Collision quenching effects in nitrogen and helium excited by a 30-keV electron beam

The quenching cross section for the 0-0 first negative band of nitrogen is determined for temperatures between 78 K and 300 K. As the temperature increases above 78 K, the quenching reaches a maximum at approximately 140 K and then decreases as 300 K is approached. At temperatures on the order of 5000 K, quenching is reported to increase with temperature and must therefore reach a minimum at some intermediate temperature between 300 K and 5000 K. By comparison, quenching of the 5016 A helium line increases continuously over the temperature range 78 K to 300 K

Hemophilia Gene Therapy: Approaching the First Licensed Product

The clinical potential of hemophilia gene therapy has now been pursued for the past 30 years, and there is a realistic expectation that this goal will be achieved within the next couple of years with the licensing of a gene therapy product. While recent late phase clinical trials of hemophilia gene therapy have shown promising results, there remain a number of issues that require further attention with regard to both efficacy and safety of this therapeutic approach. In this review, we present information relating to the current status of the field and focus attention on the unanswered questions for hemophilia gene therapy and the future challenges that need to be overcome to enable the widespread application of this treatment paradigm

Mastering the game of Go without human knowledge

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo

Clinical and laboratory variability in a cohort of patients diagnosed with type 1 VWD in the United States

Von Willebrand disease (VWD) is the most common inherited bleeding disorder, and type 1 VWD is the most common VWD variant. Despite its frequency, diagnosis of type 1 VWD remains the subject of much debate. In order to study the spectrum of type 1 VWD in the United States, the Zimmerman Program enrolled 482 subjects with a previous diagnosis of type 1 VWD without stringent laboratory diagnostic criteria. VWF laboratory testing and full length VWF gene sequencing were performed for all index cases and healthy control subjects in a central laboratory. Bleeding phenotype was characterized using the ISTH Bleeding Assessment Tool. At study entry, 64% of subjects had VWF:Ag or VWF:RCo below the lower limit of normal, while 36% had normal VWF levels. VWF sequence variations were most frequent in subjects with VWF:Ag < 30 IU/dL (82%) while subjects with type 1 VWD and VWF:Ag ≥ 30 IU/dL had an intermediate frequency of variants (44%). Subjects whose VWF testing was normal at study entry had a similar rate of sequence variations as the healthy controls at 14% of subjects. All subjects with severe type 1 VWD and VWF:Ag ≤ 5 IU/dL had an abnormal bleeding score, but otherwise bleeding score did not correlate with VWF:Ag level. Subjects with a historical diagnosis of type 1 VWD had similar rates of abnormal bleeding scores compared to subjects with low VWF levels at study entry. Type 1 VWD in the United States is highly variable, and bleeding symptoms are frequent in this population

Groundwater chemistry of the Weaber Plain (Goomig Farmlands): baseline results 2010–13

The Ord River Irrigation Area (ORIA) is located in the north-east of the Kimberley region of Western Australia, near the town of Kununurra. The irrigation area was established in 1963 and over time developed to the current extent of 14 000 hectares (ha). The Weaber Plain (Goomig Farmlands) area is located north-north-east of the existing irrigation area, 30km from Kununurra, and has been identified as being suitable for irrigated agriculture for many decades. However, it was not until 2009, with state government support, that the 7400ha project commenced, with construction starting in 2010. State and Australian government environmental approvals required the proponent to install a groundwater monitoring network and develop a groundwater management plan. The environmental approvals required seasonal monitoring of groundwater to establish baseline groundwater chemistry conditions. The monitoring bores were sampled for up to three years and showed a large variation in water type and water quality across the Weaber and Knox Creek plains

Groundwater chemistry of the Weaber Plain: preliminary results

In 2008, the Ord Irrigation Expansion Project was approved by the Western Australian Government to develop irrigated agriculture on the Weaber Plain. Construction of the M2 supply channel connecting the ORIA and the Weaber Plain, and the final period of irrigation design, environmental management and related approval processes, commenced later in 2009. This process followed a protracted period of public and private industry planning and environmental assessment (Kinhill 2000). This report summarises an analysis of groundwater salinity trends on the Ivanhoe and Weaber plains and the preliminary results of an intensive water-quality sampling program carried out in 2010 as part of Phase 1 of the project. The purpose of this report is to provide interim results to inform groundwater management plans required as part of the approval process for the development of the Weaber Plain

Progress in the molecular biology of inherited bleeding disorders

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73391/1/j.1365-2516.2008.01718.x.pd

vrAIn: a deep learning approach tailoring computing and radio resources in virtualized RANs

Proceeding of: 25th Annual International Conference on Mobile Computing and Networking (MobiCom'19), October 21-25, 2019, Los Cabos, Mexico.The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex dependencies between computing and radio resources make vRAN resource control particularly daunting. We present vrAIn, a dynamic resource controller for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and signal quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map (encoded) contexts into resource control decisions. We have implemented vrAIn using an open-source LTE stack over different platforms. Our results show that vrAIn successfully derives appropriate compute and radio control actions irrespective of the platform and context: (i) it provides savings in computational capacity of up to 30% over CPU-unaware methods; (ii) it improves the probability of meeting QoS targets by 25% over static allocation policies using similar CPU resources in average; (iii) upon CPU capacity shortage, it improves throughput performance by 25% over state-of-the-art schemes; and (iv) it performs close to optimal policies resulting from an offline oracle. To the best of our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs, and the first approach to a model-free solution that does not need to assume any particular vRAN platform or system conditions.The work of University Carlos III of Madrid was supported by H2020 5GMoNArch project (grant agreement no. 761445) and H2020 5G-TOURS project (grant agreement no. 856950). The work of NEC Laboratories Europe was supported by H2020 5GTRANSFORMER project (grant agreement no. 761536) and 5GROWTH project (grant agreement no. 856709). The work of University of Cartagena was supported by Grant AEI/FEDER TEC2016-76465-C2-1-R (AIM) and Grant FPU14/03701.Publicad