A GAN Approach for Anomaly Detection in Spacecraft Telemetries

In spacecraft health management a large number of time series is acquired and used for on-board units surveillance and for historical data analysis. The early detection of abnormal behaviors in telemetry data can prevent failures in the spacecraft equipment. In this paper we present an advanced monitoring system that was carried out in partnership with Thales Alenia Space Italia S.p.A, a leading industry in the field of spacecraft manufacturing. In particular, we developed an anomaly detection algorithm based on Generative Adversarial Networks, that thanks to their ability to model arbitrary distributions in high dimensional spaces, allow to capture complex anomalies avoiding the burden of hand crafted feature extraction. We applied this method to detect anomalies in telemetry data collected from a simulator of a Low Earth Orbit satellite. One of the strengths of the proposed approach is that it does not require any previous knowledge on the signal. This is particular useful in the context of anomaly detection where we do not have a model of the anomaly. Hence the only assumption we made is that an anomaly is a pattern that lives in a lower probability region of the data space

A Developmental Perspective on Community Service in Adolescence

A substantial number of U.S. adolescents currently participate in community service and there is increased national interest in service programs. This article assesses the assumption of developmental benefits to service participants by critically reviewing 44 empirical studies. It offers a theoretical framework for understanding the findings by connecting them to identity development and delineating three pertinent concepts: agency, social relatedness, and moral-political awareness. These concepts are applied to studies that investigate: ( 1) the characteristics and motivations of participants, ( 2) the effects of service, and ( 3) the process of service. The findings support the conclusion that service activities which provide opportunities for intense experiences and social interactions are often associated with prosocial development. The findings also point to the need for more studies focused on particular service programs and on relationships between service providers and those served

Activity-based protein profiling reveals off-target proteins of the FAAH inhibitor BIA 10-2474

A recent phase 1 trial of the fatty acid amide hydrolase (FAAH) inhibitor BIA 10-2474 led to the death of one volunteer and produced mild-to-severe neurological symptoms in four others. Although the cause of the clinical neurotoxicity is unknown, it has been postulated, given the clinical safety profile of other tested FAAH inhibitors, that off-target activities of BIA 10-2474 may have played a role. Here we use activity-based proteomicmethods to determine the protein interaction landscape of BIA 10-2474 in human cells and tissues. This analysis revealed that the drug inhibits several lipases that are not targeted by PF04457845, a highly selective and clinically tested FAAH inhibitor. BIA 10-2474, but not PF04457845, produced substantial alterations in lipid networks in human cortical neurons, suggesting that promiscuous lipase inhibitors have the potential to cause metabolic dysregulation in the nervous system

Remote sensing imagery analysis of the lacustrine system of Iberá wetland

The little-known lacustrine ecosystem of Ibera wetland was examined using satellite-measured reflectance to determine several limnological variables (Secchi depth, Sd; nephelometric turbidity, Tn; dissolved organic matter, DOM). The spatial and temporal analysis has shown the main relationships between the local forcing factors and the ecological state of the aquatic ecosystems. The spatial geomorphologic characteristics of the wetland were strongly related to the spatial distribution of the limnological variables. According to this, the macrosystem has been classified into three large regions that enclose lakes that share diverse characteristics. (i) Northwestern region encloses few open water areas, mainly of small size. (ii) Rounded large lakes (probably originated as oxbow floodplain lakes) are characteristics of the Northeastern region. (iii) Elongated large lakes (probably originated as levee floodplain lakes) are characteristics of the Southern region. These lakes showed the highest Sd and DOM values and the lowest Tn. The different water drainage of the regions seems to be the main cause of the limnological differences of the wetland. The described spatial classification agrees also with other ecological characteristics such as the degree of macrophytes development or composition of the fish community. Different disturbed local areas, overlapping the regional classification, were also identified in the borders of the wetland. These areas have been related with the presence of anthropogenic activities, revealing the sensitivity of these shallow lakes. The whole system showed a relatively homogenous seasonal pattern of water transparency related with solar irradiance cycle. The variability of active floodplains lakes of the Upper Paraná was found to be more strongly influenced by the rainfall, presenting a more unpredictable behaviour

New Concepts Of Automated Anomaly Detection In Space Operations Through ML-Based Techniques

Spacecraft health management is a key component to ensure the safety and mission operation life of a satellite complex system. The health monitoring task is pursued exploiting telemetry data, collected using various sensor reading fromonboard devices, that can be analyzed to retrieve and early detect anomalies which can lead to critical failures. The traditional monitoring methods, based on simple threshold checks, are now facing with lots of difficulties the increased complexity of the spacecraft, requiring updated and intelligent systems based on data-driven approaches. In this paper we propose different ML-based methods that contribute to the generation of an intelligent anomaly detector, that can face up the numerous telemetry data. Finally we focus on how to optimize and implement t he developed models on constrained hardware, representative of spacecraft processors

AIG1 and ADTRP are Atypical Integral Membrane Hydrolases that Degrade Bioactive FAHFAs

Enzyme classes may contain outlier members that share mechanistic, but not sequence or structural relatedness with more common representatives. The functional annotation of such exceptional proteins can be challenging. Here, we use activity-based profiling to discover that the poorly characterized multipass transmembrane proteins AIG1 and ADTRP are atypical hydrolytic enzymes that depend on conserved threonine and histidine residues for catalysis. Both AIG1 and ADTRP hydrolyze bioactive fatty-acid esters of hydroxy-fatty acids (FAHFAs), but not other major classes of lipids. We discover multiple cell-active, covalent inhibitors of AIG1 and show that these agents block FAHFA hydrolysis in mammalian cells. These results indicate that AIG1 and ADTRP are founding members of an evolutionarily conserved class of transmembrane threonine hydrolases involved in bioactive lipid metabolism. More generally, our findings demonstrate how chemical proteomics can excavate potential cases of convergent/parallel protein evolution that defy conventional sequence- and structure-based predictions

Fuzzy Logic Techniques for Blotch Feature Evaluation in Dermoscopy Images

Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2