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Abstract Background To assess the capability of multimodal apparent diffusion (MAD) weighted magnetic resonance imaging (MRI) to distinguish between malignant and benign breast lesions, and to predict Ki-67 expression level in breast cancer. Methods This retrospective study was conducted with 93 patients who had postoperative pathology-confirmed breast cancer or benign breast lesions. MAD images were acquired using a 3.0 T MRI scanner with 16 b values. The MAD parameters, as flow (fF, DF), unimpeded (fluid) (fUI), hindered (fH, DH, and αH), and restricted (fR, DR), were calculated. The differences of the parameters were compared by Mann–Whitney U test between the benign/malignant lesions and high/low Ki-67 expression level. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). Results The fR in the malignant lesions was significantly higher than in the benign lesions (P = 0.001), whereas the fUI and DH were found to be significantly lower (P = 0.007 and P

Abstract 3D point cloud data, as an immersive detailed data source, has been increasingly used in numerous applications. To deal with the computational and storage challenges of this data, it needs to be compressed before transmission, storage, and processing, especially in real-time systems. Instead of decoding the compressed data stream and subsequently conducting downstream tasks on the decompressed data, analyzing point clouds directly in their compressed domain has attracted great interest. In this paper, we dive into the realm of compressed point cloud classification (CPCC), aiming to achieve high point cloud classification accuracy in a bitrate-saving way by ensuring the bit stream contains a high degree of representative information of the point cloud. Edge information is one of the most important and representative attributes of the point cloud because it can display the outlines or main shapes. However, extracting edge points or information from point cloud models is challenging due to their irregularity and sparsity. To address this challenge, we adopt an advanced edge-sampling method that enhances existing state-of-the-art (SOTA) point cloud edge-sampling techniques based on attention mechanisms and consequently develop a novel CPCC method “CPCC-PES” that focuses on point cloud’s edge information. The result obtained on the benchmark ModelNet40 dataset shows that our model has superior rate-accuracy trade-off performance than SOTA works. Specifically, our method achieves over 90% Top-1 Accuracy with a mere 0.08 bits-per-point (bpp), marking a remarkable over 96% reduction in BD-bitrate compared with specialized codecs. This means that our method only consumes 20% of the bitrate of other SOTA works while maintaining comparable accuracy. Furthermore, we propose a new evaluation metric named BD-Top-1 Accuracy to evaluate the trade-off performance between bitrate and Top-1 Accuracy for future CPCC research.

In higher plants, the regulatory roles of cAMP (cyclic adenosine 3′,5′-monophosphate) signaling remain elusive until now. Cellular cAMP levels are generally much lower in higher plants than in animals and transiently elevated for triggering downstream signaling events. Moreover, plant adenylate cyclase (AC) activities are found in different moonlighting multifunctional proteins, which may pose additional complications in distinguishing a specific signaling role for cAMP. Here, we have developed rapeseed (Brassica napus L.) transgenic plants that overexpress an inducible plant-origin AC activity for generating high AC levels much like that in animal cells, which served the genetic model disturbing native cAMP signaling as a whole in plants. We found that overexpression of the soluble AC activity had significant impacts on the contents of indole-3-acetic acid (IAA) and stress phytohormones, i.e. jasmonic acid (JA), abscisic acid (ABA), and salicylic acid (SA) in the transgenic plants. Acute induction of the AC activity caused IAA overaccumulation, and upregulation of TAA1 and CYP83B1 in the IAA biosynthesis pathways, but also simultaneously the hyper-induction of PR4 and KIN2 expression indicating activation of JA and ABA signaling pathways. We observed typical overgrowth phenotypes related to IAA excess in the transgenic plants, including significant increases in plant height, internode length, width of leaf blade, petiole length, root length, and fresh shoot biomass, as well as the precocious seed development, as compared to wild-type plants. In addition, we identified a set of 1465 cAMP-responsive genes (CRGs), which are most significantly enriched in plant hormone signal transduction pathway, and function mainly in relevance to hormonal, abiotic and biotic stress responses, as well as growth and development. Collectively, our results support that cAMP elevation impacts phytohormone homeostasis and signaling, and modulates plant growth and development. We proposed that cAMP signaling may be critical in configuring the coordinated regulation of growth and development in higher plants.

BackgroundThe systemic immuno-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are widely used and have been shown to be predictive indicators of various diseases. Diabetic nephropathy (DN), retinopathy (DR), and peripheral neuropathy (DPN) are the most prominent and common microvascular complications, which have seriously negative impacts on patients, families, and society. Exploring the associations with these three indicators and diabetic microvascular complications are the main purpose.MethodsThere were 1058 individuals with type 2 diabetes mellitus (T2DM) in this retrospective cross-sectional study. SII, NLR, and PLR were calculated. The diseases were diagnosed by endocrinologists. Logistic regression and subgroup analysis were applied to evaluate the association between SII, NLP, and PLR and diabetic microvascular complications.ResultsSII, NLR, and PLR were significantly associated with the risk of DN [odds ratios (ORs): 1.52, 1.71, and 1.60, respectively] and DR [ORs: 1.57, 1.79, and 1.55, respectively] by multivariate logistic regression. When NLR ≥2.66, the OR was significantly higher for the risk of DPN (OR: 1.985, 95% confidence interval: 1.29–3.05). Subgroup analysis showed no significant positive associations across different demographics and comorbidities, including sex, age, hypertension, HbA1c (glycated hemoglobin), and dyslipidemia.ConclusionThis study found a positive relationship between NLR and DN, DR, and DPN. In contrast, SII and PLR were found to be only associated with DN and DR. Therefore, for the diagnosis of diabetic microvascular complications, SII, NLR and PLR are highly valuable.

Vegetation restoration is an effective measure to cope with global climate change and promote soil carbon sequestration. However, during vegetation restoration, the turnover and properties of carbon within various aggregates change. The effects of plant source carbon input on surface soil and subsurface soil may be different. Thus, the characteristics of carbon components in aggregates are affected. Therefore, the research object of this study is the Robinia pseudoacacia forest located in 16–47a of the Loess Plateau, and compared with farmland. The change characteristics of organic carbon functional groups in 0–20 cm, 20–40 cm, and 40–60 cm soil layers were analyzed by Fourier near infrared spectroscopy, and the relationship between the chemical structure of organic carbon and the content of organic carbon components in soil aggregates was clarified, and the mechanism affecting the distribution of organic carbon components in soil aggregates was revealed in the process of vegetation restoration. The results show the following: (1) The stability of surface aggregates is sensitive, while that of deep aggregates is weak. Vegetation restoration increased the surface soil organic carbon content by 1.97~3.78 g·kg−1. (2) After vegetation restoration, the relative contents of polysaccharide functional groups in >0.25 mm aggregates were significantly reduced, while the relative contents of aromatic and aliphatic functional groups of organic carbon were significantly increased. The opposite is true for aggregates smaller than 0.25 mm. (3) With the increase in soil depth, the effect of litter on organic carbon gradually decreased, while the effect of root input on the accumulation of inert carbon in deep soil was more lasting.

Perfluorosulfonated anionic ionomers are known for their robust ion conductivity and chemical and mechanical stability. However, the structure and transport property degradation of perfluorinated cationic polymers (PfCPs) are not well understood. Herein, we propose an ion-exchange strategy to identify the structural degradation, ion transport mechanisms, and architectural reformation of PfCPs. Particularly, we demonstrate that the utility of a –SO2–N+ strategy employing the Menshutkin reaction cannot yield reliable PfCPs and anion-exchange membranes, but can yield an unreliable zwitterionic intermediate (cations–anions molar ratio is approximately 7.6%). Moreover, the degradation products were efficiently reformed as proton exchange membranes (PEMs), and the as-reformed PEMs achieved an ion-exchange capacity (IEC) value (0.89 mmol g−1), meanwhile retaining more than 94.7% of their initial capacity. Furthermore, the fuel cell assembled with reformed PEMs displayed a power density of 0.91 Wcm−2 at 2.32 A cm−2, which was 90.1% of that of the robust perfluorosulfonic acid PEMs. Our combined findings shed some fresh light on the state of understanding of the structure–property relationship in PfCPs.

Abstract Background Inevitably developed resistance of the third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) limited its clinical benefit on non-small cell lung cancer (NSCLC). Upfront combination therapy is promising to prevent this resistance. Compelling clinical evidence indicated the failure of third-generation EGFR TKIs combined with either immunotherapy or antiangiogenic agents. In comparison, combined treatment of third-generation EGFR TKIs and chemotherapy might be a favorable choice. Herein, we systematically analyzed and compared the effects of pemetrexed and a novel third-generation EGFR TKI aumolertinib combined in different sequences, subsequently revealed the potential mechanisms and proved the optimal combination schedule with clinical retrospective study. Methods Three combination schedules involving pemetrexed and aumolertinib in different sequences were developed. Their inhibition effects on cell proliferation and metastasis were firstly compared upon three human NSCLC cell lines in vitro, by cell counting kit-8, colony formation, wound healing and transwell assays respectively. Further evaluation in vivo was proceeded upon H1975 and HCC827 xenograft model. Gene and protein expression were detected by Q-PCR and western blot. Drug concentration was determined by LC–MS/MS. VEGF secretion was determined by ELISA. Tumor vessel was visualized by immunofluorescence. Lastly, a clinical retrospective study was raised with 65 patients’ data. Results The combination of pemetrexed and aumolertinib exhibited a sequence-dependent and EGFR mutant-dependent synergistic effect in vitro and in vivo. Only treatment with aumolertinib following pemetrexed (P-A) exhibited synergistic effect with stronger anti-tumor growth and anti-metastasis ability than monotherapy and also other combination sequences. This synergism could exclusively be observed in H1975 and HCC827 but not A549. Pathway analysis showed that P-A significantly enhanced the suppression of EGFR pathway. In addition, our results intriguingly found an obvious reduction of VEGF secretion and the accompanying normalization of the intratumor vessel, consequently increasing intratumoral accumulation of pemetrexed in P-A group. Finally, the clinical retrospective study verified the synergistic effect of P-A combination by significantly superior tumor response than aumolertinib monotherapy. Conclusion Aumolertinib-pemetrexed combined therapy is promising for EGFR mutant NSCLC but only in right administration sequence. P-A could become an advantageous combination strategy in clinical with synergistic inhibition of tumor growth and metastasis.

Identifying tiny objects from extremely low-resolution (LR) unmanned-aerial-vehicle-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this article, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on low-frequency (LF) components and high-frequency (HF) components. To address this LF-HF inconsistency problem, we propose a dual-stream representation learning generative adversarial network (DRL-GAN). The core idea is to produce enhanced image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected `WIDER-SHIP' dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average.

Identifying tiny objects with extremely low resolution is generally considered a very challenging task even for human vision, due to limited information presented inside the object areas. There have been very limited attempts in recent years to deal with low-resolution recognition. The existing solutions rely on either generating super-resolution images or learning multi-scale features. However, their performance improvement becomes very limited, especially when the resolution becomes very low. In this paper, we propose a Representation Learning Generative Adversarial Network (RL-GAN) to generate super image representation that is optimized for recognition. Our solution deals with the classical vision task of object recognition in the distance. We evaluate our idea on the challenging task of low-resolution object recognition. Comparison of experimental results conducted on public and our newly created WIDER-SHIP datasets demonstrate the effectiveness of our RL-GAN, which improves the classification results significantly, with 10-15% gain on average, compared with benchmark solutions.

Feature correspondence selection, which aims to seek as many true matches (i.e., inliers) as possible from a given putative set while minimizing false matches (i.e., outliers), is crucial to many feature-matching based tasks in computer vision. It remains a challenging problem how to deal with putative sets with low inlier ratios. To address this problem, in this paper, we propose a novel correspondence selection strategy, which is guided by Grid-based Motion Statistics (GMS). We first adopt the GMS to generate a small correspondence set with a high inlier ratio. Then, an accurate geometric model is built using the above correspondence set. Finally, the built geometric model is used to filter the given putative correspondence set to obtain true correspondences. The experimental results on benchmark datasets demonstrate that our proposed approach outperforms the state-of-the-art approaches for putative sets with various inlier ratios, especially for cases with low inlier ratios.

Benefiting from the advances in object detection in remote sensing, detecting objects in images captured by drones has achieved promising performance in recent years. However, drone-view object detection in rainy weather conditions (Rainy DroneDet) remains a challenge, as small-sized objects blurred by rain streaks offer a little valuable information for robust detection. In this paper, we propose a Collaborative Deraining Network called “CoDerainNet”, which simultaneously and interactively trains a deraining subnetwork and a droneDet subnetwork to improve the accuracy of Rainy DroneDet. Furthermore, we propose a Collaborative Teaching paradigm called “ColTeaching”, which leverages rain-free features extracted by the Deraining Subnetwork and teaches the DroneDet Subnetwork such features, to remove rain-specific interference in features for DroneDet. Due to the lack of an existing dataset for Rainy DroneDet, we built three drone datasets, including two synthetic datasets, namely RainVisdrone and RainUAVDT, and one real drone dataset, called RainDrone. Extensive experiment results on the three rainy datasets show that CoDerainNet can significantly reduce the computational costs of state-of-the-art (SOTA) object detectors while maintaining detection performance comparable to these SOTA models.

Benefiting from the advancement of deep neural networks (DNNs), detecting objects from drone-view images has achieved great success in recent years. It is a very challenging task to deploy such DNN-based detectors on drones in real-life applications due to their excessive computational costs and limited onboard computational resources. Large redundant computation exists because existing drone-view detectors infer all inputs with nearly identical computation. Detectors with less complexity can be sufficient for a large portion of inputs, which contain a small number of sparse distributed large-size objects. Therefore, a drone-view detector supporting input-aware inference, i.e., capable of dynamically adapting its architecture to different inputs, is highly desirable. In this work, we present a Dynamic Context Collection Network (DyCC-Net), which can perform input-aware inference by dynamically adapting its structure to inputs of different levels of complexities. DyCC-Net can significantly improve inference efficiency by skipping or executing a context collector conditioned on the complexity of the input images. Furthermore, since the weakly supervised learning strategy for computational resource allocation lacks of supervision, models may execute the computationally-expensive context collector even for easy images to minimize the detection loss. We present a Pseudo-label-based semi-supervised Learning strategy (Pseudo Learning), which uses automatically generated pseudo labels as supervision signals, to determine whether to perform context collector according to the input. Extensive experiment results on VisDrone2021 and UAVDT, show that our DyCC-Net can detect objects in drone-captured images efficiently. The proposed DyCC-Net reduces the inference time of state-of-the-art (SOTA) drone-view detectors by over 30 percent, and DyCC-Net outperforms them by 1.94% in AP75.

Rehmannia glutinosa has important medicinal value; terpenoid is one of the main active components in R. glutinosa. In this study, iTRAQ technique was used to analyze the relative abundance of proteins in roots of R. glutinosa, and 6,752 reliable proteins were quantified. GO enrichment results indicated that most proteins were involved in metabolic process or cellular process, 57.63% proteins had catalytic activity, and 65.80% proteins were enriched in membrane-bounded organelle. In roots of R. glutinosa, there were 38 KEGG enrichments with significance, more DEPs were found in some pathways, especially the proteasome pathway and TCA cycle with 15.0% DEPs between elongation stage and expansion stage of roots. Furthermore, five KEGG pathways of terpenoid synthesis were found. Most prenyltransferases belong to FPP/GGPP synthase family, involved in terpenoid backbone biosynthesis, and all interacted with biotin carboxylase CAC2. Compared with that at the elongation stage, many prenyltransferases exhibited higher expression at the expansion stage or maturation stage of roots. In addition, eight FPP/GGPP synthase encoding genes were cloned from R. glutinosa, namely FPPS, FPPS1, GGPS, GGPS3, GGPS4, GGPS5, GPPS and GPPS2, introns were also found in FPPS, FPPS1, GGPS5 and GGPS2, and FPP/GPP synthases were more conservative in organisms, especially in viridiplantae, in which the co-occurrence of GPPS or GPPS2 was significantly higher in plants. Further analysis found that FPP/GGPP synthases of R. glutinosa were divided into three kinds, GGPS, GPPS and FPPS, and their gene expression was significantly diverse in different varieties, growth periods, or tissues of R. glutinosa. Compared with that of GGPS, the expression of GPPS and FPPS was much higher in R. glutinosa, especially at the expansion stage and maturation stage. Thus, the synthesis of terpenoids in roots of R. glutinosa is intricately regulated and needs to be further studied.

Introduction: Thiazide diuretics are among the most widely used antihypertensive medications worldwide. Thiazide-induced hyponatremia (TIH) is 1 of their most clinically significant adverse effects. A priori TIH must result from excessive saliuresis and/or water reabsorption. We hypothesized that pathways regulating the thiazide-sensitive sodium-chloride cotransporter NCC and the water channel aquaporin-2 (AQP2) may be involved. Our aim was to assess whether patients with TIH would show evidence of altered NCC and AQP2 expression in urinary extracellular vesicles (UEVs), and also whether abnormalities of renal sodium reabsorption would be evident using endogenous lithium clearance (ELC). Methods: Blood and urine samples were donated by patients admitted to hospital with acute symptomatic TIH, after recovery to normonatremia, and also from normonatremic controls on and off thiazides. Urinary extracellular vesicles were isolated and target proteins evaluated by western blotting and by nanoparticle tracking analysis. Endogenous lithium clearance was assessed by inductively coupled plasma mass spectrometry. Results: Analysis of UEVs by western blotting showed that patients with acute TIH displayed reduced total NCC and increased phospho-NCC and AQP2 relative to appropriate control groups; smaller differences in NCC and AQP2 expression persisted after recovery from TIH. These findings were confirmed by nanoparticle tracking analysis. Renal ELC was lower in acute TIH compared to that in controls and convalescent case patients. Conclusion: Reduced NCC expression and increased AQP2 expression would be expected to result in saliuresis and water reabsorption in TIH patients. This study raises the possibility that UEV analysis may be of diagnostic utility in less clear-cut cases of thiazide-associated hyponatremia, and may help to identify patients at risk for TIH before thiazide initiation. Keywords: diuretic, hypertension, hyponatremia, sodium, thiazide, urinary extracellular vesicles

Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It is also considered a very challenging task due to the typically cluttered background and diverse dimensions of the foreground targets, especially small object areas that contain only very limited information. Multi-scale representation learning presents a remarkable approach to recognizing small objects. However, this strategy ignores the combination of the sub-parts in an object and also suffers from the background interference in the feature fusion process. To this end, we propose a Fine-grained Target Focusing Network (FiFoNet) which can effectively select a combination of multi-scale features for an object and block background interference, which further revitalizes the differentiability of the multi-scale feature representation. Furthermore, we propose a Global–Local Context Collector (GLCC) to extract global and local contextual information and enhance low-quality representations of small objects. We evaluate the performance of the proposed FiFoNet on the challenging task of object detection in UAV images. A comparison of the experiment results on three datasets, namely VisDrone2019, UAVDT, and our VisDrone_Foggy, demonstrates the effectiveness of FiFoNet, which outperforms the ten baseline and state-of-the-art models with remarkable performance improvements. When deployed on an edge device NVIDIA JETSON XAVIER NX, our FiFoNet only takes about 80 milliseconds to process an drone-captured image.

Person re-identification (ReID), aiming to identify people among multiple camera views, has attracted an increasing attention due to the potential of application in surveillance security. Large variations in subjects' postures, view angles, and illuminating conditions as well as non-ideal human detection significantly increase the difficulty of person ReID. Learning a robust metric for measuring the similarity between different person images is another under-addressed problem. In this paper, following the recent success of part-based models, in order to generate a discriminative and robust feature representation, we first propose to learn global and weighted local body-part features from pedestrian images. Then, in the training phase, angular loss and part-level classification loss are employed jointly as a similarity measure to train the network, which significantly improves the robustness of the resultant network against feature variance. Experimental results on several benchmark data sets demonstrate that our method outperforms the state-of-the-art methods.

Cyclic AMP (cAMP) is a pivotal signaling molecule existing in almost all living organisms. However, the mechanism of cAMP signaling in plants remains very poorly understood. Here, we employ the engineered activity of soluble adenylate cyclase to induce cellular cAMP elevation in Arabidopsis thaliana plants and identify 427 cAMP-responsive genes (CRGs) through RNA-seq analysis. Induction of cellular cAMP elevation inhibits seed germination, disturbs phytohormone contents, promotes leaf senescence, impairs ethylene response, and compromises salt stress tolerance and pathogen resistance. A set of 62 transcription factors are among the CRGs, supporting a prominent role of cAMP in transcriptional regulation. The CRGs are significantly overrepresented in the pathways of plant hormone signal transduction, MAPK signaling, and diterpenoid biosynthesis, but they are also implicated in lipid, sugar, K+, nitrate signaling, and beyond. Our results provide a basic framework of cAMP signaling for the community to explore. The regulatory roles of cAMP signaling in plant plasticity are discussed.

In the presence of oxygen (O2) the model bacterium Escherichia coli is able to conserve energy by aerobic respiration. Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons. When E. coli encounters environments with different O2 availabilities, the expression of the genes encoding the alternative terminal oxidases, the cydAB and cyoABCDE operons, are regulated by two O2-responsive transcription factors, ArcA (an indirect O2 sensor) and FNR (a direct O2 sensor). It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid. In this study, an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E. coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures. The molecules of O2, transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E. coli cell. The model implies that there are two barriers that dampen the response of FNR to O2, i.e. consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR. Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression.

Fine text detection plays a crucial role in a text detection algorithm as it is capable of removing the false alarms while keeping the detected text lines in coarse text detection. Good performance of a machine learning-based fine text detection heavily depends on the powerful feature to depict the characteristics of text. In this paper, a novel texture-based descriptor, named IT-LBP, is proposed by considering horizontal, vertical, diagonal and anti-diagonal directions of character strokes to better describe the texture of text. The new feature demonstrates its superiority by comparing with other texture-based features. The new feature is used to train an SVM classifier to further filter out non-text candidates. The ICDAR 2011 born-digital image dataset is used to evaluate and demonstrate the performance of the proposed method. Following the same performance evaluation criteria, the proposed method outperforms the winner algorithm of the ICDAR 2011 Robust Reading Competition Challenge 1.

Gradient-based edge detection is a straightforward method to identify the edge points in the original grey-level image. It is intuitive that in the human vision system the edge points always appear where the grey-level value is greatly changed. Spiral Architecture is a relatively new image data structure that is inspired from anatomical considerations of the primate's vision. In Spiral Architecture, each image is represented as a collection of hexagonal pixels. Edge detection on Spiral Architecture has features of fast computation and accurate localization. In this paper, we present and compare gradient-based edge detection algorithms on Spiral Architecture. The experimental results show that the edge detection on Spiral Architecture outperforms that on traditional square image structure.