The composition of liquid atmospheric pressure matrix-assisted laser desorption/ionization matrices and its effect on ionization in mass spectrometry

New liquid atmospheric pressure (AP) matrix-assisted laser desorption/ionization (MALDI) matrices that produce predominantly multiply charged ions have been developed and evaluated with respect to their performance for peptide and protein analysis by mass spectrometry (MS). Both the chromophore and the viscous support liquid in these matrices were optimized for highest MS signal intensity, S/N values and maximum charge state. The best performance in both protein and peptide analysis was achieved employing light diols as matrix support liquids (e.g. ethylene glycol and propylene glycol). Investigating the influence of the chromophore, it was found that 2,5-dihydroxybenzoic acid resulted in a higher analyte ion signal intensity for the analysis of small peptides; however larger molecules (>17kDa) were undetectable. For larger molecules, a sample preparation based on α-cyano-4-hydroxycinnammic acid as the chromophore was developed and multiply protonated analytes with charge states of more than 50 were detected. Thus, for the first time it was possible to detect with MALDI MS proteins as large as ~80kDa with a high number of charge states, i.e. m/z values below 2000. Systematic investigations of various matrix support liquids have revealed a linear dependency between laser threshold energy and surface tension of the liquid MALDI sample

The role of dispersal constraints in the assembly of salt-marsh communities

Esther Chang onderzocht de invloed van zaadverspreidingsfactoren op de soortensamen­stelling binnen plantengemeenschappen op de kwelder, in het bijzonder op Schiermonnik­oog. Zij concludeert onder andere dat het grote vermogen van stormen om zaden te kunnen verbreiden het vóórkomen van soorten op meer groeiplaatsen lijkt te versterken, maar tevens het aantal individuen van soorten te beperken door het wegspoelen van veel zaden uit bronpopulaties

Predicting fatigue and psychophysiological test performance from speech for safety-critical environments

Automatic systems for estimating operator fatigue have application in safety-critical environments. A system which could estimate level of fatigue from speech would have application in domains where operators engage in regular verbal communication as part of their duties. Previous studies on the prediction of fatigue from speech have been limited because of their reliance on subjective ratings and because they lack comparison to other methods for assessing fatigue. In this paper, we present an analysis of voice recordings and psychophysiological test scores collected from seven aerospace personnel during a training task in which they remained awake for 60 h. We show that voice features and test scores are affected by both the total time spent awake and the time position within each subject’s circadian cycle. However, we show that time spent awake and time-of-day information are poor predictors of the test results, while voice features can give good predictions of the psychophysiological test scores and sleep latency. Mean absolute errors of prediction are possible within about 17.5% for sleep latency and 5–12% for test scores. We discuss the implications for the use of voice as a means to monitor the effects of fatigue on cognitive performance in practical applications

Production and analysis of multiply charged negative ions by liquid atmospheric pressure matrix-assisted laser desorption/ionization mass spectrometry

RATIONALE: Liquid AP-MALDI has been shown to enable the production of ESI-like multiply charged analyte ions with little sample consumption and long-lasting, robust ion yield for sensitive analysis by mass spectrometry. Previous reports have focused on positive ion production. Here, we report an initial optimisation of liquid AP-MALDI for ESI-like negative ion production and its application to the analysis of peptides/proteins, DNA and lipids. METHODS: The instrumentation employed for this study is identical to that of earlier liquid AP-MALDI MS studies for positive analyte ion production with a simple non-commercial AP ion source that is attached to a Waters Synapt G2-Si mass spectrometer and incorporates a heated ion transfer tube. The preparation of liquid MALDI matrices is similar to positive ion mode analysis but has been adjusted for negative ion mode by changing the chromophore to 3-aminoquinoline and 9-aminoacridine for further improvements. RESULTS: For DNA, liquid AP-MALDI MS analysis benefited from switching to 9-aminoacridine-based MALDI samples and the negative ion mode, increasing the number of charges by up to a factor of 2 and the analyte ion signal intensities by more than ten-fold compared to the positive ion mode. The limit of detection was recorded at around 10fmol for ATGCAT. For lipids, negative ion mode analysis provided a fully orthogonal set of detected lipids. CONCLUSIONS: Negative ion mode is a sensitive alternative to positive ion mode in liquid AP-MALDI MS analysis. In particular, the analysis of lipids and DNA benefited from the complementarity of the detected lipid species and the vastly greater DNA ion signal intensities in negative ion mode

AUTOMATIC LIP-READING OF HEARING IMPAIRED PEOPLE

Inability to use speech interfaces greatly limits the deaf and hearing impaired people in the possibility of human-machine interaction. To solve this problem and to increase the accuracy and reliability of the automatic Russian sign language recognition system it is proposed to use lip-reading in addition to hand gestures recognition. Deaf and hearing impaired people use sign language as the main way of communication in everyday life. Sign language is a structured form of hand gestures and lips movements involving visual motions and signs, which is used as a communication system. Since sign language includes not only hand gestures, but also lip movements that mimic vocalized pronunciation, it is of interest to investigate how accurately such a visual speech can be recognized by a lip-reading system, especially considering the fact that the visual speech of hearing impaired people is often characterized with hyper-articulation, which should potentially facilitate its recognition. For this purpose, thesaurus of Russian sign language (TheRusLan) collected in SPIIRAS in 2018–19 was used. The database consists of color optical FullHD video recordings of 13 native Russian sign language signers (11 females and 2 males) from “Pavlovsk boarding school for the hearing impaired”. Each of the signers demonstrated 164 phrases for 5 times. This work covers the initial stages of this research, including data collection, data labeling, region-of-interest detection and methods for informative features extraction. The results of this study can later be used to create assistive technologies for deaf or hearing impaired people

Аналитический обзор аудиовизуальных систем для определения средств индивидуальной защиты на лице человека

Начиная с 2019 года все страны мира столкнулись со стремительным распространением пандемии, вызванной коронавирусной инфекцией COVID-19, борьба с которой продолжается мировым сообществом и по настоящее время. Несмотря на очевидную эффективность средств индивидуальной защиты органов дыхания от заражения коронавирусной инфекцией, многие люди пренебрегают использованием защитных масок для лица в общественных местах. Поэтому для контроля и своевременного выявления нарушителей общественных правил здравоохранения необходимо применять современные информационные технологии, которые будут детектировать защитные маски на лицах людей по видео- и аудиоинформации. В статье приведен аналитический обзор существующих и разрабатываемых интеллектуальных информационных технологий бимодального анализа голосовых и лицевых характеристик человека в маске. Существует много исследований на тему обнаружения масок по видеоизображениям, также в открытом доступе можно найти значительное количество корпусов, содержащих изображения лиц как без масок, так и в масках, полученных различными способами. Исследований и разработок, направленных на детектирование средств индивидуальной защиты органов дыхания по акустическим характеристикам речи человека пока достаточно мало, так как это направление начало развиваться только в период пандемии, вызванной коронавирусной инфекцией COVID-19. Существующие системы позволяют предотвратить распространение коронавирусной инфекции с помощью распознавания наличия/отсутствия масок на лице, также данные системы помогают в дистанционном диагностировании COVID-19 с помощью обнаружения первых симптомов вирусной инфекции по акустическим характеристикам. Однако, на сегодняшний день существует ряд нерешенных проблем в области автоматического диагностирования симптомов COVID-19 и наличия/отсутствия масок на лицах людей. В первую очередь это низкая точность обнаружения масок и коронавирусной инфекции, что не позволяет осуществлять автоматическую диагностику без присутствия экспертов (медицинского персонала). Многие системы не способны работать в режиме реального времени, из-за чего невозможно производить контроль и мониторинг ношения защитных масок в общественных местах. Также большинство существующих систем невозможно встроить в смартфон, чтобы пользователи могли в любом месте произвести диагностирование наличия коронавирусной инфекции. Еще одной основной проблемой является сбор данных пациентов, зараженных COVID-19, так как многие люди не согласны распространять конфиденциальную информацию

AUTOMATIC DETECTION AND RECOGNITION OF 3D MANUAL GESTURES FOR HUMAN-MACHINE INTERACTION

In this paper, we propose an approach to detect and recognize 3D one-handed gestures for human-machine interaction. The logical structure of the modules of the system for recording a gestural database is described. The logical structure of the database of 3D gestures is presented. Examples of frames showing gestures in the format of Full High Definition, in the map depth mode and in the infrared illustrated. Models of a deep convolutional network for detecting faces and hand shapes are described. The results of automatic detection of the area with the face and the shape of the hand are given. Identified the distinctive features of the gesture at a certain point in time. The process of recognizing 3D one-handed gestures is described. Due to its versatility, this method can be used in tasks of biometrics, computer vision, machine learning, automatic systems of face recognition, sign languages

Protein identification using a nanoUHPLC-AP-MALDI MS/MS workflow with CID of multiply charged proteolytic peptides

Liquid AP-MALDI can produce predominantly multiply charged ESI-like ions and stable durable analyte ion yields with samples allowing good shot-to-shot reproducibility and exhibiting self-healing properties during laser irradiation. In this study, LC-MALDI MS/MS workflows that utilize multiply charged ions are reported for the first time and compared with standard LC-ESI MS/MS for bottom-up proteomic analysis. The proposed method is compatible with trifluoroacetic acid as an LC ion pairing reagent and allows multiple MS/MS acquisitions of the LC-separated samples without substantial sample consumption. In addition, the method facilitates the storage of fully spotted MALDI target plates for months without significant sample degradation

Neural network-based method for visual recognition of driver’s voice commands using attention mechanism

Visual speech recognition or automated lip-reading systems actively apply to speech-to-text translation. Video data proves to be useful in multimodal speech recognition systems, particularly when using acoustic data is difficult or not available at all. The main purpose of this study is to improve driver command recognition by analyzing visual information to reduce touch interaction with various vehicle systems (multimedia and navigation systems, phone calls, etc.) while driving. We propose a method of automated lip-reading the driver’s speech while driving based on a deep neural network of 3DResNet18 architecture. Using neural network architecture with bi-directional LSTM model and attention mechanism allows achieving higher recognition accuracy with a slight decrease in performance. Two different variants of neural network architectures for visual speech recognition are proposed and investigated. When using the first neural network architecture, the result of voice recognition of the driver was 77.68 %, which was lower by 5.78 % than when using the second one the accuracy of which was 83.46 %. Performance of the system which is determined by a real-time indicator RTF in the case of the first neural network architecture is equal to 0.076, and the second — RTF is 0.183 which is more than two times higher. The proposed method was tested on the data of multimodal corpus RUSAVIC recorded in the car. Results of the study can be used in systems of audio-visual speech recognition which is recommended in high noise conditions, for example, when driving a vehicle. In addition, the analysis performed allows us to choose the optimal neural network model of visual speech recognition for subsequent incorporation into the assistive system based on a mobile device