Global governance approaches to addressing illegal logging: Uptake and lessons learned

One of the most challenging tasks facing development agencies, trade ministries, environmental groups, social activists and forest-focused business interests seeking to ameliorate illegal logging and related timber trade is to identify and nurture promising global governance interventions capable of helping improve compliance to governmental policies and laws at national, subnational and local levels. This question is especially acute for developing countries constrained by capacity challenges and “weak states” (Risse, 2011). This chapter seeks to shed light on this task by asking four related questions: How do we understand the emergence of illegal logging as a matter of global interest? What are the types of global interventions designed to improve domestic legal compliance? How have individual states responded to these global efforts? What are the prospects for future impacts and evolution? We proceed in the following steps. Following this introduction, step two reviews how the problem of “illegal logging” emerged on the international agenda. Step three reviews leading policy interventions that resulted from this policy framing. Step four reviews developments in selected countries/regions around the world according to their place on the global forest products supply chain: consumers (United States, Europe and Australia); middle of supply chain manufacturers (China and South Korea) and producers (Russia; Indonesia; Brazil and Peru; Ghana, Cameroon and the Republic of Congo). We conclude by reflecting on key trends that emerge from this review relevant for understanding the conditions through which legality might make a difference in addressing critical challenges

CD24 Is Not Required for Tumor Initiation and Growth in Murine Breast and Prostate Cancer Models

CD24 is a small, heavily glycosylated, GPI-linked membrane protein, whose expression has been associated with the tumorigenesis and progression of several types of cancer. Here, we studied the expression of CD24 in tumors of MMTV-PyMT, Apc1572/T+ and TRAMP genetic mouse models that spontaneously develop mammary or prostate carcinoma, respectively. We found that CD24 is expressed during tumor development in all three models. In MMTV-PyMT and Apc1572T/+ breast tumors, CD24 was strongly but heterogeneously expressed during early tumorigenesis, but decreased in more advanced stages, and accordingly was increased in poorly differentiated lesions compared with well differentiated lesions. In prostate tumors developing in TRAMP mice, CD24 expression was strong within hyperplastic lesions in comparison with non-hyperplastic regions, and heterogeneous CD24 expression was maintained in advanced prostate carcinomas. To investigate whether CD24 plays a functional role in tumorigenesis in these models, we crossed CD24 deficient mice with MMTV-PyMT, Apc1572T/+ and TRAMP mice, and assessed the influence of CD24 deficiency on tumor onset and tumor burden. We found that mice negative or positive for CD24 did not significantly differ in terms of tumor initiation and burden in the genetic tumor models tested, with the exception of Apc1572T/+ mice, in which lack of CD24 reduced the mammary tumor burden slightly but significantly. Together, our data suggest that while CD24 is distinctively expressed during the early development of murine mammary and prostate tumors, it is not essential for the formation of tumors developing in MMTV-PyMT, Apc1572T/+ and TRAMP mice

Deep Generative Models for Bayesian Inference on High-Rate Sensor Data:Applications in Automotive Radar and Medical Imaging

Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restoration, such as denoising, inpainting, and super-resolution. In recent years, generative modeling for Bayesian inference on sensory data has also gained traction. Nevertheless, the direct application of generative modeling techniques initially designed for natural images on raw sensory data is not straightforward, requiring solutions that deal with high dynamic range signals acquired from multiple sensors or arrays of sensors that interfere with each other, and that typically acquire data at a very high rate. Moreover, the exact physical data-generating process is often complex or unknown. As a consequence, approximate models are used, resulting in discrepancies between model predictions and the observations that are non-Gaussian, in turn complicating the Bayesian inverse problem. Finally, sensor data is often used in real-time processing or decision-making systems, imposing stringent requirements on, e.g., latency and throughput. In this paper, we will discuss some of these challenges and offer approaches to address them, all in the context of high-rate real-time sensing applications in automotive radar and medical imaging.Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restoration, such as denoising, inpainting, and super-resolution. In recent years, generative modeling for Bayesian inference on sensory data has also gained traction. Nevertheless, the direct application of generative modeling techniques initially designed for natural images on raw sensory data is not straightforward, requiring solutions that deal with high dynamic range signals acquired from multiple sensors or arrays of sensors that interfere with each other, and that typically acquire data at a very high rate. Moreover, the exact physical data-generating process is often complex or unknown. As a consequence, approximate models are used, resulting in discrepancies between model predictions and the observations that are non-Gaussian, in turn complicating the Bayesian inverse problem. Finally, sensor data is often used in real-time processing or decision-making systems, imposing stringent requirements on, e.g., latency and throughput. In this paper, we will discuss some of these challenges and offer approaches to address them, all in the context of high-rate real-time sensing applications in automotive radar and medical imaging

National laboratory-based surveillance system for antimicrobial resistance: a successful tool to support the control of antimicrobial resistance in the Netherlands

An important cornerstone in the control of antimicrobial resistance (AMR) is a well-designed quantitative system for the surveillance of spread and temporal trends in AMR. Since 2008, the Dutch national AMR surveillance system, based on routine data from medical microbiological laboratories (MMLs), has developed into a successful tool to support the control of AMR in the Netherlands. It provides background information for policy making in public health and healthcare services, supports development of empirical antibiotic therapy guidelines and facilitates in-depth research. In addition, participation of the MMLs in the national AMR surveillance network has contributed to sharing of knowledge and quality improvement. A future improvement will be the implementation of a new semantic standard together with standardised data transfer, which will reduce errors in data handling and enable a more real-time surveillance. Furthermore, the

Design of the Verbiest trial: cost-effectiveness of surgery versus prolonged conservative treatment in patients with lumbar stenosis

Background: Degenerative changes of lumbar spine anatomy resulting in the encroachment of neural structures are often regarded progressive, ultimately necessitating decompressive surgery. However the natural course is not necessarily progressive and the efficacy of a variety of nonsurgical interventions has also been described. At present there is insufficient data to compare surgical and nonsurgical interventions in terms of their relative benefit and safety. Previous attempts failed to provide clear clinical recommendations or to distinguish subgroups that substantially benefit from a certain treatment strategy. We present the design of a randomized controlled trial on (cost-) effectiveness of surgical decompression versus prolonged conservative treatment in patients with neurogenic intermittent claudication caused by lumbar stenosis. Methods/Design. The aim of the Verbiest trial is to evaluate the effectiveness of prolonged conservative treatment compared to decompressive surgery. The study is a multi-center randomized controlled trial with two parallel groups design. Patients (age over 50) presenting