The PEDtracker: An Automatic Staging Approach for Drosophila melanogaster Larvae

The post-embryonal development of arthropod species, including crustaceans and insects, is characterized by ecdysis or molting. This process defines growth stages and is controlled by a conserved neuroendocrine system. Each molting event is divided in several critical time points, such as pre-molt, molt, and post-molt, and leaves the animals in a temporarily highly vulnerable state while their cuticle is re-hardening. The molting events occur in an immediate ecdysis sequence within a specific time window during the development. Each sub-stage takes only a short amount of time, which is generally in the order of minutes. To find these relatively short behavioral events, one needs to follow the entire post-embryonal development over several days. As the manual detection of the ecdysis sequence is time consuming and error prone, we designed a monitoring system to facilitate the continuous observation of the post-embryonal development of the fruit fly Drosophila melanogaster. Under constant environmental conditions we are able to observe the life cycle from the embryonic state to the adult, which takes about 10 days in this species. Specific processing algorithms developed and implemented in Fiji and R allow us to determine unique behavioral events on an individual level—including egg hatching, ecdysis and pupation. In addition, we measured growth rates and activity patterns for individual larvae. Our newly created RPackage PEDtracker can predict critical developmental events and thus offers the possibility to perform automated screens that identify changes in various aspects of larval development. In conclusion, the PEDtracker system presented in this study represents the basis for automated real-time staging and analysis not only for the arthropod development

Semi-automatic staging area for high-quality structured data extraction from scientific literature

In this study, we propose a staging area for ingesting new superconductors' experimental data in SuperCon that is machine-collected from scientific articles. Our objective is to enhance the efficiency of updating SuperCon while maintaining or enhancing the data quality. We present a semi-automatic staging area driven by a workflow combining automatic and manual processes on the extracted database. An anomaly detection automatic process aims to pre-screen the collected data. Users can then manually correct any errors through a user interface tailored to simplify the data verification on the original PDF documents. Additionally, when a record is corrected, its raw data is collected and utilised to improve machine learning models as training data. Evaluation experiments demonstrate that our staging area significantly improves curation quality. We compare the interface with the traditional manual approach of reading PDF documents and recording information in an Excel document. Using the interface boosts the precision and recall by 6% and 50%, respectively to an average increase of 40% in F1-score.Comment: 5 tables, 9 figures, 31 page

Model-free Control and Automatic Staging of Variable Refrigerant Flow System with Multiple Outdoor Units

For efficient operation of a variable refrigerant flow (VRF) air conditioning system with multiple outdoor units (ODUs), we propose a model-free control strategy based on extremum seeking control along with automatic staging control logic. The proposed strategy is evaluated with a representative VRF system consisting of 12 indoor units (IDUs) and three ODUs. The IDU zone temperature is regulated by EEV opening, and the compressor pressure is regulated by compressor speed. To optimize load sharing among multiple ODUs in operation, a set of bypass valves (BPVs) are added to the suction side of the compressors to manipulate refrigerant flow distribution among different compressors as needed. A penalty-function based multivariable extremum seeking control (ESC) method is used for real-time optimization of system operation. The performance index as the ESC feedback is the total power of the compressors, the ODU fans and the IDU fans, augmented with penalties for securing minimum superheat at the suction side of compressors. The manipulated inputs include the compressor suction pressure setpoint, the openings of BPVs at the suction side of the compressors, and a uniform setpoint of fan speed for all ODUs. As for the ESC feedback, the compressor power is normalized by its capacity. A set of control strategies for staging on/off particular ODUs is developed based on the compressor speed of the operating ODUs. Under increasing load, if the operating compressor(s) speed exceeds the higher limit of operation speed range (80% of rated speed), an additional ODU turned on to meet the load demand. Under decreasing load, it is desirable to turn off the least efficient ODU in a model-free fashion. In this study, an ESC based ODU staging-off strategy is proposed, for which the compressor shaft power normalized by the rated capacity is adopted as the ESC input. In addition to the compressor pressure setpoints and ODU fan speeds, the manipulated inputs of ESC also include the openings of suction-side BPVs in order to optimize load sharing among the multiple ODUs. With online optimization of ODU load sharing based on the normalized compressor power, the ESC can drive less efficient compressor(s) to operate at lower speed/capacity. If the compressor speed of an ODU falls below the preset lower limit of operational speed range (e.g. 20% of the rated speed) for long enough time, this ODU will be turned off. A dynamic simulation model of the multi-ODU VRF system is developed with Dymola and TIL Library. Simulation studies have been performed to evaluate the proposed ESC strategy for energy efficient operation during constant load patterns and the control logic for staging on and off ODU during load increase and decrease. The total power searched by the ESC is shown to be close to that obtained by a genetic algorithm based global optimization procedure in Dymola. Also, ESC is shown to be able to turn off least efficient ODU during load decrease without model knowledge. The load-sharing BPV at the compressor suction-side demonstrates bearable pressure loss except for the scenarios of large split ratio

New applications of late fusion methods for EEG signal processing

[EN] Decision fusion consists in the combination of the outputs of multiple classifiers into a common decision that is more precise or stable. In most cases, however, only classical fusion techniques are considered. This work compares the performance of several state-of-the-art fusion methods on new applications of automatic stage classification of several neuropsychological tests. The tests were staged into three classes: stimulus display, retention interval, and subject response. The considered late fusion methods were: alpha integration; copulas; Dempster-Shafer combination; independent component analysis mixture models; and behavior knowledge space. Late fusion was able to improve the performance for the task, with alpha integration yielding the most stable result.This work was supported by Generalitat Valenciana under grant PROMETEO/2019/109 and Spanish Administration and European Union grant TEC2017-84743-P.Safont, G.; Salazar Afanador, A.; Vergara Domínguez, L. (2019). New applications of late fusion methods for EEG signal processing. IEEE. 617-621. https://doi.org/10.1109/CSCI49370.2019.00116S61762

The GRB Library: Grid Computing with Globus in C

none5In this paper we describe a library layered on top of basic Globus services. The library provides high level services, can be used to develop both web-based and desktop grid applications, it is relatively small and very easy to use. We show its usefulness in the context of a web-based Grid Resource Broker developed using the library as a building block, and in the context of a metacomputing experiment demonstrated at the SuperComputing 2000 conference.Aloisio G.; Cafaro M.; Blasi E.; De Paolis L.; Epicoco I.Aloisio, Giovanni; Cafaro, Massimo; Blasi, E.; DE PAOLIS, Lucio Tommaso; Epicoco, Ital

Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent. Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved by 10% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging. This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation. This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.Comment: 20 pages, 3 figure

Multiclass Alpha Integration of Scores from Multiple Classifiers

[EN] Alpha integration methods have been used for integrating stochastic models and fusion in the context of detection (binary classification). Our work proposes separated score integration (SSI), a new method based on alpha integration to perform soft fusion of scores in multiclass classification problems, one of the most common problems in automatic classification. Theoretical derivation is presented to optimize the parameters of this method to achieve the least mean squared error (LMSE) or the mínimum probability of error (MPE). The proposed alpha integrationmethod was tested on several sets of simulated and real data. The first set of experiments used synthetic data to replicate a problem of automatic detection and classification of three types of ultrasonic pulses buried in noise (four-class classification). The second set of experiments analyzed two databases (one publicly available and one private) of real polysomnographic records from subjects with sleep disorders. These records were automatically staged in wake, rapid eye movement (REM) sleep, and non-REM sleep (three-class classification). Finally, the third set of experiments was performed on a publicly available database of single-channel real electroencephalographic data that included epileptic patients and healthy controls in five conditions (five-class classification). In all cases, alpha integration performed better than the considered single classifiers and classical fusion techniques.This work was supported by the Spanish Administration and European Union under grant TEC2017-84743-P and Generalitat Valenciana under grant PROMETEO II/2014/032.Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L. (2019). Multiclass Alpha Integration of Scores from Multiple Classifiers. Neural Computation. 31(4):806-825. https://doi.org/10.1162/neco_a_01169S80682531