Soil Contamination Interpretation by the Use of Monitoring Data Analysis

The presented study deals with the interpretation of soil quality monitoring data using hierarchical cluster analysis (HCA) and principal components analysis (PCA). Both statistical methods contributed to the correct data classification and projection of the surface (0–20 cm) and subsurface (20–40 cm) soil layers of 36 sampling sites in the region of Burgas, Bulgaria. Clustering of the variables led to formation of four significant clusters corresponding to possible sources defining the soil quality like agricultural activity, industrial impact, fertilizing, etc. Two major clusters were found to explain the sampling site locations according to soil composition—one cluster for coastal and mountain sites and another—for typical rural and industrial sites. Analogous results were obtained by the use of PCA. The advantage of the latter was the opportunity to offer more quantitative interpretation of the role of identified soil quality sources by the level of explained total variance. The score plots and the dendrogram of the sampling sites indicated a relative spatial homogeneity according to geographical location and soil layer depth. The high-risk areas and pollution profiles were detected and visualized using surface maps based on Kriging algorithm

Calcium Regulation of EGF-Induced ERK5 Activation: Role of Lad1-MEKK2 Interaction

The ERK5 cascade is a MAPK pathway that transmits both mitogenic and stress signals, yet its mechanism of activation is not fully understood. Using intracellular calcium modifiers, we found that ERK5 activation by EGF is inhibited both by the depletion and elevation of intracellular calcium levels. This calcium effect was found to occur upstream of MEKK2, which is the MAP3K of the ERK5 cascade. Co-immunoprecipitation revealed that EGF increases MEKK2 binding to the adaptor protein Lad1, and this interaction was reduced by the intracellular calcium modifiers, indicating that a proper calcium concentration is required for the interactions and transmission of EGF signals to ERK5. In vitro binding assays revealed that the proper calcium concentration is required for a direct binding of MEKK2 to Lad1. The binding of these proteins is not affected by c-Src-mediated phosphorylation on Lad1, but slightly affects the Tyr phosphorylation of MEKK2, suggesting that the interaction with Lad1 is necessary for full Tyr phosphorylation of MEKK2. In addition, we found that changes in calcium levels affect the EGF-induced nuclear translocation of MEKK2 and thereby its effect on the nuclear ERK5 activity. Taken together, these findings suggest that calcium is required for EGF-induced ERK5 activation, and this effect is probably mediated by securing proper interaction of MEKK2 with the upstream adaptor protein Lad1

Cytoskeletal protein kinases: titin and its relations in mechanosensing

Titin, the giant elastic ruler protein of striated muscle sarcomeres, contains a catalytic kinase domain related to a family of intrasterically regulated protein kinases. The most extensively studied member of this branch of the human kinome is the Ca2+–calmodulin (CaM)-regulated myosin light-chain kinases (MLCK). However, not all kinases of the MLCK branch are functional MLCKs, and about half lack a CaM binding site in their C-terminal autoinhibitory tail (AI). A unifying feature is their association with the cytoskeleton, mostly via actin and myosin filaments. Titin kinase, similar to its invertebrate analogue twitchin kinase and likely other “MLCKs”, is not Ca2+–calmodulin-activated. Recently, local protein unfolding of the C-terminal AI has emerged as a common mechanism in the activation of CaM kinases. Single-molecule data suggested that opening of the TK active site could also be achieved by mechanical unfolding of the AI. Mechanical modulation of catalytic activity might thus allow cytoskeletal signalling proteins to act as mechanosensors, creating feedback mechanisms between cytoskeletal tension and tension generation or cellular remodelling. Similar to other MLCK-like kinases like DRAK2 and DAPK1, TK is linked to protein turnover regulation via the autophagy/lysosomal system, suggesting the MLCK-like kinases have common functions beyond contraction regulation

Internet Users\u2019 Privacy Concerns And Beliefs About Government Surveillance: An Exploratory Study Of Differences Between Italy And The United States

The study examines differences in individual\u2019s privacy concerns and beliefs about government surveillance in Italy and the United States. By incorporating aspects of multiple cultural theories, we argue that for both countries, the user\u2019s decision to conduct e-commerce transactions on the Internet is influenced by privacy concerns, perceived need for government surveillance that would secure the Internet environment from fraud, crime and terrorism, and balancing concerns about government intrusion. An empirical model was tested using LISREL structural equation modeling and multigroup analysis. The results support the hypotheses with regard to direction and relative magnitude of the relationships. Italians exhibit lower Internet privacy concerns than individuals in the U.S., lower perceived need for government surveillance, and higher concerns about government intrusion. The relationships among the model constructs are also different across the two countries. Implications of the findings and directions for future work are discussed

Memory clustering using persistent homology for multimodality- and discontinuity-sensitive learning of optimal control warm-starts

Shooting methods are an efficient approach to solving nonlinear optimal control problems. As they use local optimization, they exhibit favorable convergence when initialized with a good warm-start but may not converge at all if provided with a poor initial guess. Recent work has focused on providing an initial guess from a learned model trained on samples generated during an offline exploration of the problem space. However, in practice, the solutions contain discontinuities introduced by system dynamics or the environment. Additionally, in many cases, multiple equally suitable, i.e., multimodal, solutions exist to solve a problem. Classic learning approaches smooth across the boundary of these discontinuities and thus generalize poorly. In this work, we apply tools from algebraic topology to extract information on the underlying structure of the solution space. In particular, we introduce a method based on persistent homology to automatically cluster the dataset of precomputed solutions to obtain different candidate initial guesses. We then train a mixture-of-experts within each cluster to predict state and control trajectories to warm-start the optimal control solver and provide a comparison with modality-agnostic learning. We demonstrate our method on a cartpole toy problem and a quadrotor avoiding obstacles, and show that clustering samples based on inherent structure improves the warm-start quality

Sparsity-inducing optimal control via differential dynamic programming

Optimal control is a popular approach to synthesize highly dynamic motion. Commonly, L 2 regularization is used on the control inputs in order to minimize energy used and to ensure smoothness of the control inputs. However, for some systems, such as satellites, the control needs to be applied in sparse bursts due to how the propulsion system operates. In this paper, we study approaches to induce sparsity in optimal control solutions—namely via smooth L 1 and Huber regularization penalties. We apply these loss terms to state-of-the-art Differential Dynamic Programming (DDP)-based solvers to create a family of sparsity-inducing optimal control methods. We analyze and compare the effect of the different losses on inducing sparsity, their numerical conditioning, their impact on convergence, and discuss hyperparameter settings. We demonstrate our method in simulation and hardware experiments on canonical dynamics systems, control of satellites, and the NASA Valkyrie humanoid robot. We provide an implementation of our method and all examples for reproducibility on GitHub