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

BIO-ACCUMULATION AND RELEASE OF MERCURY IN VIGNA MUNGO (L.) HEPPER SEEDLINGS

Effect of mercury on the seedling of Vigna mungo seedlings was studied by culturing the seedlings in Hoagland medium artificially contaminated with 5 and 10mM Mercuric Chloride. Histochemical localization of the mercury in shoot and root tissues was done by staining with dithizone and quantitative analyses of mercury content accumulated in root, stem and leaf tissues were done using mercury analyser. Localization of mercury was observed as coloured masses in the cells of root and stem. Stem tissues of seedlings showed anatomical modification in the epidermal cells as trichomes. Patterns of bioaccumulation of mercury was root> stem> leaves revealing feeble translocation to the shoot system. A comparison of residual mercury content retained in the growth medium after sample harvesting and quantity accumulated in the plant body reveals that some quantity of mercury is lost presumably through the trichomes developed on the stem and/ or through stomata of the leaves

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

Matter-Wave Solitons in an F=1 Spinor Bose-Einstein Condensate

Following our previous work [J. Ieda, T. Miyakawa, M. Wadati, cond-mat/0404569] on a novel integrable model describing soliton dynamics of an F=1 spinor Bose--Einstein condensate, we discuss in detail the properties of the multi-component system with spin-exchange interactions. The exact multiple bright soliton solutions are obtained for the system where the mean-field interaction is attractive (c_0 < 0) and the spin-exchange interaction is ferromagnetic (c_2 < 0). A complete classification of the one-soliton solution with respect to the spin states and an explicit formula of the two-soliton solution are presented. For solitons in polar state, there exists a variety of different shaped solutions including twin peaks. We show that a "singlet pair" density can be used to distinguish those energetically degenerate solitons. We also analyze collisional effects between solitons in the same or different spin state(s) by computing the asymptotic forms of their initial and final states. The result reveals that it is possible to manipulate the spin dynamics by controlling the parameters of colliding solitons.Comment: 12 pages, 9 figures, to appear in J. Phys. Soc. Jpn. Vol.73 No.11 (2004

Diffuse Reflectance Spectroscopic Approach for the Characterization of Soil Aggregate Size Distribution

Assessment of soil structure and soil aggregation remains a challenging task. Routine methods such as dry- and wet-sieving approaches are generally time consuming and tedious, which calls for a robust, fast, and nondestructive method of soil aggregate characterization. Over the last two decades, diffuse reflectance spectroscopy (DRS) has emerged as a rapid and noninvasive technique for soil characterization. Combined with chemometric and data-mining algorithms, it provides an effective way of measuring several soil attributes and has the added advantage of being amenable to a remote sensing mode of operation. The objective of this study was to determine if the DRS approach could be used as a rapid, noninvasive technique to estimate soil aggregate characteristics. The DRS approach was examined for the estimation of soil aggregate characteristics such as the geometric mean diameter and two statistical parameters of the lognormal aggregate size distribution (ASD) functions using 910 soil samples from India representing three important soil groups. Results showed that the geometric mean diameter and the median aggregate size parameter provided excellent predictions, with ratio of performance deviation (RPD) values ranging from 1.99 to 2.28. The RPD value for the standard deviation of the ASD ranged from 1.36 to 1.72, suggesting moderate prediction. It was further observed that soil aggregates influence the incident electromagnetic radiation on soils primarily in the visible region and to some extent the shortwave- and near-infrared regions. Electronic transitions of Fe-bearing minerals, clay minerals, and C–H functional groups of organic matter may be responsible for modifying the spectral reflectance from soils in addition to the self-shadowing effects of surface roughness. The results of this study suggest that the chemometric approach may be combined with DRS to estimate soil aggregate size characteristics

Hospitalization for Heart Failure in the United States, UK, Taiwan, and Japan: An International Comparison of Administrative Health Records on 413,385 Individual Patients

BACKGROUND: Registries show international variations in the characteristics and outcome of patients with heart failure (HF) but national samples are rarely large, and case-selection may be biased due to enrolment in academic centres. National administrative datasets provide large samples with a low risk of bias. In this study, we compared the characteristics, healthcare resource utilization (HRU) and outcomes of patients with primary HF hospitalizations (HFH) using electronic health records (EHR) from four high-income countries (USA, UK, Taiwan, Japan) on three continents. METHODS AND RESULTS: We used EHR to identify unplanned HFH between 2012-2014. We identified 231,512, 10,991, 36,900 and 133,982 patients with a primary HFH from USA, UK, Taiwan and Japan, respectively. HFH per 100,000 population was highest in USA and lowest in Taiwan. Patients in Taiwan and Japan were older but fewer were obese or had chronic kidney disease. LOHS was shortest in USA (median 4 days) and longer in UK, Taiwan and Japan (medians 7, 9 and 17 days, respectively). HRU during hospitalization was highest in Japan and lowest in UK. Crude and direct standardized in-hospital mortality was lowest in USA (direct standardized rates: 1.8 [95%CI:1.7-1.9]%)and progressively higher in Taiwan (direct standardized rates: 3.9 [95%CI:3.8-4.1]%), UK (direct standardized rates: 6.4 [95%CI:6.1-6.7]%) and Japan (direct standardized rates: 6.7 [95%CI:6.6-6.8]%). 30-day all-cause (25.8%) and HF (7.2%) readmissions were highest in USA and lowest in Japan (11.9% and 5.1% respectively). CONCLUSION: Marked international variations in patient characteristics, HRU and clinical outcome exist; understanding them might inform health care policy and international trial design

Comparative analysis of mycobacterium and related actinomycetes yields insight into the evolution of mycobacterium tuberculosis pathogenesis

<p>Abstract</p> <p>Background</p> <p>The sequence of the pathogen <it>Mycobacterium tuberculosis </it>(<it>Mtb</it>) strain <it>H37Rv </it>has been available for over a decade, but the biology of the pathogen remains poorly understood. Genome sequences from other <it>Mtb </it>strains and closely related bacteria present an opportunity to apply the power of comparative genomics to understand the evolution of <it>Mtb </it>pathogenesis. We conducted a comparative analysis using 31 genomes from the Tuberculosis Database (TBDB.org), including 8 strains of <it>Mtb </it>and <it>M. bovis</it>, 11 additional Mycobacteria, 4 Corynebacteria, 2 Streptomyces, <it>Rhodococcus jostii RHA1, Nocardia farcinia, Acidothermus cellulolyticus, Rhodobacter sphaeroides, Propionibacterium acnes</it>, and <it>Bifidobacterium longum</it>.</p> <p>Results</p> <p>Our results highlight the functional importance of lipid metabolism and its regulation, and reveal variation between the evolutionary profiles of genes implicated in saturated and unsaturated fatty acid metabolism. It also suggests that DNA repair and molybdopterin cofactors are important in pathogenic Mycobacteria. By analyzing sequence conservation and gene expression data, we identify nearly 400 conserved noncoding regions. These include 37 predicted promoter regulatory motifs, of which 14 correspond to previously validated motifs, as well as 50 potential noncoding RNAs, of which we experimentally confirm the expression of four.</p> <p>Conclusions</p> <p>Our analysis of protein evolution highlights gene families that are associated with the adaptation of environmental Mycobacteria to obligate pathogenesis. These families include fatty acid metabolism, DNA repair, and molybdopterin biosynthesis. Our analysis reinforces recent findings suggesting that small noncoding RNAs are more common in Mycobacteria than previously expected. Our data provide a foundation for understanding the genome and biology of <it>Mtb </it>in a comparative context, and are available online and through TBDB.org.</p