Developing a Laser Induced Liquid Beam Ion Desorption Spectral Database as Reference for Spaceborne Mass Spectrometers

Spaceborne impact ionization mass spectrometers, such as the Cosmic Dust Analyzer on board the past Cassini spacecraft or the SUrface Dust Analyzer being built for NASA's upcoming Europa Clipper mission, are of crucial importance for the exploration of icy moons in the Solar System, such as Saturn's moon Enceladus or Jupiter's moon Europa. For the interpretation of data produced by these instruments, analogue experiments on Earth are essential. To date, thousands of laboratory mass spectra have been recorded with an analogue experiment for impact ionization mass spectrometers. Simulation of mass spectra of ice grains in space is achieved by a Laser Induced Liquid Beam Ion Desorption (LILBID) approach. The desorbed cations or anions are analyzed in a time-of-flight mass spectrometer. The amount of unstructured raw data is increasingly challenging to sort, process, interpret and compare with data from space. Thus far this has been achieved manually for individual mass spectra because no database containing the recorded reference spectra was available. Here we describe the development of a comprehensive, extendable database containing cation and anion mass spectra from the laboratory LILBID facility. The database is based on a Relational Database Management System with a web server interface and enables filtering of the laboratory data using a wide range of parameters. The mass spectra can be compared not only with data from past and future space missions but also mass spectral data generated by other, terrestrial, techniques. The validated and approved subset of the database is available for general public (https://lilbid-db.planet.fu-berlin.de)

Developing a Laser Induced Liquid Beam Ion Desorption Spectral Database as Reference for Spaceborne Mass Spectrometers

Spaceborne impact ionization mass spectrometers, such as the Cosmic Dust Analyzer on board the past Cassini spacecraft or the SUrface Dust Analyzer being built for NASA's upcoming Europa Clipper mission, are of crucial importance for the exploration of icy moons in the Solar System, such as Saturn's moon Enceladus or Jupiter's moon Europa. For the interpretation of data produced by these instruments, analogue experiments on Earth are essential. To date, thousands of laboratory mass spectra have been recorded with an analogue experiment for impact ionization mass spectrometers. Simulation of mass spectra of ice grains in space is achieved by a Laser Induced Liquid Beam Ion Desorption (LILBID) approach. The desorbed cations or anions are analyzed in a time-of-flight mass spectrometer. The amount of unstructured raw data is increasingly challenging to sort, process, interpret and compare with data from space. Thus far this has been achieved manually for individual mass spectra because no database containing the recorded reference spectra was available. Here we describe the development of a comprehensive, extendable database containing cation and anion mass spectra from the laboratory LILBID facility. The database is based on a Relational Database Management System with a web server interface and enables filtering of the laboratory data using a wide range of parameters. The mass spectra can be compared not only with data from past and future space missions but also mass spectral data generated by other, terrestrial, techniques. The validated and approved subset of the database is available for general public (https://lilbid-db.planet.fu-berlin.de)

Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping

Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application values in the maritime industry. However, using such big data from the Automatic Identification System (AIS) for accurate VTF prediction remains challenging. Deep training networks can learn valuable features from extensive historical data. This paper proposes a new learning-based prediction network, improved Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with similarity grouping, including three views. To effectively enable the training network to capture the temporal and periodic (i.e. a spatial attribute) change characteristics of VTF, the CNN and LSTM are employed to compose spatial and temporal views, respectively. Hence, the original one-dimensional data is transformed into a matrix (hour of the day ✕ day) to adapt the input of the proposed methodology. In practical applications, VTF of multiple adjacent target regions need to be predicted simultaneously, and the changes of VTF in different areas may influence each other. To explore their hidden relationships, the similarity grouping view aims to find the target area that exhibits the most similarity with the VTF change trend of the current research area. Furthermore, similar information is combined with the features generated from the other two views to obtain the prediction results. In summary, the new advantage lies in mining the spatiotemporal attributes of data and fusing the similarity information of adjacent regions. Comparative experiments with eleven other methods on realistic VTF datasets show that the proposed method demonstrates superior prediction accuracy and stability performance

The role of the lateral hypothalamic neural outputs in motivated behaviour

Comprendre comment le cerveau traite l’information est l’une des questions les plus intrigantes auxquelles les neurosciences modernes sont confrontées. L’étude actuelle vise à caractériser comment l’hypothalamus latéral (HL) traite l’information vers des cibles cérébrales en aval afin de guider des réponses comportementales appropriées. L’HL est une zone du cerveau qui régule des comportements vitaux tels que les fonctions autonomes et endocriniennes, l’équilibre homéostatique, la régulation du métabolisme et les cycles veille-sommeil. De plus, des études récentes soulignent son importance dans le traitement de l’aversion et de la récompense. L’HL envoie des projections neurales à de nombreux noyaux cérébraux connus pour traiter des signaux qui jouent un rôle important pour guider et orchestrer des réponses comportementales appropriées. Des expériences visant a déterminer les connections afferentes et efférentesde l’HL ont démontré que trois noyaux cérébraux importants reçoivent des projections importantes de l’HL. Il s’agit de l’habenula latéral (HbL), de l’aire tegmentale ventrale (ATV) et du noyau raphe dorsal (NRD). L’HbL est le principal centre de déception du cerveau : son activité augmente spécifiquement lorsqu’un animal est soumis à des stimuli aversifs ou en l’absence de récompenses attendues, jouant un rôle important dans la signalisation d’une erreur de prédiction de punition. L’ATV dopaminergique est le principal centre de récompense du cerveau. Il joue un rôle important dans l’encodage de la valeur des récompenses, l’apprentissage du renforcement et la motivation. Le NRD est le principal centre de sérotonine jouant un rôle important dans le traitement des émotions et les réponses adaptatives. Pour examiner spécifiquement la contribution des sorties neuronales de l’HL chez les souris en mouvement libre, nous avons utilisé une technique d’imagerie avancée du calcium – système de photométrie à fibres. La photométrie à fibres est une approche puissante qui combine des indicateurs de calcium codés génétiquement et des fibres optiques multimodes pour monitorer l’activité neuronale chez les animaux en mouvement libre, ce qui est essentiel pour comprendre comment des groupes spécifiques de neurones sont impliqués dans le contrôle ou la réponse à une action ou à un stimulus. Dans le premier chapitre, je présente un protocole qui a été développé pour une détection fiable des signaux de calcium à l’aide d’un système de photométrie multifibre. Le protocole détaille les composantes d’un système de photométrie multifibres, la méthode pour accéder aux structures profondes du cerveau pour délivrer et collecter la lumière, et une méthode pour prendre en compte les artefacts de mouvement avant et pendant les enregistrements. En outre,je présente un algorithme de traitement des signaux enregistrés qui tient compte des sources communes d’artefacts qui sont inévitables pendant les enregistrements. Dans le deuxième chapitre, je présente les résultats de l’étude du rôle fonctionnel de trois sorties neurales de l’HL vers le NRD, l’ATV et l’HbL. En utilisant le protocole décrit dans le premier chapitre, l’activité dans les voies HL→NRD, HL→ ATV et HL→HbL a été simultanément enregistrée lors de réponses comportamentales dans des contextes d’aversion et de récompense. Nous avons constaté que l’activité à ces trois sorties neurales de l’HL augmentait avec des stimuli et des signaux prédictifs de stimuli aversifs. L’activité neuronale augmente également lors des réponses comportementales motivées spontanées et diminue lors de l’immobilité comportementale. L’activation optogénétique indépendante des terminaisons axonales de l’HL au niveau de l’HbL, l’ATV ou le NRD était suffisante pour augmenter la mobilité, mais a eu des effets différents dans d’autres tests comportementaux. Dans l’ensemble, nous proposons que l’HL envoie des signaux complémentaires aux cibles en aval pour traiter les informations engagées pour promouvoir des comportements motivés. En annexe, je présente un ensemble d’analyse de données python qui a été développé pour traiter tous les enregistrements de photométrie à fibre optique présentés dans l’étude actuelle. Cet ensemble permet de combiner, de stocker et d’analyser les enregistrements de plusieurs souris, essais et différentes expériences avec diverses mesures, événements comportementaux et stimuli de manière standardisée.Understanding how brain processes information is the one of the most intriguing questions that modern neuroscience faces. The current study aims to characterize how the lateral hypothalamus (LH) processes information to downstream brain targets to guide proper behavioral responses. The LH is a brain area that regulates vital behaviors such as autonomic and endocrine functions, homeostatic balance, regulation of metabolism, and sleep-wake cycles. Moreover, recent studies point out its importance in aversive and appetitive processing. The LH sends neural projections to many brain nuclei known to process signals that play important roles to guide and orchestrate proper behavior responses. Tracing experiments demonstrated that three important brain nuclei receive significant inputs from the LH, the lateral habenula (LHb), the ventral tegmental area (VTA), and the dorsal raphe nucleus (DRN). The LHb is the main disappointment center of the brain: its activity specifically increases when an animalis presented an aversive stimuli or in the absence of expected rewards, playing an important role in signaling punishment prediction error. The dopaminergic VTA is the main brain reward center. It plays important roles in reward-value encoding, reinforcement learning and motivation. The DRN is the main serotonin center playing and important role in emotion processing and adaptive responses. To specifically examine the contribution of LH neural outputs in freely moving mice, we used an advanced calcium imaging technique – fiber photometry system. Fiber photometry is a powerful approach that combines genetically encoded calcium indicators and multimode optical fibers to monitor neuronal activity in freely moving animals, which is critical to understand how specific groups of neurons play in directing or responding to an action or a stimulus. In the first chapter, I present a protocol that was developed for reliable detection of calcium signal using a camera-based multi-fiber photometry system. The protocol details the components of a multi-fiber photometry system, a method to access deep brain structures to deliverand collect light, and a method to account for motion artifacts before and during recordings. Additionally, I present an algorithm for processing of recorded signals that accounts common sources of artefacts that are inevitable during recordings. In the second chapter, I present results of the investigation of the functional role of three LH outputs to the DRN, VTA, and LHb. Using the protocol described in the first chapter, activity in the LH→DRN, LH→ VTA and LH→LHb pathways were simultaneously recorded during motivated responses in aversive and appetitive contexts. We found that these three LH neural outputs increased activity with aversive stimuli and cues predicting them. The neural activity also increased at onsets of spontaneous motivated behavior responses and decreased duringbehavioral immobility. Independent optogenetic activation of axon terminals in LHb, VTA,or DRN was sufficient to increase mobility, but had different effects in other behavioural tests. Altogether, we propose that LH sends complementary signals to the downstream targets to process information engaged in motivated behaviors. In the annex, I present a data analysis python package that was developed to process all fiber photometry recordings presented in the current study. The package allow to combine, store, and analyze recordings from multiple mice, trials, and different experiments with various measurements, behavioural events, and stimuli in a standardized way