Context-Aware Chart Element Detection

As a prerequisite of chart data extraction, the accurate detection of chart basic elements is essential and mandatory. In contrast to object detection in the general image domain, chart element detection relies heavily on context information as charts are highly structured data visualization formats. To address this, we propose a novel method CACHED, which stands for Context-Aware Chart Element Detection, by integrating a local-global context fusion module consisting of visual context enhancement and positional context encoding with the Cascade R-CNN framework. To improve the generalization of our method for broader applicability, we refine the existing chart element categorization and standardized 18 classes for chart basic elements, excluding plot elements. Our CACHED method, with the updated category of chart elements, achieves state-of-the-art performance in our experiments, underscoring the importance of context in chart element detection. Extending our method to the bar plot detection task, we obtain the best result on the PMC test dataset.Comment: Published in ICDAR 2023. Code and model are available at https://github.com/pengyu965/ChartDet

AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System

User behavior and feature interactions are crucial in deep learning-based recommender systems. There has been a diverse set of behavior modeling and interaction exploration methods in the literature. Nevertheless, the design of task-aware recommender systems still requires feature engineering and architecture engineering from domain experts. In this work, we introduce AMER, namely Automatic behavior Modeling and interaction Exploration in Recommender systems with Neural Architecture Search (NAS). The core contributions of AMER include the three-stage search space and the tailored three-step searching pipeline. In the first step, AMER searches for residual blocks that incorporate commonly used operations in the block-wise search space of stage 1 to model sequential patterns in user behavior. In the second step, it progressively investigates useful low-order and high-order feature interactions in the non-sequential interaction space of stage 2. Finally, an aggregation multi-layer perceptron (MLP) with shortcut connection is selected from flexible dimension settings of stage~3 to combine features extracted from the previous steps. For efficient and effective NAS, AMER employs the one-shot random search in all three steps. Further analysis reveals that AMER's search space could cover most of the representative behavior extraction and interaction investigation methods, which demonstrates the universality of our design. The extensive experimental results over various scenarios reveal that AMER could outperform competitive baselines with elaborate feature engineering and architecture engineering, indicating both effectiveness and robustness of the proposed method

Development and validation of a deep learning-based model to distinguish acetabular fractures on pelvic anteroposterior radiographs

Objective: To develop and test a deep learning (DL) model to distinguish acetabular fractures (AFs) on pelvic anteroposterior radiographs (PARs) and compare its performance to that of clinicians.Materials and methods: A total of 1,120 patients from a big level-I trauma center were enrolled and allocated at a 3:1 ratio for the DL model’s development and internal test. Another 86 patients from two independent hospitals were collected for external validation. A DL model for identifying AFs was constructed based on DenseNet. AFs were classified into types A, B, and C according to the three-column classification theory. Ten clinicians were recruited for AF detection. A potential misdiagnosed case (PMC) was defined based on clinicians’ detection results. The detection performance of the clinicians and DL model were evaluated and compared. The detection performance of different subtypes using DL was assessed using the area under the receiver operating characteristic curve (AUC).Results: The means of 10 clinicians’ sensitivity, specificity, and accuracy to identify AFs were 0.750/0.735, 0.909/0.909, and 0.829/0.822, in the internal test/external validation set, respectively. The sensitivity, specificity, and accuracy of the DL detection model were 0.926/0.872, 0.978/0.988, and 0.952/0.930, respectively. The DL model identified type A fractures with an AUC of 0.963 [95% confidence interval (CI): 0.927–0.985]/0.950 (95% CI: 0.867–0.989); type B fractures with an AUC of 0.991 (95% CI: 0.967–0.999)/0.989 (95% CI: 0.930–1.000); and type C fractures with an AUC of 1.000 (95% CI: 0.975–1.000)/1.000 (95% CI: 0.897–1.000) in the test/validation set. The DL model correctly recognized 56.5% (26/46) of PMCs.Conclusion: A DL model for distinguishing AFs on PARs is feasible. In this study, the DL model achieved a diagnostic performance comparable to or even superior to that of clinicians

Decoding the spermatogonial stem cell niche under physiological and recovery conditions in adult mice and humans

The intricate interaction between spermatogonial stem cell (SSC) and testicular niche is essential for maintaining SSC homeostasis; however, this interaction remains largely uncharacterized. In this study, to characterize the underlying signaling pathways and related paracrine factors, we delineated the intercellular interactions between SSC and niche cell in both adult mice and humans under physiological conditions and dissected the niche-derived regulation of SSC maintenance under recovery conditions, thus uncovering the essential role of C-C motif chemokine ligand 24 and insulin-like growth factor binding protein 7 in SSC maintenance. We also established the clinical relevance of specific paracrine factors in human fertility. Collectively, our work on decoding the adult SSC niche serves as a valuable reference for future studies on the aetiology, diagnosis, and treatment of male infertility.</p

Low-Theta Electroencephalography Coherence Predicts Cigarette Craving in Nicotine Addiction

Addicts are often vulnerable to drug use in the presence of drug cues, which elicit significant drug cue reactivity. Mounting neuroimaging evidence suggests an association between functional magnetic resonance imaging connectivity networks and smoking cue reactivity; however, there is still little understanding of the electroencephalography (EEG) coherence basis of smoking cue reactivity. We therefore designed two independent experiments wherein nicotine-dependent smokers performed a smoking cue reactivity task during EEG recording. Experiment I showed that a low-theta EEG coherence network occurring 400–600 ms after onset during long-range (mainly between frontal and parieto-occipital) scalp regions, which was involved in smoking cue reactivity. Moreover, the average coherence of this network was significantly correlated with participants’ level of cigarette craving. In experiment II, we tested an independent group of smokers and demonstrated that the low-theta coherence network significantly predicted changes in individuals’ cigarette craving. Thus, the low-theta EEG coherence in smokers’ brains might be a biomarker of smoking cue reactivity and can predict addiction behavior

Comparative analysis of physiological variations and genetic architecture for cold stress response in soybean germplasm

Soybean (Glycine max L.) is susceptible to low temperatures. Increasing lines of evidence indicate that abiotic stress-responsive genes are involved in plant low-temperature stress response. However, the involvement of photosynthesis, antioxidants and metabolites genes in low temperature response is largely unexplored in Soybean. In the current study, a genetic panel of diverse soybean varieties was analyzed for photosynthesis, chlorophyll fluorescence and leaf injury parameters under cold stress and control conditions. This helps us to identify cold tolerant (V100) and cold sensitive (V45) varieties. The V100 variety outperformed for antioxidant enzymes activities and relative expression of photosynthesis (Glyma.08G204800.1, Glyma.12G232000.1), GmSOD (GmSOD01, GmSOD08), GmPOD (GmPOD29, GmPOD47), trehalose (GmTPS01, GmTPS13) and cold marker genes (DREB1E, DREB1D, SCOF1) than V45 under cold stress. Upon cold stress, the V100 variety showed reduced accumulation of H2O2 and MDA levels and subsequently showed lower leaf injury compared to V45. Together, our results uncovered new avenues for identifying cold tolerant soybean varieties from a large panel. Additionally, we identified the role of antioxidants, osmo-protectants and their posttranscriptional regulators miRNAs such as miR319, miR394, miR397, and miR398 in Soybean cold stress tolerance

A Model of a MAPK•Substrate Complex in an Active Conformation: A Computational and Experimental Approach

The mechanisms by which MAP kinases recognize and phosphorylate substrates are not completely understood. Efforts to understand the mechanisms have been compromised by the lack of MAPK-substrate structures. While MAPK-substrate docking is well established as a viable mechanism for bringing MAPKs and substrates into close proximity the molecular details of how such docking promotes phosphorylation is an unresolved issue. In the present study computer modeling approaches, with restraints derived from experimentally known interactions, were used to predict how the N-terminus of Ets-1 associates with ERK2. Interestingly, the N-terminus does not contain a consensus-docking site ((R/K)2-3-X2-6-ΦA-X-ΦB, where Φ is aliphatic hydrophobic) for ERK2. The modeling predicts that the N-terminus of Ets-1 makes important contributions to the stabilization of the complex, but remains largely disordered. The computer-generated model was used to guide mutagenesis experiments, which support the notion that Leu-11 and possibly Ile-13 and Ile-14 of Ets-1 1-138 (Ets) make contributions through binding to the hydrophobic groove of the ERK2 D-recruiting site (DRS). Based on the modeling, a consensus-docking site was introduced through the introduction of an arginine at residue 7, to give the consensus 7RK-X2-ΦA-X-ΦB13. This results in a 2-fold increase in kcat/Km for the phosphorylation of Ets by ERK2. Similarly, the substitution of the N-terminus for two different consensus docking sites derived from Elk-1 and MKK1 also improves kcat/Km by two-fold compared to Ets. Disruption of the N-terminal docking through deletion of residues 1-23 of Ets results in a 14-fold decrease in kcat/Km, with little apparent change in kcat. A peptide that binds to the DRS of ERK2 affects Km, but not kcat. Our kinetic analysis suggests that the unstructured N-terminus provides 10-fold uniform stabilization of the ground state ERK2•Ets•MgATP complex and intermediates of the enzymatic reaction

Simultaneous Refinement of Primary Si and Modification of Eutectic Si in A390 Alloy Assisting by Sr-Modifier and Serpentine Pouring Channel Process

In this study, A390 alloy was prepared using the combined process of a water-cooled copper serpentine pouring channel (SPC) and strontium (Sr) modifier, in order to simultaneously refine primary silicon (Si) and modify eutectic silicon (Si). The nucleation and growth mechanisms of the Si phase were discussed by morphology analysis and non-isothermal analytical kinetics. The results indicate that the size of primary Si is refined to 25.2&ndash;28.5 &micro;m and the morphology of eutectic Si is modified from acicular into fibrous. The serpentine pouring channel process stimulates primary Si nucleation due to chilling effect and has no influence on eutectic Si nucleation. Impacts of Sr-modifier on primary and eutectic Si are similar, including three aspects: (1) poisoning of the nucleation site; (2) decreasing the interface energy between Si phase and liquid; (3) raising the activation energy for diffusion across solid-liquid interface. The content of Sr determines which one of the three aspects mentioned above is the dominant factor to promote or restrain the nucleation and growth of the primary and eutectic Si in hypereutectic Al-Si alloy