Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

Causality between sarcopenia and diabetic nephropathy: a bidirectional Mendelian randomization study

Background and purposeObservational studies have shown that sarcopenia and diabetic nephropathy (DN), are closely related; however, the causal relationship is unclear. This study aims to address this issue using a bidirectional Mendelian randomization (MR) study.MethodologyWe data from genome-wide association studies including appendicular lean mass (n = 244,730), grip strength (right: n = 461,089, left: n = 461026), walking speed (n = 459,915), and DN (3283 cases and 181,704 controls) to conduct a bidirectional MR study. First, we conducted a Forward MR analysis to evaluate the causality of sarcopenia on the risk of DN from the genetic perspective with appendicular lean mass, grip strength, and walking speed as exposure and DN as the outcome. Then, DN as the exposure, we performed a Reverse MR analysis to determine whether DN impacted the appendicular lean mass, grip strength, and walking speed of the appendices. Finally, a series of sensitivity studies, such as heterogeneity tests, pleiotropy evaluations, and Leave-one-out analyses, were conducted to assess the MR analysis’s accuracy further.ResultsAccording to a forward MR analysis, a genetically predicted decrease in appendicular lean mass is associated with an increased risk of developing DN risk (inverse variance weighting[IVW]: odd ratio [OR] = 0.863, 95% confidence interval [CI] 0.767-0.971; P = 0.014). According to reverse MR results, grip strength decreased as DN progressed (IVW: right β = 0.003, 95% CI: - 0.021 to - 0.009, P = 5.116e-06; left β = 0.003, 95% CI: - 0.024 to - 0.012, P = 7.035e-09). However, the results of the other MR analyses were not statistically different.ConclusionNotably, our findings suggest that the causal relationship between sarcopenia and DN cannot be generalized. According to analysis of the individual characteristic factors of sarcopenia, reducing in appendicular lean mass increases the risk of developing DN and DN is linked to reduced grip strength. But overall, there is no causal relationship between sarcopenia and DN, because the diagnosis of sarcopenia cannot be determined by one of these factors alone

Discovery and identification of potential biomarkers of pediatric Acute Lymphoblastic Leukemia

<p>Abstract</p> <p>Background</p> <p>Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.</p> <p>Methods</p> <p>Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.</p> <p>Results</p> <p>A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).</p> <p>Conclusion</p> <p>Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.</p

Exploring Semantic Concept Co-Occurrences for Image Based Applications

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined high level image semantic representation. Through experiments in applications including automatic image annotation, semantic image retrieval, moth species identification and multi-pedestrian tracking on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the proposed image semantic representation in comparison with state-of-the-art approaches

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

Semantic-visual concept relatedness and co-occurrences for image retrieval

This paper introduces a novel approach that allows the re-trieval of complex images by integrating visual and seman-tic concepts. The basic idea consists of three aspects. First, we measure the relatedness of semantic and visual concepts and select the visually separable semantic concepts as ele-ments in the proposed image signature representation. Sec-ond, we demonstrate the existence of concept co-occurrence patterns. We propose to uncover those underlying patterns by detecting the communities in a network structure. Third, we leverage the visual and semantic correspondence and the co-occurrence patterns to improve the accuracy and efficiency for image retrieval. We perform experiments on two popular datasets that confirm the effectiveness of our approach. Index Terms — Image retrieval, image semantics, con-cept signature, complex image