Hurdles to the Court: The Doctrine of Standing under Statutory Violations

Standing is a precondition for any suit brought in federal court. This Commentary analyzes a Supreme Court case, Spokeo, Inc. v. Robins, which will address whether a violation of a federal statute grants a plaintiff standing to sue. The Author argues that such a violation is sufficient for establishing standing because the plaintiff suffered an injury-in-fact which the legisture intended to prevent. That harm is both traceable to the violation and redressible by statute. Thus, the requisite elements of constitutional standing exist in this case. Such a holding follows from existing standing jurisprudence and ensures that plaintiffs can have their day in court

Variability of Western Australian isolates of Sclerotinia sclerotiorum and the potential of Local Biological Control Agents

Stem rot disease caused by Sclerotinia sclerotiorum has emerged as a serious problem for canola (Brassica napus L.) production in Western Australia (WA) over the past few years where crop losses can be up to 40% in the worst affected crops. The biological characteristics and pathogenicity of the pathogen in WA is poorly understood. Also the potential for local biological control agents (BCAs) to be used in the management of the disease has not been explored. This paper provides preliminary data in these fields. One hundred and forty isolates of S. sclerotiorum were collected from WA canola growing regions for identification of biological characteristics which include colour of mycelia, growth rate, production of sclerotia, and pathogenicity. Other fungal isolates with potential biological control activity were collected from southern regions of WA. Colour of mycelia of Sclerotinia isolates varied from white, yellowish white, greyish white, brownish white, grey, dark grey to brown. Each isolate had its 24 and 48 hour growth rate recorded after sub-culture on PDA + ampicillin medium. ANOVA showed highly significant differences between growth rates of isolates 24 and 48 hours after being sub-cultured (P≤0.001). There were significant differences in number of sclerotia produced by each isolate. Two potential fungal biological control agents were found in WA, namely isolate KEN1 and isolate MTB1. These local fungal BCAs were found to be effective in inhibiting in vitro both the growth and ability to produce sclerotia of S. sclerotiorum

Molecular electronics exploiting sharp structure in the electrode density-of-states. Negative differential resistance and Resonant Tunneling in a poled molecular layer on Al/LiF electrodes

Density-functional calculations are used to clarify the role of an ultrathin LiF layer on Al electrodes used in molecular electronics. The LiF layer creates a sharp density of states (DOS), as in a scanning-tunneling microscope (STM) tip. The sharp DOS, coupled with the DOS of the molecule leads to negative differential resistance (NDR). Electron transfer between oriented molecules occurs via resonant tunneling. The I-V characteristic for a thin-film of tris (8-hydroxyquinoline)- aluminum (AlQ) molecules, oriented using electric-field poling, and sandwiched between two Al/LiF electrodes is in excellent agreement with theory. This molecular device presents a new paradigm for a convenient, robust, inexpensive alternative to STM or mechanical break-junction structures.Comment: 5 pages, 3 figure

Root disease under intensive cereal production systems

This Bulletin describes symptoms and control methods for the most commonly encountered cereal root diseases in Western Australia: rhizoctonia bare patch root lesion nematode take-all fusarium crown rot cereal cyst nematode common root rot pythium root rot Diseases caused by pathogenic micro-organisms can significantly reduce the yield of cereals. Some of these diseases are uncommon while others occur over a large area of the Western Australian wheatbelt every year. The most prevalent root diseases of cereals in Western Australia are rhizoctonia bare patch, root lesion nematode and take-all. Less widespread are fusarium crown rot, cereal cyst nematode, common root rot and pythium root rot.https://researchlibrary.agric.wa.gov.au/bulletins/1085/thumbnail.jp

Machine learning-based available bandwidth estimation

Today’s Internet Protocol (IP), the Internet’s network-layer protocol, provides a best-effort service to all users without any guaranteed bandwidth. However, for certain applications that have stringent network performance requirements in terms of bandwidth, it is significantly important to provide Quality of Ser- vice (QoS) guarantees in IP networks. The end-to-end available bandwidth of a network path, i.e., the residual capacity that is left over by other traffic, is deter- mined by its tight link, that is the link that has the minimal available bandwidth. The tight link may differ from the bottleneck link, i.e., the link with the minimal capacity. Passive and active measurements are the two fundamental approaches used to estimate the available bandwidth in IP networks. Unlike passive measurement tools that are based on the non-intrusive monitoring of traffic, active tools are based on the concept of self-induced congestion. The dispersion, which arises when packets traverse a network, carries information that can reveal relevant network characteristics. Using a fluid-flow probe gap model of a tight link with First-in, First-out (FIFO) multiplexing, accepted probing tools measure the packet dispersion to estimate the available bandwidth. Difficulties arise, how- ever, if the dispersion is distorted compared to the model, e.g., by non-fluid traffic, multiple tight links, clustering of packets due to interrupt coalescing and inaccurate time-stamping in general. It is recognized that modeling these effects is cumbersome if not intractable. To alleviate the variability of noise-afflicted packet gaps, the state-of-the-art bandwidth estimation techniques use post-processing of the measurement results, e.g., averaging over several packet pairs or packet trains, linear regression, or a Kalman filter. These techniques, however, do not overcome the basic as- sumptions of the deterministic fluid model. While packet trains and statistical post-processing help to reduce the variability of available bandwidth estimates, these cannot resolve systematic deviations such as the underestimation bias in case of random cross traffic and multiple tight links. The limitations of the state-of-the-art methods motivate us to explore the use of machine learning in end-to-end active and passive available bandwidth estimation. We investigate how to benefit from machine learning while using standard packet train probes for active available bandwidth estimation. To reduce the amount of required training data, we propose a regression-based scale- invariant method that is applicable without prior calibration to networks of arbitrary capacity. To reduce the amount of probe traffic further, we implement a neural network that acts as a recommender and can effectively select the probe rates that reduce the estimation error most quickly. We also evaluate our method with other regression-based supervised machine learning techniques. Furthermore, we propose two different multi-class classification-based meth- ods for available bandwidth estimation. The first method employs reinforcement learning that learns through the network path’s observations without having a training phase. We formulate the available bandwidth estimation as a single-state Markov Decision Process (MDP) multi-armed bandit problem and implement the ε-greedy algorithm to find the available bandwidth, where ε is a parameter that controls the exploration vs. exploitation trade-off. We propose another supervised learning-based classification method to ob- tain reliable available bandwidth estimates with a reduced amount of network overhead in networks, where available bandwidth changes very frequently. In such networks, reinforcement learning-based method may take longer to con- verge as it has no training phase and learns in an online manner. We also evaluate our method with different classification-based supervised machine learning techniques. Furthermore, considering the correlated changes in a network’s traffic through time, we apply filtering techniques on the estimation results in order to track the available bandwidth changes. Active probing techniques provide flexibility in designing the input struc- ture. In contrast, the vast majority of Internet traffic is Transmission Control Protocol (TCP) flows that exhibit a rather chaotic traffic pattern. We investigate how the theory of active probing can be used to extract relevant information from passive TCP measurements. We extend our method to perform the estima- tion using only sender-side measurements of TCP data and acknowledgment packets. However, non-fluid cross traffic, multiple tight links, and packet loss in the reverse path may alter the spacing of acknowledgments and hence in- crease the measurement noise. To obtain reliable available bandwidth estimates from noise-afflicted acknowledgment gaps we propose a neural network-based method. We conduct a comprehensive measurement study in a controlled network testbed at Leibniz University Hannover. We evaluate our proposed methods under a variety of notoriously difficult network conditions that have not been included in the training such as randomly generated networks with multiple tight links, heavy cross traffic burstiness, delays, and packet loss. Our testing results reveal that our proposed machine learning-based techniques are able to identify the available bandwidth with high precision from active and passive measurements. Furthermore, our reinforcement learning-based method without any training phase shows accurate and fast convergence to available band- width estimates

Pathogenicity test of Western Australian isolates of Sclerotinia sclerotiorum in canola

Stem rot disease caused by Sclerotinia sclerotiorum has emerged as a serious problem on canola (Brassica napus L.) production in Western Australia (WA) over the past few years where crop losses can be up to 40% in the worst affected crops. Hundreds of isolates of S. sclerotiorum have been collected from different canola growing regions of WA. As the majority of WA isolates of S. sclerotiorum have not been analyzed for their genetic characterization, analysis of genetic variation of WA isolates will be undertaken using classical and molecular techniques such as pathogenicity test, mycelial compatibility groups (MCGs), ITS sequencing, and cluster analysis. The experiments which started in March 2013, aim to use classical and molecular tools to identify groups of WA isolates of S. sclerotiorum from which isolates will be selected for the main studies on the management of S. sclerotiorum in canola. Accurate information of genetic diversity through research on characterization of the pathogen will lead to better understanding of the pathogen and will also benefit the breeding programs particularly aiming at breeding for disease resistance and moreover, could lead to developing better techniques for managing the disease. The paper provides an outline of the experiments and preliminary results

Spontaneous ruptured splenic artery aneurysm: a case report

Splenic artery aneurysms are rare. We discuss a case of a 58-year-old gentleman presenting with collapse and shock secondary to spontaneous splenic artery aneurysm rupture. Patient underwent laparotomy and splenectomy then discharged home within a week of presentation