Network effects, cooperation and entrepreneurial innovation in China

The rapid rise of an innovative private manufacturing economy in China challenges standard economic explanations of growth, which typically assume the existence of well-defined formal institutions such as property rights and company laws safeguarding investor and creditor interests. We highlight the social structure of cooperation that enables innovative activity in private manufacturing firms when formal property rights protection remains weak. We show how network effects linked to inter-firm cooperation in industrial clusters allowed private entrepreneurs to quickly develop reliable business norms to reduce the inherent risk of malfeasance and contract breach in formal and informal collaborative efforts. Survey data from a sample of 700 manufacturing firms located in China’s Yangzi Delta region confirms that both formal and informal types of inter-firm collaboration are effective, though in different areas of innovative activity

19th Annual Conference on Legal Issues For Financial Institutions

Materials from the 19th Annual Conference on Legal Issues For Financial Institutions held by UK/CLE in May 1999

Central role for MCP-1/CCL2 in injury-induced inflammation revealed by in vitro, in silico, and clinical studies

The translation of in vitro findings to clinical outcomes is often elusive. Trauma/hemorrhagic shock (T/HS) results in hepatic hypoxia that drives inflammation. We hypothesize that in silico methods would help bridge in vitro hepatocyte data and clinical T/HS, in which the liver is a primary site of inflammation. Primary mouse hepatocytes were cultured under hypoxia (1% O 2) or normoxia (21% O2) for 1-72 h, and both the cell supernatants and protein lysates were assayed for 18 inflammatory mediators by Luminex™ technology. Statistical analysis and data-driven modeling were employed to characterize the main components of the cellular response. Statistical analyses, hierarchical and k-means clustering, Principal Component Analysis, and Dynamic Network Analysis suggested MCP-1/CCL2 and IL-1α as central coordinators of hepatocyte-mediated inflammation in C57BL/6 mouse hepatocytes. Hepatocytes from MCP-1-null mice had altered dynamic inflammatory networks. Circulating MCP-1 levels segregated human T/HS survivors from non-survivors. Furthermore, T/HS survivors with elevated early levels of plasma MCP-1 post-injury had longer total lengths of stay, longer intensive care unit lengths of stay, and prolonged requirement for mechanical ventilation vs. those with low plasma MCP-1. This study identifies MCP-1 as a main driver of the response of hepatocytes in vitro and as a biomarker for clinical outcomes in T/HS, and suggests an experimental and computational framework for discovery of novel clinical biomarkers in inflammatory diseases. © 2013 Ziraldo et al

Diversity and carbon storage across the tropical forest biome

Tropical forests are global centres of biodiversity and carbon storage. Many tropical countries aspire to protect forest to fulfil biodiversity and climate mitigation policy targets, but the conservation strategies needed to achieve these two functions depend critically on the tropical forest tree diversity-carbon storage relationship. Assessing this relationship is challenging due to the scarcity of inventories where carbon stocks in aboveground biomass and species identifications have been simultaneously and robustly quantified. Here, we compile a unique pan-tropical dataset of 360 plots located in structurally intact old-growth closed-canopy forest, surveyed using standardised methods, allowing a multi-scale evaluation of diversity-carbon relationships in tropical forests. Diversity-carbon relationships among all plots at 1 ha scale across the tropics are absent, and within continents are either weak (Asia) or absent (Amazonia, Africa). A weak positive relationship is detectable within 1 ha plots, indicating that diversity effects in tropical forests may be scale dependent. The absence of clear diversity-carbon relationships at scales relevant to conservation planning means that carbon-centred conservation strategies will inevitably miss many high diversity ecosystems. As tropical forests can have any combination of tree diversity and carbon stocks both require explicit consideration when optimising policies to manage tropical carbon and biodiversity.Additional co-authors: Kofi Affum-Baffoe, Shin-ichiro Aiba, Everton Cristo de Almeida, Edmar Almeida de Oliveira, Patricia Alvarez-Loayza, Esteban Álvarez Dávila, Ana Andrade, Luiz E. O. C. Aragão, Peter Ashton, Gerardo A. Aymard C., Timothy R. Baker, Michael Balinga, Lindsay F. Banin, Christopher Baraloto, Jean-Francois Bastin, Nicholas Berry, Jan Bogaert, Damien Bonal, Frans Bongers, Roel Brienen, José Luís C. Camargo, Carlos Cerón, Victor Chama Moscoso, Eric Chezeaux, Connie J. Clark, Álvaro Cogollo Pacheco, James A. Comiskey, Fernando Cornejo Valverde, Eurídice N. Honorio Coronado, Greta Dargie, Stuart J. Davies, Charles De Canniere, Marie Noel Djuikouo K., Jean-Louis Doucet, Terry L. Erwin, Javier Silva Espejo, Corneille E. N. Ewango, Sophie Fauset, Ted R. Feldpausch, Rafael Herrera, Martin Gilpin, Emanuel Gloor, Jefferson S. Hall, David J. Harris, Terese B. Hart, Kuswata Kartawinata, Lip Khoon Kho, Kanehiro Kitayama, Susan G. W. Laurance, William F. Laurance, Miguel E. Leal, Thomas Lovejoy, Jon C. Lovett, Faustin Mpanya Lukasu, Jean-Remy Makana, Yadvinder Malhi, Leandro Maracahipes, Beatriz S. Marimon, Ben Hur Marimon Junior, Andrew R. Marshall, Paulo S. Morandi, John Tshibamba Mukendi, Jaques Mukinzi, Reuben Nilus, Percy Núñez Vargas, Nadir C. Pallqui Camacho, Guido Pardo, Marielos Peña-Claros, Pascal Pétronelli, Georgia C. Pickavance, Axel Dalberg Poulsen, John R. Poulsen, Richard B. Primack, Hari Priyadi, Carlos A. Quesada, Jan Reitsma, Maxime Réjou-Méchain, Zorayda Restrepo, Ervan Rutishauser, Kamariah Abu Salim, Rafael P. Salomão, Ismayadi Samsoedin, Douglas Sheil, Rodrigo Sierra, Marcos Silveira, J. W. Ferry Slik, Lisa Steel, Hermann Taedoumg, Sylvester Tan, John W. Terborgh, Sean C. Thomas, Marisol Toledo, Peter M. Umunay, Luis Valenzuela Gamarra, Ima Célia Guimarães Vieira, Vincent A. Vos, Ophelia Wang, Simon Willcock & Lise Zemagh

A Climate-Change Policy Induced Shift from Innovations in Energy Production to Energy Savings

We develop an endogenous growth model with capital, labor and energy as production factors and three productivity variables that measure accumulated innovations for energy production, energy savings, and neutral growth. All markets are complete and perfect, except for research, for which we assume that the marginal social value exceeds marginal costs by factor four. The model constants are calibrated so that the model reproduces the relevant trends over the 1970-2000 period. The model contains a simple climate module, and is used to assess the impact of Induced Technological Change (ITC) for a policy that aims at a maximum level of atmospheric CO2 concentration (450 ppmv). ITC is shown to reduce the required carbon tax by about a factor 2, and to reduce costs of such a policy by about factor 10. Numerical simulations show that knowledge accumulation shifts from energy production to energy saving technology

Long-term thermal sensitivity of Earth’s tropical forests

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio