CO2 laser surgery in osteomyelitis

Spirituality is a critical component of the holistic mind-body-spirit model embraced by Hospice. Hospice chaplains, as part of the caregiving team, must understand their abilities and limitations in providing spiritual guidance to others who may differ in religious and spiritual beliefs.     

Multiband properties of a fractal tree antenna generated byelectrochemical deposition

Experimental results on a fractal tree-like antenna are reported. The fractal structure is generated by electrochemical deposition. The antenna presents a multiband behaviour with a denser band distribution than the previously reported Sierpinski fractal antenna. The matched frequencies are related to the length distribution over the fractal shape     

Association of IgG1 Antibody Clearance with FcγRIIA Polymorphism and Platelet Count in Infliximab-Treated Patients

The FcγRIIA/CD32A is mainly expressed on platelets, myeloid and several endothelial cells. Its affinity is considered insufficient for allowing significant binding of monomeric IgG, while its H131R polymorphism (histidine > arginine at position 131) influences affinity for multimeric IgG2. Platelet FcγRIIA has been reported to contribute to IgG-containing immune-complexe clearance. Given our finding that platelet FcγRIIA actually binds monomeric IgG, we investigated the role of platelets and FcγRIIA in IgG antibody elimination. We used pharmacokinetics analysis of infliximab (IgG1) in individuals with controlled Crohn’s disease. The influence of platelet count and FcγRIIA polymorphism was quantified by multivariate linear modelling. The infliximab half-life increased with R allele number (13.2, 14.4 and 15.6 days for HH, HR and RR patients, respectively). It decreased with increasing platelet count in R carriers: from ≈20 days (RR) and ≈17 days (HR) at 150 × 10⁹/L, respectively, to ≈13 days (both HR and RR) at 350 × 10⁹/L. Moreover, a flow cytometry assay showed that infliximab and monomeric IgG1 bound efficiently to platelet FcγRIIA H and R allotypes, whereas panitumumab and IgG2 bound poorly to the latter. We propose that infliximab (and presumably any IgG1 antibody) elimination is partly due to an unappreciated mechanism dependent on binding to platelet FcγRIIA, which is probably tuned by its affinity for IgG2.     

Mangrove forests as a nature-based solution for coastal flood protection: Biophysical and ecological considerations

Nature-based coastal protection is increasingly recognised as a potentially sustainable and cost-effective solution to reduce coastal flood risk. It uses coastal ecosystems such as mangrove forests to create resilient designs for coastal flood protection. However, to use mangroves effectively as a nature-based measure for flood risk reduction, we must understand the biophysical processes that govern risk reduction capacity through mangrove ecosystem size and structure. In this perspective, we evaluate the current state of knowledge on local physical drivers and ecological processes that determine mangrove functioning as part of a nature-based flood defence. We show that the forest properties that comprise coastal flood protection are well-known, but models cannot yet pinpoint how spatial heterogeneity of the forest structure affects the capacity for wave or surge attenuation. Overall, there is relatively good understanding of the ecological processes that drive forest structure and size, but there is a lack of knowledge on how daily bed-level dynamics link to long-term biogeomorphic forest dynamics, and on the role of combined stressors influencing forest retreat. Integrating simulation models of forest structure under changing physical (e.g. due to sea-level change) and ecological drivers with hydrodynamic attenuation models will allow for better projections of long-term natural coastal protection.     

Learning Sets of Antecedent-restricted Functional and Multivalued Dependencies with Queries

Identifying dependencies that hold in relational databases is essential to produce good databases designs. In particular, functional and multivalued dependencies are used to obtain relation schemes that satisfy the 4th normal form, a property that is considered satisfactory for most applications. It is known that the class of sets of functional dependencies is learnable in the exact model of learning with queries. Also a subclass of multivalued dependencies, the class of consequent-restricted multivalued dependencies, has been shown to be learnable in this model. In this paper, we present an algorithm that learns a class that contains sets of both functional and multivalued dependencies under some restrictions imposed on the antecedents of dependencies. We also show, as a by-product, an algorithm that learns a non-trivial subclass of 2-quasi Horn formulas, closely related to the class just mentioned.     

Information fusion for automotive applications – An overview

This article focusses on the fusion of information from various automotive sensors like radar, video, and lidar for enhanced safety and traffic efficiency. Fusion is not restricted to data from sensors onboard the same vehicle but vehicular communication systems allow to propagate and fuse information with sensor data from other vehicles or from the road infrastructure as well. This enables vehicles to perceive information from regions that are hardly accessible otherwise and represents the basis for cooperative driving maneuvers. While the Bayesian framework builds the basis for information fusion, automobile environments are characterized by their a priori unknown topology, i.e., the number, type, and structure of the perceived objects is highly variable. Multi-object detection and tracking methods are a first step to cope with this challenge. Obviously, the existence or non-existence of an object is of paramount importance for safe driving. Such decisions are highly influenced by the association step that assigns sensor measurements to object tracks. Methods that involve multiple sequences of binary assignments are compared with soft-assignment strategies. Finally, fusion based on finite set statistics that (theoretically) avoid an explicit association are discussed.     

A genetic solution based on lexicographical goal programming for a multiobjective job shop with uncertainty

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly active schedules. Experimental results are presented for several problem instances, solved by the GA according to the proposed model, considering three objectives: makespan, tardiness and idleness. The results illustrate the potential of the proposed multiobjective model and genetic algorithm.     

Learning a subclass of k-quasi-Horn formulas with membership queries

Boolean formulas have been widely studied in the field of learning theory. We focus on the model of learning with queries, and study a restriction of the class of k-quasi-Horn formulas, that is, conjunctive normal form formulas where the number of unnegated literals per clause is at most k. This class is known to be as hard to learn as the general class CNF of conjunctive normal form formulas. By imposing some constraints, we define a fragment of this logic that can be learned using only membership queries. Also we prove that none of these constraints makes by itself the class learnable.