Miracle-dz/UniFAR-FTUs huggingface dataset:
Tomato Dynamin Related Protein 2A Associates With LeEIX2 and Enhances PRR Mediated Defense by Modulating Receptor Trafficking

The endocytic trafficking pathway is employed by the plant to regulate immune responses, and is often targeted by pathogen effectors to promote virulence. The model system of the tomato receptor-like protein (RLP) LeEIX2 and its ligand, the elicitor EIX, employs endocytosis to transmit receptor-mediated signals, with some of the signaling events occurring directly from endosomal compartments. Here, to explore the trafficking mechanism of LeEIX2-mediated immune signaling, we used a proteomic approach to identify LeEIX2-associating proteins. We report the identification of SlDRP2A, a dynamin related protein, as an associating partner for LeEIX2. SlDRP2A localizes at the plasma membrane. Overexpression of SlDRP2A increases the sub-population of LeEIX2 in VHAa1 endosomes, and enhances LeEIX2- and FLS2-mediated defense. The effect of SlDRP2A on induction of plant immunity highlights the importance of endomembrane components and endocytosis in signal propagation during plant immune responses.
Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and Fingerprint Map

This paper presents analysis of object location accuracy of a mobile device on the basis of the iBeacon technology. The research starts with radio signal strength indicator analysis along the corridor in order to create a path loss model for iBeacon. Two cases are taken into account: line of sight and non-line of sight for model creation. For both cases two tests: Chi-square, Shapiro-Wilk have been performed. It has also been checked if the HCI (Host Controller Interface) is a source with a memory. Acquired data have been filtered with different type of filters, e.g. median, moving average and then compared. Next, the authors evaluated the indoor positioning trilateration algorithms with the use of created model for exemplary hall. The RSSI map (radiomap) was created and the logarithm propagation model was designed. The logarithmic model estimated distance with average error 1.09m for 1 – 9m and 1.75m for 1-20m and after trilateration, the positions with average error 2.45m was achieved. A statistical analysis for acquiring data led to the final conclusion which enhanced knowledge about positioning based on the popular iBeacon technology.
Improving Models of Care for Diabetes in Pregnancy: Experience of Current Practice in Far North Queensland, Australia

Aims: To map health practitioners' experiences and describe knowledge regarding screening and management of Diabetes in Pregnancy (DIP) in Far North Queensland, Australia. Methods: Mixed methods including a cross-sectional survey (101 respondents) and 8 focus groups with 61 health practitioners. All participants provided clinical care for women with DIP. Results: A wide range of healthcare professionals participated; 96% worked with Indigenous women, and 63% were from regional or remote work settings. Universal screening for gestational diabetes at 24–28 weeks gestation was reported as routine with 87% using a 75 g Oral Glucose Tolerance Test. Early screening for DIP was reported by 61% although there was large variation in screening methods and who should be screened <24 weeks. Health practitioners were confident providing lifestyle advice (88%), dietary, and blood glucose monitoring education (67%, 81%) but only 50% were confident giving insulin education. Electronic medical records were used by 80% but 55% also used paper records. Dissatisfaction with information from hospitals was reported by 40%. In the focus groups improving communication and information technology systems were identified as key areas. Other barriers described were difficulties in care coordination and access for remote women. Conclusions: Communication, information technology systems, coordination of care, and education for health professionals are key areas that will be addressed by a complex health systems intervention being undertaken by the DIP Partnership in North Queensland.
The Design and Analysis of Message Authentication and Authenticated Encryption Schemes

Awareness of the significance of securing communication and data has increased dramatically due to the countless examples showing that systems with little or no protection can and will be attacked. Lack of adoption, or improper use of strong cryptographic techniques could be attributed to the fact that cryptographic solutions are not efficient enough, impose impractical constraints on their use, or their analysis does not align with how they are used in practice. This thesis studies message authentication and authenticated encryption algorithms, which are symmetric-key solutions to providing data integrity and confidentiality. A formal study is performed of how security degrades when authenticated encryption algorithms are implemented in environments where theoretical assumptions might not be met, the so-called nonce abuse and release of unverified plaintext settings. Designs for authenticated encryption schemes are analyzed, including our designs COPA and COBRA, while keeping efficiency constraints in mind. Additionally, limits imposed by constrained environments, which commonly appear in applications for the internet of things, are considered, and discussed in the context of message authentication algorithms. A new design is introduced, LightMAC, which enables keys to be used longer than typically possible, and an existing construction, PMAC, is analyzed in depth for its potential to provide more security than what was commonly thought.
iFEED: Interactive Feature Extraction for Engineering Design

One of the major challenges faced by the decision maker in the design of complex engineering systems is information overload. Effective exploration of the design space requires the generation and analysis of numerous design alternatives, each represented by multiple design variables and corresponding objective values. When the size and dimensionality of the data exceeds a certain level, a designer may become overwhelmed and no longer be able to perceive or understand the underlying dynamics of the design problem at hand, which can result in premature or poor design selection. There exist various visual analytic tools designed to relieve the information overload, such as BrickViz, Cloud Visualization, ATSV, and LIVE, to name a few. However, most of them do not explicitly support the discovery of key knowledge about the mapping between the design space and the objective space, such as the set of high-level design features that drive most of the trade-offs between objectives. In this paper, we introduce a new interactive method, called iFEED, that supports the designer in the process of high-level knowledge discovery in a large, multiobjective design space. The primary goal of the method is to iteratively mine the design space dataset for driving features, i.e., combinations of design variables that appear to consistently drive designs towards specific target regions in the design space set by the user. This is implemented using a data mining algorithm that mines interesting patterns in the form of association rules. The extracted patterns are then used to build a surrogate classification model based on a decision tree that predicts whether a design is likely to be located in the target region of the tradespace or not based on the driving features only. The classification performance and the compactness of this tree are used as figures of merit to evaluate the set of driving features, and are provided to the user to facilitate iteration. Initially, the extracted patterns that constitute test nodes of the classification tree are likely to be primitive (simple combinations of design variables). The proposed method enables the user to build and test more compact and higher-level driving features through an interactive process. Higher level driving features will result in more compact classification trees while improving classification performance, helping the user to gain insights and build a mental model of how design variables are mapped into objective values. The validity and effectiveness of such approach are tested on a controlled experiment with human subjects.
Paucimannosidic glycoepitopes inhibit tumorigenic processes in glioblastoma multiforme

Glioblastoma multiforme is an aggressive cancer type with poor patient outcomes. Interestingly, we reported previously a novel association between the little studied paucimannosidic N-linked glycoepitope and glioblastoma. Paucimannose has only recently been detected in vertebrates where it exhibits a very restricted tumor-specific expression. Herein, we demonstrate for the first time a very high protein paucimannosylation in human grade IV glioblastoma and U-87MG and U-138MG glioblastoma cells. Furthermore, we revealed the involvement of paucimannosidic epitopes in tumorigenic processes including cell proliferation, migration, invasion and adhesion. Finally, we identified AHNAK which is discussed as a tumor suppressor as the first paucimannose-carrying protein in glioblastoma and show the involvement of AHNAK in the observed paucimannose-dependent effects. This study is the first to provide evidence of a protective role of paucimannosylation in glioblastoma, a relationship that with further in vivo support may have far reaching benefits for patients suffering from this often fatal disease.
Object detection and localization with Artificial Foveal Visual Attention

In the last decades, in order to make the processing of a scene more efficient, biologically inspired approaches have been proposed. Visual attention models are being studied and actively developed in order to reduce the complexity and computational time of the existing methods. We propose a biologically inspired model that combines a single pre-trained CNN architecture with an artificial foveal visual system that performs simultaneously the classification and localization of objects in images. This model is based on the fact that only a small part of the image is processed with high resolution at each time so we load a foveated image in the network and successively employ feed-forward passes to determine the class labels and then via backward propagation determine the object possible locations according to each semantic label. By directing the attention to the center of the proposed location we mimic the human saccadic eye movements. In the results obtained we used the ILSVRC 2012 validation data set in a GoogLeNet CNN. We demonstrate that for non-centered objects the gain of the classification performance between iterations is significant showing that when mimicking the human visual behaviour of foveation, saccades are needed to integrate the information at each time.
Efficient Reward-Based Learning through Body Representation in a Spiking Neural Network

Brain-body interactions guide the development of behavioral and cognitive functions. Sensory signals during behavior are relayed to the brain and evoke neural activity. This feedback is important for the organization of neural networks via neural plasticity, which in turn facilitates the generation of motor commands for new behaviors. In this study, we investigated how brain-body interactions develop and affect reward-based learning. We constructed a spiking neural network (SNN) model for the reward-based learning of canonical babbling, i.e., combination of a vowel and consonant. Motor commands to a vocal simulator were generated by SNN output and auditory signals representing the vocalized sound were fed back into the SNN. Synaptic weights in the SNN were updated using spike-timing-dependent plasticity (STDP). Connections from the SNN to the vocal simulator were modulated based on reward signals in terms of saliency of the vocalized sound. Our results showed that, under auditory feedback, STDP enabled the model to rapidly acquire babbling-like vocalization. We found that some neurons in the SNN were more highly activated during vocalization of a consonant than during other sounds. That is, neural dynamics in the SNN adapted to task-related articulator movements. Accordingly, body representation in the SNN facilitated brain-body interaction and accelerated the acquisition of babbling behavior.
Intrinsically Motivated Agent Behavior in a Swarm

Intrinsically motivated artificial agents are capable of open-ended exploration and cumulative learning. Swarm Intelligence is the intelligent behavior demonstrated by a group of simple agents that can solve complex problems as a group. There is relatively little work examining intrinsically motivated agents in a swarm setting. It includes a lack of metrics to measure the effect of intrinsic motivation in a swarm. This paper presents a model for a flock of agents capable of novelty detection and taking action to maximize immediate novelty. The intrinsically motivated behavior of these agents is examined in a simulated gallery environment. We also introduce behavior metrics to quantify the motivated behavior. Our results demonstrate the effectiveness of these metrics to determine the effects of the motivation mechanism as well as the exploratory behavior induced by it.
Autonomous learning of cyclovergence control based on Active Efficient Coding

A central aspect of the development of visual perception is the autonomous calibration of various kinds of eye movements including saccadic, pursuit, or vergence eye movements. An important but less well-studied class of eye movements are so-called torsional eye movements, where the eyes rotate around the line of sight. In humans, such torsional eye movements obey certain lawful relationships such as Listing's Law. However, it is still an open question how these eye movements develop and what learning processes may contribute to their development. Here we propose a model of the development of torsional eye movements based on the active efficient coding (AEC) framework. AEC models the joint development of sensory encoding and movements of the sense organs to maximize the overall coding efficiency of the perceptual system. Our results demonstrate that optimizing coding efficiency in this way leads to torsional eye movements consistent with Listing's Law describing torsional eye movements in humans. This suggests that humanoid robots aiming to maximize the coding efficiency of their visual systems could also benefit from physical or simulated torsional eye movements.
Deep Reinforcement Learning by Parallelizing Reward and Punishment using the MaxPain Architecture

Traditionally, reinforcement learning treats punishments as negative rewards. However, in biological decision systems, some evidence shows that animals have separate systems for rewards and punishments. The MaxPain architecture parallelizes the predictions of rewards and punishments and scales them into dual-attribute policies, and has been shown to both improve the learning speed and the learning of safer behaviors. This paper extends the MaxPain architecture into a deep reinforcement learning framework using convolutional neural networks to approximate two action-value functions. To derive the behavioral policy, we consider the mixture distributions of the policies computed from the two action-value functions. For evaluation, we compare the MaxPain architecture with count-based exploration and a reward-decomposing structure called Hybrid Reward Architecture (HRA) in grid-world navigation and vision-based navigation in a U-shape maze in the Gazebo robot simulation environment. The simulation results show the superiority of the MaxPain approach over the count-based method because the MaxPain agents efficiently avoid dead-end states by predicting future punishments. In addition, the MaxPain agents learn safe behaviors, while the HRA agents learn similar behaviors, as in the case of no punishments.
Semantic Query Languages for Knowledge-Based Web Services in a Construction Context

Since the early 2000s, different frameworks were set up to enable web-based collaboration in building projects. Unfortunately, none of these initiatives was granted a long life. Recently, however, the use of web technologies in the building industry has been gaining momentum again, considered some promising technologies for reaching a more interoperable BIM practice. Specifically, this relates to (1) Linked Data and Semantic Web technologies, and (2) cloud-based applications. In order to combine these into a network of interlinked applications and datastores, an agreed-upon mechanism for automatic communication and data retrieval needs to be used. Apart from the W3C standard SPARQL, often considered too high a threshold for developers to implement, there are some recent GraphQL-based solutions that simplify the querying process and its implementation into web services. In this paper, we review two recent open source technologies based on GraphQL, that enable to query Linked Data on the web: GraphQL-LD and HyperGraphQL.
Learning of situation dependent prediction toward acquiring physical causality

Physical causality is one of the most important knowledge that human babies learn ﬁrst after birth through interaction with the surrounding environment. The properties of object movement changes depending on the situation, and so the agent should change its prediction. This paper proposes a learning model which predicts the movement of an attended object depending on the environment around the object. The predictor is formed by three main layered associative modules: (a) an environment module, which recognizes the attended object and its surrounding environment; (b) a predictor module , which antici-pates the movement of the attended object depending on the surrounding environment; (c) an attention module which implements bottom-up and top-down attention processes. The proposed method is applied to the robot, and its prediction faculty and adaptability are examined in the simulation and actual environment.
Regulation of Pseudomonas aeruginosa-Mediated Neutrophil Extracellular Traps

Pseudomonas aeruginosa is the most prevalent opportunistic pathogen in the airways of cystic fibrosis (CF) patients. The pulmonary disorder is characterized by recurrent microbial infections and an exaggerated host inflammatory immune response led primarily by influx of neutrophils. Under these conditions, chronic colonization with P. aeruginosa is associated with diminished pulmonary function and increased morbidity and mortality. P. aeruginosa has a wide array of genetic mechanisms that facilitate its persistent colonization of the airway despite extensive innate host immune responses. Loss of function mutations in the quorum sensing regulatory gene lasR have been shown to confer survival advantage and a more pathogenic character to P. aeruginosa in CF patients. However, the strategies used by LasR-deficient P. aeruginosa to modulate neutrophil-mediated bactericidal functions are unknown. We sought to understand the role of LasR in P. aeruginosa-mediated neutrophil extracellular trap (NET) formation, an important anti-microbial mechanism deployed by neutrophils, the first-line responder in the infected airway. We observe mechanistic and phenotypic differences between NETs triggered by LasR-sufficient and LasR-deficient P. aeruginosa strains. We uncover that LasR-deficient P. aeruginosa strains fail to induce robust NET formation in both human and murine neutrophils, independently of bacterial motility or LPS expression. LasR does not mediate NET release via downstream quorum sensing signaling pathways but rather via transcriptional regulation of virulence factors, including, but not restricted to, LasB elastase and LasA protease. Finally, our studies uncover the differential requirements for NADPH oxidase in NET formation triggered by different P. aeruginosa strains.
Model-Based Analysis of Serverless Applications

Serverless computing is a relatively new execution model where the cloud platform provider manages the allocation of resources for containerized functions dynamically. This evolving paradigm is called Function as a Service (FaaS). The statelessness of these functions enables the application to be scaled up elastically in the case of peak loads. They can be tested easily in isolation, but the behavior arising by integrating them to an application is both hard to predict and test. The parallel execution of the functions and the shift of its state to data storages can cause several workflows accessing the same data. These workflows are hard to detect, particularly for complex applications. Therefore, we suggest an approach for modelling an existing serverless application based on a specialized graph holding all relevant features. Our serverless-specific model can be applied during the whole life cycle of a complex application and offers a good basis for this specific class of applications. It helps to optimize an existing system by identifying hot spots, supports the generation of test cases and can be used to monitor an existing system. Furthermore, we show how the generation of the model can be automated by realizing a tool supporting Amazon's AWS Lambda.
A mechanism for spatial and temporal earthquake clustering

[1] The Gutenberg-Richter law states that the size-frequency distribution of earthquakes follows a power law. This trend is usually justified using spring-block models, where slips with the appropriate statistics of sizes have been numerically observed. However, prominent spatial and temporal clustering features of earthquakes, as those implied by the Omori law of aftershocks, are not accounted for by this kind of model unless they are complemented with ad hoc assumptions, such as stress recovery laws after slip events, or the phenomenological rate-and-state equations to describe friction. We show that when a mechanism of structural relaxation is incorporated into a spring-block model, realistic earthquake patterns following the Gutenberg-Richter and Omori laws are obtained. Moreover, features well known from laboratory friction experiments, such as velocity weakening and increase of static friction with contact time, appear as a consequence of the relaxational mechanism as well, without making any a priori assumptions on the velocity dependence of the friction force in the model. In this way, our model shows that a single physical mechanism may be a unifying concept behind the Gutenberg-Richter and Omori laws and the rate-and-state equations of rock friction.
XMM-NEWTON DISCOVERY OF 2.6 s PULSATIONS IN THE SOFT GAMMA-RAY REPEATER SGR 1627−41

After nearly a decade of quiescence, the soft gamma-ray repeater SGR 1627−41 reactivated on 2008 May 28 with a bursting episode followed by a slowly decaying enhancement of its persistent emission. To search for the still unknown spin period of this SGR taking advantage of its high flux state, we performed on 2008 September 27–28 a 120 ks long X-ray observation with the XMM–Newton satellite. Pulsations with P = 2.594578(6) s were detected at a higher than 6σ confidence level, with a double-peaked pulse profile. The pulsed fraction in the 2–12 keV range is 19% ± 3% and 24% ± 3% for the fundamental and the second harmonic, respectively. The observed 2–10 keV flux is 3.4 × 10−13 erg cm−2 s−1, still a factor of ∼ 5 above the quiescent pre-burst-activation level, and the spectrum is well fitted by an absorbed power law plus blackbody model (photon index Γ ≃ 0.6, blackbody temperature kT ≃ 0.5 keV, and absorption NH ≈ 1.2 × 1023 cm−2). We also detected a shell of diffuse soft X-ray emission which is likely associated with the young supernova remnant G337.0−0.1.