Identification of Hotspot Mutations in the N Gene of SARS-CoV-2 in Russian Clinical Samples That May Affect the Detection by Reverse Transcription-PCR

Sensitive and reliable diagnostic test systems based on real-time PCR are of great importance in the fight against the ongoing SARS-CoV-2 pandemic. The genetic variability of the SARS-CoV-2 virus leads to the accumulation of mutations, some of which may affect the sensitivity of modern PCR assays. The aim of this study was to search in Russian clinical samples for new mutations in SARS-CoV-2 gene N that can affect the detection by RT-PCR. In this study, the polymorphisms in the regions of the target gene N causing failed or poor detection of the target N in the RT-PCR assay on 12 selected samples were detected. Sequencing the entire N and E genes in these samples along with other 195 samples that were positive for both target regions was performed. Here, we identified a number of nonsynonymous mutations and one novel deletion in the N gene that affected the ability to detect a target in the N gene as well a few mutations in the E gene of SARS-CoV-2 that did not affect detection. Sequencing revealed that majority of the mutations in the N gene were located in the variable region between positions 193 and 235 aa, inside and nearby the phosphorylated serine-rich region of the protein N. This study highlights the importance of the further characterization of the genetic variability and evolution of gene N, the most common target for detecting SARS-CoV-2. The use of at least two targets for detecting SARS-CoV-2, including one for the E gene, will be necessary for reliable diagnostics.

CN-waterfall: a deep convolutional neural network for multimodal physiological affect detection

Affective computing solutions, in the literature, mainly rely on machine learning methods designed to accurately detect human affective states. Nevertheless, many of the proposed methods are based on handcrafted features, requiring sufficient expert knowledge in the realm of signal processing. With the advent of deep learning methods, attention has turned toward reduced feature engineering and more end-to-end machine learning. However, most of the proposed models rely on late fusion in a multimodal context. Meanwhile, addressing interrelations between modalities for intermediate-level data representation has been largely neglected. In this paper, we propose a novel deep convolutional neural network, called CN-Waterfall, consisting of two modules: Base and General. While the Base module focuses on the low-level representation of data from each single modality, the General module provides further information, indicating relations between modalities in the intermediate- and high-level data representations. The latter module has been designed based on theoretically grounded concepts in the Explainable AI (XAI) domain, consisting of four different fusions. These fusions are mainly tailored to correlation- and non-correlation-based modalities. To validate our model, we conduct an exhaustive experiment on WESAD and MAHNOB-HCI, two publicly and academically available datasets in the context of multimodal affective computing. We demonstrate that our proposed model significantly improves the performance of physiological-based multimodal affect detection.

Diversifying Emotional Experience by Layered Interfaces in Affective Interactive Installations

This paper aims to improve users’ experience in affective interactive installations through the diversification of interfaces. With logically organized hierarchical experience, diverse interfaces with emotion data as inputs enhance users’ emotional interaction to be more natural and immersive. By using facial affect detection technology, an installation with diverse input interfaces was tested with an organic formal setting. Mechanical flowers and support structure based on the organic form were deployed as its physical output for a multitude of sensorial dimensions. With actions of the mechanical flowers, such as blooming, closing, rotating, glowing and blinking, a layered experiential sequence was created and the atmosphere of the installation was evaluated to be more engaging. In this way, the layered complexity of information was transferred to users’ immersive emotional experience. We believe that the practices in this work can contribute to deeper emotional engagement with users and add new layers of emotional interactivity.

Shedding New Light on Mountainous Forest Growth: A Cross-Scale Evaluation of the Effects of Topographic Illumination Correction on 25 Years of Forest Cover Change across Nepal

Accurate remote sensing of mountainous forest cover change is important for myriad social and ecological reasons, but is challenged by topographic and illumination conditions that can affect detection of forests. Several topographic illumination correction (TIC) approaches have been developed to mitigate these effects, but existing research has focused mostly on whether TIC improves forest cover classification accuracy and has usually found only marginal gains. However, the beneficial effects of TIC may go well beyond accuracy since TIC promises to improve detection of low illuminated forest cover and thereby normalize measurements of the amount, geographic distribution, and rate of forest cover change regardless of illumination. To assess the effects of TIC on the extent and geographic distribution of forest cover change, in addition to classification accuracy, we mapped forest cover across mountainous Nepal using a 25-year (1992–2016) gap-filled Landsat time series in two ways—with and without TIC (i.e., nonTIC)—and classified annual forest cover using a Random Forest classifier. We found that TIC modestly increased classifier accuracy and produced more conservative estimates of net forest cover change across Nepal (−5.2% from 1992–2016) TIC. TIC also resulted in a more even distribution of forest cover gain across Nepal with 3–5% more net gain and 4–6% more regenerated forest in the least illuminated regions. These results show that TIC helped to normalize forest cover change across varying illumination conditions with particular benefits for detecting mountainous forest cover gain. We encourage the use of TIC for satellite remote sensing detection of long-term mountainous forest cover change.

An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.