Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection

Object detection on VHR remote sensing images plays a vital role in applications such as urban planning, land resource management, and rescue missions. The large-scale variation of the remote-sensing targets is one of the main challenges in VHR remote-sensing object detection. Existing methods improve the detection accuracy of high-resolution remote sensing objects by improving the structure of feature pyramids and adopting different attention modules. However, for small targets, there still be seriously missed detections due to the loss of key detail features. There is still room for improvement in the way of multiscale feature fusion and balance. To address this issue, this paper proposes two novel modules: Guided Attention and Tucker Bilinear Attention, which are applied to the stages of early fusion and late fusion respectively. The former can effectively retain clean key detail features, and the latter can better balance features through semantic-level correlation mining. Based on two modules, we build a new multi-scale remote sensing object detection framework. No bells and whistles. The proposed method largely improves the average precisions of small objects and achieves the highest mean average precisions compared with 9 state-of-the-art methods on DOTA, DIOR, and NWPU VHR-10.Code and models are available at https://github.com/Shinichict/GTNet.Comment: arXiv admin note: text overlap with arXiv:1705.06676, arXiv:2209.13351 by other author

The Developing Blueberry Industry in China

The present situation of blueberry industry in China was summarized. The six main blueberry cultivation areas in China were reviewed and practical suggestions were made. Reference and guidance for water management of rabbiteye blueberry in Yangtze river basin was provided, and water physiological characteristics and water requirement of blueberry were also clarified so as to provide scientific management of blueberry. Effects of vinegar residue on soil physical and chemical properties, enzymatic activities, growth of blueberry, nutrient uptake, and fruit quality were studied. The effect of vinegar residue on the growth of blueberry and the mechanism revealed from the perspective of soil amelioration were also discussed from the results

Wettability Alteration of Sandstone by Chemical Treatments

Liquid condensation in the reservoir near a wellbore may kill gas production in gas-condensate reservoirs when pressure drops lower than the dew point. It is clear from investigations reported in the literature that gas production could be improved by altering the rock wettability from liquid-wetness to gas-wetness. In this paper, three different fluorosurfactants FG1105, FC911, and FG40 were evaluated for altering the wettability of sandstone rocks from liquid-wetting to gas-wetting using contact angle measurement. The results showed that FG40 provided the best wettability alteration effect with a concentration of 0.3% and FC911 at the concentration of 0.3%

Boosting the Transferability of Adversarial Attacks with Global Momentum Initialization

Deep neural networks are vulnerable to adversarial examples, which attach human invisible perturbations to benign inputs. Simultaneously, adversarial examples exhibit transferability under different models, which makes practical black-box attacks feasible. However, existing methods are still incapable of achieving desired transfer attack performance. In this work, from the perspective of gradient optimization and consistency, we analyze and discover the gradient elimination phenomenon as well as the local momentum optimum dilemma. To tackle these issues, we propose Global Momentum Initialization (GI) to suppress gradient elimination and help search for the global optimum. Specifically, we perform gradient pre-convergence before the attack and carry out a global search during the pre-convergence stage. Our method can be easily combined with almost all existing transfer methods, and we improve the success rate of transfer attacks significantly by an average of 6.4% under various advanced defense mechanisms compared to state-of-the-art methods. Eventually, we achieve an attack success rate of 95.4%, fully illustrating the insecurity of existing defense mechanisms

Ideal serum non-ceruloplasmin bound copper prediction for long-term treated patients with Wilson disease: a nomogram model

PurposeThis study aimed to explore the factors associated with the optimal serum non-ceruloplasmin bound copper (NCBC) level and develop a flexible predictive model to guide lifelong therapy in Wilson disease (WD) and delay disease progression.MethodsWe retrospectively collected clinical data from 144 patients hospitalized in the Encephalopathy Center of the first affiliated hospital of Anhui University of Chinese Medicine between May 2012 and April 2023. Independent variables were selected using variate COX and LASSO regressions, followed by multivariate COX regression analysis. A predictive nomogram was constructed and validated using the concordance index (C-index), calibration curves, and clinical decision curve analysis, of which nomogram pictures were utilized for model visualization.ResultsA total of 61 (42.36%) patients were included, with an average treatment duration of 55.0 (range, 28.0, 97.0) months. Multivariate regression analysis identified several independent risk factors for serum NCBC level, including age of diagnosis, clinical classification, laminin liver stiffness measurement, and copper to zinc ratio in 24-h urinary excretion. The C-index indicated moderate discriminative ability (48 months: 0.829, 60 months: 0.811, and 72 months: 0.819). The calibration curves showed good consistency and calibration; clinical decision curve analysis demonstrated clinically beneficial threshold probabilities at different time intervals.ConclusionThe predictive nomogram model can predict serum NCBC level; consequently, we recommend its use in clinical practice to delay disease progression and improve the clinical prognosis of WD

Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma

Background and purposeThe presence of microvascular invasion (MVI) is a crucial indicator of postoperative recurrence in patients with hepatocellular carcinoma (HCC). Detecting MVI before surgery can improve personalized surgical planning and enhance patient survival. However, existing automatic diagnosis methods for MVI have certain limitations. Some methods only analyze information from a single slice and overlook the context of the entire lesion, while others require high computational resources to process the entire tumor with a three-dimension (3D) convolutional neural network (CNN), which could be challenging to train. To address these limitations, this paper proposes a modality-based attention and dual-stream multiple instance learning(MIL) CNN.Materials and methodsIn this retrospective study, 283 patients with histologically confirmed HCC who underwent surgical resection between April 2017 and September 2019 were included. Five magnetic resonance (MR) modalities including T2-weighted, arterial phase, venous phase, delay phase and apparent diffusion coefficient images were used in image acquisition of each patient. Firstly, Each two-dimension (2D) slice of HCC magnetic resonance image (MRI) was converted into an instance embedding. Secondly, modality attention module was designed to emulates the decision-making process of doctors and helped the model to focus on the important MRI sequences. Thirdly, instance embeddings of 3D scans were aggregated into a bag embedding by a dual-stream MIL aggregator, in which the critical slices were given greater consideration. The dataset was split into a training set and a testing set in a 4:1 ratio, and model performance was evaluated using five-fold cross-validation.ResultsUsing the proposed method, the prediction of MVI achieved an accuracy of 76.43% and an AUC of 74.22%, significantly surpassing the performance of the baseline methods.ConclusionOur modality-based attention and dual-stream MIL CNN can achieve outstanding results for MVI prediction

Probing the Internal pH and Permeability of a Carboxysome Shell

The carboxysome is a protein-based nanoscale organelle in cyanobacteria and many proteobacteria, which encapsulates the key CO2-fixing enzymes ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and carbonic anhydrase (CA) within a polyhedral protein shell. The intrinsic self-assembly and architectural features of carboxysomes and the semipermeability of the protein shell provide the foundation for the accumulation of CO2 within carboxysomes and enhanced carboxylation. Here, we develop an approach to determine the interior pH conditions and inorganic carbon accumulation within an α-carboxysome shell derived from a chemoautotrophic proteobacterium Halothiobacillus neapolitanus and evaluate the shell permeability. By incorporating a pH reporter, pHluorin2, within empty α-carboxysome shells produced in Escherichia coli, we probe the interior pH of the protein shells with and without CA. Our in vivo and in vitro results demonstrate a lower interior pH of α-carboxysome shells than the cytoplasmic pH and buffer pH, as well as the modulation of the interior pH in response to changes in external environments, indicating the shell permeability to bicarbonate ions and protons. We further determine the saturated HCO3- concentration of 15 mM within α-carboxysome shells and show the CA-mediated increase in the interior CO2 level. Uncovering the interior physiochemical microenvironment of carboxysomes is crucial for understanding the mechanisms underlying carboxysomal shell permeability and enhancement of Rubisco carboxylation within carboxysomes. Such fundamental knowledge may inform reprogramming carboxysomes to improve metabolism and recruit foreign enzymes for enhanced catalytical performance

Synthetic engineering of a new biocatalyst encapsulating [NiFe]-hydrogenases for enhanced hydrogen production

Hydrogenases are microbial metalloenzymes capable of catalyzing the reversible interconversion between molecular hydrogen and protons with high efficiency, and have great potential in the development of new electrocatalysts for renewable...</jats:p

Reprogramming bacterial protein organelles as a nanoreactor for hydrogen production

Compartmentalization is a ubiquitous building principle in cells, which permits segregation of biological elements and reactions. The carboxysome is a specialized bacterial organelle that encapsulates enzymes into a virus-like protein shell and plays essential roles in photosynthetic carbon fixation. The naturally designed architecture, semi-permeability, and catalytic improvement of carboxysomes have inspired rational design and engineering of new nanomaterials to incorporate desired enzymes into the protein shell for enhanced catalytic performance. Here, we build large, intact carboxysome shells (over 90 nm in diameter) in the industrial microorganism Escherichia coli by expressing a set of carboxysome protein-encoding genes. We develop strategies for enzyme activation, shell self-assembly, and cargo encapsulation to construct a robust nanoreactor that incorporates catalytically active [FeFe]-hydrogenases and functional partners within the empty shell for the production of hydrogen. We show that shell encapsulation and the internal microenvironment of the new catalyst facilitate hydrogen production of the encapsulated oxygen-sensitive hydrogenases. The study provides insights into the assembly and formation of carboxysomes and paves the way for engineering carboxysome shell-based nanoreactors to recruit specific enzymes for diverse catalytic reactions