Transfer of an Esterase-Resistant Receptor Analog to the Surface of Influenza C Virions Results in Reduced Infectivity Due to Aggregate Formation

AbstractA synthetic sialic acid,N-acetyl-9-thioacetamidoneuraminic acid (9-ThioAcNeu5Ac), is recognized by influenza C virus as a receptor determinant but—in contrast to the natural receptor determinant,N-acetyl-9-O-acetylneuraminic acid—is resistant to inactivation by the viral acetylesterase. This sialic acid analog was used to analyze the importance of the receptor-destroying enzyme of influenza C virus in keeping the viral surface free of receptor determinants. Enzymatic transfer of 9-ThioAcNeu5Ac to the surface of influenza C virions resulted in the loss of the hemagglutinating activity. The ability to agglutinate erythrocytes was restored when the synthetic sialic acid was released from the viral surface by neuraminidase treatment. Infectivity of influenza C virus containing surface-bound 9-ThioAcNeu5Ac was reduced about 20-fold. Sedimentation analysis as well as electron microscopy indicated that virions resialylated with the esterase-resistant sialic acid analog formed virus aggregates. These results indicate that the receptor-destroying enzyme of influenza C virus is required to avoid the presence of receptor determinants on the virion surface and thus to prevent aggregate formation and a reduction of the infectious titer

Innovative mid-infrared detector concepts

Gas sensing is a key technology with applications in various industrial, medical and environmental areas. Optical detection mechanisms allow for a highly selective, contactless and fast detection. For this purpose, rotational-vibrational absorption bands within the mid infrared (MIR) spectral region are exploited and probed with appropriate light sources. During the past years, the development of novel laser concepts such as interband cascade lasers (ICLs) and quantum cascade lasers (QCLs) has driven a continuous optimization of MIR laser sources. On the other hand side, there has been relatively little progress on detectors in this wavelength range. Here, we study two novel and promising GaSb-based detector concepts: Interband cascade detectors (ICD) and resonant tunneling diode (RTD) photodetectors. ICDs are a promising approach towards highly sensitive room temperature detection of MIR radiation. They make use of the cascading scheme that is enabled by the broken gap alignment of the two binaries GaSb and InAs. The interband transition in GaSb/InAs-superlattices (SL) allows for normal incidence detection. The cut-off wavelength, which determines the low energy detection limit, can be engineered via the SL period. RTD photodetectors act as low noise and high speed amplifiers of small optically generated electrical signals. In contrast to avalanche photodiodes, where the gain originates from multiplication due to impact ionization, in RTD photodetectors a large tunneling current is modulated via Coulomb interaction by the presence of photogenerated minority charge carriers. For both detector concepts, first devices operational at room temperature have been realized.Publisher PD

Room temperature operation of GaSb-based resonant tunneling diodes by prewell injection

The authors are grateful for financial support by the state of Bavaria, the German Ministry of Education and Research (BMBF) within the national project HIRT (FKZ 13XP5003B).We present room temperature resonant tunneling of GaSb/AlAsSb double barrier resonant tunneling diodes with pseudomorphically grown prewell emitter structures comprising the ternary compound semiconductors GaInSb and GaAsSb. At room temperature, resonant tunneling is absent for diode structures without prewell emitters. The incorporation of Ga0.84In0.16Sb and GaAs0.05Sb0.95 prewell emitters leads to room temperature resonant tunneling with peak‐to‐valley current ratios of 1.45 and 1.36 , respectively. The room temperature operation is attributed to the enhanced Γ ‐L‐valley energy separation and consequently depopulation of L‐valley states in the conduction band of the ternary compound emitter prewell with respect to bulk GaSb.PostprintPeer reviewe

GaSb/AlAsSb resonant tunneling diodes with GaAsSb emitter prewells

The authors are grateful for financial support by the state of Bavaria, and the German Ministry of Education and Research (BMBF) within the national project HIRT (FKZ 13XP5003B).We investigate the electronic transport properties of GaSb/AlAsSb double barrier resonant tunneling diodes with pseudomorphically grown ternary GaAsxSb1-x emitter prewells over a broad temperature range. At room temperature, resonant tunneling is observed and the peak to valley current ratio (PVCR) is enhanced with increasing As mole fraction from 1.88 (GaAs0.07Sb0.93 prewell), to 2.08 (GaAs0.09Sb0.91 prewell) up to 2.36 (GaAs0.11Sb0.89 prewell). The rise in PVCR is attributed to an enhanced carrier density at the Γ-valley within the emitter prewell. On the contrary at cryogenic temperatures, increasing the As mole fractions reduces the PVCR. At a temperature of T = 4.2 K, reference samples without incorporation of an emitter prewell containing As show PVCRs up to 20.4. We attribute the reduced PVCR to a degraded crystal quality of the resonant tunneling structure caused by As incorporation and subsequently an enhanced defect scattering at the interfaces.PostprintPeer reviewe

What's in a face: Automatic facial coding of untraines study participants compared to standardized inventories

Automatic facial coding (AFC) is a novel research tool to automatically analyze emotional facial expressions. AFC can classify emotional expressions with high accuracy in standardized picture inventories of intensively posed and prototypical expressions. However, classification of facial expressions of untrained study participants is more error prone. This discrepancy requires a direct comparison between these two sources of facial expressions. To this end, 70 untrained participants were asked to express joy, anger, surprise, sadness, disgust, and fear in a typical laboratory setting. Recorded videos were scored with a well-established AFC software (FaceReader, Noldus Information Technology). These were compared with AFC measures of standardized pictures from 70 trained actors (i.e., standardized inventories). We report the probability estimates of specific emotion categories and, in addition, Action Unit (AU) profiles for each emotion. Based on this, we used a novel machine learning approach to determine the relevant AUs for each emotion, separately for both datasets. First, misclassification was more frequent for some emotions of untrained participants. Second, AU intensities were generally lower in pictures of untrained participants compared to standardized pictures for all emotions. Third, although profiles of relevant AU overlapped substantially across the two data sets, there were also substantial differences in their AU profiles. This research provides evidence that the application of AFC is not limited to standardized facial expression inventories but can also be used to code facial expressions of untrained participants in a typical laboratory setting

Sharpening emitter localization in front of a tuned mirror

Single-molecule localization microscopy (SMLM) aims for maximized precision and a high signal-to-noise ratio1. Both features can be provided by placing the emitter in front of a metal-dielectric nanocoating that acts as a tunedmirror2–4. Here, we demonstrate that a higher photon yield at a lower background on biocompatible metal-dielectric nanocoatings substantially improves SMLM performance and increases the localization precision by up to a factor oftwo. The resolution improvement relies solely on easy-to-fabricate nanocoatings on standard glass coverslips and is spectrally and spatially tunable by the layer design and wavelength, as experimentally demonstrated for dual-color SMLM in cells.Publisher PDFPeer reviewe

Automatic facial expression recognition in standardized and non-standardized emotional expressions

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions.publishe