Automatic prediction of emotion promises to revolutionise human-computer
interaction. Recent trends involve fusion of multiple data modalities - audio,
visual, and physiological - to classify emotional state. However, in practice,
collection of physiological data `in the wild' is currently limited to
heartbeat time series of the kind generated by affordable wearable heart
monitors. Furthermore, real-world applications of emotion prediction often
require some measure of uncertainty over model output, in order to inform
downstream decision-making. We present here an end-to-end deep learning model
for classifying emotional valence from unimodal heartbeat time series. We
further propose a Bayesian framework for modelling uncertainty over these
valence predictions, and describe a probabilistic procedure for choosing to
accept or reject model output according to the intended application. We
benchmarked our framework against two established datasets and achieved peak
classification accuracy of 90%. These results lay the foundation for
applications of affective computing in real-world domains such as healthcare,
where a high premium is placed on non-invasive collection of data, and
Hybrid approach catches lightPlant chloroplasts enclose two major photosynthetic processes: light reactions, which generate the energy carriers adenosine triphosphate and reduced nicotinamide dinucleotide phosphate (NADPH), and dark reactions, which use these molecules to fix carbon dioxide and build biomass. Miller et al. appropriated natural components, thylakoid membranes from spinach, for the light reactions and showed that these could be coupled to a synthetic enzymatic cycle that fixes carbon dioxide within water-in-oil droplets. The composition of the droplets could be tuned and optimized and the metabolic activity monitored in real time by NADPH fluorescence (see the Perspective by Gaut and Adamala). These chloroplast-mimicking droplets bring together natural and synthetic components in a small space and are amenable to further functionalization to perform complex biosynthetic tasks.Science, this issue p. 649; see also p. 587Nature integrates complex biosynthetic and energy-converting tasks within compartments such as chloroplasts and mitochondria. Chloroplasts convert light into chemical energy, driving carbon dioxide fixation. We used microfluidics to develop a chloroplast mimic by encapsulating and operating photosynthetic membranes in cell-sized droplets. These droplets can be energized by light to power enzymes or enzyme cascades and analyzed for their catalytic properties in multiplex and real time. We demonstrate how these microdroplets can be programmed and controlled by adjusting internal compositions and by using light as an external trigger. We showcase the capability of our platform by integrating the crotonyl–coenzyme A (CoA)/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, a synthetic network for carbon dioxide conversion, to create an artificial photosynthetic system that interfaces the natural and the synthetic biological worlds.Natural photosynthetic components power a synthetic CO2 fixation pathway in picoliter droplets.Natural photosynthetic components power a synthetic CO2 fixation pathway in picoliter droplets.
Background: The SARS-CoV-2 pandemic has swept the world and poses a significant global threat to lives and livelihoods, with over 16 million confirmed cases and at least 650 000 deaths from COVID-19 in the first 7 months of the pandemic. Developing tools to measure seroprevalence and understand protective immunity to SARS-CoV-2 is a priority. We aimed to develop a serological assay using plant-derived recombinant viral proteins, which represent important tools in less-resourced settings. Methods: We established an indirect enzyme-linked immunosorbent assay (ELISA) using the S1 and receptor-binding domain (RBD) portions of the spike protein from SARS-CoV-2, expressed in Nicotiana benthamiana. We measured antibody responses in sera from South African patients (n=77) who had tested positive by PCR for SARS-CoV-2. Samples were taken a median of six weeks after the diagnosis, and the majority of participants had mild and moderate COVID-19 disease. In addition, we tested the reactivity of pre-pandemic plasma (n=58) and compared the performance of our in-house ELISA with a commercial assay. We also determined whether our assay could detect SARS-CoV-2-specific IgG and IgA in saliva. Results: We demonstrate that SARS-CoV-2-specific immunoglobulins are readily detectable using recombinant plant-derived viral proteins, in patients who tested positive for SARS-CoV-2 by PCR. Reactivity to S1 and RBD was detected in 51 (66%) and 48 (62%) of participants, respectively. Notably, we detected 100% of samples identified as having S1-specific antibodies by a validated, high sensitivity commercial ELISA, and OD values were strongly and significantly correlated between the two assays. For the pre-pandemic plasma, 1/58 (1.7%) of samples were positive, indicating a high specificity for SARS-CoV-2 in our ELISA. SARS-CoV-2-specific IgG correlated significantly with IgA and IgM responses. Endpoint titers of S1- and RBD-specific immunoglobulins ranged from 1:50 to 1:3200. S1-specific IgG and IgA were found in saliva samples from convalescent volunteers. Conclusions: We demonstrate that recombinant SARS-CoV-2 proteins produced in plants enable robust detection of SARS-CoV-2 humoral responses. This assay can be used for seroepidemiological studies and to measure the strength and durability of antibody responses to SARS-CoV-2 in infected patients in our setting.
PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE (PDAT) is an enzyme that catalyzes the transfer of a fatty acyl moiety from the sn-2 position of a phospholipid to the sn-3-position of sn-1,2-diacylglyerol, thus forming triacylglycerol and a lysophospholipid. Although the importance of PDAT in triacylglycerol biosynthesis has been illustrated in some previous studies, the evolutionary relationship of plant PDATs has not been studied in detail. In this study, we investigated the evolutionary relationship of the PDAT gene family across the green plants using a comparative phylogenetic framework. We found that the PDAT candidate genes are present in all examined green plants, including algae, lowland plants (a moss and a lycophyte), monocots, and eudicots. Phylogenetic analysis revealed the evolutionary division of the PDAT gene family into seven major clades. The separation is supported by the conservation and variation in the gene structure, protein properties, motif patterns, and/or selection constraints. We further demonstrated that there is a eudicot-wide PDAT gene expansion, which appears to have been mainly caused by the eudicot-shared ancient gene duplication and subsequent species-specific segmental duplications. In addition, selection pressure analyses showed that different selection constraints have acted on three core eudicot clades, which might enable paleoduplicated PDAT paralogs to either become nonfunctionalized or develop divergent expression patterns during evolution. Overall, our study provides important insights into the evolution of the plant PDAT gene family and explores the evolutionary mechanism underlying the functional diversification among the core eudicot PDAT paralogs.