Agriculture Beyond Food: Experiences from Indonesia

The ABF programme addresses one of today’s major societal challenges, how to achieve a sustainable and inclusive biobased economy, with high-level scientific research on the thin lines between food and non-food, commodities and waste products, livelihood opportunities and risks, and local and global economy. This book provides insights into the main issues and key questions relating to the biobased economy, reflects on the objectives of the ABF programme, and offers policy recommendations. It summarises the projects conducted within the three major clusters at the heart of the programme: migration and forest transformation, breakthroughs in biofuel production technology, and the commoditisation of an alternative biofuel crop. The book ends with a number of lessons learned from the ABF programme on interdisciplinary programming

Rational design of highly potent broad-spectrum enterovirus inhibitors targeting the nonstructural protein 2C

There is a great need for antiviral drugs to treat enterovirus (EV) and rhinovirus (RV) infections, which can be severe and occasionally life-threatening. The conserved nonstructural protein 2C, which is an AAA+ ATPase, is a promising target for drug development. Here, we present a structure-activity relationship study of a previously identified compound that targets the 2C protein of EV-A71 and several EV-B species members, but not poliovirus (PV) (EV-C species). This compound is structurally related to the Food and Drug Administration (FDA)-approved drug fluoxetine—which also targets 2C—but has favorable chemical properties. We identified several compounds with increased antiviral potency and broadened activity. Four compounds showed broad-spectrum EV and RV activity and inhibited contemporary strains of emerging EVs of public health concern, including EV-A71, coxsackievirus (CV)-A24v, and EV-D68. Importantly, unlike (S)-fluoxetine, these compounds are no longer neuroactive. By raising resistant EV-A71, CV-B3, and EV-D68 variants against one of these inhibitors, we identified novel 2C resistance mutations. Reverse engineering of these mutations revealed a conserved mechanism of resistance development. Resistant viruses first acquired a mutation in, or adjacent to, the α2 helix of 2C. This mutation disrupted compound binding and provided drug resistance, but this was at the cost of viral fitness. Additional mutations at distantly localized 2C residues were then acquired to increase resistance and/or to compensate for the loss of fitness. Using computational methods to identify solvent accessible tunnels near the α2 helix in the EV-A71 and PV 2C crystal structures, a conserved binding pocket of the inhibitors is proposed

PERSONAL NETWORK SAMPLING, OUTDEGREE ANALYSIS AND MULTILEVEL ANALYSIS - INTRODUCING THE NETWORK CONCEPT IN STUDIES OF HIDDEN POPULATIONS

Populations, such as heroin and cocaine users, the homeless and the like (hidden populations), are among the most difficult populations to which to apply classic random sampling procedures. A frequently used data collection method for these hidden populations is the snowball procedure. The prerequisite for using this procedure is the existence of a network among the members of the population. Most studies of hidden populations treat the existence of networks as implicit, and subsequently the analysis remains at a qualitative level. In this article a practical approach to these populations is described that can be used simultaneously not only as a tool for locating a reasonable number of members of a hidden population but also as a tool for analysing aspects of network structure. By taking into account the personal network in the sampling design (personal network sampling) as well as in the analyses (outdegree analysis and multilevel analysis), meaningful quantitative information about aspects of network structure will be obtained. This approach is illustrated by data from a cocaine users project in the Dutch city of Rotterdam

Recovery of speed of information processing in closed-head-injury patients

After severe traumatic brain injury, patients almost invariably demonstrate a slowing of reaction time, reflecting a slowing of central information processing. Methodological problems associated with the traditional method for the analysis of longitudinal data (MANOVA) severely complicate studies on cognitive recovery. It is argued that multilevel models are often better suited for the analysis of improvement over time in clinical settings. Multilevel models take into account individual differences in both overall performance level and recovery. These models enable individual predictions for the recovery of speed of information processing. Recovery is modelled in a group of closed-head-injury patients (N = 24). Recovery was predicted by age and severity of injury, as indicated by coma duration. Over a period up to 44 months post trauma, reaction times were found to decrease faster for patients with longer coma duration