Dynamic Factored Particle Filtering for Context-Specific Correlations

In order to control any system one needs to know the system's current state. In many real-world scenarios the state of the system cannot be determined with certainty due to the sensors being noisy or simply missing. In cases like these one needs to use probabilistic inference techniques to compute the likely states of the system and because such cases are common, there are lots of techniques to choose from in the field of Artificial Intelligence. Formally, we must compute a probability distribution function over all possible states. Doing this exactly is difficult because the number of states is exponential in the number of variables in the system and because the joint PDF may not have a closed form. Many approximation techniques have been developed over the years, but none ideally suited the problem we faced. Particle filtering is a popular scheme that approximates the joint PDF over the variables in the system by a set of weighted samples. It works even when the joint PDF has no closed form and the size of the sample can be adjusted to trade off accuracy for computation time. However, with many variables the size of the sample required for a good approximation can still become prohibitively large. Factored particle filtering uses the structure of variable dependencies to split the problem into many smaller subproblems and scales better if such decomposition is possible. However, our problem was unusual because some normally independent variables would become strongly correlated for short periods of time. This dynamically-changing dependency structure was not handled effectively by existing techniques. Considering variables to be always correlated meant the problem did not scale, considering them to be always independent introduced errors too large to tolerate. It was necessary to develop an approach that would utilize variables' independence whenever possible, but not introduce large errors when variables become correlated. We have developed a new technique for monitoring the state of the system for a class of systems with context-specific correlations. It is based on the idea of caching the context in which correlations arise and otherwise keeping the variables independent. Our evaluation shows that our technique outperforms existing techniques and is the first viable solution for the class of problems we consider

Predictions of Heat Transfer and Flow Circulations in Differentially Heated Liquid Columns With Applications to Low-Pressure Evaporators

Numerical computations are presented for the temperature and velocity distributions of two differentially heated liquid columns with liquor depths of 0.1 m and 2.215 m, respectively. The temperatures in the liquid columns vary considerably with respect to position for pure conduction, free convection, and nucleate boiling cases using one-dimensional (1D) thermal resistance networks. In the thermal resistance networks the solutions are not sensitive to the type of condensing and boiling heat transfer coefficients used. However, these networks are limited and give no indication of velocity distributions occurring within the liquor. To alleviate this issue, two-dimensional (2D) axisymmetric and three-dimensional (3D) computational fluid dynamics (CFD) simulations of the test rigs have been performed. The axisymmetric conditions of the 2D simulations produce unphysical solutions; however, the full 3D simulations do not exhibit these behaviors. There is reasonable agreement for the predicted temperatures, heat fluxes, and heat transfer coefficients when comparing the boiling case of the 1D thermal resistance networks and the CFD simulations

Detection of cannabinoid receptor type 2 in native cells and zebrafish with a highly potent, cell-permeable fluorescent probe.

Despite its essential role in the (patho)physiology of several diseases, CB2R tissue expression profiles and signaling mechanisms are not yet fully understood. We report the development of a highly potent, fluorescent CB2R agonist probe employing structure-based reverse design. It commences with a highly potent, preclinically validated ligand, which is conjugated to a silicon-rhodamine fluorophore, enabling cell permeability. The probe is the first to preserve interspecies affinity and selectivity for both mouse and human CB2R. Extensive cross-validation (FACS, TR-FRET and confocal microscopy) set the stage for CB2R detection in endogenously expressing living cells along with zebrafish larvae. Together, these findings will benefit clinical translatability of CB2R based drugs

Highly Selective Drug-Derived Fluorescent Probes for the Cannabinoid Receptor Type 1 (CB1R)

The cannabinoid receptor type 1 (CB1R) is pivotal within the endocannabinoid system regulating various signaling cascades with effects in appetite regulation, pain perception, memory formation, and thermoregulation. Still, understanding of CB1R’s cellular signaling, distribution, and expression dynamics is very fragmentary. Real-time visualization of CB1R is crucial for addressing these questions. Selective drug-like CB1R ligands with a defined pharmacological profile were investigated for the construction of CB1R fluorescent probes using a reverse design-approach. A modular design concept with a diethyl glycine-based building block as the centerpiece allowed for the straightforward synthesis of novel probe candidates. Validated by computational docking studies, radioligand binding, and cAMP assay, this systematic approach allowed for the identification of novel pyrrole-based CB1R fluorescent probes. Application in fluorescence-based target-engagement studies and live cell imaging exemplify the great versatility of the tailored CB1R probes for investigating CB1R localization, trafficking, pharmacology, and its pathological implications

Transition-Metal-Catalyzed Synthesis of Spirolactones

Spiranoid lactone structures can frequently be observed as scaffold segments of various biochemical compounds and drugs of natural origin. Examples of these structures have been identified among terpenoids, alkaloids, steroids, carbohydrates, and many other natural products. Such a broad natural diversity and biological activity allows a wide spectrum of these systems to be attractive targets for synthetic and medicinal chemists. Covering a broad spectrum of recognized approaches toward the design of spirolactones established over the past several decades, this review focuses on transition-metal-catalyzed synthesis, which is the most prominent methodology reported to date.1 Introduction2 Patterned Approaches2.1 Cyclocarbonylation2.2 Hydroalkylation/Arylation of Hydroxy α,β-Acetylenic Esters2.3 [2+2+2]-Cyclotrimerization: Rapid Access to Spirobenzofuranone Scaffolds2.4 Cyclization of Allenoic Acids/Allenoates2.5 Cycloisomerization–Oxidation of Homopropargyl Alcohols2.6 Hydroalkoxylation of Alkynoic or Alkenoic Acids2.7 C–H Carbonylation: Access to Spirobenzofuranone and Spiroisochromanone Derivatives2.8 Alkylative Spirolactonization of α,β-Unsaturated Esters2.9 Olefin Ring-Closing Metathesis2.10 Reductive Opening of Epoxides2.11 Intramolecular C–H Insertion3 Nonpatterned Approaches3.1 Azomethine Ylide Cycloaddition3.2 Hydrohydroxyalkylation of Vicinal Diols3.3 Photoredox Catalysis: C-Alkylation of Alcohols3.4 Carbonylative Spirolactonization of Hydroxycyclopropanols3.5 Copper-Catalyzed Alkylation of β-Keto Esters4 Conclusion</jats:p

Particle-water heat transfer during explosive volcanic eruptions

Thermal interaction between volcanic particles and water during explosive eruptions has been quantified using a numerical heat transfer model for spherical particles. The model couples intraparticle conduction with heat transfer from the particle surface by boiling water in order to explore heat loss with time for a range of particle diameters. The results are combined with estimates of particle settling times to provide insight into heat removal during eruption from samples of volcanic particles produced by explosive eruption. Heat removal is restricted by resistance to heat transfer from the volcanic particles with intraparticle thermal conduction important for large particles and surface cooling by boiling dominating for small particles. In most cases, volcanic particles approach thermal equilibrium with the surrounding fluid during an explosive eruption. Application of the results to a sample from the Gjalp 1996, Iceland eruption indicates that, relative to 0 degrees C, 70-80% of the heat is transferred from the particles to boiling water during the settling time before burial in the stratigraphic succession. The implication is that, for subglacial explosive eruptions, much of the heat content of the magma is coupled into melting ice extremely rapidly. If all particles of the Gjalp 1996 deposit were cooled to the local boiling point by the end of the eruption then approximately 78% of the initial heat content was removed from the erupting magma during the eruption. This is consistent with calorimetric calculations based on volumes of ice melted during and after the eruption