1,721,003 research outputs found
The Perfect Match: Assessment of Sample Collection Efficiency for Immunological and Molecular Findings in Different Types of Fabrics
Full text linkBody fluid identification at crime scenes can be crucial in retrieving the appropriate evidence that leads to the perpetrator and, in some cases, the victim. For this purpose, immunochromatographic tests are simple, fast and suitable for crime scenes. The potential sample is retrieved with a swab, normally a cotton swab, moistened in a specific buffer. Nonetheless, there are other swab types available, which have been proven to be efficient for DNA isolation and analysis. The aim of this study is to evaluate the efficiency of different swab types for body fluid identification as well as DNA isolation and characterization. Fifty microliters of human saliva were deposited in three different types of fabric (denim, cotton, and polyester). After 24 h at room temperature, samples were recovered by applying three different swab types, and the tests were performed. Subsequently, total DNA was recovered from the sample buffer. Cotton swabs performed worse in denim and cotton fabrics in both immunochromatography tests and DNA yield. No differences were observed for polyester. In contrast, and except for two replicates, it was possible to obtain a full DNA profile per fabric and swab type, and to identify the mtDNA haplogroup. In this paper, the impact of swab types on body fluid identification through the application of immunochromatographic tests is analyzed for the first time. This work corroborates previous research related to the influence of swab types in nuclear DNA isolation and characterization
A Counterfactual Approach to Modeling Consumer Outcomes With and Without Payment for Order Flow
No full textWe study the market mechanism of payment for order flow, forming a theoretical framework for understanding the spreads in equilibrium with and without payment for order flow. Our goal is to determine conditions under which retail customers are better off in a market without payment for order flow compared to with payment for order flow, and combine this with market constraints that allow payment for order flow to exist. We propose a general model into which different market characteristics can be plugged in to determine equilibria, formed based on changing levels of nontoxic flow into the open exchange based on the presence or absence of payment for order flow. After solving for our general model, we examine an SEC proposal for amendments to the structure of payment for order flow, and also examine strategic behavior in the equilibrium with payment for order flow and its implications on retail customer outcomes. Finally, we examine practical limitations of our model and look to future work for continued development of our understanding of payment for order flow
Data Analysis for Stock Price Prediction
No full textIn the field of applied mathematics, modeling random processes naturally arises in
a variety of settings. Specifically within the context of financial markets, an intriguing
pursuit is to model patterns of stock prices — which are characterized by high levels
of noise and randomness. This thesis attempts to provide a sound methodology
to forecast stock returns by examining a wide range of information and defining a
systematic approach.
In attempting to uncover patterns in movements of stock prices and forecast returns,
I first engineer a variety of different input features which are derived from
sources ranging from the historical time series of stock prices to alternative sources of
information such as options data and analyst recommendations. In this thesis, I note
the input features which provide the strongest insights in forecasting returns through
several different methods. After analyzing the strongest input variables, I reassess
if the obvious methodology of forecasting stock prices through a regression model is
attainable. After this, an initial model is fit to the data. Consistent with the ”No Free
Lunch” theorem — which states that there is no optimal algorithm for all problems
— I test a variety of different machine learning algorithms to see which works best
on my dataset, evaluating several different metrics. After identifying the best performing
model, a variety of different tests are conducted to improve the performance
of the model — including outlier detection and removal, feature elimination, and dimensionality
reduction. Finally, I optimize the output of the model in a distinctive
manner that is best suitable for future work
Envisioning Automated Glaucoma Screening: Domain Generalization for Deep Learning-Based Glaucoma Classification
No full textRapid advancements in deep learning algorithms for computer vision tasks have produced powerful models that can accurately classify diseases from medical images across a variety of specialties. In the field of ophthalmology, these systems have the potential to enable large-scale eye disease screening programs in areas that lack access to vision care or trained specialists. However, deep learning models must be able to generalize to unseen domains, such as retinal images from different healthcare institutions, machines, and patient populations, before they can be deployed for population-wide clinical screening. In this thesis, we analyze domain shift across four public retinal image datasets and investigate its effect on baseline glaucoma classification model performance. We find that domain shift severely degrades classification ability, and, specifically, image features extracted during model training do not generalize to out-of-domain images. We further test three state-of-the-art domain generalization methods and find that one method marginally improves model generalization, though not to an adequate level for clinical use. In the final chapter, we connect the findings of our research to broader themes in global health and health policy. Overall, this thesis begins to fill a noticeable gap in domain generalization research for deep learning-based glaucoma classification
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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