The Ever-Changing Landscape of Informed Consent and Whether the Obligation to Explain a Procedure to the Patient May Be Delegated

Informed consent is an integral part of the shared decision making process and requires a patient be informed of the benefits, risks and alternatives to a medical procedure. This information, which requirement has been codified into the law and practice of every healthcare provider, helps a patient decide whether to proceed with the recommended treatment plan. Informed consent has its foundation in the ethical notion of patient autonomy and fundamental human rights. After all, it is the patient’s decision to determine what may be done to his or her body and to ascertain the risks and benefits before undertaking a procedure. On the other hand, a physician’s role is to act as a facilitator in the patient’s decision making process by providing information about the planned treatment and to answer questions. While the roles of the patient and physician seem clearly defined, a number of barriers present challenges in creating a process that guarantees a patient understands a test or procedure. This includes ineffective communication between the doctor and patient. The first part of this article will explore the liability of various health care providers who participate in the informed consent process, such as the physician, nurse, physician assistant and hospital. The second section will examine whether the treating physician may delegate the duty to explain the risks and alternatives of a procedure to another. The controversial decision of Shinal v. Toms, which mandates that the doctor must have a one-on–one exchange with the patient in order to secure a valid informed consent, will also be explored. This recent ruling has sent shock waves throughout the medical community causing a reexamination of their informed consent policies

Cluster Algorithm Renormalization Group Study of Universal Fluctuations in the 2D Ising Model

In this paper we propose a novel method to study critical systems numerically by a combined collective-mode algorithm and Renormalization Group on the lattice. This method is an improved version of MCRG in the sense that it has all the advantages of cluster algorithms. As an application we considered the 2D Ising model and studied wether scale invariance or universality are possible underlying mechanisms responsible for the approximate "universal fluctuations" close to a so-called bulk temperature $T^*(L)$. "Universal fluctuations" was first proposed in [1] and stated that the probability density function of a global quantity for very dissimilar systems, like a confined turbulent flow and a 2D magnetic system, properly normalized to the first two moments, becomes similar to the "universal distribution", originally obtained for the magnetization in the 2D XY model in the low temperature region. The results for the critical exponents and the renormalization group flow of the probability density function are very accurate and show no evidence to support that the approximate common shape of the PDF should be related to both scale invariance or universal behavior.Comment: 6 pages, 4 figures and 3 table

South-North trade, intellectual property jurisdictions, and freedom to operate in agricultural research on staple crops:

A biotechnology revolution is proceeding in tandem with international proliferation of intellectual property regimes and rights. Does the intellectual property impede agricultural research conducted in, or of consequence for, developing countries? This question has important spatial dimensions that link the location of production, the pattern of international trade, and the jurisdiction of intellectual property. Our main conclusion is that the current concerns about the freedom to operate in agricultural research oriented towards food crops for the developing world are exaggerated. Rights to intellectual property are confined to the jurisdictions where they are granted, and, presently, many of the intellectual property (IP) rights for biotechnologies potentially useful to developing-country agricultural producers are valid only in developed countries. IP problems might arise in technologies destined for crops grown in developing countries unencumbered by IP restrictions, if those crops are subsequently exported to countries in which IP is likely to prevail. Thus freedom to trade is also part of the IP story. However, using international production and trade data in the 15 crops critical to food security throughout the developing world, we show that exports from developing to developed countries are generally dwarfed by production and consumption in the developing world, the value of these exports is concentrated in a few crops and a few exporting countries, and the bulk of these exports go to Western Europe. Thus for now, most LDC researchers can focus primarily on domestic IPR in determining their freedom to operate with respect to food staples.Intellectual property., Biotechnology., Agricultural research., Trade regulation.,

Quantifying offshore fore-arc deformation and splay-fault slip using drowned Pleistocene shorelines, Arauco Bay, Chile

Indexación: Web of Science; Scopus.Most of the deformation associated with the seismic cycle in subduction zones occurs offshore and has been therefore difficult to quantify with direct observations at millennial timescales. Here we study millennial deformation associated with an active splay-fault system in the Arauco Bay area off south central Chile. We describe hitherto unrecognized drowned shorelines using high-resolution multibeam bathymetry, geomorphic, sedimentologic, and paleontologic observations and quantify uplift rates using a Landscape Evolution Model. Along a margin-normal profile, uplift rates are 1.3 m/ka near the edge of the continental shelf, 1.5 m/ka at the emerged Santa María Island, −0.1 m/ka at the center of the Arauco Bay, and 0.3 m/ka in the mainland. The bathymetry images a complex pattern of folds and faults representing the surface expression of the crustal-scale Santa María splay-fault system. We modeled surface deformation using two different structural scenarios: deep-reaching normal faults and deep-reaching reverse faults with shallow extensional structures. Our preferred model comprises a blind reverse fault extending from 3 km depth down to the plate interface at 16 km that slips at a rate between 3.0 and 3.7 m/ka. If all the splay-fault slip occurs during every great megathrust earthquake, with a recurrence of ~150–200 years, the fault would slip ~0.5 m per event, equivalent to a magnitude ~6.4 earthquake. However, if the splay-fault slips only with a megathrust earthquake every ~1000 years, the fault would slip ~3.7 m per event, equivalent to a magnitude ~7.5 earthquake. ©2017. American Geophysical Union.http://onlinelibrary.wiley.com/doi/10.1002/2016JB013339/epd

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

Biological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on recent insights in neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on adaptive spiking neurons. These spiking neurons efficiently encode information in spike-trains using a form of Asynchronous Pulsed Sigma-Delta coding while homeostatically optimizing their firing rate. In the proposed paradigm of spiking neuron computation, neural adaptation is tightly coupled to synaptic plasticity, to ensure that downstream neurons can correctly decode upstream spiking neurons. We show that this type of network is inherently able to carry out asynchronous and event-driven neural computation, while performing identical to corresponding artificial neural networks (ANNs). In particular, we show that these adaptive spiking neurons can be drop in replacements for ReLU neurons in standard feedforward ANNs comprised of such units. We demonstrate that this can also be successfully applied to a ReLU based deep convolutional neural network for classifying the MNIST dataset. The ASNN thus outperforms current Spiking Neural Networks (SNNs) implementations, while responding (up to) an order of magnitude faster and using an order of magnitude fewer spikes. Additionally, in a streaming setting where frames are continuously classified, we show that the ASNN requires substantially fewer network updates as compared to the corresponding ANN