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The red and far-red light photoreceptor phytochrome B (phyB) transmits light signals following cytosol-to-nuclear translocation to regulate transcriptional networks therein. This necessitates changes in protein-protein interactions of phyB in the cytosol, about which little is presently known. Via introduction of a nucleus-excluding G767R mutation into the dominant, constitutively active phyBY276H (YHB) allele, we explore the functional consequences of expressing a cytosol-localized YHBG767R variant in transgenic Arabidopsis seedlings. We show that YHBG767R elicits selective constitutive photomorphogenic phenotypes in dark-grown phyABCDE null mutants, wild type and other phy-deficient genotypes. These responses include light-independent apical hook opening, cotyledon unfolding, seed germination and agravitropic hypocotyl growth with minimal suppression of hypocotyl elongation. Such phenotypes correlate with reduced PIF3 levels, which implicates cytosolic targeting of PIF3 turnover or PIF3 translational inhibition by YHBG767R. However, as expected for a cytoplasm-tethered phyB, YHBG767R elicits reduced light-mediated signaling activity compared with similarly expressed wild-type phyB in phyABCDE mutant backgrounds. YHBG767R also interferes with wild-type phyB light signaling, presumably by formation of cytosol-retained and/or otherwise inactivated heterodimers. Our results suggest that cytosolic interactions with PIFs play an important role in phyB signaling even under physiological conditions.

Laminaria japonica (LJ) is a common seaweed, which has been widely used as functional food and medicine, but its pharmaceutical activities in angiogenic regulation are unclear. Here, different lines of studies provide evidence to the suppressive effects of LJ extract in VEGF-medicated angiogenic function. In cellular level, LJ extract significantly inhibited VEGF-mediated endothelial cell proliferation, migration, invasion and tube formation. For in vivo verification, LJ extract suppressed the VEGF-induced formation of sub-intestinal vessels in zebrafish embryo model. In signalling cascades, LJ extract decreased the amount of phosphorylated VEGF receptor-2, as triggered by VEGF, and subsequently some key related downstream elements were obviously inactivated. In parallel, NO production and ROS formation, as induced by VEGF, were significantly attenuated. Thus, these preclinical findings promoted the possible development of LJ extract as a potential therapeutic agent for prevention and clinical treatment of angiogenesis-related diseases. © 2024 The Authors

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Discrete Mathematics and Optimization

As a variant of Graph Neural Networks (GNNs), Unfolded GNNs offer enhanced interpretability and flexibility over traditional designs. Nevertheless, they still suffer from scalability challenges when it comes to the training cost. Although many methods have been proposed to address the scalability issues, they mostly focus on per-iteration efficiency, without worst-case convergence guarantees. Moreover, those methods typically add components to or modify the original model, thus possibly breaking the interpretability of Unfolded GNNs. In this paper, we propose HERTA: a High-Efficiency and Rigorous Training Algorithm for Unfolded GNNs that accelerates the whole training process, achieving a nearly-linear time worst-case training guarantee. Crucially, HERTA converges to the optimum of the original model, thus preserving the interpretability of Unfolded GNNs. Additionally, as a byproduct of HERTA, we propose a new spectral sparsification method applicable to normalized and regularized graph Laplacians that ensures tighter bounds for our algorithm than existing spectral sparsifiers do. Experiments on real-world datasets verify the superiority of HERTA as well as its adaptability to various loss functions and optimizers.

Parameter-efficient finetuning (PEFT) is a key technique for adapting large language models (LLMs) to downstream tasks. In this paper, we study leveraging knowledge graph embeddings to improve the effectiveness of PEFT. We propose a knowledgeable adaptation method called KnowLA. It inserts an adaptation layer into an LLM to integrate the embeddings of entities appearing in the input text. The adaptation layer is trained in combination with LoRA on instruction data. Experiments on six benchmarks with two popular LLMs and three knowledge graphs demonstrate the effectiveness and robustness of KnowLA. We show that \modelname can help activate the relevant parameterized knowledge in an LLM to answer a question without changing its parameters or input prompts., Comment: Accepted in the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024)

The dielectric response function and its inverse are crucial physical quantities in materials science. We propose an accurate and efficient strategy to invert the dielectric function matrix. The GW approximation, a powerful approach to accurately describe many-body excited states, is taken as an application to demonstrate accuracy and efficiency. We incorporate the interpolative separable density fitting (ISDF) algorithm with Sherman--Morrison--Woodbury (SMW) formula to accelerate the inversion process by exploiting low-rank properties of dielectric function in plane-wave GW calculations. Our ISDF--SMW strategy produces accurate quasiparticle energies with $O(N_{\mathrm{r}}N_{\mathrm{e}}^2)$ computational cost $(N_{\mathrm{e}}$ is the number of electrons and $N_{\mathrm{r}}=100$--$1000N_{\mathrm{e}}$ is the number of grid points) with negligible small error of $0.03$~eV for both complex molecules and solids. This new strategy for inverting the dielectric matrix can be \(50\times\) faster than the current state-of-the-art implementation in BerkeleyGW, resulting in two orders of magnitude speedup for total GW calculations.

The objective of image super-resolution is to generate clean and high-resolution images from degraded versions. Recent advancements in diffusion modeling have led to the emergence of various image super-resolution techniques that leverage pretrained text-to-image (T2I) models. Nevertheless, due to the prevalent severe degradation in low-resolution images and the inherent characteristics of diffusion models, achieving high-fidelity image restoration remains challenging. Existing methods often exhibit issues including semantic loss, artifacts, and the introduction of spurious content not present in the original image. To tackle this challenge, we propose Cascaded diffusion for Super-Resolution, CasSR , a novel method designed to produce highly detailed and realistic images. In particular, we develop a cascaded controllable diffusion model that aims to optimize the extraction of information from low-resolution images. This model generates a preliminary reference image to facilitate initial information extraction and degradation mitigation. Furthermore, we propose a multi-attention mechanism to enhance the T2I model's capability in maximizing the restoration of the original image content. Through a comprehensive blend of qualitative and quantitative analyses, we substantiate the efficacy and superiority of our approach.

Autonomous driving demands high-quality LiDAR data, yet the cost of physical LiDAR sensors presents a significant scaling-up challenge. While recent efforts have explored deep generative models to address this issue, they often consume substantial computational resources with slow generation speeds while suffering from a lack of realism. To address these limitations, we introduce RangeLDM, a novel approach for rapidly generating high-quality range-view LiDAR point clouds via latent diffusion models. We achieve this by correcting range-view data distribution for accurate projection from point clouds to range images via Hough voting, which has a critical impact on generative learning. We then compress the range images into a latent space with a variational autoencoder, and leverage a diffusion model to enhance expressivity. Additionally, we instruct the model to preserve 3D structural fidelity by devising a range-guided discriminator. Experimental results on KITTI-360 and nuScenes datasets demonstrate both the robust expressiveness and fast speed of our LiDAR point cloud generation.

We study the generalization capability of nearly-interpolating linear regressors: $\boldsymbol{\beta}$'s whose training error $\tau$ is positive but small, i.e., below the noise floor. Under a random matrix theoretic assumption on the data distribution and an eigendecay assumption on the data covariance matrix $\boldsymbol{\Sigma}$, we demonstrate that any near-interpolator exhibits rapid norm growth: for $\tau$ fixed, $\boldsymbol{\beta}$ has squared $\ell_2$-norm $\mathbb{E}[\|{\boldsymbol{\beta}}\|_{2}^{2}] = \Omega(n^{\alpha})$ where $n$ is the number of samples and $\alpha >1$ is the exponent of the eigendecay, i.e., $\lambda_i(\boldsymbol{\Sigma}) \sim i^{-\alpha}$. This implies that existing data-independent norm-based bounds are necessarily loose. On the other hand, in the same regime we precisely characterize the asymptotic trade-off between interpolation and generalization. Our characterization reveals that larger norm scaling exponents $\alpha$ correspond to worse trade-offs between interpolation and generalization. We verify empirically that a similar phenomenon holds for nearly-interpolating shallow neural networks., Comment: AISTATS 2024

Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories. In this paper, we propose a novel generative and fine-tuning framework, LTGC, to handle long-tail recognition via leveraging generated content. Firstly, inspired by the rich implicit knowledge in large-scale models (e.g., large language models, LLMs), LTGC leverages the power of these models to parse and reason over the original tail data to produce diverse tail-class content. We then propose several novel designs for LTGC to ensure the quality of the generated data and to efficiently fine-tune the model using both the generated and original data. The visualization demonstrates the effectiveness of the generation module in LTGC, which produces accurate and diverse tail data. Additionally, the experimental results demonstrate that our LTGC outperforms existing state-of-the-art methods on popular long-tailed benchmarks., Comment: CVPR 2024, Oral

Generative 3D part assembly involves understanding part relationships and predicting their 6-DoF poses for assembling a realistic 3D shape. Prior work often focus on the geometry of individual parts, neglecting part-whole hierarchies of objects. Leveraging two key observations: 1) super-part poses provide strong hints about part poses, and 2) predicting super-part poses is easier due to fewer superparts, we propose a part-whole-hierarchy message passing network for efficient 3D part assembly. We first introduce super-parts by grouping geometrically similar parts without any semantic labels. Then we employ a part-whole hierarchical encoder, wherein a super-part encoder predicts latent super-part poses based on input parts. Subsequently, we transform the point cloud using the latent poses, feeding it to the part encoder for aggregating super-part information and reasoning about part relationships to predict all part poses. In training, only ground-truth part poses are required. During inference, the predicted latent poses of super-parts enhance interpretability. Experimental results on the PartNet dataset show that our method achieves state-of-the-art performance in part and connectivity accuracy and enables an interpretable hierarchical part assembly. Code is available at https://github.com/pkudba/3DHPA.

E-type cyclins E1 (CcnE1) and E2 (CcnE2) are regulatory subunits of cyclin-dependent kinase 2 (Cdk2) and thought to control the transition of quiescent cells into the cell cycle. Initial findings indicated that CcnE1 and CcnE2 have largely overlapping functions for cancer development in several tumor entities including hepatocellular carcinoma (HCC). In the present study, we dissected the differential contributions of CcnE1, CcnE2, and Cdk2 for initiation and progression of HCC in mice and patients. To this end, we tested the HCC susceptibility in mice with constitutive deficiency for CcnE1 or CcnE2 as well as in mice lacking Cdk2 in hepatocytes. Genetic inactivation of CcnE1 largely prevented development of liver cancer in mice in two established HCC models, while ablation of CcnE2 had no effect on hepatocarcinogenesis. Importantly, CcnE1-driven HCC initiation was dependent on Cdk2. However, isolated primary hepatoma cells typically acquired independence on CcnE1 and Cdk2 with increasing progression in vitro, which was associated with a gene signature involving secondary induction of CcnE2 and up-regulation of cell cycle and DNA repair pathways. Importantly, a similar expression profile was also found in HCC patients with elevated CcnE2 expression and poor survival. In general, overall survival in HCC patients was synergistically affected by expression of CcnE1 and CcnE2, but not through Cdk2. Our study suggests that HCC initiation specifically depends on CcnE1 and Cdk2, while HCC progression requires expression of any E-cyclin, but no Cdk2., German Research Foundation, Ramón y Cajal Fellowship, Ministerio de Economía, Comercio y Empresa. Retos, Genomics Facility and by the Confocal Microscopy Facility of the Interdisciplinary Center for Clinical Research (IZKF), Faculty of Medicine at RWTH Aachen University, Depto. de Inmunología, Oftalmología y ORL, Fac. de Medicina, TRUE, pub

Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks. These works tend to sample and group points to create smaller point sets locally and mainly focus on extracting local features through GCNs, while ignoring the relationship between point sets. In this paper, we propose the Dynamic Hop Graph Convolution Network (DHGCN) for explicitly learning the contextual relationships between the voxelized point parts, which are treated as graph nodes. Motivated by the intuition that the contextual information between point parts lies in the pairwise adjacent relationship, which can be depicted by the hop distance of the graph quantitatively, we devise a novel self-supervised part-level hop distance reconstruction task and design a novel loss function accordingly to facilitate training. In addition, we propose the Hop Graph Attention (HGA), which takes the learned hop distance as input for producing attention weights to allow edge features to contribute distinctively in aggregation. Eventually, the proposed DHGCN is a plug-and-play module that is compatible with point-based backbone networks. Comprehensive experiments on different backbones and tasks demonstrate that our self-supervised method achieves state-of-the-art performance. Our source code is available at: https://github.com/Jinec98/DHGCN., Comment: Accepted to AAAI 2024

The convenience of 3D sensors has led to an increase in the use of 3D point clouds in various applications. However, the differences in acquisition devices or scenarios lead to divergence in the data distribution of point clouds, which requires good generalization of point cloud representation learning methods. While most previous methods rely on domain adaptation, which involves fine-tuning pre-trained models on target domain data, this may not always be feasible in real-world scenarios where target domain data may be unavailable. To address this issue, we propose InvariantOODG, which learns invariability between point clouds with different distributions using a two-branch network to extract local-to-global features from original and augmented point clouds. Specifically, to enhance local feature learning of point clouds, we define a set of learnable anchor points that locate the most useful local regions and two types of transformations to augment the input point clouds. The experimental results demonstrate the effectiveness of the proposed model on 3D domain generalization benchmarks.

Multi-aspect controllable text generation aims to control the generated texts in attributes from multiple aspects (e.g., "positive" from sentiment and "sport" from topic). For ease of obtaining training samples, existing works neglect attribute correlations formed by the intertwining of different attributes. Particularly, the stereotype formed by imbalanced attribute correlations significantly affects multi-aspect control. In this paper, we propose MAGIC, a new multi-aspect controllable text generation method with disentangled counterfactual augmentation. We alleviate the issue of imbalanced attribute correlations during training using counterfactual feature vectors in the attribute latent space by disentanglement. During inference, we enhance attribute correlations by target-guided counterfactual augmentation to further improve multi-aspect control. Experiments show that MAGIC outperforms state-of-the-art baselines in both imbalanced and balanced attribute correlation scenarios. Our source code and data are available at https://github.com/nju-websoft/MAGIC., Comment: Accepted in the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)

Recent years have witnessed the deployment of code language models (LMs) in various code intelligence tasks such as code completion. Yet, it is challenging for pre-trained LMs to generate correct completions in private repositories. Previous studies retrieve cross-file context based on import relations or text similarity, which is insufficiently relevant to completion targets. In this paper, we propose a dataflow-guided retrieval augmentation approach, called DraCo, for repository-level code completion. DraCo parses a private repository into code entities and establishes their relations through an extended dataflow analysis, forming a repo-specific context graph. Whenever triggering code completion, DraCo precisely retrieves relevant background knowledge from the repo-specific context graph and generates well-formed prompts to query code LMs. Furthermore, we construct a large Python dataset, ReccEval, with more diverse completion targets. Our experiments demonstrate the superior accuracy and applicable efficiency of DraCo, improving code exact match by 3.43% and identifier F1-score by 3.27% on average compared to the state-of-the-art approach., Comment: Accepted in the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)

This work addresses data-driven inverse optimization (IO), where the goal is to estimate unknown parameters in an optimization model from observed decisions that can be assumed to be optimal or near-optimal solutions to the optimization problem. The IO problem is commonly formulated as a large-scale bilevel program that is notoriously difficult to solve. Deviating from traditional exact solution methods, we propose a derivative-free optimization approach based on Bayesian optimization, which we call BO4IO, to solve general IO problems. We treat the IO loss function as a black box and approximate it with a Gaussian process model. Using the predicted posterior function, an acquisition function is minimized at each iteration to query new candidate solutions and sequentially converge to the optimal parameter estimates. The main advantages of using Bayesian optimization for IO are two-fold: (i) it circumvents the need of complex reformulations of the bilevel program or specialized algorithms and can hence enable computational tractability even when the underlying optimization problem is nonconvex or involves discrete variables, and (ii) it allows approximations of the profile likelihood, which provide uncertainty quantification on the IO parameter estimates. We apply the proposed method to three computational case studies, covering different classes of forward optimization problems ranging from convex nonlinear to nonconvex mixed-integer nonlinear programs. Our extensive computational results demonstrate the efficacy and robustness of BO4IO to accurately estimate unknown model parameters from small and noisy datasets. In addition, the proposed profile likelihood analysis has proven to be effective in providing good approximations of the confidence intervals on the parameter estimates and assessing the identifiability of the unknown parameters.

Recently, there has been a surge of international interest in extraterrestrial exploration targeting the Moon, Mars, the moons of Mars, and various asteroids. This contribution discusses how current state-of-the-art Earth-based testing for designing rovers and landers for these missions currently leads to overly optimistic conclusions about the behavior of these devices upon deployment on the targeted celestial bodies. The key misconception is that gravitational offset is necessary during the \textit{terramechanics} testing of rover and lander prototypes on Earth. The body of evidence supporting our argument is tied to a small number of studies conducted during parabolic flights and insights derived from newly revised scaling laws. We argue that what has prevented the community from fully diagnosing the problem at hand is the absence of effective physics-based models capable of simulating terramechanics under low gravity conditions. We developed such a physics-based simulator and utilized it to gauge the mobility of early prototypes of the Volatiles Investigating Polar Exploration Rover (VIPER), which is slated to depart for the Moon in November 2024. This contribution discusses the results generated by this simulator, how they correlate with physical test results from the NASA-Glenn SLOPE lab, and the fallacy of the gravitational offset in rover and lander testing. The simulator developed is open sourced and made publicly available for unfettered use; it can support principled studies that extend beyond trafficability analysis to provide insights into in-situ resource utilization activities, e.g., digging, bulldozing, and berming in low gravity.

Facts extraction is pivotal for constructing knowledge graphs. Recently, the increasing demand for temporal facts in downstream tasks has led to the emergence of the task of temporal fact extraction. In this paper, we specifically address the extraction of temporal facts from natural language text. Previous studies fail to handle the challenge of establishing time-to-fact correspondences in complex sentences. To overcome this hurdle, we propose a timeline-based sentence decomposition strategy using large language models (LLMs) with in-context learning, ensuring a fine-grained understanding of the timeline associated with various facts. In addition, we evaluate the performance of LLMs for direct temporal fact extraction and get unsatisfactory results. To this end, we introduce TSDRE, a method that incorporates the decomposition capabilities of LLMs into the traditional fine-tuning of smaller pre-trained language models (PLMs). To support the evaluation, we construct ComplexTRED, a complex temporal fact extraction dataset. Our experiments show that TSDRE achieves state-of-the-art results on both HyperRED-Temporal and ComplexTRED datasets., Comment: Accepted to ACL2024 main conference

An innovative design method of conformal array antennas is presented by utilizing characteristic mode analysis (CMA) in this work. A single-layer continuous perfect electric conductor under bending conditions is conducted by CMA to evaluate the variations in operating performance. By using this method, the design process of a conformal array is simplified. The results indicate that the operating performance of the antenna with single-layer metal radiation structure remains stable within a certain range of curvature. Subsequently, an infinite array element using single-layer metal radiation structure is designed, operating in ultra-wideband and dual polarization. Following, an 8 * 8 ultra-wideband dual-polarized cylindrical-conformal array (UDCA) is developed by wrapping the planar arrays to a cylindric surface, which has a stable operating performance even at a curvature radius as small as 100 mm. Finally, a physical prototype is cost-effectively fabricated by novel manufacturing solutions that stack three-layer conformal substrate. The experimental result demonstrates that the proposed UDCA with a 1.2{\lambda} curvature radius operates at 3.6~9.6 GHz (90.9%) and achieves 60{\deg} wide-angle scanning in two principal planes, which provides a practical and promising solution for conformal array applications. The insights derived from the CMA offer a direction for further advancement in conformal antenna research.

Macadamia nuts are an abundant source of the monounsaturated fats (59 %), oleic (50 %−65 %) and palmitoleic acid (12 %−30 %). As macadamia is generally an outcrossing species, this study focuses on the effect of both the maternal (seed parent) and paternal (pollen parent) genotype on the accumulation of the principal fatty acids in macadamia using a controlled 3 × 3 pollination trial. Both the seed parent and pollen parent were observed to significantly impact the fatty acid profile of macadamia nut kernels. In addition, the general combining abilities (GCA) of the parental cultivars and specific combining abilities (SCA) of parental crosses were determined, as well as identifying maternal/paternal combinations that could significantly increase palmitoleic acid and oleic acid, or decrease saturated fat concentration. These results provide evidence for the first time that pollen source has a significant impact on the fatty acid profile of macadamia kernels, and that previous reporting of the fatty acid profiles of macadamia cultivars is likely to be variable, due to the unknown genotype of pollen sources.

Diesel exhaust has long been of health concern due to established toxicity including carcinogenicity in humans. However, the precise components of diesel engine emissions that drive carcinogenesis are still unclear. Limited work has suggested that nitrated polycyclic aromatic hydrocarbons (NPAHs) such as 1-nitropyrene and 2-nitrofluorene may be more abundant in diesel exhaust. The present study aimed to examine whether urinary amino metabolites of these NPAHs were associated with high levels of diesel engine emissions and urinary mutagenicity in a group of highly exposed workers including both smokers and nonsmokers. Spot urine samples were collected immediately following a standard work shift from each of the 54 diesel engine testers and 55 non-tester controls for the analysis of five amino metabolites of NPAHs, and cotinine (a biomarker of tobacco smoke exposure) using liquid chromatography-mass spectrometry. An overnight urine sample was collected in a subgroup of non-smoking participants for mutagenicity analysis using strain YG1041 in the Salmonella (Ames) mutagenicity assay. Personal exposure to fine particles (PM 2.5) and more-diesel-specific constituents (elemental carbon and soot) was assessed for the engine testers by measuring breathing-zone concentrations repeatedly over several full work shifts. Results showed that it was 12.8 times more likely to detect 1-aminopyrene and 2.9 times more likely to detect 2-aminofluorene in the engine testers than in unexposed controls. Urinary concentrations of 1-aminopyrene were significantly higher in engine testers (p < 0.001), and strongly correlated with soot and elemental carbon exposure as well as mutagenicity tested in strain YG1041 with metabolic activation (p < 0.001). Smoking did not affect 1-aminopyrene concentrations and 1-aminopyrene relationships with diesel exposure. In contrast, both engine emissions and smoking affected 2-aminofluorene concentrations. The results confirm that urinary 1-aminopyr

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

BACKGROUND: We previously showed that exposure to 5-methylchrysene (5MC) and other methylated polycyclic aromatic hydrocarbons (PAHs) best explains lung cancer risks in a case-control study among non-smoking women using smoky coal in China. Time-related factors (e.g., age at exposure) and non-linear relations were not explored.OBJECTIVE: We investigated the relation between coal-derived air pollutants and lung cancer mortality using data from a large retrospective cohort.METHODS: Participants were smoky (bituminous) or smokeless (anthracite) coal users from a cohort of 42,420 subjects from four communes in XuanWei. Follow-up was from 1976 to 2011, during which 4,827 deaths from lung-cancer occurred. Exposures were predicted for 43 different pollutants. Exposure clusters were identified using hierarchical clustering. Cox regression was used to estimate exposure-response relations for 5MC, while effect modification by age at exposure was investigated for cluster prototypes. A Bayesian penalized multi-pollutant model was fitted on a nested case-control sample, with more restricted models fitted to investigate non-linear exposure-response relations.RESULTS: We confirmed the strong exposure-response relation for 5MC (Hazard Ratio [95% Confidence Interval] = 2.5 [2.4, 2.6] per standard-deviation (SD)). We identified four pollutant clusters, with all but two PAHs in a single cluster. Exposure to PAHs in the large cluster was associated with a higher lung cancer mortality rate (HR [95%CI] = 2.4 [2.2, 2.6] per SD), while exposure accrued before 18 years of age appeared more important than adulthood exposures. Results from the multi-pollutant model identified anthanthrene (ANT) and benzo(a)chrysene (BaC) as risk factors. 5MC remained strongly associated with lung cancer in models that included ANT and BaC and also benzo(a)pyrene (BaP).CONCLUSION: We confirmed the link between PAH exposures and lung cancer in smoky coal users and found exposures

This systematic literature review attempts to examine the current status, unsolved issues and future trend of the comparative study of request speech act for the past ten years (2012-2022). Based on the analysis of 186 studies, the findings are: 1) the status quo is that intralingual comparative studies of request are still very few and previous researches reply on DCT and CCSARP instruments too much; 2) unsolved issues include further investigation of intralingual comparative request, Asian languages, and the development of new or more advanced instrument; and 3) future trend is that more comparative studies of request need to be conducted on Asian languages, especially Mandarin and Malay from intralingual perspective and new or more advanced data collection and analysis instruments should be applied. This systematic literature review could help identify the research gap and unsolved problems in the current literature that calls for the further investigation of comparative request speech act.

In this paper, we propose a new form of multidisciplinary academic collaboration that goes beyond the traditional modes of knowledge exchange. We argue that most research collaboration today is based on interactions between closely related disciplines, in which researchers share data, methods, and insights within a common framework or problem. However, such collaboration may not foster the development of the communication and management skills essential to a multi-disciplinary research career. Therefore, we suggest establishing a network of researchers from divergent, yet complementary, disciplines who are interested in improving these skills through regular interactions and feedback. The main goal of this network is not to conduct research or address a specific research question, but to create a learning environment where researchers can enhance their interdisciplinary competencies through the diverse perspectives and experiences of their peers. Moreover, a multidisciplinary group of early-career professionals provides a space for collaborations to flourish. In this paper, we also offer practical advice for researchers who wish to join or create a similar network.

BackgroundDeep brain stimulation (DBS) of the globus pallidus interna (GPi) is an effective therapy for select patients with primary dystonia. DBS programming for dystonia is often challenging due to variable time to symptomatic improvement or stimulation induced side effects (SISE) such as capsular or optic tract activation which can prolong device optimization.ObjectiveTo characterize the safety and tolerability of active recharge biphasic DBS (bDBS) in primary dystonia and to compare it to conventional clinical DBS (clinDBS).MethodsTen subjects with primary dystonia and GPi DBS underwent a single center, double blind, nonrandomized crossover study comparing clinDBS versus bDBS. The testing occurred over two-days. bDBS and clinDBS were administered on separate days and each was activated for 6 h. Rating scales were collected by video recording and scored by four blinded movement disorders trained neurologists.ResultsThe bDBS paradigm was safe and well-tolerated in all ten subjects. There were no persistent SISE reported. The mean change in the Unified Dystonia Rating Scale after 4 h of stimulation was greater in bDBS when compared to clinDBS (-6.5 vs 0.3, p < 0.04).ConclusionIn this pilot study, we demonstrated that biphasic DBS is a novel stimulation paradigm which can be administered safely. The biphasic waveform revealed a greater immediate improvement. Further studies are needed to determine whether this immediate improvement persists with chronic stimulation or if clinDBS will eventually achieve similar levels of improvement to bDBS over time.

Cervical carcinoma (CC) is the second most prevalent gynecologic cancer in females across the world. To obtain a better understanding of the mechanisms underlying the development of CC, high-resolution label-free mass spectrometry was performed on CC and adjacent normal tissues from eight patients. A total of 2631 proteins were identified, and 46 significant differently expressed proteins (DEPs) were found between CC and normal tissues (p < 0.01, fold change >10 or <0.1). Ingenuity pathway analysis revealed that the majority of the proteins were involved in the regulation of eIF4 and p70S6K signaling and mTOR signaling. Among 46 DEPs, Integrin beta 6 (ITGB6), PPP1CB, TMPO, PTGES3 (P23) and DTX3L were significantly upregulated, while Desmin (DES) was significantly downregulated in CC tissues compared with the adjacent normal tissues. In in vivo and in vitro experiments, DTX3L knockdown suppressed CC cell proliferation, migration, invasion and xenograft tumorigenesis, and enhanced cell apoptosis. Combination of silencing DTX3L and cisplatin treatment induced higher apoptosis percentage compared to cisplatin treatment alone. Moreover, DTX3L silencing inhibited the PI3K/AKT/mTOR signal pathway. Thus, our results suggested DTX3L could regulate CC progression through the PI3K/AKT/mTOR signal pathway and is potentially a novel biomarker and therapeutic target for CC., De två första författarna delar förstaförfattarskapet.

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Multitemporal polarimertic SAR is considered to be very effective in crop classification and cultivated land detection, which has received much attention from researchers. Currently, for most multitemporal polarimetric SAR data classification methods, the simultaneous temporal–polarimetric–spatial feature extraction capability has not been exploited sufficiently. Also, the diversity of different time and different polarimetric features has not been taken into account sufficiently. In this letter, we propose a classification model that combines a dual-stream network as a temporal–polarimetric–spatial feature extraction module with vision transformer (ViT) called temporal–polarimetric–spatial transformer (TPST) to address the above problems. Second, a 3-D convolutional attention module that enables the network to weight the temporal dimension, polarimetric feature dimension and spatial dimension is developed, according to their importance. Experimental results on both the UAVSAR and RADARSAT-2 datasets show that the proposed method outperforms ResNet., This work was supported by the National Natural Science Foundation of China under Grant 62201027 and Grant 62271034., Peer Reviewed, Postprint (author's final draft)

A dual-polarized antenna with wide impedance bandwidth and high selectivity is presented for the use of 5G sub-6GHz base stations. By strategically introducing a novel coupled dual-mode grid structure, two additional resonant modes (One is in-band, another one is out-of-band) as well as two radiation nulls can be obtained to enhance the impedance bandwidth and out-of-band rejection level of the widely used tightly coupled cross-dipole antenna. The two radiation nulls introduced by the grid structure are located at the two edges of the desired frequency band. To further improve the gain suppression level in higher out-of-band, a new radiation null is obtained by symmetrically introducing four T-shaped strips. Apart from these additional radiation nulls, this antenna also exhibits two inherent radiation nulls originating from its feed structure and reflector. The measured results demonstrate that the proposed antenna exhibits a wide impedance bandwidth of 39.5% (ranging from 3.07 GHz to 4.58 GHz), excellent isolation of over 31 dB, and a notable out-of-band rejection level exceeding 17.6 dB and 19.2 dB in the frequency bands of 1.7 GHz-2.7 GHz and 4.8 GHz- 5.0 GHz.

A dual-polarized antenna with multimode resonance and high selectivity is proposed in this paper to cover the 5G sub-6 GHz bands. The proposed antenna achieves dual wide impedance bandwidth characteristics by incorporating a dual mode coupled patch and four planar coupled strips around the driven patch. Through the four resonant modes of these structures, the antenna effectively covers the two desired frequency bands. Moreover, the electric/magnetic coupling between the driven patch, dual mode coupled patch, and planar coupled strips enables the creation of three radiation nulls that suppress unwanted radiation. To further improve the out-of-band rejection level and half power beamwidth, four shorted strips are introduced around the radiator. The introduction of these strips results in a 4th radiation null at higher out-of-band frequencies and expands the antenna's half power beamwidth from 52° to 62°. To demonstrate the feasibility of the design, both the proposed antenna and its array were manufactured and tested. Measured results show that the filtering element was able to operate within frequency bands of 3.24-3.83 GHz (16.7%) and 4.74-5.30 GHz (11.2%) with a reference of |Sdd11| < -14 dB. The input ports exhibited a high level of isolation, measuring 40 dB. Furthermore, the four radiation nulls proved effective in suppressing out-of-band radiation.

Jiang SB, He XJ, Xia YJ, et al. Onco Targets Ther. 2016;9:2305-2315. We, the Editors and Publisher of OncoTargets and Therapy, have retracted the following article. Since publication, concerns have been raised about the integrity of the data in the article. When approached for an explanation, the authors were cooperative but were unable to address the concerns raised and have not been able to provide sufficient supporting information. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article. The authors were informed of this decision and do not agree with the retraction. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.

Parkinson’s disease (PD) is a progressive central nervous system disease with a common motor symptom of gait disturbance in PD, which is more pronounced in the later stages. Although FDA-approved treatments, including dopaminergic pharmacotherapy, deep brain stimulation, and rehabilitation, have some benefits in improving gait dysfunction, a fair amount of advanced PD patients can develop a disability, social isolation, and high mortality and morbidity. Recently, clinicians and scientists have applied music to clinical therapy, namely music therapy. It has been used as a unique rehabilitation tool to improve PD-induced gait and speech disorders. Based on relevant studies in recent years, this paper reviews the published literature about music in treating gait disorders and speech problems in PD patients. Additionally, we discuss current studies’ limitations and emphasize the future potential research fields.

In this paper, we propose a simple energy decaying iterative thresholding algorithm to solve the two-phase minimum compliance problem. The material domain is implicitly represented by its characteristic function, and the problem is formulated into a minimization problem by the principle of minimum complementary energy. We prove that the energy is decreasing in each iteration. Two effective continuation schemes are proposed to avoid trapping into the local minimum. Numerical results on 2D isotropic linear material demonstrate the effectiveness of the proposed methods.

A simple prediction of the well-known unification model of active galactic nuclei is that a sample of sources should exhibit an anti-correlation between the solid angle of the dusty torus and of the ionization cone (as the sum of them shall equal 4$\pi$), which however has never been detected. In this work, we analyze the correlation between [OIII] 5007 narrow emission line equivalent width and $L_{\rm IR}(\lambda)/L_{\rm bol}$ for a large sample of luminous quasars. For the first time, we detect a clear intrinsic anti-correlation between them, which immediately verifies the torus/ionization-cone geometry in luminous quasars. More interestingly, the anti-correlation significantly weakens with increasing wavelength from $\sim$ 2 to 12 $\mu$m, and disappears at $\sim$ 12 $\mu$m. Simulations show a cool dust component (in addition to equatorial torus) with its strength positively correlating with the solid angle of the ionization cone is required to explain the observations. This shows that the polar dust seen in nearby active galaxies also exists in luminous quasars, with its contribution to total dust emission increasing with $\lambda$ (from $\sim$ 2 to 12 $\mu$m) and reaching between 39%-62% (model dependent) at rest frame 12 $\mu$m. Our findings provide a unique approach to map the otherwise spatially unresolvable inner structure of quasars., Comment: 10 pages, 8 figures, accepted for publication in MNRAS

Federated Learning (FL) recently emerges as a paradigm to train a global machine learning model across distributed clients without sharing raw data. Knowledge Graph (KG) embedding represents KGs in a continuous vector space, serving as the backbone of many knowledge-driven applications. As a promising combination, federated KG embedding can fully take advantage of knowledge learned from different clients while preserving the privacy of local data. However, realistic problems such as data heterogeneity and knowledge forgetting still remain to be concerned. In this paper, we propose FedLU, a novel FL framework for heterogeneous KG embedding learning and unlearning. To cope with the drift between local optimization and global convergence caused by data heterogeneity, we propose mutual knowledge distillation to transfer local knowledge to global, and absorb global knowledge back. Moreover, we present an unlearning method based on cognitive neuroscience, which combines retroactive interference and passive decay to erase specific knowledge from local clients and propagate to the global model by reusing knowledge distillation. We construct new datasets for assessing realistic performance of the state-of-the-arts. Extensive experiments show that FedLU achieves superior results in both link prediction and knowledge forgetting., Comment: Accepted in the ACM Web Conference (WWW 2023)

Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC. DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. By coupling DMN with finite elements, we have developed a highly accurate and efficient data-driven approach, which predicts nonlinear behaviors of composite materials and structures at a computational speed orders-of-magnitude faster than the high-fidelity direct numerical simulation. To model industrial-scale SFRC products, transfer learning is utilized to generate a unified DMN database, which effectively captures the effects of injection molding-induced fiber orientations and volume fractions on the overall composite properties. Numerical examples are presented to demonstrate the promising performance of this LS-DYNA machine learning-based multiscale method for SFRC modeling., Comment: Final version of this manuscript is published in Journal of Engineering Mechanics. Wei, H., Wu, C. T., Hu, W., Su, T. H., Oura H., Nishi, M., Naito T., Chung S., Shen L. (2023). LS-DYNA machine learning-based multiscale method for nonlinear modeling of short-fiber-reinforced composites. Journal of Engineering Mechanics. 149(3): 04023003. https://doi.org/10.1061/JENMDT.EMENG-6945

Xiaoyu Wu,1,&ast; Danfei Ji,1,&ast; Zefan Wang,1,&ast; Wenhua Yu,2 Quan Du,2 Wei Hu,3 Yongke Zheng,3 Xiaoqiao Dong,2 Fanghui Chen4 1The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 2Department of Neurosurgery, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 3Department of Intensive Care Unit, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 4Emergency Department, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaoqiao Dong; Fanghui Chen, Email dxqhyy@163.com; wangxiaoleihzsy@163.comBackground: NADPH oxidase 2 (NOX2) is highly expressed in injured brain tissues. We determined serum NOX2 levels of aneurysmal subarachnoid hemorrhage (aSAH) patients and further investigated correlation of serum NOX2 levels with disease severity, delayed cerebral ischemia (DCI) plus prognosis after aSAH.Methods: Serum NOX2 levels were measured in 123 aSAH patients and 123 healthy controls. World Federation of Neurological Surgeons scale (WFNS) score and modified Fisher (mFisher) score were utilized to assess disease severity. Modified Rankin scale (mRS) score was used to evaluate the clinical prognosis at 90 days after aSAH. Relations of serum NOX2 levels to DCI and 90-day poor prognosis (mRS score of 3– 6) were analyzed using multivariate analysis. Receiver operating characteristic curve (ROC) was built to evaluate the prognostic predictive capability.Results: Serum NOX2 levels in aSAH patients, compared with healthy controls, were significantly increased, and were independently correlated with WFNS score, mFisher score and post-stroke 90-day mRS score. Patients with poor prognosis or DCI had significantly