The Dynamics of Wealth Inequality and the Effect of Income Distribution

The rapid increase of wealth inequality in the past few decades is one of the most disturbing social and economic issues of our time. Studying its origin and underlying mechanisms is essential for policy aiming to control and even reverse this trend. In that context, controlling the distribution of income, using income tax or other macroeconomic policy instruments, is generally perceived as effective for regulating the wealth distribution. We provide a theoretical tool, based on the realistic modeling of wealth inequality dynamics, to describe the effects of personal savings and income distribution on wealth inequality. Our theoretical approach incorporates coupled equations, solved using iterated maps to model the dynamics of wealth and income inequality. Notably, using the appropriate historical parameter values we were able to capture the historical dynamics of wealth inequality in the United States during the course of the 20th century. It is found that the effect of personal savings on wealth inequality is substantial, and its major decrease in the past 30 years can be associated with the current wealth inequality surge. In addition, the effect of increasing income tax, though naturally contributing to lowering income inequality, might contribute to a mild increase in wealth inequality and vice versa. Plausible changes in income tax are found to have an insignificant effect on wealth inequality, in practice. In addition, controlling the income inequality, by progressive taxation, for example, is found to have a very small effect on wealth inequality in the short run. The results imply, therefore, that controlling income inequality is an impractical tool for regulating wealth inequality.     

Microbiome-related aspects of locust density-dependent phase transition

Locust plagues are a notorious, ancient phenomenon. These swarming pests tend to aggregate and perform long migrations, decimating cultivated fields along their path. When population density is low, however, the locusts will express a cryptic, solitary, non-aggregating phenotype that is not considered a pest. Although the transition from the solitary to the gregarious phase has been well studied, associated shifts in the locust's microbiome have yet to be addressed. Here, using 16S rRNA amplicon sequencing, we compared the bacterial composition of solitary desert locusts before and after a phase transition. Our findings revealed that the microbiome is altered during the phase transition, and that a major aspect of this change is the acquisition of Weissella (Firmicutes). Our findings led us to hypothesize that the locust microbiome plays a role in inducing aggregation behaviour, contributing to the formation and maintenance of a swarm. Employing a mathematical model, we demonstrate the potential evolutionary advantage of inducing aggregation under different conditions; specifically, when the aggregation-inducing microbe exhibits a relatively high horizontal transmission rate. This is the first report of a previously unknown and important aspect of locust phase transition, demonstrating that the phase shift includes a shift in the gut and integument bacterial composition.     

Conformational proofreading: the impact of conformational changes on the specificity of molecular recognition

To perform recognition, molecules must locate and specifically bind their targets within a noisy biochemical environment with many look-alikes. Molecular recognition processes, especially the induced-fit mechanism, are known to involve conformational changes. This raises a basic question: Does molecular recognition gain any advantage by such conformational changes? By introducing a simple statistical-mechanics approach, we study the effect of conformation and flexibility on the quality of recognition processes. Our model relates specificity to the conformation of the participant molecules and thus suggests a possible answer: Optimal specificity is achieved when the ligand is slightly off target; that is, a conformational mismatch between the ligand and its main target improves the selectivity of the process. This indicates that deformations upon binding serve as a conformational proofreading mechanism, which may be selected for via evolution.     

Generic inhibition of amyloidogenic proteins by two naphthoquinone-tryptophan hybrid molecules

Amyloid formation is associated with several human diseases including Alzheimer's disease (AD), Parkinson's disease, Type 2 Diabetes, and so forth, no disease modifying therapeutics are available for them. Because of the structural similarities between the amyloid species characterizing these diseases, (despite the lack of amino acid homology) it is believed that there might be a common mechanism of toxicity for these conditions. Thus, inhibition of amyloid formation could be a promising disease-modifying therapeutic strategy for them. Aromatic residues have been identified as crucial in formation and stabilization of amyloid structures. This finding was corroborated by high-resolution structural studies, theoretical analysis, and molecular dynamics simulations. Amongst the aromatic entities, tryptophan was found to possess the most amyloidogenic potential. We therefore postulate that targeting aromatic recognition interfaces by tryptophan could be a useful approach for inhibiting the formation of amyloids. Quinones are known as inhibitors of cellular metabolic pathways, to have anti- cancer, anti-viral and anti-bacterial properties and were shown to inhibit aggregation of several amyloidogenic proteins in vitro. We have previously described two quinone-tryptophan hybrids which are capable of inhibiting amyloid-beta, the protein associated with AD pathology, both in vitro and in vivo. Here we tested their generic properties and their ability to inhibit other amyloidogenic proteins including α-synuclein, islet amyloid polypeptide, lysozyme, calcitonin, and insulin. Both compounds showed efficient inhibition of all five proteins examined both by ThT fluorescence analysis and by electron microscope imaging. If verified in vivo, these small molecules could serve as leads for developing generic anti-amyloid drugs.     

RecA-mediated homology search as a nearly optimal signal detection system

Homologous recombination facilitates the exchange of genetic material between homologous DNA molecules. This crucial process requires detecting a specific homologous DNA sequence within a huge variety of heterologous sequences. The detection is mediated by RecA in E. coli, or members of its superfamily in other organisms. Here, we examine how well the RecA-DNA interaction is adjusted to its task. By formulating the DNA recognition process as a signal detection problem, we find the optimal value of binding energy that maximizes the ability to detect homologous sequences. We show that the experimentally observed binding energy is nearly optimal. This implies that the RecA-induced deformation and the binding energetics are fine-tuned to ensure optimal sequence detection. Our analysis suggests a possible role for DNA extension by RecA, in which deformation enhances detection. The present signal detection approach provides a general recipe for testing the optimality of other molecular recognition systems.     

Faster algorithms for optimal multiple sequence alignment based on pairwise comparisons

Multiple sequence alignment (MSA) is one of the most fundamental problems in computational molecular biology. The running time of the best known scheme for finding an optimal alignment, based on dynamic programming, increases exponentially with the number of input sequences. Hence, many heuristics were suggested for the problem. We consider a version of the MSA problem where the goal is to find an optimal alignment in which matches are restricted to positions in predefined matching segments. We present several techniques for making the dynamic programming algorithm more efficient, while still finding an optimal solution under these restrictions. We prove that it suffices to find an optimal alignment of the predefined sequence segments, rather than single letters, thereby reducing the input size and thus improving the running time. We also identify "shortcuts" that expedite the dynamic programming scheme. Empirical study shows that, taken together, these observations lead to an improved running time over the basic dynamic programming algorithm by 4 to 12 orders of magnitude, while still obtaining an optimal solution. Under the additional assumption that matches between segments are transitive, we further improve the running time for finding the optimal solution by restricting the search space of the dynamic programming algorithm     

Human T cells express a functional ionotropic glutamate receptor GluR3, and glutamate by itself triggers integrin-mediated adhesion to laminin and fibronectin and chemotactic migration

T cells may encounter glutamate, the major excitatory neurotransmitter in the nervous system, when patrolling the brain and in glutamate-rich peripheral organs. Moreover, glutamate levels increase in the CNS in many pathological conditions in which T cells exert either beneficial or detrimental effects. We discovered that normal human T cells, human T leukemia cells, and mouse anti-myelin basic protein T cells express high levels of glutamate ion channel receptor (ionotropic) of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) subtype 3 (GluR3). The evidence for GluR3 on T cells includes GluR3-specific RT-PCR, Western blot, immunocytochemical staining and flow cytometry. Sequencing showed that the T cell-expressed GluR3 is identical with the brain GluR3. Glutamate (10 nM), in the absence of any additional molecule, triggered T cell function: integrin-mediated T cell adhesion to laminin and fibronectin, a function normally performed by activated T cells only. The effect of glutamate was mimicked by AMPA receptor-agonists and blocked specifically by the selective receptor-antagonists 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) and 6-nitro-7-sulfamoylbenzo[f]quinoxalin-2,3-dione (NBQX), and by relevant anti-integrin mAbs. Glutamate also increased the CXCR4-mediated T cell chemotactic migration toward the key chemokine CXCL12/stromal cell-derived factor-1. GluR3 expression on normal, cancer and autoimmune-associated T cells and the ability of glutamate to directly activate T cell function could be of substantial scientific and clinical importance to normal neuroimmune dialogues and to CNS diseases and injury, and especially to: 1) T cell transmigration to the CNS and patrolling in the brain, 2) T cell-mediated multiple sclerosis, and 3) autoimmune epilepsy, as neurotoxic anti-GluR3 Abs are found and suspected to cause/potentiate seizures and neuropathology in several types of human epilepsies. Thus far, GluR3 was found only on neurons and glia cells; our results reveal a novel peripheral source of this antigenic receptor.     

Computational and experimental identification of C. elegans microRNAs

MicroRNAs (miRNAs) constitute an extensive class of noncoding RNAs that are thought to regulate the expression of target genes via complementary base-pair interactions. To date, cloning has identified over 200 miRNAs from diverse eukaryotic organisms. Despite their success, such biochemical approaches are skewed toward identifying abundant miRNAs, unlike genome-wide, sequence-based computational predictions. We developed informatic methods to predict miRNAs in the C. elegans genome using sequence conservation and structural similarity to known miRNAs and generated 214 candidates. We confirmed the expression of four new miRNAs by Northern blotting and used a more sensitive PCR approach to verify the expression of ten additional candidates. Based on hypotheses underlying our computational methods, we estimate that the C. elegans genome may encode between 140 and 300 miRNAs and potentially many more.     

Aerial manoeuvrability in wingless gliding ants (Cephalotes atratus)

In contrast to the patagial membranes of gliding vertebrates, the aerodynamic surfaces used by falling wingless ants to direct their aerial descent are unknown. We conducted ablation experiments to assess the relative contributions of the hindlegs, midlegs and gaster to gliding success in workers of the Neotropical arboreal ant Cephalotes atratus (Hymenoptera: Formicidae). Removal of hindlegs significantly reduced the success rate of directed aerial descent as well as the glide index for successful flights. Removal of the gaster alone did not significantly alter performance relative to controls. Equilibrium glide angles during successful targeting to vertical columns were statistically equivalent between control ants and ants with either the gaster or the hindlegs removed. High-speed video recordings suggested possible use of bilaterally asymmetric motions of the hindlegs to effect body rotations about the vertical axis during targeting manoeuvre. Overall, the control of gliding flight was remarkably robust to dramatic anatomical perturbations, suggesting effective control mechanisms in the face of adverse initial conditions (e.g. falling upside down), variable targeting decisions and turbulent wind gusts during flight.     

Spatial Generalization in Operant Learning: Lessons from Professional Basketball

In operant learning, behaviors are reinforced or inhibited in response to the consequences of similar actions taken in the past. However, because in natural environments the “same” situation never recurs, it is essential for the learner to decide what “similar” is so that he can generalize from experience in one state of the world to future actions in different states of the world. The computational principles underlying this generalization are poorly understood, in particular because natural environments are typically too complex to study quantitatively. In this paper we study the principles underlying generalization in operant learning of professional basketball players. In particular, we utilize detailed information about the spatial organization of shot locations to study how players adapt their attacking strategy in real time according to recent events in the game. To quantify this learning, we study how a make \ miss from one location in the court affects the probabilities of shooting from different locations. We show that generalization is not a spatially-local process, nor is governed by the difficulty of the shot. Rather, to a first approximation, players use a simplified binary representation of the court into 2 pt and 3 pt zones. This result indicates that rather than using low-level features, generalization is determined by high-level cognitive processes that incorporate the abstract rules of the game.