Exploiting visual cues for safe and flexible cyber-physical production systems

Human workers are envisioned to work alongside robots and other intelligent factory modules, and fulfill supervision tasks in future smart factories. Technological developments, during the last few years, in the field of smart factory automation have introduced the concept of cyber-physical systems, which further expanded to cyber-physical production systems. In this context, the role of collaborative robots is significant and depends largely on the advanced capabilities of collision detection, impedance control, and learning new tasks based on artificial intelligence. The system components, collaborative robots, and humans need to communicate for collective decision-making. This requires processing of shared information keeping in consideration the available knowledge, reasoning, and flexible systems that are resilient to the real-time dynamic changes on the industry floor as well as within the communication and computer network infrastructure. This article presents an ontology-based approach to solve industrial scenarios for safety applications in cyber-physical production systems. A case study of an industrial scenario is presented to validate the approach in which visual cues are used to detect and react to dynamic changes in real time. Multiple scenarios are tested for simultaneous detection and prioritization to enhance the learning surface of the intelligent production system with the goal to automate safety-based decisions

Sialyl Residues Modulate LPS-Mediated Signaling through the Toll-Like Receptor 4 Complex

We previously reported that neuraminidase (NA) pretreatment of human PBMCs markedly increased their cytokine response to lipopolysaccharide (LPS). To study the mechanisms by which this occurs, we transfected HEK293T cells with plasmids encoding TLR4, CD14, and MD2 (three components of the LPS receptor complex), as well as a NFκB luciferase reporting system. Both TLR4 and MD2 encoded by the plasmids are α-2,6 sialylated. HEK293T cells transfected with TLR4/MD2/CD14 responded robustly to the addition of LPS; however, omission of the MD2 plasmid abrogated this response. Addition of culture supernatants from MD2 (sMD2)-transfected HEK293T cells, but not recombinant, non-glycosylated MD2 reconstituted this response. NA treatment of sMD2 enhanced the LPS response as did NA treatment of the TLR4/CD14-transfected cell supplemented with untreated sMD2, but optimal LPS-initiated responses were observed with NA-treated TLR4/CD14-transfected cells supplemented with NA-treated sMD2. We hypothesized that removal of negatively charged sialyl residues from glycans on the TLR4 complex would hasten the dimerization of TLR4 monomers required for signaling. Co-transfection of HEK293T cells with separate plasmids encoding either YFP- or FLAG-tagged TLR4, followed by treatment with NA and stimulation with LPS, led to an earlier and more robust time-dependent dimerization of TLR4 monomers on co-immunoprecipitation, compared to untreated cells. These findings were confirmed by fluorescence resonance energy transfer (FRET) analysis. Overexpression of human Neu1 increased LPS-initiated TLR4-mediated NFκB activation and a NA inhibitor suppressed its activation. We conclude that (1) sialyl residues on TLR4 modulate LPS responsiveness, perhaps by facilitating clustering of the homodimers, and that (2) sialic acid, and perhaps other glycosyl species, regulate MD2 activity required for LPS-mediated signaling. We speculate that endogenous sialidase activity mobilized during cell activation may play a role in this regulation

The relationship between the time of cerebral desaturation episodes and outcome in aneurysmal subarachnoid haemorrhage: a preliminary study.

In this preliminary study we investigated the relationship between the time of cerebral desaturation episodes (CDEs), the severity of the haemorrhage, and the short-term outcome in patients with aneurysmal subarachnoid haemorrhage (aSAH). Thirty eight patents diagnosed with aneurysmal subarachnoid haemorrhage were analysed in this study. Regional cerebral oxygenation (rSO2) was assessed using near infrared spectroscopy (NIRS). A CDE was defined as rSO2 < 60% with a duration of at least 30 min. The severity of the aSAH was assessed using the Hunt and Hess scale and the short-term outcome was evaluated utilizing the Glasgow Outcome Scale. CDEs were found in 44% of the group. The total time of the CDEs and the time of the longest CDE on the contralateral side were longer in patients with severe versus moderate aSAH [h:min]: 8:15 (6:26-8:55) versus 1:24 (1:18-4:18), p = 0.038 and 2:05 (2:00-5:19) versus 0:48 (0:44-2:12), p = 0.038. The time of the longest CDE on the ipsilateral side was longer in patients with poor versus good short-term outcome [h:min]: 5:43 (3:05-9:36) versus 1:47 (0:42-2:10), p = 0.018. The logistic regression model for poor short-term outcome included median ABP, the extent of the haemorrhage in the Fisher scale and the time of the longest CDE. We have demonstrated that the time of a CDE is associated with the severity of haemorrhage and short-term outcome in aSAH patients. A NIRS measurement may provide valuable predictive information and could be considered as additional method of neuromonitoring of patients with aSAH

The Role of Response Elements Organization in Transcription Factor Selectivity: The IFN-β Enhanceosome Example

What is the mechanism through which transcription factors (TFs) assemble specifically along the enhancer DNA? The IFN-β enhanceosome provides a good model system: it is small; its components' crystal structures are available; and there are biochemical and cellular data. In the IFN-β enhanceosome, there are few protein-protein interactions even though consecutive DNA response elements (REs) overlap. Our molecular dynamics (MD) simulations on different motif combinations from the enhanceosome illustrate that cooperativity is achieved via unique organization of the REs: specific binding of one TF can enhance the binding of another TF to a neighboring RE and restrict others, through overlap of REs; the order of the REs can determine which complexes will form; and the alternation of consensus and non-consensus REs can regulate binding specificity by optimizing the interactions among partners. Our observations offer an explanation of how specificity and cooperativity can be attained despite the limited interactions between neighboring TFs on the enhancer DNA. To date, when addressing selective TF binding, attention has largely focused on RE sequences. Yet, the order of the REs on the DNA and the length of the spacers between them can be a key factor in specific combinatorial assembly of the TFs on the enhancer and thus in function. Our results emphasize cooperativity via RE binding sites organization

Differential gene expression in mouse primary hepatocytes exposed to the peroxisome proliferator-activated receptor α agonists

BACKGROUND: Fibrates are a unique hypolipidemic drugs that lower plasma triglyceride and cholesterol levels through their action as peroxisome proliferator-activated receptor alpha (PPARα) agonists. The activation of PPARα leads to a cascade of events that result in the pharmacological (hypolipidemic) and adverse (carcinogenic) effects in rodent liver. RESULTS: To understand the molecular mechanisms responsible for the pleiotropic effects of PPARα agonists, we treated mouse primary hepatocytes with three PPARα agonists (bezafibrate, fenofibrate, and WY-14,643) at multiple concentrations (0, 10, 30, and 100 μM) for 24 hours. When primary hepatocytes were exposed to these agents, transactivation of PPARα was elevated as measured by luciferase assay. Global gene expression profiles in response to PPARα agonists were obtained by microarray analysis. Among differentially expressed genes (DEGs), there were 4, 8, and 21 genes commonly regulated by bezafibrate, fenofibrate, and WY-14,643 treatments across 3 doses, respectively, in a dose-dependent manner. Treatments with 100 μM of bezafibrate, fenofibrate, and WY-14,643 resulted in 151, 149, and 145 genes altered, respectively. Among them, 121 genes were commonly regulated by at least two drugs. Many genes are involved in fatty acid metabolism including oxidative reaction. Some of the gene changes were associated with production of reactive oxygen species, cell proliferation of peroxisomes, and hepatic disorders. In addition, 11 genes related to the development of liver cancer were observed. CONCLUSION: Our results suggest that treatment of PPARα agonists results in the production of oxidative stress and increased peroxisome proliferation, thus providing a better understanding of mechanisms underlying PPARα agonist-induced hepatic disorders and hepatocarcinomas

Unique establishment of procephalic head segments is supported by the identification of cis-regulatory elements driving segment-specific segment polarity gene expression in Drosophila

Anterior head segmentation is governed by different regulatory mechanisms than those that control trunk segmentation in Drosophila. For segment polarity genes, both initial mode of activation as well as cross-regulatory interactions among them differ from the typical genetic circuitry in the trunk and are unique for each of the procephalic segments. In order to better understand the segment-specific gene network responsible for the procephalic expression of the earliest active segment polarity genes wingless and hedgehog, we started to identify and analyze cis-regulatory DNA elements of these genes. For hedgehog, we could identify a cis-regulatory element, ic-CRE, that mediates expression specifically in the posterior part of the intercalary segment and requires promoter-specific interaction for its function. The intercalary stripe is the last part of the metameric hedgehog expression pattern that appears during embryonic development, which probably reflects the late and distinct establishment of this segment. The identification of a cis-regulatory element that is specific for one head segment supports the mutant-based observation that the expression of segment polarity genes is governed by a unique gene network in each of the procephalic segments. This provides further indication that the anterior-most head segments represent primary segments, which are set up independently, in contrast to the secondary segments of the trunk, which resemble true repetitive units

Active preference-based learning of reward functions

Our goal is to efficiently learn reward functions encoding a human's preferences for how a dynamical system should act. There are two challenges with this. First, in many problems it is difficult for people to provide demonstrations of the desired system trajectory (like a high-DOF robot arm motion or an aggressive driving maneuver), or to even assign how much numerical reward an action or trajectory should get. We build on work in label ranking and propose to learn from preferences (or comparisons) instead: the person provides the system a relative preference between two trajectories. Second, the learned reward function strongly depends on what environments and trajectories were experienced during the training phase. We thus take an active learning approach, in which the system decides on what preference queries to make. A novel aspect of our work is the complexity and continuous nature of the queries: continuous trajectories of a dynamical system in environments with other moving agents (humans or robots). We contribute a method for actively synthesizing queries that satisfy the dynamics of the system. Further, we learn the reward function from a continuous hypothesis space by maximizing the volume removed from the hypothesis space by each query. We assign weights to the hypothesis space in the form of a log-concave distribution and provide a bound on the number of iterations required to converge. We show that our algorithm converges faster to the desired reward compared to approaches that are not active or that do not synthesize queries in an autonomous driving domain. We then run a user study to put our method to the test with real people

Planning for cars that coordinate with people: leveraging effects on human actions for planning and active information gathering over human internal state

Traditionally, autonomous cars treat human-driven vehicles like moving obstacles. They predict their future trajectories and plan to stay out of their way. While physically safe, this results in defensive and opaque behaviors. In reality, an autonomous car’s actions will actually affect what other cars will do in response, creating an opportunity for coordination. Our thesis is that we can leverage these responses to plan more efficient and communicative behaviors. We introduce a formulation of interaction with human-driven vehicles as an underactuated dynamical system, in which the robot’s actions have consequences on the state of the autonomous car, but also on the human actions and thus the state of the human-driven car. We model these consequences by approximating the human’s actions as (noisily) optimal with respect to some utility function. The robot uses the human actions as observations of her underlying utility function parameters. We first explore learning these parameters offline, and show that a robot planning in the resulting underactuated system is more efficient than when treating the person as a moving obstacle. We also show that the robot can target specific desired effects, like getting the person to switch lanes or to proceed first through an intersection. We then explore estimating these parameters online, and enable the robot to perform active information gathering: generating actions that purposefully probe the human in order to clarify their underlying utility parameters, like driving style or attention level. We show that this significantly outperforms passive estimation and improves efficiency. Planning in our model results in coordination behaviors: the robot inches forward at an intersection to see if can go through, or it reverses to make the other car proceed first. These behaviors result from the optimization, without relying on hand-coded signaling strategies. Our user studies support the utility of our model when interacting with real users

Planning for cars that coordinate with people: leveraging effects on human actions for planning and active information gathering over human internal state

Traditionally, autonomous cars treat human-driven vehicles like moving obstacles. They predict their future trajectories and plan to stay out of their way. While physically safe, this results in defensive and opaque behaviors. In reality, an autonomous car’s actions will actually affect what other cars will do in response, creating an opportunity for coordination. Our thesis is that we can leverage these responses to plan more efficient and communicative behaviors. We introduce a formulation of interaction with human-driven vehicles as an underactuated dynamical system, in which the robot’s actions have consequences on the state of the autonomous car, but also on the human actions and thus the state of the human-driven car. We model these consequences by approximating the human’s actions as (noisily) optimal with respect to some utility function. The robot uses the human actions as observations of her underlying utility function parameters. We first explore learning these parameters offline, and show that a robot planning in the resulting underactuated system is more efficient than when treating the person as a moving obstacle. We also show that the robot can target specific desired effects, like getting the person to switch lanes or to proceed first through an intersection. We then explore estimating these parameters online, and enable the robot to perform active information gathering: generating actions that purposefully probe the human in order to clarify their underlying utility parameters, like driving style or attention level. We show that this significantly outperforms passive estimation and improves efficiency. Planning in our model results in coordination behaviors: the robot inches forward at an intersection to see if can go through, or it reverses to make the other car proceed first. These behaviors result from the optimization, without relying on hand-coded signaling strategies. Our user studies support the utility of our model when interacting with real users