Two photon annihilation operators and squeezed vacuum

Inverses of the harmonic oscillator creation and annihilation operators by their actions on the number states are introduced. Three of the two photon annihilation operators, viz., a(sup +/-1)a, aa(sup +/-1), and a(sup 2), have normalizable right eigenstates with nonvanishing eigenvalues. The eigenvalue equation of these operators are discussed and their normalized eigenstates are obtained. The Fock state representation in each case separates into two sets of states, one involving only the even number states while the other involving only the odd number states. It is shown that the even set of eigenstates of the operator a(sup +/-1)a is the customary squeezed vacuum S(sigma) O greater than

The Function of NM23-H1/NME1 and Its Homologs in Major Processes Linked to Metastasis

International audienc

The European Cystic Fibrosis Society Patient Registry:valuable lessons learned on how to sustain a disease registry

Background: Disease registries have the invaluable potential to provide an insight into the natural history of the disease under investigation, to provide useful information (e.g. through health indicators) for planning health care services and to identify suitable groups of patients for clinical trials enrolment. However, the establishment and maintenance of disease registries is a burdensome initiative from economical and organisational points of view and experience sharing on registries management is important to avoid waste of resources. The aim of this paper is to discuss the problems embedded in the institution and management of an international disease registry to warn against common mistakes that can derail the best of intentions: we share the experience of the European Cystic Fibrosis Society Patient Registry, which collects data on almost 30,000 patients from 23 countries. Methods: We discuss the major problems that researchers often encounter in the creation and management of disease registries: definition of the aims the registry has to reach, definition of the criteria for patients referral to the registry, definition of the information to record, set up of a data quality process, handling of missing data, maintenance of data confidentiality, regulation of data use and dissemination of research results. Results: We give examples on how many crucial aspects were solved by the European Cystic Fibrosis Society Patient Registry regarding objectives, inclusion criteria and variables definition, data management, data quality controls, missing data handling, confidentiality maintenance, data use and results dissemination. Conclusions: We suggest an extensive literature research and discussions in working groups with different stake holders, including patient representatives, on the objectives, inclusion criteria and the information to record. We propose to pilot the recording of few variables and test the applicability of their definition first. The use of a shared electronic platform for data collection that automatically computes derived variables, and automatically performs basic data quality controls is a good data management practice, that also helps in reducing missing data. We found crucial for success the collaboration with existing national and international registries, cystic fibrosis organisations and patients' associations

Creating longitudinal datasets and cleaning existing data identifiers in a cystic fibrosis registry using a novel Bayesian probabilistic approach from astronomy

Patient registry data are commonly collected as annual snapshots that need to be amalgamated to understand the longitudinal progress of each patient. However, patient identifiers can either change or may not be available for legal reasons when longitudinal data are collated from patients living in different countries. Here, we apply astronomical statistical matching techniques to link individual patient records that can be used where identifiers are absent or to validate uncertain identifiers. We adopt a Bayesian model framework used for probabilistically linking records in astronomy. We adapt this and validate it across blinded, annually collected data. This is a high-quality (Danish) sub-set of data held in the European Cystic Fibrosis Society Patient Registry (ECFSPR). Our initial experiments achieved a precision of 0.990 at a recall value of 0.987. However, detailed investigation of the discrepancies uncovered typing errors in 27 of the identifiers in the original Danish sub-set. After fixing these errors to create a new gold standard our algorithm correctly linked individual records across years achieving a precision of 0.997 at a recall value of 0.987 without recourse to identifiers. Our Bayesian framework provides the probability of whether a pair of records belong to the same patient. Unlike other record linkage approaches, our algorithm can also use physical models, such as body mass index curves, as prior information for record linkage. We have shown our framework can create longitudinal samples where none existed and validate pre-existing patient identifiers. We have demonstrated that in this specific case this automated approach is better than the existing identifiers

Robust Prediction Model for Multidimensional and Unbalanced Datasets

Data Mining is a promising field and is applied in multiple domains for its predictive capabilities. Data in the real world cannot be readily used for data mining as it suffers from the problems of multidimensionality, unbalance and missing values. It is difficult to use its predictive capabilities by novice users. It is difficult for a beginner to find the relevant set of attributes from a large pool of data available. The paper presents a Robust Prediction Model that finds a relevant set of attributes; resolves the problems of unbalanced and multidimensional real-life datasets and helps in finding patterns for informed decision making. Model is tested upon five different datasets in the domain of Health Sector, Education, Business and Fraud Detection. The results showcase the robust behaviour of the model and its applicability in various domains.Comment: 9 page

Unified Prediction Model for Employability in Indian Higher Education System

Educational Data Mining has become extremely popular among researchers in last decade. Prior effort in this area was only directed towards prediction of academic performance of a student. Very less number of researches are directed towards predicting employability of a student i.e. prediction of students performance in campus placements at an early stage of enrollment. Furthermore, existing researches on students employability prediction are not universal in approach and is either based upon only one type of course or University/Institute. Henceforth, is not scalable from one context to another. With the necessity of unification, data of professional technical courses namely Bachelor in Engineering/Technology and Masters in Computer Applications students have been collected from 17 states of India. To deal with such a data, a unified predictive model has been developed and applied on 17 states datasets. The research done in this paper proves that model has universal application and can be applied to various states and institutes pan India with different cultural background and course structure. This paper also explores and proves statistically that there is no significant difference in Indian Education System with respect to states as far as prediction of employability of students is concerned. Model provides a generalized solution for student employability prediction in Indian Scenario.Comment: 9 page

Maternal and perinatal outcome in pregnancy complicated by obstetric cholestasis: study from a tertiary care centre in North India

Background: Intrahepatic cholestasis of pregnancy (IHCP) is the most common pregnancy related liver disorder. It typically presents with troublesome itching and can lead to complications for both mother and foetus. Present study was carried out to study the incidence of Obstetric Cholestasis and its fetomaternal outcome in a tertiary care hospital.Methods: It was a prospective epidemiological study during a period of one year (May 2020 to April 2021) over 120 pregnant ladies suffering from pruritus and detected as having Obstetric Cholestasis. They were followed up and maternal as well as perinatal outcome recorded. Appropriate statistical analysis done as applicable.Results: The incidence of Obstetric Cholestasis in our hospital was 9.3%. Majority of cases delivered at term (78.3%). 41.6% patients delivered vaginally, 43.3% had emergency caesarean section, and 2.5% patients had instrumental delivery. Maternal morbidities are due to sleep disturbance (60%), coagulation abnormality (13.3%), increase chance of operative delivery (55.8%) and postpartum haemorrhage (12.5%). Neonatal complications include meconium aspiration (46.6%), NICU admission (36.6%), prematurity (5%) and perinatal mortality (3.3%).Conclusions: Cholestasis of pregnancy causes maternal pruritus with impaired liver function tests. Maternal morbidity is increased in terms of increased caesarean section rates and discomfort due to pruritus. A timely intervention at 37-38 weeks will reduce the adverse perinatal outcome.