Integrability of a conducting elastic rod in a magnetic field

We consider the equilibrium equations for a conducting elastic rod placed in a uniform magnetic field, motivated by the problem of electrodynamic space tethers. When expressed in body coordinates the equations are found to sit in a hierarchy of non-canonical Hamiltonian systems involving an increasing number of vector fields. These systems, which include the classical Euler and Kirchhoff rods, are shown to be completely integrable in the case of a transversely isotropic rod; they are in fact generated by a Lax pair. For the magnetic rod this gives a physical interpretation to a previously proposed abstract nine-dimensional integrable system. We use the conserved quantities to reduce the equations to a four-dimensional canonical Hamiltonian system, allowing the geometry of the phase space to be investigated through Poincar\'e sections. In the special case where the force in the rod is aligned with the magnetic field the system turns out to be superintegrable, meaning that the phase space breaks down completely into periodic orbits, corresponding to straight twisted rods.Comment: 19 pages, 1 figur

N, NH, and NH2 radical densities in a remote Ar-NH3-SiH4 plasma and their role in silicon nitride deposition

The densities of N, NH, and NH2 radicals in a remote Ar-NH3-SiH4 plasma used for high-rate silicon nitride deposition were investigated for different gas mixts. and plasma settings using cavity ringdown absorption spectroscopy and threshold ionization mass spectrometry. For typical deposition conditions, the N, NH, and NH2 radical densities are on the order of 1012 cm-3 and the trends with NH3 flow, SiH4 flow, and plasma source current are reported. We present a feasible reaction pathway for the prodn. and loss of the NHx radicals that is consistent with the exptl. results. Furthermore, mass spectrometry revealed that the consumption of NH3 was typically 40%, while it was over 80% for SiH4. On the basis of the measured N densities we deduced the recombination and sticking coeff. for N radicals on a silicon nitride film. Using this sticking coeff. and reported surface reaction probabilities of NH and NH2 radicals, we conclude that N and NH2 radicals are mainly responsible for the N incorporation in the silicon nitride film, while Si atoms are most likely brought to the surface in the form of SiHx radicals. [on SciFinder (R)

The performance of COBRA, a decision rule to predict the need for intensive care interventions in intentional drug overdose

BACKGROUND: COBRA was developed as a decision rule to predict which patients visiting the emergency department (ED) following intentional drug overdose will not require intensive care unit (ICU) interventions. COBRA uses parameters from five vital systems (cardiac conduction, oxygenation, blood pressure, respiration, and awareness) that are readily available in the ED. COBRA recommends against ICU admission when all these parameters are normal. OBJECTIVE: The primary aim of this study was to determine the negative predictive value (NPV) of COBRA in predicting ICU interventions. Secondary outcomes were the sensitivity, specificity and positive predictive value (PPV), and the observation time required for a reliable prediction. DESIGN: Observational cohort study. SETTINGS AND PARTICIPANTS: Patients with a reported intentional overdose with drugs having potential acute effects on neurological, circulatory or ventilatory function were included, and data necessary to complete the decision rule was collected. The attending physician in the ED made the actual admission decision, on the basis of clinical judgement. COBRA was measured 0, 3 and 6 h after arrival at the ED. OUTCOME MEASURES: Need for ICU interventions (treatment of convulsion; defibrillation; mechanical or noninvasive ventilation; intravenous administration of vasopressive agents, antiarrhythmics, atropine, calcium, magnesium or sedation; continuous hemofiltration or administration of antagonist/antidote and fluid resuscitation). MAIN RESULTS: Of 230 new cases (144 unique patients), 59 were immediately referred to the psychiatric services and/or sent home by the attending physician, 27 went to a regular ward, and 144 were admitted to the ICU. Of these 144 cases, 40 required one or more ICU interventions. By the time the first parameters were collected, the NPV of COBRA was 95.6%. After 3 h of observation, NPV was 100%, while sensitivity, specificity and PPV were 100, 61.1 and 35.1%, respectively. None of these values improved by prolonging the observation time to 6 h. CONCLUSION: In patients with a reported intentional overdose with drugs having potential acute effects on neurological, circulatory or ventilatory function, the COBRA decision rule showed good performances in predicting the need for intensive care interventions, with a NPV of 100% after 3 h of observation

Quantitative Virus-Associated RNA Detection to Monitor Oncolytic Adenovirus Replication

Oncolytic adenoviruses are in development as immunotherapeutic agents for solid tumors. Their efficacy is in part dependent on their ability to replicate in tumors. It is, however, difficult to obtain evidence for intratumoral oncolytic adenovirus replication if direct access to the tumor is not possible. Detection of systemic adenovirus DNA, which is sometimes used as a proxy, has limited value because it does not distinguish between the product of intratumoral replication and injected virus that did not replicate. Therefore, we investigated if detection of virus-associated RNA (VA RNA) by RT-qPCR on liquid biopsies could be used as an alternative. We found that VA RNA is expressed in adenovirus-infected cells in a replication-dependent manner and is secreted by these cells in association with extracellular vesicles. This allowed VA RNA detection in the peripheral blood of a preclinical in vivo model carrying adenovirus-injected human tumors and on liquid biopsies from a human clinical trial. Our results confirm that VA RNA detection in liquid biopsies can be used for minimally invasive assessment of oncolytic adenovirus replication in solid tumors in vivo.</p

Risk Factors for Tremor in a Population of Patients with Severe Mental Illness:An 18-year Prospective Study in a Geographically Representative Sample (The Curacao Extrapyramidal Syndromes Study XI)

BACKGROUND: The aim was to assess incidence, prevalence and risk factors of medication-induced tremor in African-Caribbean patients with severe mental illness (SMI).METHOD: A prospective study of SMI patients receiving care from the only mental health service of the previous Dutch Antilles. Eight clinical assessments, over 18 years, focused on movement disorders, medication use, and resting tremor (RT) and (postural) action tremor (AT). Risk factors were modeled with logistic regression for both current (having) tremor and for tremor at the next time point (developing). The latter used a time-lagged design to assess medication changes prior to a change in tremor state.RESULTS: Yearly tremor incidence rate was 2.9% and mean tremor point prevalence was 18.4%. Over a third of patients displayed tremor during the study. Of the patients, 5.2% had AT with 25% of cases persisting to the next time point, while 17.1% of patients had RT of which 65.3% persisted. When tremor data were examined in individual patients, they often had periods of tremor interspersed with periods of no tremor. Having RT was associated with age (OR=1.07 per year; 95% confidence interval 1.03-1.11), sex (OR=0.17 for males; 0.05-0.78), cocaine use (OR=10.53; 2.22-49.94), dyskinesia (OR=0.90; 0.83-0.97), and bradykinesia (OR=1.16; 1.09-1.22). Developing RT was strongly associated with previous measurement RT (OR=9.86; 3.80-25.63), with previous RT severity (OR=1.22; 1.05-1.41), and higher anticholinergic load (OR= 1.24; 1.08-1.43). Having AT was associated with tremor-inducing medication (OR= 4.54; 1.90-10.86), cocaine use (OR=14.04; 2.38-82.96), and bradykinesia (OR=1.07; 1.01-1.15). Developing AT was associated with, previous AT severity (OR=2.62 per unit; 1.64-4.18) and tremor reducing medication (OR=0.08; 0.01-0.55).CONCLUSIONS: Long-stay SMI patients are prone to developing tremors, which show a relapsing-remitting course. Differentiation between RT and AT is important as risk factors differ and they require different prevention and treatment strategies.</p

Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium

Purpose: Coronary artery calcium (CAC) score has shown to be an accurate predictor of future cardiovascular events. Early detection by CAC scoring might reduce the number of deaths by cardiovascular disease (CVD). Automatically excluding scans which test negative for CAC could significantly reduce the workload of radiologists. We propose an algorithm that both excludes negative scans and segments the CAC. Method: The training and internal validation data were collected from the ROBINSCA study. The external validation data were collected from the ImaLife study. Both contain annotated low-dose non-contrast cardiac CT scans. 60 scans of participants were used for training and 2 sets of 50 CT scans of participants without CAC and 50 CT scans of participants with an Agatston score between 10 and 20 were collected for both internal and external validation. The effect of dilated convolutional layers was tested by using 2 CNN architectures. We used the patient-level accuracy as metric for assessing the accuracy of our pipeline for detection of CAC and the Dice coefficient score as metric for the segmentation of CAC. Results: Of the 50 negative cases in the internal and external validation set, 62 % and 86 % were classified correctly, respectively. There were no false negative predictions. For the segmentation task, Dice Coefficient scores of 0.63 and 0.84 were achieved for the internal and external validation datasets, respectively. Conclusions: Our algorithm excluded 86 % of all scans without CAC. Radiologists might need to spend less time on participants without CAC and could spend more time on participants that need their attention

Qualitative Evaluation of Common Quantitative Metrics for Clinical Acceptance of Automatic Segmentation:a Case Study on Heart Contouring from CT Images by Deep Learning Algorithms

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance

The Mouse Functional Genome Database (MfunGD): functional annotation of proteins in the light of their cellular context

MfunGD () provides a resource for annotated mouse proteins and their occurrence in protein networks. Manual annotation concentrates on proteins which are found to interact physically with other proteins. Accordingly, manually curated information from a protein–protein interaction database (MPPI) and a database of mammalian protein complexes is interconnected with MfunGD. Protein function annotation is performed using the Functional Catalogue (FunCat) annotation scheme which is widely used for the analysis of protein networks. The dataset is also supplemented with information about the literature that was used in the annotation process as well as links to the SIMAP Fasta database, the Pedant protein analysis system and cross-references to external resources. Proteins that so far were not manually inspected are annotated automatically by a graphical probabilistic model and/or superparamagnetic clustering. The database is continuously expanding to include the rapidly growing amount of functional information about gene products from mouse. MfunGD is implemented in GenRE, a J2EE-based component-oriented multi-tier architecture following the separation of concern principle