Isolation and characterization of an insertion element-like repetitive sequence specific for Mycobacterium tuberculosis complex

We report the characterization of an insertion-like repetitive sequence containing the clone of Mycobacterium tuberculosis. This repetitive sequence contains seven inverted repeats. Restriction fragment length polymorphism studies using this probe have shown that it is not a highly polymorphic probe but rather shows conservative fingerprint pattern. Out of the 150 strains tested, only three showed different fingerprint patterns. It has several direct and inverted repeats. Homology studies of the putative protein coding region show that this repeat element might code for a metalloproteinase of M. tuberculosis. Homology studies also implicate this repeat element to be from a very essential region of the M. tuberculosis genome participating in recombination. This repeat has been found to be an ideal target for polymerase chain reaction to detect M. tuberculosis

Cryoablation for Small Renal Masses

Advances in imaging techniques (CT and MRI) and widespread use of imaging especially ultrasound scanning have resulted in a dramatic increase in the detection of small renal masses. While open partial nephrectomy is still the reference standard for the management of these small renal masses, its associated morbidity has encouraged clinicians to exploit the advancements in minimally invasive ablative techniques. The last decade has seen the rapid development of laparoscopic partial nephrectomy and novel ablative techniques such as, radiofrequency ablation (RFA), high-intensity focused ultrasound (HIFU), and cryoablation (CA). In particular, CA for small renal masses has gained popularity as it combines nephron-sparing surgery with a minimally invasive approach. Studies with up to 5-year followup have shown an overall and cancer-specific 5-year survival of 82% and 100%, respectively. This manuscript will focus on the principles and clinical applications of cryoablation of small renal masses, with detailed review of relevant literature

Significance of HOMO and LUMO studies on dye doped glycene lithium sulphate (GLS) crystals for non-linear optical applications

The ideal material that could have potential applications in non-linear optical (NLO) devices should possess the combination of large non-linear figure of merit for frequency conversion, high laser damage threshold, fast optical response time, wide phase matchable angle, architectural flexibility for molecular design and morphology, optical transparency and high mechanical strength. The stability of glycene lithium sulphate (GLS) single crystal has been improved by doping organic dyes. The structural, chemical, optical, mechanical and non-linear optical properties of the dye doped crystals have been analyzed with the characterization studies such as powder XRD, FT-IR, UV-Visible and SHG measurements, respectively. NMR, HOMO and LUMO energies have been performed by time dependent density functional theory (TD-DFT) approach. The Mulliken charge analysis indicates that the sulphur atoms of the benzene ring and the OH group attached to the ring are the main reactive centers of glycene lithium sulphate. And the temperature dependence of the thermodynamic properties of constant pressure (Cp), entropy (S) and enthalpy change (ΔH0→T) for glycene lithium sulphate have also been determined

Development of DNA probes for M. tuberculosis

Attempts were made to develop DNA probes for M. tuberculosis. Random library of M. tuberculosis was constructed in plasmid pGEM -4. Selection of recombinant clones was made by hybridisation with 32P labelled M. tuberculosis probe. Ten recombinant clones were selected on the basis of strong signals from the random library. These 10 clones named pTRC1-10 were subjected to tests for specificity and sensitivity. On this basis, pTRC4 was chosen and this is also, useful in restriction fragment length polymorphism (RFLP) studies

Restriction fragment length polymorphism of Mycobacterium tuberculosis strains from various regions of India, using direct repeat probe

Intraspecies differentiation was studied on 68 M. tuberculosis strains obtained from 6 states of India by restriction fragment length polymorphism (RFLP) using a direct repeat probe (DR probe) hybridised with Alu I digest of DNA. Most strains showed polymorphism based patterns that comprised between 2 to 7 bands and were grouped into 26 RFLP types. Of the 11 strains tested from Amritsar, 8 were RFLP type 5; the remaining 3 were of type 11 and were exclusively confined to this region. The strains from other regions were more heterogeneous. We confirm that DR-associated RFLP can be an excellent tool for the differentiation of M. tuberculosis strains. Depending on their geographical origin, these strains can be differentiated to a large extent by DR fingerprinting

Predictability improvement of Scheduled Flights Departure Time Variation using Supervised Machine Learning

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air traffic flow management slot changes. This paper presents a model that predicts departure time variance based on the previous leg departure time using a combination of exponential moving average and machine learning methods. The model correctly classifies the departure time (Early, On Time, Delay) based on the previous leg departure state, allowing the ATFM system to measure the arrival time of a capacity constrained airport with greater accuracy and better assess demand requirements. The results show that the proposed model with M5P Regression tree provides the best results, with Mean Absolute Error and Root Mean Square Error (RMSE) of 3.43 and 4.83, respectively, indicating a 50% improvement over previous research findings. Whereas, with logistic regression, the classification of departure time (Early, On Time, Delay) is achieved a better accuracy of 91 %, which is higher than previous works

Prediction of Gate In Time of Scheduled Flights and Schedule Conformance using Machine Learning-based Algorithms

Prediction of Gate to Gate block time for scheduled flights is considered as one of the challenging tasks in Air Traffic Flow Management (ATFM)system. Establishing an effective and practically reliable model to manage the problem of block time variation is a significant work. The airlines do tend to pad or inflate block time to Actual Block time to calculate Schedule block times which is approved by aviation regulator. This will lead to flaws in air traffic flow strategic decision-making and in turn affect the efficiency, estimation and undesirable delays, which leads to traffic congestion and inefficient ground delay programs. This study evaluates the effectiveness of nonlinear and time varying regression models to predict block time with minimal attributes in order to solve the problem of difficulty in predicting the block time variation. The key research outcome of this paper is to trace the temporal variations of flying time for different aircraft types and to predict the variation of actual arrival time from the scheduled arrival time at the destination airport. Ultimately, a combination of M5P regression model and logistic regression model is proposed to predict early, delayed and on-time conformity with approved schedules. Analysis based on a realistic data set of a domestic airport pair (Mumbai International Airport and New Delhi International Airport) in India shows that the proposed model is able to predict in block time at the time of departure with an accuracy of minutes for of test instances. As a result of the scheduled arrival time performance (early, delayed and timely) has been classified accurately using Logistic regression Classifier of machine learning. The test results show that the proposed model uses a minimum number of attributes and less computational time to more accurately predict the actual arrival time and scheduled arrival performance without details on the weather

Using the Hadamard and related transforms for simplifying the spectrum of the quantum baker's map

We rationalize the somewhat surprising efficacy of the Hadamard transform in simplifying the eigenstates of the quantum baker's map, a paradigmatic model of quantum chaos. This allows us to construct closely related, but new, transforms that do significantly better, thus nearly solving for many states of the quantum baker's map. These new transforms, which combine the standard Fourier and Hadamard transforms in an interesting manner, are constructed from eigenvectors of the shift permutation operator that are also simultaneous eigenvectors of bit-flip (parity) and possess bit-reversal (time-reversal) symmetry.Comment: Version to appear in J. Phys. A. Added discussions; modified title; corrected minor error

Symmetrically coupled higher-order nonlinear Schroedinger equations: singularity analysis and integrability

The integrability of a system of two symmetrically coupled higher-order nonlinear Schr\"{o}dinger equations with parameter coefficients is tested by means of the singularity analysis. It is proven that the system passes the Painlev\'{e} test for integrability only in ten distinct cases, of which two are new. For one of the new cases, a Lax pair and a multi-field generalization are obtained; for the other one, the equations of the system are uncoupled by a nonlinear transformation.Comment: 12 pages, LaTeX2e, IOP style, final version, to appear in J.Phys.A:Math.Ge