caused by image altering pathologies, metal artifacts, or other interferences with routine conditions. The proposed algorithm outperforms existing but sophisticated approaches and is easily implemented at the same time.

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