Nomogram Including Elastography for Prediction of Contralateral Central Lymph Node Metastasis in Solitary Papillary Thyroid Carcinoma Preoperatively.

BACKGROUND: It is controversial whether contralateral prophylactic central neck dissection (PCND) should be performed for patients with solitary and clinical lymph node negative (cN0) papillary thyroid carcinoma (PTC) although routine ipsilateral PCND is required. OBJECTIVE: The aim of this study was to develop an improved nomogram including clinical features, ultrasound, and acoustic radiation force impulse (ARFI) elastography for the prediction of contralateral central lymph node metastasis (CLNM) in patients with solitary and cN0 PTC in the preoperative period. MATERIALS AND METHODS: A total of 340 patients were retrospectively included as the training cohort and 170 patients as the external validation cohort. Patients were grouped according to the pathological results of contralateral CLNM. The association between the clinical characteristics, ultrasound, and ARFI elastography and the risk for contralateral CLNM were analyzed. A nomogram was established based on the result of multivariable logistic analysis to predict the risk of contralateral CLNM, which was assessed by internal and external validation. RESULTS: CLNM was found in 213 patients (41.8%), among whom 142 (27.8%) had ipsilateral CLNM and 95 (18.6%) had contralateral CLNM (including 68 (13.3%) with bilateral CLNM). Multivariable analysis revealed that patients with younger age, male gender, larger tumor size, closer distance from the capsule, microcalcification, and larger SWVmean were independent predictors associated with the contralateral CLNM (P < 0.05), which was served as the basis of the nomogram. It showed good discrimination (C-index: 0.856) and calibration (χ2 = 9.028, P = 0.340, Hosmer-Lemeshow test) in the training cohort, and good discrimination was maintained in the external validation cohort (C-index: 0.792). CONCLUSION: The nomogram utilizing the features of ultrasound combined with ARFI elastography in preoperatively predicting the risk of contralateral CLNM in patients with solitary and cN0 PTC was established, which showed superior performance both in internal and external validation.

Antimicrobial resistance of 3 types of gram-negative bacteria isolated from hospital surfaces and the hands of health care workers.

BACKGROUND: There has been an increased focus in recent years on antimicrobial resistance of bacteria isolated from clinical samples. However, resistance of bacteria from hospital environments has been less frequently investigated. METHODS: According to hygienic standard for disinfection in hospitals, samples were collected from hospital inanimate surfaces and the hands of health care workers after daily cleaning. An automatic microorganism analyzer was used to identify bacteria and test for antimicrobial susceptibility. Polymerase chain reaction was used to detect antimicrobial resistance genes. RESULTS: The detection rate of bacteria in general wards was significantly higher than that in intensive care units. The isolates were predominantly gram-negative (GN) bacteria, with Pseudomonas aeruginosa, Enterobacter cloacae, and Klebsiella pneumoniae being the most common. P aeruginosa isolates from other surfaces were much higher than those from medical instruments. E cloacae was isolated more frequently from the hands of other staff than medical staff. Most P aeruginosa and K pneumoniae were resistant to sulfonamides and ß-lactam antimicrobials. Only 1 strain of P aeruginosa and 1 strain of K pneumoniae showed multiple antimicrobials resistance. CONCLUSIONS: The GN bacteria isolated from hospital environments demonstrate variable resistance to antimicrobials.

Assemble new object detector with few examples.

Learning a satisfactory object detector generally requires sufficient training data to cover the most variations of the object. In this paper, we show that the performance of object detector is severely degraded when training examples are limited. We propose an approach to handle this issue by exploring a set of pretrained auxiliary detectors for other categories. By mining the global and local relationships between the target object category and auxiliary objects, a robust detector can be learned with very few training examples. We adopt the deformable part model proposed by Felzenszwalb and simultaneously explore the root and part filters in the auxiliary object detectors under the guidance of the few training examples from the target object category. An iterative solution is introduced for such a process. The extensive experiments on the PASCAL VOC 2007 challenge data set show the encouraging performance of the new detector assembled from those related auxiliary detectors.

[Soil physical properties of different hedgerow systems in upper reaches of Yangtze River].

Based on the investigation of present hedgerows in the upper reaches of Yangtze River, this paper analyzed the soil physical properties at different positions of three kinds of hedgerows (arbor, shrub, and grass). Comparing with those between the hedgerows, the soil physical properties within the hedgerows improved significantly. The average values of soil porosity, moisture content, saturated conductivity, water stable aggregates content, anti-erodibility index, anti-scouribility index, and clay content within the arbor, grass, and shrub hedgerows increased by 18.8%, 30.1%, 12.9%, 139.3%, 108.3%, 95.9%, and 25.5%, and the soil bulk density and sand content averagely decreased by 17.3% and 9.6%, respectively. The soil properties within the three hedgerows differed significantly. The soil anti-scouribility index within arbor hedgerow was the highest; the soil porosity, moisture content, saturated conductivity, anti-scouribility index, water-stable aggregates content, and clay content within the shrub hedgerow were higher than those within the tree and grass hedgerows; while the soil bulk density within the shrub hedgerows was lower than that within the tree and grass hedgerows. Because of the differences in the affecting degree of hedgerow on the soil physical properties at different positions of the three hedgerow systems, the related parameters presented definite horizontal variation at steep lands within, before, and behind the hedgerows, and between the hedgerows. The coefficient of variation (CV) of soil moisture content, anti-erodibility index, saturated conductivity, and clay content of arbor hedgerows was bigger than that of shrub and grass hedgerows, while the CV of soil bulk density, porosity, water-stable aggregates content, and anti-scouribility index of shrub hedgerow was bigger than that of arbor and grass hedgerows.

[Macropore characteristics and its relationships with the preferential flow in broadleaved forest soils of Simian Mountains].

Brilliant blue dyeing and water breakthrough curve were applied to study the number and distribution of macropores and their relations to the preferential flow in typical sub-tropic broad-leaved forest soils of Simian Mountains. The radii of the macropores were mainly between 0. 3 and 3.0 mm, with the macroporosities in the range of 6.3% to 10.5%, and the macropores were always distributed in aggregation with increasing soil depth. The number of the macropores in each radius interval of dye-stained areas was tenfold increase than that of blank areas. The number of the macropores with radius larger than 0.3 mm, especially larger than 1.5 mm, was the most important factor affecting the occurrence of preferential flow. Significant correlations were found between the number of macropores and the water steady effluent volume, with the highest correlation coefficients of 0.842 and 0.879 for the radii intervals of 0.7-1.5 mm and 1.5-3.0 mm, respectively. Macro-pore continuity in dye-stained areas was better than that in blank areas, especially in the radius interval of 1.5-3.0 mm, with the biggest difference of 78.31%. In dye-stained areas, the number of macropores decreased gradually with soil depth. The filler-like distribution of macropores formed an effective water pressure gradient, which resulted in the preferential transport of water.

Two-dimensional multilabel active learning with an efficient online adaptation model for image classification.

Conventional active learning dynamically constructs the training set only along the sample dimension. While this is the right strategy in binary classification, it is suboptimal for multilabel image classification. We argue that for each selected sample, only some effective labels need to be annotated while others can be inferred by exploring the label correlations. The reason is that the contributions of different labels to minimizing the classification error are different due to the inherent label correlations. To this end, we propose to select sample-label pairs, rather than only samples, to minimize a multilabel Bayesian classification error bound. We call it two-dimensional active learning because it considers both the sample dimension and the label dimension. Furthermore, as the number of training samples increases rapidly over time due to active learning, it becomes intractable for the offline learner to retrain a new model on the whole training set. So we develop an efficient online learner to adapt the existing model with the new one by minimizing their model distance under a set of multilabel constraints. The effectiveness and efficiency of the proposed method are evaluated on two benchmark data sets and a realistic image collection from a real-world image sharing Web site-Corbis.

Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval.

Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for human gait recognition and content-based image retrieval (CBIR). In this paper, we present extensions of our recently proposed marginal Fisher analysis (MFA) to address these problems. For human gait recognition, we first present a direct application of MFA, then inspired by recent advances in matrix and tensor-based dimensionality reduction algorithms, we present matrix-based MFA for directly handling 2-D input in the form of gray-level averaged images. For CBIR, we deal with the relevance feedback problem by extending MFA to marginal biased analysis, in which within-class compactness is characterized only by the distances between each positive sample and its neighboring positive samples. In addition, we present a new technique to acquire a direct optimal solution for MFA without resorting to objective function modification as done in many previous algorithms. We conduct comprehensive experiments on the USF HumanID gait database and the Corel image retrieval database. Experimental results demonstrate that MFA and its extensions outperform related algorithms in both applications.

[Genetic polymorphisms of short tandem repeat loci D4S2368, D6S1043, D9S925 from Korean ethnic group of JiLin].

OBJECTIVE: To understand the allele structure and genetic polymorphism at D4S2368, D6S1043, D9S925 short tandem repeat (STR) loci in Korean ethnic group of Jilin, and to construct a preliminary database. METHODS: The allele frequencies of the three STRs loci in 310 unrelated individuals from Korean ethnic individuals were analyzed by polymerase chain reaction (PCR) and polyacrylamide gel electrophoresis (PAGE). RESULTS: Seven, thirteen, and nine alleles were observed at D4S2368, D6S1043, and D9S925 loci, respectively, and all loci met Hardy-Weinberg equilibrium (except D6S1043). The statistical analysis of 3 STR loci showed the heterozygosities were more than 0.717, the polymorphic information contents (PIC) were more than 0.670; the combined power of discrimination (PD) and the power of exclusion (PE) were more than 0.9995 and 0.952 respectively. CONCLUSION: The three loci in this study are found to have high heterozygosity and polymorphic information content, so they can provide useful markers for genetic purposes. These results could serve as valuable data to enrich the Korean ethnic group genetic database and play an important role in Chinese population genetic application.

Multilinear discriminant analysis for face recognition.

There is a growing interest in subspace learning techniques for face recognition; however, the excessive dimension of the data space often brings the algorithms into the curse of dimensionality dilemma. In this paper, we present a novel approach to solve the supervised dimensionality reduction problem by encoding an image object as a general tensor of second or even higher order. First, we propose a discriminant tensor criterion, whereby multiple interrelated lower dimensional discriminative subspaces are derived for feature extraction. Then, a novel approach, called k-mode optimization, is presented to iteratively learn these subspaces by unfolding the tensor along different tensor directions. We call this algorithm multilinear discriminant analysis (MDA), which has the following characteristics: 1) multiple interrelated subspaces can collaborate to discriminate different classes, 2) for classification problems involving higher order tensors, the MDA algorithm can avoid the curse of dimensionality dilemma and alleviate the small sample size problem, and 3) the computational cost in the learning stage is reduced to a large extent owing to the reduced data dimensions in k-mode optimization. We provide extensive experiments on ORL, CMU PIE, and FERET databases by encoding face images as second- or third-order tensors to demonstrate that the proposed MDA algorithm based on higher order tensors has the potential to outperform the traditional vector-based subspace learning algorithms, especially in the cases with small sample sizes.

Graph embedding and extensions: a general framework for dimensionality reduction.

Over the past few decades, a large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.

Orthogonal laplacianfaces for face recognition.

Following the intuition that the naturally occurring face data may be generated by sampling a probability distribution that has support on or near a submanifold of ambient space, we propose an appearance-based face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which aims at finding a linear approximation to the eigenfunctions of the Laplace Beltrami operator on the face manifold. However, LPP is nonorthogonal, and this makes it difficult to reconstruct the data. The orthogonal locality preserving projection (OLPP) method produces orthogonal basis functions and can have more locality preserving power than LPP. Since the locality preserving power is potentially related to the discriminating power, the OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness of our proposed algorithm.

Generalized manifold-ranking-based image retrieval.

In this paper, we propose a general transductive learning framework named generalized manifold-ranking-based image retrieval (gMRBIR) for image retrieval. Comparing with an existing transductive learning method named MRBIR [12], our method could work well whether or not the query image is in the database; thus, it is more applicable for real applications. Given a query image, gMRBIR first initializes a pseudo seed vector based on neighborhood relationship and then spread its scores via manifold ranking to all the unlabeled images in the database. Furthermore, in gMRBIR, we also make use of relevance feedback and active learning to refine the retrieval result so that it converges to the query concept as fast as possible. Systematic experiments on a general-purpose image database consisting of 5,000 Corel images demonstrate the superiority of gMRBIR over state-of-the-art techniques.

A unified framework for image retrieval using keyword and visual features.

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

A memory learning framework for effective image retrieval.

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.

An efficient and effective region-based image retrieval framework.

An image retrieval framework that integrates efficient region-based representation in terms of storage and complexity and effective on-line learning capability is proposed. The framework consists of methods for region-based image representation and comparison, indexing using modified inverted files, relevance feedback, and learning region weighting. By exploiting a vector quantization method, both compact and sparse (vector) region-based image representations are achieved. Using the compact representation, an indexing scheme similar to the inverted file technology and an image similarity measure based on Earth Mover's Distance are presented. Moreover, the vector representation facilitates a weighted query point movement algorithm and the compact representation enables a classification-based algorithm for relevance feedback. Based on users' feedback information, a region weighting strategy is also introduced to optimally weight the regions and enable the system to self-improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.

Motion analysis and segmentation through spatio-temporal slices processing.

This paper presents new approaches in characterizing and segmenting the content of video. These approaches are developed based upon the pattern analysis of spatio-temporal slices. While traditional approaches to motion sequence analysis tend to formulate computational methodologies on two or three adjacent frames, spatio-temporal slices provide rich visual patterns along a larger temporal scale. We first describe a motion computation method based on a structure tensor formulation. This method encodes visual patterns of spatio-temporal slices in a tensor histogram, on one hand, characterizing the temporal changes of motion over time, on the other hand, describing the motion trajectories of different moving objects. By analyzing the tensor histogram of an image sequence, we can temporally segment the sequence into several motion coherent subunits, in addition, spatially segment the sequence into various motion layers. The temporal segmentation of image sequences expeditiously facilitates the motion annotation and content representation of a video, while the spatial decomposition of image sequences leads to a prominent way of reconstructing background panoramic images and computing foreground objects.

Learning similarity measure for natural image retrieval with relevance feedback.

A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two clusters. Images inside the boundary are ranked by their Euclidean distances to the query. The scheme is called constrained similarity measure (CSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance of the Euclidean distance measure. Two techniques, support vector machine (SVM) and AdaBoost from machine learning, are utilized to learn the boundary. They are compared to see their differences in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The CSM metric is evaluated in a large database of 10009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval.

[Refractivity of C60 composite material].

The experiment makes C60 composite material by mixing up C60 and polymethyl methacrylate. Then three minor studies are conducted: (1) Studying the relation of refractivity of the material with the change in temperature. (2) Studying the different change in refractivity with different amount of C60 in the composite material. (3) Studying the change in refractivity of the material radiated with ultraviolet radiation. A comparison is made between the above three cases. The result shows that: under different circumstances, the change in refractivity with the change of temperature differ.