Current status of sentinel lymph node biopsy in solid malignancies

Lymphatic mapping and sentinel lymph node biopsy were first reported in 1977 by Cabanas for penile cancer. Since that time, the technique has become rapidly assimilated into clinical practice. The sentinel node concept has been validated in cutaneous melanoma and breast cancer. However, follow-up data of patients from randomised trials is needed to establish the clinical significance of sentinel lymph node biopsy before accepting the procedure as a standard of care. This technique has the potential to be utilised in all solid tumours like colon, gastric, oesophageal, lung, gynaecologic, and head and neck cancer. This paper reviews the current status of sentinel lymph node biopsy in solid tumours

Predicting the Equity Premium With Dividend Ratios

Our paper reexamines the forecasting regressions which predict annual aggregate stock market returns net of the risk-free rate with lagged aggregate dividend-yield ratios and dividend-price ratios. Prior to 1990, the conditional dividend yield could reliably outperform the historical equity premium mean in predicting future equity premia *in-sample*. But our paper shows that the dividend ratios could not outperform the prevailing unconditional mean *out-of-sample*, plus any residual power was directly related to only two years, 1974 and 1975. As of 2000, even this in-sample predictive ability has disappeared. Our paper also documents changes in the time-series processes of the dividends themselves and shows that an increasing persistence of dividend-price ratio is largely responsible for weak stock return predictability.

Empirical cross-sectional asset pricing: a survey

I review the state of empirical asset pricing devoted to understanding cross-sectional differences in average rates of return. Both methodologies and empirical evidence are surveyed. Tremendous progress has been made in understanding return patterns. At the same time, there is a need to synthesize the huge amount of collected evidenc

High-performance, heteroepitaxial, nanolaminate device layers on single-crystal-like, artificial substrates and controlled self-assembly of anostructures within device layers for wide-ranging electrical and electronic applications

For many energy and electronic applications, single-crystal-like materials offer the best performance. However, in almost all cases, fabrication of single-crystal form of the relevant material is too expensive. In addition, for many applications, very long or wide materials are required a regime not accessible by conventional single-crystal growth

Streaming and Sketch Algorithms for Large Data NLP

The availability of large and rich quantities of text data is due to the emergence of the World Wide Web, social media, and mobile devices. Such vast data sets have led to leaps in the performance of many statistically-based problems. Given a large magnitude of text data available, it is computationally prohibitive to train many complex Natural Language Processing (NLP) models on large data. This motivates the hypothesis that simple models trained on big data can outperform more complex models with small data. My dissertation provides a solution to effectively and efficiently exploit large data on many NLP applications. Datasets are growing at an exponential rate, much faster than increase in memory. To provide a memory-efficient solution for handling large datasets, this dissertation show limitations of existing streaming and sketch algorithms when applied to canonical NLP problems and proposes several new variants to overcome those shortcomings. Streaming and sketch algorithms process the large data sets in one pass and represent a large data set with a compact summary, much smaller than the full size of the input. These algorithms can easily be implemented in a distributed setting and provide a solution that is both memory- and time-efficient. However, the memory and time savings come at the expense of approximate solutions. In this dissertation, I demonstrate that approximate solutions achieved on large data are comparable to exact solutions on large data and outperform exact solutions on smaller data. I focus on many NLP problems that boil down to tracking many statistics, like storing approximate counts, computing approximate association scores like pointwise mutual information (PMI), finding frequent items (like n-grams), building streaming language models, and measuring distributional similarity. First, I introduce the concept of approximate streaming large-scale language models in NLP. Second, I present a novel variant of the Count-Min sketch that maintains approximate counts of all items. Third, I conduct a systematic study and compare many sketch algorithms that approximate count of items with focus on large-scale NLP tasks. Last, I develop fast large-scale approximate graph (FLAG), a system that quickly constructs a large-scale approximate nearest-neighbor graph from a large corpus

New approaches to scaled-up carbon nanotube synthesis and nanotube-based metal composites and sensors

The first phase of the work presented in this dissertation is the development of a scaleable process for the cost-effective synthesis of single walled carbon nanotubes (SWNTs) by thermally-induced catalytic chemical vapor deposition (CVD). With the goal of understanding the growth mechanism and optimize the synthesis process, the effect of CVD and catalyst parameters on nanotube formation was investigated in detail. It was found that nucleation and growth of SWNTs occurred within a few seconds of the introduction of the carbon source, carbon monoxide, at temperatures above 675°C over a Co-Mo/MgO catalyst/support system, resulting in the formation of high quality thinly bundled SWNTs with a narrow individual nanotube diameter distribution. A simple kinetic model is proposed to explain the observed growth and exit gas (CO2) concentration data. A scaled up run using fluidized bed reactor is performed to demonstrate large SWNTs production. In the second phase of the research performed some of the CVD parameters optimized for the synthesis of pure nanotubes were used to infiltrate SWNTs as well as multiwalled carbon nanotubes (MWNTs) into catalyst precursor filled iron and aluminum matrices, respectively, to directly fabricate metal-nanotube composites. Two carbon sources, carbon monoxide and acetylene were used for the synthesis of SWNTs and MWNTs, respectively. The yield strength of iron-carbon nanotube composites showed substantial enhancement of up to 45% and 36 % with 1 wt % of infiltrated SWNTs and MWNTs, respectively, relative to that of similarly treated pure iron samples of the same piece density without carbon nanotubes. Vickers hardness measurements showed an increase of 74% and 96% for iron composites filled with SWNTs and MWNTs, respectively. The use of a mixed feed of CO and acetylene resulted in carbide-free fabrication of the nanocomposites. A reaction mechanism supporting the observed carbide-free growth is also presented. In the third phase of the research performed, a SWNT fabrication protocol using CVD growth or electrophoretic deposition was employed for integrating nanotubes as biosensor and chemical gas sensor probes. For biosensor probes, vertically aligned SWNTs were grown or deposited on metal interconnects (Cr/Co), at precise locations, which were patterned on quartz substrates using photo- and e-beam Iithogrpahy to make electrical connections to each SWNT/bundle individually. Gas sensor probes were fabricated using individually suspended SWNTs contacted by Cr/Au pads as source and drain field effect transistor components for the monitoring of NO2 vapors. The adsorption of an electron donating gas such as NO2 on the SWNT sidewalls shifts the Fermi level of the p-type semiconducting nanotubes, consequently changing their electrical conductivity. Experimental results showed that sensor response to NO2 (at 10-300 ppm levels) was of the order of a few seconds at 100 ppm, and was reversible and reproducible. Recovery of the sensor response was achieved by heating the sensors at 120 °C for a period of 10-12 hours indicating physisorption of the NO2 molecules on the nanotube sidewalls

Role of catalyst and substrate in synthesis of single wall carbon nanotubes

The synthesis of single wall carbon nanotubes (SWNTs) by the catalyticdisproportionation of carbon monoxide (CO) (i.e., Boudouard reaction) at I atm pressureand 700-800 C, has been systematically investigated in order to determine whether theprocess can be used for the large scale production. SWNT diameter distribution andmorphology were studied by Raman scattering, and scanning and transmission electronmicroscopy, respectively. X-ray diffraction was used to determine levels of the remnantcatalyst and support. Synthesis experiments were conducted using a three-stage processwith hydrogen to reduce the catalyst-precursor and argon to cool the system. Two typesof catalyst/support synthesis were studied: (1) Combustion synthesis to form catalyst onhigh surface area MgO support, and (2) Tetraethoxysilane synthesis to form catalyst onarrayed 200 nm silica opal support. Cobalt to molybdenum atomic weight ratios of 1:4and 5:1 respectively were found to be most effective for the selective production ofSVVNTs, and by contrast with previous observations in the literature, cobalt alone ascatalyst was also found to be very effective under these synthesis conditions.Molybdenum alone is not active, but having some Mo in combination with Co increasesSWNT yields. This suggests that Mo plays the role of a promoter by preventing thesegregation of the active Co particles. MgO support could be easily removed using 4 MHCl as confirmed by X-ray diffraction measurements, whereas silica support removalrequires more aggressive HF treatment, which is likely to chemically damage the SWNTs. The process using MgO support would therefore be scaleable for SWNT production. In this study this was preliminarily demonstrated by the production of gram quantities of highly pure SWNTs. Future work would need to focus on increasing SWNT yields and process continuity, by using a fluidized bed to facilitate point-to-point contact between gaseous precursor and catalyst particles, and admixing CO with small amounts of the more labile CH4 precursor to enhance the kinetics of CO disproportionation

Synchronized exchange of material and information

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2003.Includes bibliographical references (leaves 39-41).Commerce is all about the carefully managed exchange of material, money, and information. Traditionally, the connection between material and information has been tenuous, with humans acting as the intermediaries. This has made the supply chain inefficient and expensive. The Auto-lID Center has created a stronger, automatic link between inanimate objects and computers. This thesis completes the information exchange, or feedback loop, which makes commerce possible. Specifically, it identifies a framework for information exchange alongside material exchange using Savant-to-Savant communication. Messaging standards will need to support the Auto-ID Center's technology, and this thesis suggests how to augment existing and emerging communication standards to accomplish this feat. Finally, to address the issue of increasing information management, this thesis analyzes the aggregation database, an IT infrastructure component that might be of value to organizations. The outcome of this thesis is an understanding of the various issues necessary to develop a secure, efficient and robust system for tracking and automatically confirming material exchange.by Amit Goyal.M.Eng