In Vivo Molecular Imaging in Retinal Disease

There is an urgent need for early diagnosis in medicine, whereupon effective treatments could prevent irreversible tissue damage. The special structure of the eye provides a unique opportunity for noninvasive light-based imaging of ocular fundus vasculature. To detect endothelial injury at the early and reversible stage of adhesion molecule upregulation, some novel imaging agents that target retinal endothelial molecules were generated. In vivo molecular imaging has a great potential to impact medicine by detecting diseases or screening disease in early stages, identifying extent of disease, selecting disease and patient-specific therapeutic treatment, applying a directed or targeted therapy, and measuring molecular-specific effects of treatment. Current preclinical findings and advances in instrumentation such as endoscopes and microcatheters suggest that these molecular imaging modalities have numerous clinical applications and will be translated into clinical use in the near future

Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomised controlled trial findings

Objectives To determine whether observational studies that use an electronic medical record database can provide valid results of therapeutic effectiveness and to develop new methods to enhance validity

Cluster Analysis Based on Bipartite Network

Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy c-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster number c in advance. The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. The experimental results on artificial and real data sets show the validity of the proposed method

What Makes Good Open-Vocabulary Detector: A Disassembling Perspective

Open-vocabulary detection (OVD) is a new object detection paradigm, aiming to localize and recognize unseen objects defined by an unbounded vocabulary. This is challenging since traditional detectors can only learn from pre-defined categories and thus fail to detect and localize objects out of pre-defined vocabulary. To handle the challenge, OVD leverages pre-trained cross-modal VLM, such as CLIP, ALIGN, etc. Previous works mainly focus on the open vocabulary classification part, with less attention on the localization part. We argue that for a good OVD detector, both classification and localization should be parallelly studied for the novel object categories. We show in this work that improving localization as well as cross-modal classification complement each other, and compose a good OVD detector jointly. We analyze three families of OVD methods with different design emphases. We first propose a vanilla method,i.e., cropping a bounding box obtained by a localizer and resizing it into the CLIP. We next introduce another approach, which combines a standard two-stage object detector with CLIP. A two-stage object detector includes a visual backbone, a region proposal network (RPN), and a region of interest (RoI) head. We decouple RPN and ROI head (DRR) and use RoIAlign to extract meaningful features. In this case, it avoids resizing objects. To further accelerate the training time and reduce the model parameters, we couple RPN and ROI head (CRR) as the third approach. We conduct extensive experiments on these three types of approaches in different settings. On the OVD-COCO benchmark, DRR obtains the best performance and achieves 35.8 Novel AP$_{50}$, an absolute 2.8 gain over the previous state-of-the-art (SOTA). For OVD-LVIS, DRR surpasses the previous SOTA by 1.9 AP$_{50}$ in rare categories. We also provide an object detection dataset called PID and provide a baseline on PID

Modeling Quantum Entanglements in Quantum Language Models

Recently, a Quantum Language Model (QLM) was proposed to model term dependencies upon Quantum Theory (QT) framework and successively applied in Information Retrieval (IR). Nevertheless, QLM's dependency is based on co-occurrences of terms and has not yet taken into account the Quantum Entanglement (QE), which is a key quantum concept and has a significant cognitive implication. In QT, an entangled state can provide a more complete description for the nature of realities, and determine intrinsic correlations of considered objects globally, rather than those co-occurrences on the surface. It is, however, a real challenge to decide and measure QE using the classical statistics of texts in a post-measurement configuration. In order to circumvent this problem, we theoretically prove the connection between QE and statistically Unconditional Pure Dependence (UPD). Since UPD has an implementable deciding algorithm, we can in turn characterize QE by extracting the UPD patterns from texts. This leads to a measurable QE, based on which we further advance the existing QLM framework. We empirically compare our model with related models, and the results demonstrate the effectiveness of our model

Semisupervised Community Detection by Voltage Drops

Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal is OV+E for the sparse network with V vertices and E edges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods

Relationship between Microstructure and Properties of Cu-Cr-Ag-(Ce) Alloy Using Microscopic Investigation

Microstructure, precipitation hardening response, and mechanical and physical properties of Cu-Cr-Ag alloy and Cu-Cr-Ag-Ce alloy have been investigated using transmission electron microscopy, scanning electron microscope, optical microscope, electrical conductivity analysis, and tensile test. The influence of element Ce on the matrix refinement, impurity removal, and precipitation in the Cu-Cr-Ag alloys has been analyzed. The experimental results show that the strength and electrical conductivity of Ce containing alloys are greater than those of Ce-free alloys after each processing step. Improvement of strength and electrical conductivity of the Cu-Cr-Ag alloy by adding Ce element is attributed to removing oxygen and sulfur from as-cast alloy