Linguistic Structures and Economic Outcomes

Linguistic structures have recently started to attract attention from economists as determinants of economic phenomena. This paper provides the first comprehensive review of this nascent literature and its achievements so far. First, we explore the complex connections between language, culture, thought and behaviour. Then, we summarize the empirical evidence on the relationship between linguistic structures and economic and social outcomes. We follow up with a discussion of data, empirical design and identification. The paper concludes by discussing implications for future research and policy

Stabilizing versus destabilizing the microtubules: A double-edge sword for an effective cancer treatment option?

Microtubules are dynamic and structural cellular components involved in several cell functions, including cell shape, motility, and intracellular trafficking. In proliferating cells, they are essential components in the division process through the formation of the mitotic spindle. As a result of these functions, tubulin and microtubules are targets for anticancer agents. Microtubule-targeting agents can be divided into two groups: microtubule-stabilizing, and microtubule-destabilizing agents. The former bind to the tubulin polymer and stabilize microtubules, while the latter bind to the tubulin dimers and destabilize microtubules. Alteration of tubulin-microtubule equilibrium determines the disruption of the mitotic spindle, halting the cell cycle at the metaphase-anaphase transition and, eventually, resulting in cell death. Clinical application of earlier microtubule inhibitors, however, unfortunately showed several limits, such as neurological and bone marrow toxicity and the emergence of drug-resistant tumor cells. Here we review several natural and synthetic microtubule-targeting agents, which showed antitumor activity and increased efficacy in comparison to traditional drugs in various preclinical and clinical studies. Cryptophycins, combretastatins, ombrabulin, soblidotin, D-24851, epothilones and discodermolide were used in clinical trials. Some of them showed antiangiogenic and antivascular activity and others showed the ability to overcome multidrug resistance, supporting their possible use in chemotherapy

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions

Machine learning–driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades

Performance of arthroscopic irrigation systems assessed with automatic blood detection

Biomechanical EngineeringMechanical, Maritime and Materials Engineerin

Squealer Dealers: The Market for Information in Federal Drug Trafficking Prosecutions

Federal data on drug trafficking sentences are used to determine factors that affect market quantities of providing information against other defendants (i.e., defendant probabilities of receiving testimony-related sentence reductions) and market prices of information (i.e., the sizes of such sentence reductions). Women and better-educated defendants experience high demand (higher quantities and prices) for information. Blacks, Hispanics, and non-U.S. citizens experience low demand. Defendants expecting longer sentences have higher supply of information. Conditional on expected sentence, crack dealers, high-level dealers, and dealers with long criminal histories experience low demand, while low-level dealers experience high demand. Women of all races experience high demand for information

Threshold effect of foreign direct investment on environmental degradation

The aim of this paper is to investigate the threshold effect of foreign direct investment (FDI) on environmental degradation. In empirical analysis, FDI and environmental degradation are jointly determined under the given threshold variable and other exogenous variables. Using carbon dioxide (CO2) emissions per capita as a proxy for environmental degradation, the results show that increasing FDI worsens CO2 emissions after a threshold level of corruption has been reached. Our results demonstrate that increasing FDI will increase CO2 emissions when the degree of corruptibility is relatively high. The study suggests that further FDI and improved environmental quality are competing rather than compatible objectives in high-corruption countries and are compatible rather than competing objectives in low-corruption countries. Higher trade liberalization in low-corruption countries could contribute to negative environmental consequences because of the increased output or economic activity which results from increased trade. The robustness estimation confirms the evidence that pollution and economic development increase together up to a certain income level, after which the trend reverses.info:eu-repo/semantics/publishedVersio