The sensitivity of agricultural productivity to climate has not been sufficiently quantified. The total factor productivity (TFP) of the US agricultural economy has grown continuously for over half a century, with most of the growth typically attributed to technical change. Many studies have examined the effects of local climate on partial productivity measures such as crop yields and economic returns, but these measures cannot account for national-level impacts. Quantifying the relationships between TFP and climate is critical to understanding whether current US agricultural productivity growth will continue into the future. We analyze correlations between regional climate variations and national TFP changes, identify key climate indices, and build a multivariate regression model predicting the growth of agricultural TFP based on a physical understanding of its historical relationship with climate. We show that temperature and precipitation in distinct agricultural regions and seasons explain ∼70% of variations in TFP growth during 1981-2010. To date, the aggregate effects of these regional climate trends on TFP have been outweighed by improvements in technology. Should these relationships continue, however, the projected climate changes could cause TFP to drop by an average 2.84 to 4.34% per year under medium to high emissions scenarios. As a result, TFP could fall to pre-1980 levels by 2050 even when accounting for present rates of innovation. Our analysis provides an empirical foundation for integrated assessment by linking regional climate effects to national economic outcomes, offering a more objective resource for policy making.total factor productivity | agricultural economy | economic growth | climate impacts | crop yield A long-standing challenge of climate impact assessment has been to determine how climate has influenced the agricultural economy, and how its effects may change in the future. Climate affects agriculture regionally, depending not only on local weather factors but also on specific crops, livestock, and related goods and services, as well as agricultural systems, infrastructures, and interventions. Aggregating these disparate and potentially contradictory regional impacts into larger-scale economic outcomes is particularly difficult because the ultimate consequences are influenced by market fluctuations and policy incentives. As a result, understanding of how climate has influenced the agricultural economy is limited, making projection of the future under climate change extremely uncertain.This uncertainty is reflected in the lack of consensus regarding the overall impacts of climate change on US agriculture (1, 2). In general, studies follow two approaches, both focusing on partial productivity measures or local economic indicators. One approach seeks to determine the impact of weather shocks on common partial productivity measures such as crop yield (3-7). These studies tend to show that weather variability substantially influences local crop production. The other approach aims to iden...

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Cigarette smoke contains reactive oxygen (ROS) that can cause oxidative stress. It increases the number of apoptotic and necrotic lung cells and further induces the development of chronic airway disease. In this study, we investigated the effects of cigarette smoke extract (CSE) on apoptosis in human bronchial epithelial cells (BEAS-2B). CSE exposure induced ROS generation and p38 mitogen-activated protein kinase (MAPK) activation that are associated with the activation of apoptosis-regulating signal kinase 1 (ASK-1). N-acetylcysteine (a general antioxidant) attenuated the CSE-induced ASK-1 and p38 MAPK activation and cell apoptosis, suggesting a triggering role of ROS in ASK-1/p38 MAPK activation during apoptotic progression. In contrast, the inhibition and knockdown of p38 attenuated the expression of anti-oxidant master NF-E2-related factor 2 (Nrf-2) and CSE-induced apoptosis, suggesting that p38 MAPK modulates Nrf-2 expression and presumably prevents cell apoptosis. Taken together, the data presented in this manuscript demonstrate that the ROS-dependent ASK-1/p38 signaling cascade regulates CSE-induced BEAS-2B cell apoptosis. In addition, anti-oxidative Nrf-2 is also up-regulated by the ROS/p38 signaling cascade in this progression.

Abstract. Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or nonsmooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency.