Toward a “constitution” for behavioral policy-making

Behavioral policy interventions aimed at redirecting individuals’ behavior toward optimal choices are characterized by an important issue which is often overlooked: the lack of an instrument to define what “optimal” means. If agents are subject to behavioral biases leading them to make “wrong” choices, the policy-maker can no longer rely on the revealed preferences approach (e.g., what people choose is what people prefer) for defining a welfare criterion. In this article, we reiterate the argument put forward by some scholars that choosing a suitable welfare criterion once the link between observed choices and individuals’ preferences is broken becomes a problematic task. We review the state of the art in the literature and the possible approaches proposed to overcome the problem, concluding that a solution has not yet been reached. Moreover, we argue that the lack of an established welfare criterion characterizing behavioral policy-making could pave the way to government wanting to restrict individual freedom. In the absence of any legislative constraint for the executive, stating that what individuals choose is not what they prefer in principle justifies any freedom-reducing government intervention, since choices can be arbitrarily labeled “sub-optimal” or “welfare-reducing.” To avoid this risk without turning down the potential of behavioral policy-making, we propose that an independent committee establishes ex ante procedural rules and domains where behavioral policy-making can be implemented. The article suggests some possible examples of normative provisions characterizing this constitution-type document, such as the selective identification of the only sectors where behavioral policies could be effectively applied, the periodic evaluation of policy effects, and the use of sunset clauses

A workflow for bacterial metabolic fingerprinting and lipid profiling: application to Ciprofloxacin challenged Escherichia coli

The field of lipidomics focuses upon the non-targeted analysis of lipid composition, the process of which follows similar routines to those applied in conventional metabolic profiling, however lipidomics differs with respect to the sample preparation steps and chosen analytical platform applied to the sample analysis. Conventionally, lipidomics has applied analytical techniques such as direct infusion mass spectrometry and more recently reverse phase liquid chromatography–mass spectrometry, for the detection of mono-, di-, and tri-acyl glycerols, phospholipids, and other complex lipophilic species such as sterols. The field is rapidly expanding, especially with respect to the clinical sciences where it is known that changes of lipid composition, especially phospholipids, are commonly associated with many disease processes. As a proof of principle study, a small number of Escherichia coli isolates were selected on the basis of their sensitivity to a second generation fluoroquinolone antibiotic, known as Ciprofloxacin (E. coli isolates 161 and 171, non-ST131 isolates, which are resistant and sensitive respectively: E. coli isolates 160 and 173, ST131 sequence isolates which are resistant and susceptible respectively). It has been proposed that Ciprofloxacin may be a surface active drug that interacts at the surface-water interface of the phospholipid bi-layer where the head groups reside. Further, antibiotic resistance through intracellular exclusion is known to result in remodelling of the phospholipid membrane. Therefore, to study the effects of Ciprofloxacin on both susceptible and resistant bacterial strains, lipid profiling would present an informative approach. Control and antibiotic challenged cultures for each of the isolates were compared for changes in metabolite and lipid composition as detected by FT-IR spectroscopy and RP-UHPLC–MS, and appraised with a variety of chemometric data analysis approaches. The developed bacterial lipidomics workflow was deemed to be highly reproducible (with respect to the employed technical and analytical routines) and led to the detection of a large array of lipid classes as well as highlighting a range of significant lipid alterations that differed in regulation between susceptible and resistant E. coli isolates