PROMOTING FARM SAFETY WITH ECONOMIC AND MANAGERIAL INCENTIVES

The ex ante marginal values of management strategies for farm producers facing significant exposures to accident risks is assessed. A probit model describing the factors influencing the probability of a farm accident is estimated jointly with an ordered probit model for the severity of the accident.Farm Management,

Identification of criticality in neuronal avalanches: II. A theoretical and empirical investigation of the Driven case

The observation of apparent power laws in neuronal systems has led to the suggestion that the brain is at, or close to, a critical state and may be a self-organised critical system. Within the framework of self-organised criticality a separation of timescales is thought to be crucial for the observation of power-law dynamics and computational models are often constructed with this property. However, this is not necessarily a characteristic of physiological neural networks—external input does not only occur when the network is at rest/a steady state. In this paper we study a simple neuronal network model driven by a continuous external input (i.e. the model does not have an explicit separation of timescales from seeding the system only when in the quiescent state) and analytically tuned to operate in the region of a critical state (it reaches the critical regime exactly in the absence of input—the case studied in the companion paper to this article). The system displays avalanche dynamics in the form of cascades of neuronal firing separated by periods of silence. We observe partial scale-free behaviour in the distribution of avalanche size for low levels of external input. We analytically derive the distributions of waiting times and investigate their temporal behaviour in relation to different levels of external input, showing that the system’s dynamics can exhibit partial long-range temporal correlations. We further show that as the system approaches the critical state by two alternative ‘routes’, different markers of criticality (partial scale-free behaviour and long-range temporal correlations) are displayed. This suggests that signatures of criticality exhibited by a particular system in close proximity to a critical state are dependent on the region in parameter space at which the system (currently) resides

Identification of criticality in neuronal avalanches: I. A theoretical investigation of the non-driven case

In this paper, we study a simple model of a purely excitatory neural network that, by construction, operates at a critical point. This model allows us to consider various markers of criticality and illustrate how they should perform in a finite-size system. By calculating the exact distribution of avalanche sizes, we are able to show that, over a limited range of avalanche sizes which we precisely identify, the distribution has scale free properties but is not a power law. This suggests that it would be inappropriate to dismiss a system as not being critical purely based on an inability to rigorously fit a power law distribution as has been recently advocated. In assessing whether a system, especially a finite-size one, is critical it is thus important to consider other possible markers. We illustrate one of these by showing the divergence of susceptibility as the critical point of the system is approached. Finally, we provide evidence that power laws may underlie other observables of the system that may be more amenable to robust experimental assessment

API for connecting to captive portals

This disclosure describes an API that enables clients to connect to access-controlled WiFi networks. The WiFi service provider describes requirements to connect to the network via the API. The API enables clients to satisfy the requirements in an automatable fashion and to inspect the status of their connection. The API can be integrated into an app of a WiFi service provider, and provides a robust, standardized WiFi onboarding process and user experience. The API can also be integrated into an app of a cellular service provider for the purposes of offloading to a WiFi network. Alternately the API can function in a standalone manner

Rapid Integration Of WiFi Hotspots With A Cloud-based AAA Service

Public WiFi is typically provided by a network of smaller (local-area), independent service providers internetworked by a larger (wide-area) service provider. Currently, integration between the local-area and the wide-area service providers is a tedious process. This disclosure describes a set of vendor-agnostic application program interfaces (APIs) that enable a WiFi hotspot provider to integrate its control plane with a cloud-based authentication, authorization, and accounting (AAA) service in a self-service manner, without the direct involvement of personnel from the cloud service. This set of APIs enable a WiFi provider to self-register and self-configure its WiFi devices, such as controllers, with the cloud-based AAA service. Once registered and configured, the WiFi device can send requests to the cloud-based AAA service for creating WiFi sessions to control and account for users’ network access

A data driven approach to understanding the organization of high-level visual cortex

The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain

Enhancing thermal stability and mechanical properties of lyotropic liquid crystals through incorporation of a polymerizable surfactant.

We present a facile method to prepare thermally stable and mechanically robust crosslinked lyotropic liquid crystals (LLCs) through incorporation of a polymerizable amphiphile into a binary LLC system comprising commercially available surfactant Brij 97 and water. Thermal stability and mechanical properties of the polymerized LLCs were significantly enhanced after polymerization of the incorporated polymerizable surfactant. The effect of incorporating a polymerizable amphiphile on the phase behavior of the LLC system was studied in detail. In situ photo-rheology was used to monitor the change in the mechanical properties of the LLCs, namely the storage modulus, loss modulus, and viscosity, upon polymerization. The retention of the LLC nanostructures was evaluated by small angle X-ray scattering (SAXS). The ability to control the thermal stability and mechanical strength of LLCs simply by adding a polymerizable amphiphile, without tedious organic synthesis or harsh polymerization conditions, could prove highly advantageous in the preparation of robust nanomaterials with well-defined periodic structures

Identification of Highly Expressed, Soluble Proteins Using an Improved, High-Throughput Pooled ORF Expression Technology

This article describes an improved pooled open reading frame (ORF) expression technology (POET) that uses recombinational cloning and solution-based tandem mass spectrometry (MS/MS) to identify ORFs that yield high levels of soluble, purified protein when expressed in Escherichia coli. Using this method, three identical pools of 512 human ORFs were subcloned, purified, and transfected into three separate E. coli cultures. After bulk expression and purification, the proteins from the three separate pools were digested into tryptic peptides. Each of these samples was subsequently analyzed in triplicate using reversed-phase high-performance liquid chromatography (LC) coupled directly online with MS/MS. The abundance of each protein was determined by calculating the average exponentially modified protein abundance index (emPAI) of each protein across the three protein pools. Human proteins that consistently gave high emPAI values were subjected to small-scale expression and purification. These clones showed high levels of expression of soluble protein. Conversely, proteins that were not observed by LC-MS/MS did not show any detectable soluble expression in small-scale validation studies. Using this improved POET method allows the expression characteristics of hundreds of proteins to be quickly determined in a single experiment

Development of an efficient glucosinolate extraction method

Background: Glucosinolates, anionic sulfur rich secondary metabolites, have been extensively studied because of their occurrence in the agriculturally important brassicaceae and their impact on human and animal health. There is also increasing interest in the biofumigant properties of toxic glucosinolate hydrolysis products as a method to control agricultural pests. Evaluating biofumigation potential requires rapid and accurate quantification of glucosinolates, but current commonly used methods of extraction prior to analysis involve a number of time consuming and hazardous steps; this study aimed to develop an improved method for glucosinolate extraction. Results: Three methods previously used to extract glucosinolates from brassicaceae tissues, namely extraction in cold methanol, extraction in boiling methanol, and extraction in boiling water were compared across tissue type (root, stem leaf ) and four brassicaceae species (B. juncea, S. alba, R. sativus, and E. sativa). Cold methanol extraction was shown to perform as well or better than all other tested methods for extraction of glucosinolates with the exception of glucoraphasatin in R. sativus shoots. It was also demonstrated that lyophilisation methods, routinely used during extraction to allow tissue disruption, can reduce final glucosinolate concentrations and that extracting from frozen wet tissue samples in cold 80% methanol is more effective. Conclusions: We present a simplified method for extracting glucosinolates from plant tissues which does not require the use of a freeze drier or boiling methanol, and is therefore less hazardous, and more time and cost effective. The presented method has been shown to have comparable or improved glucosinolate extraction efficiency relative to the commonly used ISO method for major glucosinolates in the Brassicaceae species studied: sinigrin and gluconasturtiin in B. juncea; sinalbin, glucotropaeolin, and gluconasturtiin in S. alba; glucoraphenin and glucoraphasatin in R. sativus; and glucosatavin, glucoerucin and glucoraphanin in E. sativa