Power mismatch and civil conflict: an empirical investigation

This paper empirically shows that the imbalance between an ethnic group’s political and military power is crucial to understanding the likelihood that such a group engages in a conflict. We develop a novel measure of a group’s military power by combining machine learning techniques with rich data on ethnic group characteristics and outcomes of civil conflicts in Africa and the Middle East. We couple this measure with available indicators of an ethnic group’s political power as well as with a novel proxy based on information about the ethnicity of cabinet members. We find that groups characterized by a higher mismatch between military and political power are between 30% and 50% more likely to engage in a conflict against their government depending on the specification used. We also find that the effects of power mismatch are nonlinear, which is in agreement with the predictions of a simple model that accounts for the cost of conflict. Moreover, our results suggest that high-mismatched groups are typically involved in larger and centrist conflicts. The policy implication is that powersharing recommendations and institutional design policies for peace should consider primarily the reduction of power mismatches between relevant groups, rather than focusing exclusively on equalizing political power in isolation

MolFM: A Multimodal Molecular Foundation Model

Molecular knowledge resides within three different modalities of information sources: molecular structures, biomedical documents, and knowledge bases. Effective incorporation of molecular knowledge from these modalities holds paramount significance in facilitating biomedical research. However, existing multimodal molecular foundation models exhibit limitations in capturing intricate connections between molecular structures and texts, and more importantly, none of them attempt to leverage a wealth of molecular expertise derived from knowledge graphs. In this study, we introduce MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs. We propose cross-modal attention between atoms of molecular structures, neighbors of molecule entities and semantically related texts to facilitate cross-modal comprehension. We provide theoretical analysis that our cross-modal pre-training captures local and global molecular knowledge by minimizing the distance in the feature space between different modalities of the same molecule, as well as molecules sharing similar structures or functions. MolFM achieves state-of-the-art performance on various downstream tasks. On cross-modal retrieval, MolFM outperforms existing models with 12.13% and 5.04% absolute gains under the zero-shot and fine-tuning settings, respectively. Furthermore, qualitative analysis showcases MolFM's implicit ability to provide grounding from molecular substructures and knowledge graphs. Code and models are available on https://github.com/BioFM/OpenBioMed.Comment: 31 pages, 15 figures, and 15 table

Empowering AI drug discovery with explicit and implicit knowledge

Motivation: Recently, research on independently utilizing either explicit knowledge from knowledge graphs or implicit knowledge from biomedical literature for AI drug discovery has been growing rapidly. These approaches have greatly improved the prediction accuracy of AI models on multiple downstream tasks. However, integrating explicit and implicit knowledge independently hinders their understanding of molecules. Results: We propose DeepEIK, a unified deep learning framework that incorporates both explicit and implicit knowledge for AI drug discovery. We adopt feature fusion to process the multi-modal inputs, and leverage the attention mechanism to denoise the text information. Experiments show that DeepEIK significantly outperforms state-of-the-art methods on crucial tasks in AI drug discovery including drug-target interaction prediction, drug property prediction and protein-protein interaction prediction. Further studies show that benefiting from explicit and implicit knowledge, our framework achieves a deeper understanding of molecules and shows promising potential in facilitating drug discovery applications.Comment: Bioinformatic

LAMP: a micro-satellite based soft X-ray polarimeter for astrophysics

The Lightweight Asymmetry and Magnetism Probe (LAMP) is a micro-satellite mission concept dedicated for astronomical X-ray polarimetry and is currently under early phase study. It consists of segmented paraboloidal multilayer mirrors with a collecting area of about 1300 cm^2 to reflect and focus 250 eV X-rays, which will be detected by position sensitive detectors at the focal plane. The primary targets of LAMP include the thermal emission from the surface of pulsars and synchrotron emission produced by relativistic jets in blazars. With the expected sensitivity, it will allow us to detect polarization or place a tight upper limit for about 10 pulsars and 20 blazars. In addition to measuring magnetic structures in these objects, LAMP will also enable us to discover bare quark stars if they exist, whose thermal emission is expected to be zero polarized, while the thermal emission from neutron stars is believed to be highly polarized due to plasma polarization and the quantum electrodynamics (QED) effect. Here we present an overview of the mission concept, its science objectives and simulated observational results

Mutual Funds and Information Diffusion: The Role of Country-Level Governance

__Abstract__ We hypothesize that poor country-level governance, which makes public information less reliable, induces fund managers to increase their use of semi-public information. Utilizing data from international mutual funds and stocks over the 2000-2009 period, we find that semi-public information-related stock rebalancing can be five times higher in countries with the worst quality of governance than in countries with the best. The use of semi-public information increases price informativeness but also increases information asymmetry and reduces stock liquidity. It also intensifies the price impact and liquidity crunch during the recent global financial crisis

Part 2: Making the "unproven" "proven"

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Protection against alcohol-induced neuronal and cognitive damage by the PPARγ receptor agonist pioglitazone

Binge alcohol drinking has emerged as a typical phenomenon in young people. This pattern of drinking, repeatedly leading to extremely high blood and brain alcohol levels and intoxication is associated with severe risks of neurodegeneration and cognitive damage. Mechanisms involved in excitotoxicity and neuroinflammation are pivotal elements in alcohol-induced neurotoxicity. Evidence has demonstrated that PPARγ receptor activation shows anti-inflammatory and neuroprotective properties. Here we examine whether treatment with the PPARγ agonist pioglitazone is beneficial in counteracting neurodegeneration, neuroinflammation and cognitive damage produced by binge alcohol intoxication. Adult Wistar rats were subjected to a 4-day binge intoxication procedure, which is commonly used to model excessive alcohol consumption in humans. Across the 4-day period, pioglitazone (0, 30, 60mg/kg) was administered orally twice daily at 12-h intervals. Degenerative cells were detected by fluoro-jade B (FJ-B) immunostaining in brain regions where expression of pro-inflammatory cytokines was also determined. The effects of pioglitazone on cognitive function were assessed in an operant reversal learning task and the Morris water maze task. Binge alcohol exposure produced selective neuronal degeneration in the hippocampal dentate gyrus and the adjacent entorhinal cortex. Pioglitazone reduced FJ-B positive cells in both regions and prevented alcohol-induced expression of pro-inflammatory cytokines. Pioglitazone also rescued alcohol-impaired reversal learning in the operant task and spatial learning deficits in the Morris water maze. These findings demonstrate that activation of PPARγ protects against neuronal and cognitive degeneration elicited by binge alcohol exposure. The protective effect of PPARγ agonist appears to be linked to inhibition of pro-inflammatory cytokines