Notes

Structural first step toward the actual conversation involving SETD2 methyltransferase and hnRNP T paralogs pertaining to overseeing co-transcriptional splicing.
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, which underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. https://www.selleckchem.com/products/gilteritinib-asp2215.html The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. link2 In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.Action assessment, the process of evaluating how well an action is performed, is an important task in human action analysis. In fact, every type of action has specific evaluation criteria, and human experts are trained for years to correctly evaluate a single type of action. Therefore, it is difficult for a single assessment architecture to achieve high performance for all types of actions. This work addresses this problem by adaptively designing different assessment architectures for different types of actions, and the proposed approach is therefore called the adaptive action assessment. In order to facilitate our adaptive action assessment by exploiting the specific joint interactions for each type of action, a set of graph-based joint relations is learned for each type of action by means of trainable joint relation graphs built according to the human skeleton structure. In addition, we further introduce using a normalized mean squared error loss (N-MSE loss) and a Pearson loss which perform automatic score normalization for our adaptive assessment training. The experiments on four benchmarks for action assessment demonstrate the effectiveness and feasibility of the proposed method. We also demonstrate the visual interpretability of our model by visualizing the details of the assessment process.Low-light image enhancement (LLIE) aims at improving the perception or interpretability of an image captured in an environment with poor illumination. Recent advances in this area are dominated by deep learning-based solutions, where many learning strategies, network structures, loss functions, training data, etc. have been employed. In this paper, we provide a comprehensive survey to cover various aspects ranging from algorithm taxonomy to unsolved open issues. To examine the generalization of existing methods, we propose a low-light image and video dataset, in which the images and videos are taken by different mobile phones' cameras under diverse illumination conditions. Besides, for the first time, we provide a unified online platform that covers many popular LLIE methods, of which the results can be produced through a user-friendly web interface. In addition to qualitative and quantitative evaluation of existing methods on publicly available and our proposed datasets, we also validate their performance in face detection in the dark. This survey together with the proposed dataset and online platform could serve as a reference source for future study and promote the development of this research field. The proposed platform and dataset as well as the collected methods, datasets, and evaluation metrics are publicly available.Multi-modal classification (MMC) uses the information from different modalities to improve the performance of classification. Existing MMC methods can be grouped into two categories traditional methods and deep learning-based methods. The traditional methods often implement fusion in a low-level original space. Besides, they mostly focus on the inter-modal fusion and neglect the intra-modal fusion. Thus, the representation capacity of fused features induced by them is insufficient. The deep learning-based methods implement the fusion in a high-level feature space where the associations among features are considered, while the whole process is implicit and the fused space lacks interpretability. Based on these observations, we propose a novel interpretative association-based fusion method for MMC, named AF. In AF, both the association information and the high-order information extracted from feature space are simultaneously encoded into a new feature space to help to train an MMC model in an explicit manner. Moreover, AF is a general fusion framework, and most existing MMC methods can be embedded into it to improve their performance. Finally, the effectiveness and the generality of AF are validated on 22 datasets, four typically traditional MMC methods adopting best modality, early, late and model fusion strategies and a deep learning-based MMC method.Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i.e., the point cloud videos. We empirically categorize the temporal information into short-term and long-term patterns. To encode the short-term data, we present a Grid Message Passing Network (GMPNet), which considers each grid (i.e., the grouped points) as a node and constructs a k-NN graph with the neighbor grids. To update features for a grid, GMPNet iteratively collects information from its neighbors, thus mining the motion cues in grids from nearby frames. To further aggregate the long-term frames, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU), which contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module. link3 STA and TTA enhance the vanilla GRU to focus on small objects and better align the moving objects. Our overall framework supports both online and offline video object detection in point clouds. The evaluation results on the challenging nuScenes benchmark show the superior performance of our method, achieving 1st on the leaderboard without any bells and whistles, by the time the paper is submitted.
Although HIFU has been successfully applied in various clinical applications in the past two decades for the ablation of many types of tumors, one bottleneck in its wider applications is the lack of a reliable and affordable strategy to guide the therapy. This study aims at estimating the therapeutic beam path at the pre-treatment stage to guide the therapeutic procedure.

An incident beam mapping technique using passive beamforming was proposed based on a clinical HIFU system and an ultrasound imaging research system. An optimization model was created to map the cross-like beam pattern by maximizing the total energy within the mapped area. This beam mapping technique was validated by comparing the estimated focal region with the HIFU-induced actual focal region (damaged region) through simulation, in-vitro, ex-vivo and in-vivo experiments.

The results of this study showed that the proposed technique was, to a large extent, tolerant of sound speed inhomogeneities, being able to estimate the focal location with errors of 0.15 mm and 0.93 mm under in-vitro and ex-vivo situations respectively, and slightly over 1 mm under the in-vivo situation. It should be noted that the corresponding errors were 6.8 mm, 3.2 mm, and 9.9 mm respectively when the conventional geometrical method was used.

This beam mapping technique can be very helpful in guiding the HIFU therapy and can be easily applied in clinical environments with an ultrasound-guided HIFU system.

The technique is non-invasive and can potentially be adapted to other ultrasound-related beam manipulating applications.
The technique is non-invasive and can potentially be adapted to other ultrasound-related beam manipulating applications.
The potential of electromagnetic (EM) knee imaging system verified on ex-vivo pig knee joint as an essential step before clinical trials is demonstrated. The system, which includes an antenna array of eight printed biconical elements operating at the band 0.7-2.2 GHz, is portable and cost-effective. Importantly, it can provide daily monitoring and onsite real-time examinations imaging tool for knee injuries.

Six healthy hind legs from three dead adult pigs were removed at the hip and suspended in the developed system. For each pig, the right- and left-knee were scanning sequentially. Then ligament tear was emulated by injecting distilled water into the left knee joint of each pig for early (5 mL water) and mid-stage (10 mL water) injuries. The injured left knees were re-scanned. A modified multi-static fast delay, multiply and sum algorithm (MS-FDMAS) is used to reconstruct imaging of the knee. All knees connective tissues, such as anterior and posterior cruciate ligaments (ACL, PCL), lateral and medial collateral ligaments (LCL, MCL), tendons, and meniscus, are extracted from a healthy hind leg along with collected synovial fluid. The extracted tissues and fluid were characterized and modelled as their data are not available in the literature, then imported to build an equivalent model for pig knee of 1 mm3 resolution in a realistic simulation environment.

The obtained results proved potential of the proposed system to detect ligament/tendon tears.

The proposed system has the potential to detect early knee injuries in a realistic environment.

Contactless EM knee imaging system verified on ex-vivo pig joints confirms its potential to reconstruct knee images. This work lays the groundwork for clinical EM system for detecting and monitoring knee injuries.
Contactless EM knee imaging system verified on ex-vivo pig joints confirms its potential to reconstruct knee images. This work lays the groundwork for clinical EM system for detecting and monitoring knee injuries.
We aim to establish the prognostic value of metabolic parameters of the primary tumor in patients diagnosed with vulvar squamous cell carcinoma (VSCC) who underwent a pretreatment F-18 FDG PET/CT scan.

This retrospective study included 47 patients with a histopathologically confirmed diagnosis of VSCC, and who underwent a F-18 FDG PET/CT scan prior to treatment. The disease stage and age at diagnosis, and the maximum standardized uptake value (SUVmax), SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) values of the primary tumor, based on a baseline PET scan, were recorded. The relationship between these factors, and progression-free survival (PFS) and overall survival (OS) was evaluated.

The mean age of the 47 study patients was 69.6±1.9 years. Among the patients, 18 were in early stage of the disease and 29 were in the advanced stage. The age, and SUVmax, SUVmean, MTV and TLG values were statistically significantly associated with OS and PFS. Furthermore, it was noted that OS and PFS were significantly longer in the early stage patients than in the advanced stage patients, in patients with a tumor size <4cm than those with a tumor size ≥4cm, and in patients with a negative lymph node metastasis than those with a positive lymph node metastasis.

Our findings suggest that PET parameters are prognostic factors for VSCC. To the best of our knowledge, this study is the first to investigate the prognostic value of the PET parameters of primary tumors in patients with VSCC, and as such, we believe it contributes to literature.
Our findings suggest that PET parameters are prognostic factors for VSCC. To the best of our knowledge, this study is the first to investigate the prognostic value of the PET parameters of primary tumors in patients with VSCC, and as such, we believe it contributes to literature.
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