Evaluating Arabidopsis thaliana ubiquitin1 promoter activity in Solanum lycopersicum

Overexpression of genes involved in heat stress response may yield heat stress resistant plants. To bring genes into overexpression, a good constitutive promoter is key. The key question of this study was: Is the Arabidopsis thaliana ubiquitin1 promoter constitutively active in Solanum lycopersicum? S. lycopersicum plants where transformed with an A. thaliana ubiquitin1 promoter driving expression of β-glucuronidase (GUS). Six out of the ten successfully transformed explants showed expression of GUS, either in protein activity or transcript. The research shows that the A. thaliana ubiquitin1 promoter is not constitutively active in S. lycopersicum, but instead shows sporadic expression

TESTING THREE CHEMICALS FOR DETERRING CROP DAMAGE BY CRANES

Damage to planted corn seed by cranes has the potential to cause great economic loss in areas where both intersect. In 2000 the International Crane Foundation (ICF) tested limonene (LIM), methyl anthranilate (MA), and 9,10-anthraquinone (AQ) as possible replacements for the insecticides lindane and diazinon that had been used as deterrents to cranes damaging corn seed and seedlings. LIM, MA, and AQ lowered germination rates (down to 85, 90, and 92%, respectively) as compared to a germination rate of 96% in untreated corn. A 1.0% solution of AQ was effective as a crane deterrent, while LIM and MA were not. Both LIM and MA metabolized in the soil too quickly to be effective during the entire period when corn seedlings were vulnerable to crane herbivory. In 2001, a 0.5% concentration of AQ in 2 different soils (sand and organic) was tested in 2 different time periods (trial 1, 15 May to 14 June; trial 2, 26 June to 7 July 2). The concentration of AQ did not degrade to below effective levels in either soil type or in either time period. In all trials, AQ concentration of 0.5% prevented crane herbivory. Crane response to AQ-treated corn was to continue foraging in fields without damaging the planted crop. We believe AQ is an effective chemical deterrent and will prove useful for preventing crane damage to planted corn

Rate-monotonic scheduling on uniform multiprocessors

Each processor in a uniform multiprocessor machine is characterized by a speed or computing capacity, with the interpretation that a job executing on a processor with speed s for t time units completes (s×t) units of execution. The scheduling of systems of periodic tasks on uniform multiprocessor platforms using the rate-monotonic scheduling algorithm is considered here. A simple, sufficient test is presented for determining whether a given periodic task system will be successfully scheduled by algorithm upon a particular uniform multiprocessor platform — this test generalizes earlier results concerning rate-monotonic scheduling upon identical multiprocessor platforms

Design and Performance of a Permanent Magnetic Quadrupole for a Low Energy Linear Accelerator Beam Line

Permanent magnets which show the highest magnetic flux density, have been used in constructing an insertable Permanent Magnetic Quadrupole (PMQ). The PMQ is part of an electron irradiation facility for polymer research at the Eindhoven University of Technology. For polymer irradiation that requires a homogeneous dose distribution, the PMQ is inserted and the expanded electron beam will irradiate the target completely. Design criteria of the quadrupole are discussed. The quadrupole geometry has been optimised using CEDRAT finite element software. The influence of mechanical alignment errors (0.15 mm) and variations in permanent magnet properties (0.5%) on the magnetic field have been simulated. Sixteen NdFeB magnets (42x42x10 mm3) have been used to produce a quadrupole with an aperture radius of 50 mm. Before insertion, the magnetic flux density of all magnets has been determined versus magnetic field and temperature. After construction the lens strength of the quadrupole has been determined using the floating wire technique. The flux density has been measured using a Hall probe. Results show a magnetic field gradient that varies less than 0.5% within a radius of 25 mm. Alignment errors have been determined comparing simulation and measurement

Building a Machine-learning Framework to Remotely Assess Parkinson's Disease Using Smartphones.

Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple neurological systems. Traditional PD assessment is conducted by a physician during infrequent clinic visits. Using smartphones, remote patient monitoring has the potential to obtain objective behavioral data semi-continuously, track disease fluctuations, and avoid rater dependency. Smartphones collect sensor data during various active tests and passive monitoring, including balance (postural instability), dexterity (skill in performing tasks using hands), gait (the pattern of walking), tremor (involuntary muscle contraction and relaxation), and voice. Some of the features extracted from smartphone data are potentially associated with specific PD symptoms identified by physicians. To leverage large-scale cross-modality smartphone features, we propose a machine-learning framework for performing automated disease assessment. The framework consists of a two-step feature selection procedure and a generic model based on the elastic-net regularization. Using this framework, we map the PD-specific architecture of behaviors using data obtained from both PD participants and healthy controls (HCs). Utilizing these atlases of features, the framework shows promises to (a) discriminate PD participants from HCs, and (b) estimate the disease severity of individuals with PD. Data analysis results from 437 behavioral features obtained from 72 subjects (37 PD and 35 HC) sampled from 17 separate days during a period of up to six months suggest that this framework is potentially useful for the analysis of remotely collected smartphone sensor data in individuals with PD

Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones

Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying the idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time