1,184 research outputs found
Analysis of Models for Longitudinal and Clustered Binary Data
This dissertation deals with modeling and statistical analysis of longitudinal and clustered binary data. Such data consists of observations on a dichotomous response variable generated from multiple time or cluster points, that exhibit either decaying correlation or equi-correlated dependence. The current literature addresses modeling the dependence using an appropriate correlation structure, but ignores the feasible bounds on the correlation parameter imposed by the marginal means.
The first part of this dissertation deals with two multivariate probability models, the first order Markov chain model and the multivariate probit model, that adhere to the feasible bounds on the correlation. For both the models we obtain maximum likelihood estimates for the regression and correlation parameters, and study both asymptotic and small-sample properties of the estimates. Through simulations we compare the efficiency of the two methods and demonstrate that neither is uniformly superior over the other.
The second part of this dissertation deals with marginal models, an alternative to multivariate probability models. We discuss the generalized estimating equations and the quadratic inference function methods for estimating the regression parameter in marginal models. Relative efficiency calculations show these methods when compared to the likelihood estimates could result in significant loss in efficiency for highly correlated data. We also propose a modified quadratic inference function method and demonstrate through efficiency calculations this is an improvement of the original quadratic inference function approach. The final part of this dissertation deals with methods for constructing higher order Markov chain models using copulas
Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm
Hospital emergency departments frequently receive lots of bone fracture
cases, with pediatric wrist trauma fracture accounting for the majority of
them. Before pediatric surgeons perform surgery, they need to ask patients how
the fracture occurred and analyze the fracture situation by interpreting X-ray
images. The interpretation of X-ray images often requires a combination of
techniques from radiologists and surgeons, which requires time-consuming
specialized training. With the rise of deep learning in the field of computer
vision, network models applying for fracture detection has become an important
research topic. In this paper, YOLOv8 algorithm is used to train models on the
GRAZPEDWRI-DX dataset, which includes X-ray images from 6,091 pediatric
patients with wrist trauma. The experimental results show that YOLOv8 algorithm
models have different advantages for different model sizes, with YOLOv8l model
achieving the highest mean average precision (mAP 50) of 63.6\%, and YOLOv8n
model achieving the inference time of 67.4ms per X-ray image on one single CPU
with low computing power. In this way, we create "Fracture Detection Using
YOLOv8 App" to assist surgeons in interpreting X-ray images without the help of
radiologists. Our implementation code is released at
https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network
Oscillatory combustion in aero engines and modern gas turbines often has
significant adverse effects on their operation, and accurately recognizing
various oscillation modes is the prerequisite for understanding and controlling
combustion instability. However, the high-dimensional spatial-temporal data of
a complex combustion system typically poses considerable challenges to the
dynamical mode recognition. Based on a two-layer bidirectional long short-term
memory variational autoencoder (Bi-LSTM-VAE) dimensionality reduction model and
a two-dimensional Wasserstein distance-based classifier (WDC), this study
proposes a promising method (Bi-LSTM-VAE-WDC) for recognizing dynamical modes
in oscillatory combustion systems. Specifically, the Bi-LSTM-VAE dimension
reduction model was introduced to reduce the high-dimensional spatial-temporal
data of the combustion system to a low-dimensional phase space; Gaussian kernel
density estimates (GKDE) were computed based on the distribution of phase
points in a grid; two-dimensional WD values were calculated from the GKDE maps
to recognize the oscillation modes. The time-series data used in this study
were obtained from numerical simulations of circular arrays of laminar flame
oscillators. The results show that the novel Bi-LSTM-VAE method can produce a
non-overlapping distribution of phase points, indicating an effective
unsupervised mode recognition and classification. Furthermore, the present
method exhibits a more prominent performance than VAE and PCA (principal
component analysis) for distinguishing dynamical modes in complex flame
systems, implying its potential in studying turbulent combustion.Comment: research paper (18 pages, 1 table 10 figures) with supplementary
material (8 pages, 1 table, 5 figures
A Case History of Super-Large Scale Bridge Pile Foundation in Soft Soil
The Sutong Yangtze River Bridge is the longest cable-stayed bridge in the world with main span of 1088 m. The pile cap is 113.8 m by 48.1 m by 13.3 m under each pylon and the foundation is super-long large-diameter drilled pile groups consisting of 131 piles with the length of 114/117 m and the diameter of 2.8/2.5m. This paper presents the static loading tests on single piles, centrifugal model tests on pile groups, finite element analysis, cap model tests and field monitoring for the super-large scale pile foundation. The results show that: the super-long large-diameter bored pile of Sutong Bridge is floating piles, and it is difficult for the resistance at pile bottom to play. based on the comprehensive consideration of theoretical calculation, centrifugal model tests and finite element analysis, the pile group effect coefficient of the Sutong Yangtze Bridge is 0.82. The pile-head load distribution of pile foundation under main bridge pylon presents a “W” shape, which is larger at both the edges and intermediate connection and smaller at other position of pylon. According to the results of cap model tests, the soften-coordinated spatial truss model was suitable for the stress analysis of cap. The calculated results agree well with the field monitoring results
Functional studies of the group A rotavirus non-structural protein NSP4
NSP4, encoded by rotavirus genome segment 10 has been shown to be a transmembrane, endoplasmic reticulum (ER) specific N-linked glycoprotein. Consistent with its localization to the ER membrane, NSP4 was first shown to have a role in the morphogenesis of the infectious virion. The protein has also been reported to have cytotoxic activity when applied extracellularly to cells. Consequently it has been earmarked as an enterotoxin being secreted from virus-infected cells to cause early cellular pathology in the gut. The effect of expressing the NSP4 protein of group A rotaviruses in cells has been studied. It led to the rapid appearance of long cytoplasmic extrusions. Site-directed mutagenesis was used to block N-linked glycosylation at both of the known glycosylation sites near the amino terminus of NSP4. This revealed that the NSP4 induced formation of the cytoplasmic extrusions was dependent on the protein’s ability to become fully glycosylated. The cytoplasmic extrusions seen in cells expressing glycosylated NSP4 were also evident in virus-infected cells. Using real-time confocal microscopy a dynamic elongation of the cytoplasmic extrusions with a growth speed of 2 μm/min was observed in virus-infected cells. The cytoplasmic extrusions were found to contain β-tubulin and F-actin. Inhibiting their polymerization prevented the formation of the extrusions from virus-infected cells. Functional studies using Cell Tracker dyes showed that the cytoplasmic extrusions could disseminate vesicles from virus-infected cells onto the plasma membrane surface of uninfected cells. The vesicles were then found in the interior of the uninfected cells. Mono-specific antibody to NSP4 revealed the presence of the protein in the vesicles suggesting that the cytoplasmic extrusions facilitated the direct cell-cell spread of NSP4. The effect of NSP4 expression on the microtubular network of cells was analysed. It was found that NSP4 de-polymerized the microtubular network from the centre of cells and promoted the assembly of microtubules at the periphery of the cells in a glycosylation independent manner. Similar de-polymerization and re-assembly of the microtubules was observed in the virus-infected cells. Interestingly in the presence of nocodazole, tubular structures containing tubulin and viral proteins excluding NSP4 were found in virus-infected cells. A YFP-PCA assay was established to screen for cellular partners of NSP4. The functionality and the sensitivity of the assay were examined, but only two false positive colonies were isolated in the first screening. In conclusion, the function of glycosylated and unglycosylated NSP4 was examined with the former possessing the ability to promote the formation of the cytoplasmic extrusions from cells and both being capable of disrupting the microtubular network indicating that two forms of NSP4 play different roles in NSP4 function. The cytoplasmic extrusions seen in our studies may be relevant to rotavirus infection and pathogenesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Evaluation of a new chemiluminescence immunoassay for diagnosis of syphilis
<p>Abstract</p> <p>Objective</p> <p>To assess the sensitivity, specificity, and feasibility of a new chemiluminescence immunoassay (CLIA) in the diagnosis of syphilis.</p> <p>Methods</p> <p>At first, a retrospective study was conducted, using 135 documented cases of syphilis and 30 potentially interfering samples and 80 normal sera. A prospective study was also performed by testing 2, 071 unselected samples for routine screening for syphilis. CLIA was compared with a nontreponemal test (TRUST) and a treponemal test (TPPA).</p> <p>Results</p> <p>There was an agreement of 100% between CLIA and TPPA in the respective study. The percentage of agreement among the 245 sera tested was 100.0%. Compared with TPPA, the specificity of CLIA was 99.9% (1817/1819), the sensitivity of CLIA was 100.0% (244/244) in the prospective study. CLIA showed 99.5% agreement with TPPA by testing 2, 071 unselected samples. And CLIA seemed to be more sensitive than TPPA in detecting the samples of primary syphilis.</p> <p>Conclusions</p> <p>CLIA is easy to perform and the indicator results are objective and unequivocal. It may be suitable for large-scale screening as a treponemal test substituted for TPPA.</p
Distribution of dust ejected from the lunar surface into the Earth-Moon system
Aims. An asymmetric dust cloud was detected around the Moon by the Lunar Dust
Experiment on board the Lunar Atmosphere and Dust Environment Explorer mission.
We investigate the dynamics of the grains that escape the Moon and their
configuration in the Earth-Moon system.
Methods. We use a plausible initial ejecta distribution and mass production
rate for the ejected dust. Various forces, including the solar radiation
pressure and the gravity of the Moon, Earth, and Sun, are considered in the
dynamical model, and direct numerical integrations of trajectories of dust
particles are performed. The final states, the average life spans, and the
fraction of retrograde grains as functions of particle size are computed. The
number density distribution in the Earth-Moon system is obtained through
long-term simulations.
Results. The average life spans depend on the size of dust particles and show
a rapid increase in the size range between and . About ()
particles ejected from the lunar surface escape the gravity of the Moon, and
they form an asymmetric torus between the Earth and the Moon in the range
, which is offset toward the direction of
the Sun. A considerable number of retrograde particles occur in the Earth-Moon
system
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