Dinotefuran exposure alters biochemical, metabolomic, gut microbiome, and growth responses in decapoda pacific white shrimp Penaeus vannamei.

Dinotefuran, a neonicotinoid insecticide, may impact nontarget organisms such as Decapoda P. vannamei shrimp with nervous systems similar to insects. Exposing shrimp to low dinotefuran concentrations (6, 60, and 600 μg/L) for 21 days affected growth, hepatosomatic index, and survival.

Increased both cortical activation and functional connectivity after transcranial direct current stimulation in patients with post-stroke: A functional near-infrared spectroscopy study.

Background: Previous studies have shown that cognitive impairment is common after stroke. Transcranial direct current stimulation (tDCS) is a promising tool for rehabilitating cognitive impairment. This study aimed to investigate the effects of tDCS on the rehabilitation of cognitive impairment in patients with stroke.

The efficacy and safety of self-administered acupressure on respiratory tract infection in chronic kidney disease: a randomized controlled trial.

Background: Respiratory tract infection (RTI) is associated with a higher risk of kidney failure in patients with chronic kidney disease (CKD), without effective precautions. Self-administered acupressure (SAA) has been shown to potentially prevent RTI, but still lack of clinical evidence in CKD. The present randomized controlled trial assessed the efficacy and safety of SAA in preventing RTI recurrence in patients with CKD.

Control of Behavioral Arousal and Defense by a Glutamatergic Midbrain-Amygdala Pathway in Mice.

In response to external threatening signals, animals evolve a series of defensive behaviors that depend on heightened arousal. It is believed that arousal and defensive behaviors are coordinately regulated by specific neurocircuits in the central nervous system. The ventral tegmental area (VTA) is a key structure located in the ventral midbrain of mice.

Combined toxic effects of thiamethoxam on intestinal flora, transcriptome and physiology of Pacific white shrimp Litopenaeus vannamei.

The environmental accumulation of thiamethoxam has increasingly become a risk for the health of aquatic animals, especially crustacean species in the same phylum as the target pests. The lack of knowledge on the toxicity of thiamethoxam to crustaceans motivates our research to study the acute and chronic toxicity of decapod crustaceans Litopenaeus vannamei, exposed to thiamethoxam. A 28-day chronic toxicity test followed a 96 h acute toxicity test.

Global Instance Tracking: Locating Target More Like Humans.

Target tracking, the essential ability of the human visual system, has been simulated by computer vision tasks. However, existing trackers perform well in austere experimental environments but fail in challenges like occlusion and fast motion. The massive gap indicates that researches only measure tracking performance rather than intelligence.

Impact of imidacloprid exposure on the biochemical responses, transcriptome, gut microbiota and growth performance of the Pacific white shrimp Litopenaeus vannamei.

The widespread use of neonicotinoid insecticides, such as imidacloprid, in agriculture is one of the key factors for the drop in the survival of invertebrates, including decapod crustaceans. However, there is currently a lack of comprehensive studies on the chronic toxicity mechanisms in decapod crustaceans. Here, the concentration-dependent effects of imidacloprid on the physiology and biochemistry, gut microbiota and transcriptome of L.

Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation.

Panoptic segmentation (PS) is a complex scene understanding task that requires providing high-quality segmentation for both thing objects and stuff regions. Previous methods handle these two classes with semantic and instance segmentation modules separately, following with heuristic fusion or additional modules to resolve the conflicts between the two outputs. This work simplifies this pipeline of PS by consistently modeling the two classes with a novel PS framework, which extends a detection model with an extra module to predict category- and instance-aware pixel embedding (CIAE).

Learning Macromanagement in Starcraft by Deep Reinforcement Learning.

StarCraft is a real-time strategy game that provides a complex environment for AI research. Macromanagement, i.e.

GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild.

We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure [1] and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts [19], [20], [23], [26]. By releasing the large high-diversity database, we aim to provide a unified training and evaluation platform for the development of class-agnostic, generic purposed short-term trackers.

Diastereoselective Synthesis of 3,4-Dihydropyran-3-carboxamides with in Vitro Anti-inflammatory Activity.

A versatile and economical reaction of diketene (), aryl amines , cyclic 1,3-diketones , primary amines , and aryl aldehydes was explored to synthesize 3,4-dihydropyran-3-carboxamide derivatives under mild conditions. Three stereogenic centers are generated in the products, and the structure of the major diastereomer of {,,,} was identified by X-ray diffraction and 2D NMR spectroscopy. The scope and limitation investigation provided two series of (2,3,4)-chromene-3-carboxamides in good to excellent yields with high diastereoselectivity.

Fast A3RL: Aesthetics-Aware Adversarial Reinforcement Learning for Image Cropping.

Image cropping aims at improving the quality of images by removing unwanted outer areas, which is widely used in the photography and printing industry. Most previous cropping methods that don't need bounding box supervision rely on the sliding window mechanism. The sliding window method results in fixed aspect ratios and limits the shape of the cropping region.

Focal Boundary Guided Salient Object Detection.

The performance of salient object segmentation has been significantly advanced by using deep convolutional networks. However, these networks often produce blob-like saliency maps without accurate object boundaries. This is caused by the limited spatial resolution of their feature maps after multiple pooling operations, and might hinder downstream applications that require precise object shapes.

A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios.

Retrieving specific persons with various types of queries, e.g., a set of attributes or a portrait photo has great application potential in large-scale intelligent surveillance systems.

Deep Crisp Boundaries: From Boundaries to Higher-Level Tasks.

Edge detection has made significant progress with the help of deep convolutional networks (ConvNet). These ConvNet-based edge detectors have approached human level performance on standard benchmarks. We provide a systematical study of these detectors' outputs.

Mixed Supervised Object Detection with Robust Objectness Transfer.

In this paper, we consider the problem of leveraging existing fully labeled categories to improve the weakly supervised detection (WSD) of new object categories, which we refer to as mixed supervised detection (MSD). Different from previous MSD methods that directly transfer the pre-trained object detectors from existing categories to new categories, we propose a more reasonable and robust objectness transfer approach for MSD. In our framework, we first learn domain-invariant objectness knowledge from the existing fully labeled categories.

ORGM: Occlusion Relational Graphical Model for Human Pose Estimation.

Articulated human pose estimation from monocular image is a challenging problem in computer vision. Occlusion is a main challenge for human pose estimation, which is largely ignored in popular tree structured models. The tree structured model is simple and convenient for exact inference, but short in modeling the occlusion coherence especially in the case of self-occlusion.

Deep Aesthetic Quality Assessment With Semantic Information.

Human beings often assess the aesthetic quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic aesthetic quality assessment and semantic recognition. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition task offers the key to address this problem.

A Semi-Supervised Method for Surveillance-Based Visual Location Recognition.

In this paper, we are devoted to solving the problem of crossing surveillance and mobile phone visual location recognition, especially for the case that the query and reference images are captured by mobile phone and surveillance camera, respectively. Besides, we also study the influence of the environmental condition variations on this problem. To explore that problem, we first build a cross-device location recognition dataset, which includes images of 22 locations taken by mobile phones and surveillance cameras under different time and weather conditions.

Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting.

Localizing objects of interest in images when provided with only image-level labels is a challenging visual recognition task. Previous efforts have required carefully designed features and have difficulty in handling images with cluttered backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications.

High-Order Topology Modeling of Visual Words for Image Classification.

Modeling relationship between visual words in feature encoding is important in image classification. Recent methods consider this relationship in either image or feature space, and most of them incorporate only pairwise relationship (between visual words). However, in situations involving large variability in images, one cannot capture intrinsic invariance of intra-class images using low-order pairwise relationship.

Sub-nanometer periodic nonlinearity error in absolute distance interferometers.

Periodic nonlinearity which can result in error in nanometer scale has become a main problem limiting the absolute distance measurement accuracy. In order to eliminate this error, a new integrated interferometer with non-polarizing beam splitter is developed. This leads to disappearing of the frequency and/or polarization mixing.

Large-scale weakly supervised object localization via latent category learning.

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds.

Foreground object detection using top-down information based on EM framework.

In this paper, we present a novel foreground object detection scheme that integrates the top-down information based on the expectation maximization (EM) framework. In this generalized EM framework, the top-down information is incorporated in an object model. Based on the object model and the state of each target, a foreground model is constructed.

A discriminative model of motion and cross ratio for view-invariant action recognition.

Action recognition is very important for many applications such as video surveillance, human-computer interaction, and so on; view-invariant action recognition is hot and difficult as well in this field. In this paper, a new discriminative model is proposed for video-based view-invariant action recognition. In the discriminative model, motion pattern and view invariants are perfectly fused together to make a better combination of invariance and distinctiveness.