PRF-Net: A Progressive Remote Sensing Image Registration and Fusion Network.

Most of the existing fusion algorithms are not robust to unregistered input images. Even after image registration, nonlinear nonregistration may persist in the local areas of the images, leading to poor quality in the fused image. So, as to tackle these challenges, a progressive remote sensing image registration and fusion network is proposed in this article, and named PRF-Net, which is particularly useful when two images are from different platforms.

An Enzyme-Cleavable Solubilizing-Tag Facilitates the Chemical Synthesis of Mirror-Image Proteins.

Mirror-image proteins (D-proteins) are useful in biomedical research for purposes such as mirror-image screening for D-peptide drug discovery, but the chemical synthesis of many D-proteins is often low yielding due to the poor solubility or aggregation of their constituent peptide segments. Here, we report a Lys-C protease-cleavable solubilizing tag and its use to synthesize difficult-to-obtain D-proteins. Our tag is easily installed onto multiple amino acids such as Lys, Ser, Thr, and/or the N-terminal amino acid of hydrophobic D-peptides, is impervious to various reaction conditions, such as peptide synthesis, ligation, desulfurization, and transition metal-mediated deprotection, and yet can be completely removed by Lys-C protease under denaturing conditions to give the desired D-protein.

Backbone-Installed Split Intein-Assisted Ligation for the Chemical Synthesis of Mirror-Image Proteins.

Membrane-associated D-proteins are an important class of synthetic molecules needed for D-peptide drug discovery, but their chemical synthesis using canonical ligation methods such as native chemical ligation is often hampered by the poor solubility of their constituent peptide segments. Here, we describe a Backbone-Installed Split Intein-Assisted Ligation (BISIAL) method for the synthesis of these proteins, wherein the native L-forms of the N- and C-intein fragments of the unique consensus-fast (Cfa) (i.e.

SiMaN: Sign-to-Magnitude Network Binarization.

Binary neural networks (BNNs) have attracted broad research interest due to their efficient storage and computational ability. Nevertheless, a significant challenge of BNNs lies in handling discrete constraints while ensuring bit entropy maximization, which typically makes their weight optimization very difficult. Existing methods relax the learning using the sign function, which simply encodes positive weights into +1s, and -1s otherwise.

Automated morphologic analysis of intracranial and extracranial arteries using convolutional neural networks.

Objective: To develop an automated method for 3D magnetic resonance (MR) vessel wall image analysis to facilitate morphologic quantification of intra- and extracranial arteries, including vessel centerline tracking, vessel straightening and reformation, vessel wall segmentation based on convoluted neural networks (CNNs), and morphological measurement.

Methods: MR vessel wall images acquired using DANTE-SPACE sequences and corresponding time-of-flight-MRA images of 67 subjects (including 47 healthy volunteers and 20 patients with atherosclerosis) were included in this study. The centerline of the vessel was firstly extracted from the MRA images and copyed to the 3D MR vessel wall images through the registration relationship between the MRA images and the MR vessel wall images to extract, straighten, and reconstruct interested vessel segments into 2D slices.

Clinical value and molecular mechanism of AQGPs in different tumors.

Aquaglyceroporins (AQGPs), including AQP3, AQP7, AQP9, and AQP10, are transmembrane channels that allow small solutes across biological membranes, such as water, glycerol, HO, and so on. Increasing evidence suggests that they play critical roles in cancer. Overexpression or knockdown of AQGPs can promote or inhibit cancer cell proliferation, migration, invasion, apoptosis, epithelial-mesenchymal transition and metastasis, and the expression levels of AQGPs are closely linked to the prognosis of cancer patients.

A Survey on Human Cancer Categorization Based on Deep Learning.

In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases.

Synchronous Spatiotemporal Graph Transformer: A New Framework for Traffic Data Prediction.

Modeling the spatiotemporal relationship (STR) of traffic data is important yet challenging for existing graph networks. These methods usually capture features separately in temporal and spatial dimensions or represent the spatiotemporal data by adopting multiple local spatial-temporal graphs. The first kind of method mentioned above is difficult to capture potential temporal-spatial relationships, while the other is limited for long-term feature extraction due to its local receptive field.

Tiny Vehicle Detection for Mid-to-High Altitude UAV Images Based on Visual Attention and Spatial-Temporal Information.

Mid-to-high altitude Unmanned Aerial Vehicle (UAV) imagery can provide important remote sensing information between satellite and low altitude platforms, and vehicle detection in mid-to-high altitude UAV images plays a crucial role in land monitoring and disaster relief. However, the high background complexity of images and limited pixels of objects challenge the performance of tiny vehicle detection. Traditional methods suffer from poor adaptation ability to complex backgrounds, while deep neural networks (DNNs) have inherent defects in feature extraction of tiny objects with finite pixels.

A Well-Conserved Archaeal B-Family Polymerase Functions as an Extender in Translesion Synthesis.

B-family DNA polymerases (PolBs) of different groups are widespread in , and different PolBs often coexist in the same organism. Many of these PolB enzymes remain to be investigated. One of the main groups that is poorly characterized is PolB2, whose members occur in many archaea but are predicted to be inactivated forms of DNA polymerase.

Self-Supervised monocular depth and ego-Motion estimation in endoscopy: Appearance flow to the rescue.

Recently, self-supervised learning technology has been applied to calculate depth and ego-motion from monocular videos, achieving remarkable performance in autonomous driving scenarios. One widely adopted assumption of depth and ego-motion self-supervised learning is that the image brightness remains constant within nearby frames. Unfortunately, the endoscopic scene does not meet this assumption because there are severe brightness fluctuations induced by illumination variations, non-Lambertian reflections and interreflections during data collection, and these brightness fluctuations inevitably deteriorate the depth and ego-motion estimation accuracy.

Enzymatic Switching Between Archaeal DNA Polymerases Facilitates Abasic Site Bypass.

Abasic sites are among the most abundant DNA lesions encountered by cells. Their replication requires actions of specialized DNA polymerases. Herein, two archaeal specialized DNA polymerases were examined for their capability to perform translesion DNA synthesis (TLS) on the lesion, including Dpo2 of B-family, and Dpo4 of Y-family.

An Automatic Method for Epileptic Seizure Detection Based on Deep Metric Learning.

Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of epilepsy which is a life-threatening neurological disorder. Many algorithms have been proposed for the automatic detection of epileptic seizures using traditional machine learning and deep learning. Although deep learning methods have achieved great success in many fields, their performance in EEG analysis and classification is still limited mainly due to the relatively small sizes of available datasets.

Hierarchical Self-Distilled Feature Learning for Fine-Grained Visual Categorization.

Fine-grained visual categorization (FGVC) relies on hierarchical features extracted by deep convolutional neural networks (CNNs) to recognize closely alike objects. Particularly, shallow layer features containing rich spatial details are vital for specifying subtle differences between objects but are usually inadequately optimized due to gradient vanishing during backpropagation. In this article, hierarchical self-distillation (HSD) is introduced to generate well-optimized CNNs features for accurate fine-grained categorization.

A multi-scale unsupervised learning for deformable image registration.

Purpose: Image registration is a fundamental task in the area of image processing, and it is critical to many clinical applications, e.g., computer-assisted surgery.

Chemical Synthesis of a Full-Length G-Protein-Coupled Receptor β-Adrenergic Receptor with Defined Modification Patterns at the C-Terminus.

The β-adrenergic receptor (βAR) is a G-protein-coupled receptor (GPCR) that responds to the hormone adrenaline and is an important drug target in the context of respiratory diseases, including asthma. βAR function can be regulated by post-translational modifications such as phosphorylation and ubiquitination at the C-terminus, but access to the full-length βAR with well-defined and homogeneous modification patterns critical for biochemical and biophysical studies remains challenging. Here, we report a practical synthesis of differentially modified, full-length βAR based on a combined native chemical ligation (NCL) and sortase ligation strategy.

iffDetector: Inference-Aware Feature Filtering for Object Detection.

Modern convolutional neural network (CNN)-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this article, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages. We introduce a generic inference-aware feature filtering (IFF) module that can be easily combined with existing detectors, resulting in our iffDetector.

Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks.

Pathological examination is the gold standard for the diagnosis of cancer. Common pathological examinations include hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). In some cases, it is hard to make accurate diagnoses of cancer by referring only to H&E staining images.

Memory Attention Networks for Skeleton-Based Action Recognition.

Skeleton-based action recognition has been extensively studied, but it remains an unsolved problem because of the complex variations of skeleton joints in 3-D spatiotemporal space. To handle this issue, we propose a newly temporal-then-spatial recalibration method named memory attention networks (MANs) and deploy MANs using the temporal attention recalibration module (TARM) and spatiotemporal convolution module (STCM). In the TARM, a novel temporal attention mechanism is built based on residual learning to recalibrate frames of skeleton data temporally.

MIGO-NAS: Towards Fast and Generalizable Neural Architecture Search.

Neural architecture search (NAS) has achieved unprecedented performance in various computer vision tasks. However, most existing NAS methods are defected in search efficiency and model generalizability. In this paper, we propose a novel NAS framework, termed MIGO-NAS, with the aim to guarantee the efficiency and generalizability in arbitrary search spaces.

Modulated Convolutional Networks.

While the deep convolutional neural network (DCNN) has achieved overwhelming success in various vision tasks, its heavy computational and storage overhead hinders the practical use of resource-constrained devices. Recently, compressing DCNN models has attracted increasing attention, where binarization-based schemes have generated great research popularity due to their high compression rate. In this article, we propose modulated convolutional networks (MCNs) to obtain binarized DCNNs with high performance.

Learning From Architectural Redundancy: Enhanced Deep Supervision in Deep Multipath Encoder-Decoder Networks.

Deep encoder-decoders are the model of choice for pixel-level estimation due to their redundant deep architectures. Yet they still suffer from the vanishing supervision information issue that affects convergence because of their overly deep architectures. In this work, we propose and theoretically derive an enhanced deep supervision (EDS) method which improves on conventional deep supervision (DS) by incorporating variance minimization into the optimization.

Fast Class-Wise Updating for Online Hashing.

Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e.

Aligning Discriminative and Representative Features: An Unsupervised Domain Adaptation Method for Building Damage Assessment.

Building assessment is highly prioritized during rescue operations and damage relief after hurricane disasters. Although machine learning has made remarkable improvement in building damage classification, it remains challenging because classifiers must be trained using a massive amount of labeled data. Furthermore, data labeling is labor intensive, costly, and unavailable after a disaster.