Improving Cardiovascular Disease Prediction With Machine Learning Using Mental Health Data: A Prospective UK Biobank Study.

Background: Robust and accurate prediction of cardiovascular disease (CVD) risk facilitates early intervention to benefit patients. The intricate relationship between mental health disorders and CVD is widely recognized. However, existing models often overlook psychological factors, relying on a limited set of clinical and lifestyle parameters, or being developed on restricted population subsets.

Consistency-Aware Anchor Pyramid Network for Crowd Localization.

Crowd localization aims to predict the positions of humans in images of crowded scenes. While existing methods have made significant progress, two primary challenges remain: (i) a fixed number of evenly distributed anchors can cause excessive or insufficient predictions across regions in an image with varying crowd densities, and (ii) ranking inconsistency of predictions between the testing and training phases leads to the model being sub-optimal in inference. To address these issues, we propose a Consistency-Aware Anchor Pyramid Network (CAAPN) comprising two key components: an Adaptive Anchor Generator (AAG) and a Localizer with Augmented Matching (LAM).

Report of the European Medicines Agency Conference on RNA-Based Medicines.

RNA-based medicines have potential to treat a large variety of diseases, and research in the field is very dynamic. Proactively, The European Medicines Agency (EMA) organized a virtual conference on February 2, 2023 to promote the development of RNA-based medicines. The initiative addresses the goal of the EMA Regulatory Science Strategy to 2025 to "catalyse the integration of science and technology in medicines development.

Dynamic Convolution for 3D Point Cloud Instance Segmentation.

In this paper, we come up with a simple yet effective approach for instance segmentation on 3D point cloud with strong robustness. Previous top-performing methods for this task adopt a bottom-up strategy, which often involves various inefficient operations or complex pipelines, such as grouping over-segmented components, introducing heuristic post-processing steps, and designing complex loss functions. As a result, the inevitable variations of the instances sizes make it vulnerable and sensitive to the values of pre-defined hyper-parameters.

CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification.

Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque learning and reasoning process, making it difficult to trust machine diagnosis and the subsequent adoption of such algorithms in clinical setting. This is especially true when the CNN is trained with limited amount of medical data, which is a common issue as curating sufficiently large amount of annotated medical imaging data is a long and costly process.

Structured Multimodal Attentions for TextVQA.

Text based Visual Question Answering (TextVQA) is a recently raised challenge requiring models to read text in images and answer natural language questions by jointly reasoning over the question, textual information and visual content. Introduction of this new modality - Optical Character Recognition (OCR) tokens ushers in demanding reasoning requirements. Most of the state-of-the-art (SoTA) VQA methods fail when answer these questions because of three reasons: (1) poor text reading ability; (2) lack of textual-visual reasoning capacity; and (3) choosing discriminative answering mechanism over generative couterpart (although this has been further addressed by M4C).

Learning to Zoom-In via Learning to Zoom-Out: Real-World Super-Resolution by Generating and Adapting Degradation.

Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs. The SR methods learned on synthetic data do not perform well in real-world, due to the domain gap between the artificially synthesized and real LR images. Some efforts are thus taken to capture real-world image pairs.

Multi-way backpropagation for training compact deep neural networks.

Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since ResNet (He et al., 2016), we are able to train very deep CNNs as the gradient vanishing issue has been largely addressed by the introduction of skip connections.

Learning Deep Gradient Descent Optimization for Image Deconvolution.

As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominant approach is based on optimization subject to regularization functions that are either manually designed or learned from examples. Existing learning-based methods have shown superior restoration quality but are not practical enough due to their restricted and static model design.

Accurate Tensor Completion via Adaptive Low-Rank Representation.

Low-rank representation-based approaches that assume low-rank tensors and exploit their low-rank structure with appropriate prior models have underpinned much of the recent progress in tensor completion. However, real tensor data only approximately comply with the low-rank requirement in most cases, viz., the tensor consists of low-rank (e.

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs.

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio.

Research, Innovation, and Policy: An Alliance Combating Antimicrobial Resistance.

The surge in antimicrobial resistance (AMR) has created a crisis that has become top priority for public health and global policy. Researchers, developers, innovators, funders, and policymakers need to curb AMR's rising trend by acting synergistically, boosting investment in developing solutions. This science-policy interface is now taking shape.

High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare).

To optimize shoot growth and structure of cereals, we need to understand the genetic components controlling initiation and elongation. While measuring total shoot growth at high throughput using 2D imaging has progressed, recovering the 3D shoot structure of small grain cereals at a large scale is still challenging. Here, we present a method for measuring defined individual leaves of cereals, such as wheat and barley, using few images.

MPTV: Matching Pursuit-Based Total Variation Minimization for Image Deconvolution.

Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, however, often suffer from the over-smoothing issue and solution bias caused by the homogeneous penalization. In this paper, we consider addressing these issues by applying inhomogeneous regularization on different image components.

Critical knowledge gaps and research needs related to the environmental dimensions of antibiotic resistance.

There is growing understanding that the environment plays an important role both in the transmission of antibiotic resistant pathogens and in their evolution. Accordingly, researchers and stakeholders world-wide seek to further explore the mechanisms and drivers involved, quantify risks and identify suitable interventions. There is a clear value in establishing research needs and coordinating efforts within and across nations in order to best tackle this global challenge.

Video Diversion Improves Success Rate of Fundoscopic Examination in Children: A Prospective Randomized Controlled Trial.

Background: Fundoscopy is an important component of the neurological examination as it can detect pathologies such as high intracranial pressure. However, the examination can be challenging in young children. This study evaluated whether playing a video during eye examination improves the success, duration, and ease of pediatric fundoscopy.

An Embarrassingly Simple Approach to Visual Domain Adaptation.

We show that it is possible to achieve high-quality domain adaptation without explicit adaptation. The nature of the classification problem means that when samples from the same class in different domains are sufficiently close, and samples from differing classes are separated by large enough margins, there is a high probability that each will be classified correctly. Inspired by this, we propose an embarrassingly simple yet effective approach to domain adaptation-only the class mean is used to learn class-specific linear projections.

Pull Incentives for Antibacterial Drug Development: An Analysis by the Transatlantic Task Force on Antimicrobial Resistance.

New alternative market models are needed to incentivize companies to invest in developing new antibacterial drugs. In a previous publication, the Transatlantic Task Force on Antimicrobial Resistance (TATFAR) summarized the key areas of consensus for economic incentives for antibacterial drug development. That work determined that there was substantial agreement on the need for a mixture of push and pull incentives and particularly those that served to delink the revenues from the volumes sold.

FVQA: Fact-based Visual Question Answering.

Visual Question Answering (VQA) has attracted much attention in both computer vision and natural language processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited.

Prevention, diagnosis, and management of midurethral mesh sling complications.

Midurethral slings (MUS) are a proven effective treatment option for stress urinary incontinence (SUI) and have become the gold standard in most centres in North America. MUS implantation can be associated with risks that are common to all anti-incontinence surgeries, and others which are unique. This article reviews the intraoperative and the early and late postoperative risks associated with these procedures, with insights into their prevention, diagnosis, and management drawn from the literature and expert opinion.

Exploring Context with Deep Structured Models for Semantic Segmentation.

We propose an approach for exploiting contextual information in semantic image segmentation, and particularly investigate the use of patch-patch context and patch-background context in deep CNNs. We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions. Specifically, we formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches.

Image Captioning and Visual Question Answering Based on Attributes and External Knowledge.

Much of the recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. In this paper we first propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering.

Compositional Model Based Fisher Vector Coding for Image Classification.

Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) as the generative model for local features. However, the representative power of a GMM can be limited because it essentially assumes that local features can be characterized by a fixed number of feature prototypes, and the number of prototypes is usually small in FVC.

Cross-Convolutional-Layer Pooling for Image Recognition.

Recent studies have shown that a Deep Convolutional Neural Network (DCNN) trained on a large image dataset can be used as a universal image descriptor and that doing so leads to impressive performance for a variety of image recognition tasks. Most of these studies adopt activations from a single DCNN layer, usually a fully-connected layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive convolutional layers: one layer is used for local feature extraction and the other serves as guidance to pool the extracted features.