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Glucosinolates are plant secondary metabolites found in cruciferous vegetables (Brassicaceae) that are valued for their potential health benefits. Frequently consumed representatives of these vegetables, for example, are white or red cabbage, which are typically boiled before consumption. Recently, 3-alk(en)yl-4-hydroxythiazolidine-2-thiones were identified as a class of thermal glucosinolate degradation products that are formed during the boiling of cabbage. Since these newly discovered compounds are frequently consumed, this raises questions about their potential uptake and their possible bioactive functions. Therefore, 3-allyl-4-hydroxythiazolidine-2-thione (allyl HTT) and 4-hydroxy-3-(4-(methylsulfinyl) butyl)thiazolidine-2-thione (4-MSOB HTT) as degradation products of the respective glucosinolates sinigrin and glucoraphanin were investigated. After consumption of boiled red cabbage broth, recoveries of consumed amounts of the degradation products in urine collected for 24 h were 18 ± 5% for allyl HTT and 21 ± 4% for 4-MSOB HTT (mean ± SD, n = 3). To investigate the stability of the degradation products during uptake and to elucidate the uptake mechanism, both an in vitro stomach and an in vitro intestinal model were applied. The results indicate that the uptake of allyl HTT and 4-MSOB HTT occurs by passive diffusion. Both compounds show no acute cell toxicity, no antioxidant potential, and no change in NAD(P)H dehydrogenase quinone 1 (NQO1) activity up to 100 μM. However, inhibition of glycogen synthase kinases-3 (GSK-3) in the range of 20% for allyl HTT for the isoform GSK-3β and 29% for 4-MSOB HTT for the isoform GSK-3α at a concentration of 100 μM was found. Neither health-promoting nor toxic effects of 3-alk(en)yl-4-hydroxythiazolidine-2-thiones were found in the four tested assays carried out in this study, which contrasts with the properties of other glucosinolate degradation products, such as isothiocyanates.

The Sentinel-2 (S2) Toolbox permits for the automated retrieval of leaf area index (LAI). LAI assimilation into crop simulation models could aid to improve the prediction accuracy for biomass at field level. We investigated if the combined effects of assimilation date and corresponding growth stage plus observational frequency have an impact on the crop model-based simulation of water stress and biomass production. We simulated winter wheat growth in nine fields in Germany over two years. S2 LAI estimations for each field were categorized into three phases, depending on the development stage of the crop at acquisition date (tillering, stem elongation, booting to flowering). LAI was assimilated in every possible combinational setup using the ensemble Kalman filter (EnKF). We evaluated the performance of the simulations based on the comparison of measured and simulated aboveground biomass at harvest. The results showed that the effects on water stress remained largely limited, because it mostly occurred after we stopped LAI assimilation. With regard to aboveground biomass, we found that the assimilation of only one LAI estimate from either the tillering or the booting to flowering stage resulted in simulated biomass values similar or closer to measured values than in those where more than one LAI estimate from the stem elongation phase were assimilated. LAI assimilation after the tillering phase might therefore be not necessarily required, as it may not lead to the desired improvement effect.

In this paper, we present a multimodal dataset for affective computing research acquired in a human-computer interaction (HCI) setting. An experimental mobile and interactive scenario was designed and implemented based on a gamified generic paradigm for the induction of dialog-based HCI relevant emotional and cognitive load states. It consists of six experimental sequences, inducing Interest, Overload, Normal, Easy, Underload, and Frustration. Each sequence is followed by subjective feedbacks to validate the induction, a respiration baseline to level off the physiological reactions, and a summary of results. Further, prior to the experiment, three questionnaires related to emotion regulation (ERQ), emotional control (TEIQue-SF), and personality traits (TIPI) were collected from each subject to evaluate the stability of the induction paradigm. Based on this HCI scenario, the University of Ulm Multimodal Affective Corpus (uulmMAC), consisting of two homogenous samples of 60 participants and 100 recording sessions was generated. We recorded 16 sensor modalities including 4 × video, 3 × audio, and 7 × biophysiological, depth, and pose streams. Further, additional labels and annotations were also collected. After recording, all data were post-processed and checked for technical and signal quality, resulting in the final uulmMAC dataset of 57 subjects and 95 recording sessions. The evaluation of the reported subjective feedbacks shows significant differences between the sequences, well consistent with the induced states, and the analysis of the questionnaires shows stable results. In summary, our uulmMAC database is a valuable contribution for the field of affective computing and multimodal data analysis: Acquired in a mobile interactive scenario close to real HCI, it consists of a large number of subjects and allows transtemporal investigations. Validated via subjective feedbacks and checked for quality issues, it can be used for affective computing and machine learning applications.

The assimilation of LAI measurements, repeatedly taken at sub-field level, into dynamic crop simulation models could provide valuable information for precision farming applications. Commonly used updating methods such as the Ensemble Kalman Filter (EnKF) rely on an ensemble of model runs to update a limited set of state variables every time a new observation becomes available. This threatens the model’s integrity, as not the entire table of model states is updated. In this study, we present the Weighted Mean (WM) approach that relies on a model ensemble that runs from simulation start to simulation end without compromising the consistency and integrity of the state variables. We measured LAI on 14 winter wheat fields across France, Germany and the Netherlands and assimilated these observations into the LINTUL5 crop model using the EnKF and WM approaches, where the ensembles were created using one set of crop component (CC) ensemble generation variables and one set of soil and crop component (SCC) ensemble generation variables. The model predictions for total aboveground biomass and grain yield at harvest were evaluated against measurements collected in the fields. Our findings showed that (a) the performance of the WM approach was very similar to the EnKF approach when SCC variables were used for the ensemble generation, but outperformed the EnKF approach when only CC variables were considered, (b) the difference in site-specific performance largely depended on the choice of the set of ensemble generation variables, with SCC outperforming CC with regard to both biomass and grain yield, and (c) both EnKF and WM improved accuracy of biomass and yield estimates over standard model runs or the ensemble mean. We conclude that the WM data assimilation approach is equally efficient to the improvement of model accuracy, compared to the updating methods, but it has the advantage that it does not compromise the integrity and consistency of the state variables.

The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery.

BackgroundSocial interactive functions such as facial emotion recognition and smell identification have been shown to differ between women and men. However, little is known about how these differences are mirrored in patients with schizophrenia and how these abilities interact with each other and with other clinical variables in patients vs. healthy controls.MethodsStandardized instruments were used to assess facial emotion recognition [Facially Expressed Emotion Labelling (FEEL)] and smell identification [University of Pennsylvania Smell Identification Test (UPSIT)] in 51 patients with schizophrenia spectrum disorders and 79 healthy controls; furthermore, working memory functions and clinical variables were assessed.ResultsIn both the univariate and the multivariate results, illness showed a significant influence on UPSIT and FEEL. The inclusion of age and working memory in the MANOVA resulted in a differential effect with sex and working memory as remaining significant factors. Duration of illness was correlated with both emotion recognition and smell identification in men only, whereas immediate general psychopathology and negative symptoms were associated with emotion recognition only in women.ConclusionBeing affected by schizophrenia spectrum disorder impacts one’s ability to correctly recognize facial affects and identify odors. Converging evidence suggests a link between the investigated basic and social cognitive abilities in patients with schizophrenia spectrum disorders with a strong contribution of working memory and differential effects of modulators in women vs. men.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by

Affective computing aims at the detection of users' mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods. Although this is of high priority, other subject-specific variables should not be neglected. In our study, we analyzed the effect of gender, age, personality and gender roles on the extracted psychobiological features (derived from skin conductance level, facial electromyography and heart rate variability) as well as the influence on the classification results. In an experimental human-computer interaction, five different affective states with picture material from the International Affective Picture System and ULM pictures were induced. A total of 127 subjects participated in the study. Among all potentially influencing variables (gender has been reported to be influential), age was the only variable that correlated significantly with psychobiological responses. In summary, the conducted classification processes resulted in 20% classification accuracy differences according to age and gender, especially when comparing the neutral condition with four other affective states. We suggest taking age and gender specifically into account for future studies in affective computing, as these may lead to an improvement of emotion recognition accuracy.

BACKGROUND:Disordered interpersonal communication can be a serious problem in schizophrenia. Recent advances in computer-based measures allow reliable and objective quantification of nonverbal behavior. Research using these novel measures has shown that objective amounts of body and head movement in patients with schizophrenia during social interactions are closely related to the symptom profiles of these patients. In addition to and above mere amounts of movement, the degree of synchrony, or imitation, between patients and normal interactants may be indicative of core deficits underlying various problems in domains related to interpersonal communication, such as symptoms, social competence, and social functioning. METHODS:Nonverbal synchrony was assessed objectively using Motion Energy Analysis (MEA) in 378 brief, videotaped role-play scenes involving 27 stabilized outpatients diagnosed with paranoid-type schizophrenia. RESULTS:Low nonverbal synchrony was indicative of symptoms, low social competence, impaired social functioning, and low self-evaluation of competence. These relationships remained largely significant when correcting for the amounts of patients' movement. When patients showed reduced imitation of their interactants' movements, negative symptoms were likely to be prominent. Conversely, positive symptoms were more prominent in patients when their interaction partners' imitation of their movements was reduced. CONCLUSIONS:Nonverbal synchrony can be an objective and sensitive indicator of the severity of patients' problems. Furthermore, quantitative analysis of nonverbal synchrony may provide novel insights into specific relationships between symptoms, cognition, and core communicative problems in schizophrenia.

The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021-2050 compared to 1971-2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078-2087. The projected phenophases advanced by 5.5 d K(-1), showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day.