Alternative strategies to treat potato early blight

Potato, Solanum tuberosum, is a staple crop grown worldwide. Like all other plants that are cultivated in the world’s vast agricultural system, potatoes are constantly under attack by plant pathogens. Early blight is a potato disease caused by a fungal pathogen called Alternaria solani. In Sweden this pathogen is particularly problematic in the starch potato industry causing premature defoliation and reduced starch yield. The most common current treatment is application of fungicides. The focus of the research presented in this thesis has been to test and evaluate alternative ways to combat this pathogen in an applied Swedish field environment. A three-year observational study was conducted, as were multiple field trials, to achieve a broader understanding of how to manage early blight. The results of the observational study led us to design further field trials to test the importance of potassium. We found interesting differences in disease severity among the farms. The field trials consisted of evaluating cultivar tolerance, biological control measures such as the use of biocontrol agents (BCAs) and plant resistance inducers (PRIs), and the role of plant nutrients. The most important finding in this thesis is that the best treatment strategy is highly farm specific, and it is crucial to customize the treatments at a field level. The soil composition is the single largest factor that impacts the rate of infection. A sandier soil is much more likely to suffer from early blight induced yield loss and the recommended treatments should be based on the sand content of the soil in the specific field. Further results conclude that the potassium content in the soil and leaf plays a role in disease rate since a depletion caused heavier infection. The BCAs and PRIs evaluated showed potential for future alternative strategies but none of the evaluated substances proved to be efficient under field conditions. Lastly, it was observed that there are differences among starch potato cultivars currently grown, that affect the disease rate of early blight

Life as a Joke: In Defense of the Comedic Narrative

Undergraduate Winner: 1st Place, 2012. 25th Annual Carl Neureuther Student Book Collection Competitio

Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Design and Development of a Multifunctional Test Rig

In this project, the several steps in a product development process can be followed, from the first brainstorming of basic concepts to the final implementation of the manufactured product in the factory. The project was assigned by Faiveley Transport Nordic AB and its aim was to design a well functioning test rig for testing of their train brake units. The new rig’s advantages compared to old existing test rigs at Faiveley, is that it should be compact, flexible and able to test multiple train brake units at the same time. Throughout the project the methodology of Ullrich and Eppinger’s “Product Design and Development” was used at a large extent. As a first step in this methodology, target specifications were set and thereafter the concept generation could start. The designing of the test rig was divided into sub problems to be solved separately and after several iterations a final design was found. To make sure the test rig was dimensioned in a satisfying way comprehensive calculations were carried out, e.g. ANSYS calculations. After the supervisors at Faiveley approved the design it was manufactured by the company Ingenjörsfirma Jeppsson AB. When the test rig was delivered careful testing took place. The results were very positive, all components functioned as wished and the test rig responded well when applied to forces. As Faiveley wanted a new pneumatic system to drive the train brakes, this was ordered by Festo. It consisted of one control unit and ten valve units in a terminal making the device very compact. A casing was designed and manufactured to protect the sensitive equipment. Finally the target specifications were compared to those of the actual test rig. All specifications were found satisfactory and the project was considered successful

Signal quality assessment of a novel ecg electrode for motion artifact reduction

Background: The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. Methods: The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time–frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. Results: The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. Conclusions: The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring

Influence of weather conditions on the quality of ‘Ingrid Marie’ apples and their susceptibility to grey mould infection

Apple (Malus domestica) is one of the most popular fruits consumed around the world. Environmental factors influence the development and quality of apples. We determined the influence of weather conditions on the quality of ‘Ingrid Marie’ apples harvested from eight different orchards in south Sweden in the years 2015–2017 and their susceptibility to infection by grey mould (Botrytis cinerea). We infected apples and collected data on fruit firmness, starch index, weight of fruit and lesion size in addition to collecting data on temperature, rainfall, sunlight and humidity in the period April–September. High rainfall in early April, during tree flowering, and in early June, during early fruit development, correlated with improved quality, namely reduced lesion size and low firmness level. Furthermore, with humidity higher than 77% in early June apples became more tolerant to grey mould, while low temperatures and high humidity in a period from the end of August to end of September, during the end of the fruit cell enlargement stage, correlated with larger apples. We conclude that rainfall, humidity and temperature are important weather factors influencing the quality of apples and their susceptibility to grey mould. This information may help apple growers understand the effects of weather conditions on apples more in detail. From such updated information, preharvest techniques may be applied (e.g. pruning, nutrition, irrigation or drainage) to improve conditions and apple quality as well as to reduce their susceptibility to pathogen attack

Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation

Screening for atrial fibrillation (AF) with a handheld device for recording the ECG is becoming increasingly popular. The poorer signal quality of such ECGs may lead to false detection of AF, often caused by transient noise. Consequently, the need for expert review in AF screening can become extensive. A convolutional neural network (CNN) is proposed for transient noise identification in AF detection. The network is trained using the events produced by a QRS detector, classified into either true beat detections or false detections. The CNN and a low-complexity AF detector are trained and tested using the StrokeStop I database, containing 30-s ECGs from mass screening for AF in the elderly population. Performance evaluation of the CNN-based quality control using a subset of the database resulted in sensitivity, specificity, and accuracy of 96.4, 96.9, and 96.9%, respectively. By inserting the CNN before the AF detector, the false AF detections were reduced by 22.5% without any loss in sensitivity. The results show that the number of recordings calling for expert review can be significantly reduced thanks to the identification of transient noise. The reduction of false AF detections is directly linked to the time and cost spent on expert review

False Alarm Reduction in Atrial Fibrillation Screening

Early detection of AF is essential and emphasizes the significance of AF screening. However, AF detection in screening ECGs, usually recorded by handheld and portable devices, is limited because of their high susceptibility to noise. In this study, the feasibility of applying a machine learning-based quality control stage, inserted between the QRS detector and AF detector blocks, is investigated with the aim to improve AF detection. A convolutional neural network was trained to classify the detections into either true or false. False detections were excluded and an updated series of QRS complexes was fed to the AF detector. The results show that the convolutional neural network-based quality control reduces the number of false alarms by 24.8% at the cost of 1.9% decrease in sensitivity compared to AF detection without any quality control