Inhibition of Xanthomonas fragariae, Causative Agent of Angular Leaf Spot of Strawberry, through Iron Deprivation.

In commercial production settings, few options exist to prevent or treat angular leaf spot (ALS) of strawberry, a disease of economic importance and caused by the bacterial pathogen Xanthomonas fragariae. In the process of isolating and identifying X. fragariae bacteria from symptomatic plants, we observed growth inhibition of X. fragariae by bacterial isolates from the same leaf macerates. Identified as species of Pseudomonas and Rhizobium, these isolates were confirmed to suppress growth of X. fragariae in agar overlay plates and in microtiter plate cultures, as did our reference strain Pseudomonas putida KT2440. Screening of a transposon mutant library of KT2440 revealed that disruption of the biosynthetic pathway for the siderophore pyoverdine resulted in complete loss of X. fragariae antagonism, suggesting iron competition as a mode of action. Antagonism could be replicated on plate and in culture by addition of purified pyoverdine or by addition of the chelating agents tannic acid and dipyridyl, while supplementing the medium with iron negated the inhibitory effects of pyoverdine, tannic acid and dipyridyl. When co-inoculated with tannic acid onto strawberry plants, X. fragariae's ability to cause foliar symptoms was greatly reduced, suggesting a possible opportunity for iron-based management of ALS. We discuss our findings in the context of 'nutritional immunity,' the idea that plant hosts restrict pathogen access to iron, either directly, or indirectly through their associated microbiota

Inhibition of Xanthomonas fragariae, Causative Agent of Angular Leaf Spot of Strawberry, through Iron Deprivation.

In commercial production settings, few options exist to prevent or treat angular leaf spot (ALS) of strawberry, a disease of economic importance and caused by the bacterial pathogen Xanthomonas fragariae. In the process of isolating and identifying X. fragariae bacteria from symptomatic plants, we observed growth inhibition of X. fragariae by bacterial isolates from the same leaf macerates. Identified as species of Pseudomonas and Rhizobium, these isolates were confirmed to suppress growth of X. fragariae in agar overlay plates and in microtiter plate cultures, as did our reference strain Pseudomonas putida KT2440. Screening of a transposon mutant library of KT2440 revealed that disruption of the biosynthetic pathway for the siderophore pyoverdine resulted in complete loss of X. fragariae antagonism, suggesting iron competition as a mode of action. Antagonism could be replicated on plate and in culture by addition of purified pyoverdine or by addition of the chelating agents tannic acid and dipyridyl, while supplementing the medium with iron negated the inhibitory effects of pyoverdine, tannic acid and dipyridyl. When co-inoculated with tannic acid onto strawberry plants, X. fragariae's ability to cause foliar symptoms was greatly reduced, suggesting a possible opportunity for iron-based management of ALS. We discuss our findings in the context of 'nutritional immunity,' the idea that plant hosts restrict pathogen access to iron, either directly, or indirectly through their associated microbiota

ViSQOLAudio: An Objective Audio Quality Metric for Low Bitrate Codecs

Streaming services seek to optimise their use of bandwidth across audio and visual channels to maximise the quality of experience for users. This letter evaluates whether objective quality metrics can predict the audio quality for music encoded at low bitrates by comparing objective predictions with results from listener tests. Three objective metrics were benchmarked: PEAQ, POLQA, and VISQOLAudio. The results demonstrate objective metrics designed for speech quality assessment have a strong potential for quality assessment of low bitrate audio codecs

Taking Trust Seriously in Privacy Law

Trust is beautiful. The willingness to accept vulnerability to the actions of others is the essential ingredient for friendship, commerce, transportation, and virtually every other activity that involves other people. It allows us to build things, and it allows us to grow. Trust is everywhere, but particularly at the core of the information relationships that have come to characterize our modern, digital lives. Relationships between people and their ISPs, social networks, and hired professionals are typically understood in terms of privacy. But the way we have talked about privacy has a pessimism problem – privacy is conceptualized in negative terms, which leads us to mistakenly look for “creepy” new practices, focus excessively on harms from invasions of privacy, and place too much weight on the ability of individuals to opt out of harmful or offensive data practices. But there is another way to think about privacy and shape our laws. Instead of trying to protect us against bad things, privacy rules can also be used to create good things, like trust. In this paper, we argue that privacy can and should be thought of as enabling trust in our essential information relationships. This vision of privacy creates value for all parties to an information transaction and enables the kind of sustainable information relationships on which our digital economy must depend. Drawing by analogy on the law of fiduciary duties, we argue that privacy laws and practices centered on trust would enrich our understanding of the existing privacy principles of confidentiality, transparency, and data protection. Re-considering these principles in terms of trust would move them from procedural means of compliance for data extraction towards substantive principles to build trusted, sustainable information relationships. Thinking about privacy in terms of trust also reveals a principle that we argue should become a new bedrock tenet of privacy law: the Loyalty that data holders must give to data subjects. Rejuvenating privacy law by getting past Privacy Pessimism is essential if we are to build the kind of digital society that is sustainable and ultimately beneficial to all – users, governments, and companies. There is a better way forward for privacy. Trust us

Emotional processing in Parkinson's disease and anxiety: an EEG study of visual affective word processing

A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance. However, BCI systems that are applicable in real-time and outside clinical settings require high performance. Therefore, we have to improve the current methods for feature extraction. In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a linearly constrained minimum variance (LCMV) beamformer. Beamformers allow for easy incorporation of anatomical data and are applicable in real-time. A 32-channel EEG-BCI system was designed for a two-class motor imagery (MI) paradigm. We optimized a synchronous system for two untrained subjects and investigated two aspects. First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method (FDM) head model including white and gray matter, CSF, scalp and skull tissue. Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed. On the one hand, they were chosen around electrodes C3 and C4, as hand MI activity theoretically is expected here. On the other hand, they were constructed based on the actual activated regions identified by an fMRI scan. Subsequently, an asynchronous system was derived for one of the subjects and an optimal balance between speed and accuracy was found. Lastly, a real-time application was made. These systems were evaluated by their accuracy, defined as the percentage of correct left and right classifications. From the real-time application, the information transfer rate (ITR) was also determined. An accuracy of 86.60 ± 4.40% was achieved for subject 1 and 78.71 ± 0.73% for subject 2. This gives an average accuracy of 82.66 ± 2.57%. We found that the use of a personalized FDM model improved the accuracy of the system, on average 24.22% with respect to the template BEM model and on average 5.15% with respect to the personalized BEM model. Including fMRI spatial priors did not improve accuracy. Personal fine- tuning largely resolved the robustness problems arising due to the differences in head geometry and neurophysiology between subjects. A real-time average accuracy of 64.26% was reached and the maximum ITR was 6.71 bits/min. We conclude that beamformers calculated with a personalized FDM model have great potential to ameliorate feature extraction and, as a consequence, to improve the performance of real-time BCI systems