Bayesian methods to overcome the winner's curse in genetic studies

Parameter estimates for associated genetic variants, report ed in the initial discovery samples, are often grossly inflated compared to the values observed in the follow-up replication samples. This type of bias is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in studies with low power. We investigate the performance of the method with simulations and applications to four real data examples.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS373 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior

We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation. Code available at https://github.com/deepseek-ai/DreamCraft3D.Comment: Project Page: https://mrtornado24.github.io/DreamCraft3D

Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars

3D-aware generative adversarial networks (GANs) synthesize high-fidelity and multi-view-consistent facial images using only collections of single-view 2D imagery. Towards fine-grained control over facial attributes, recent efforts incorporate 3D Morphable Face Model (3DMM) to describe deformation in generative radiance fields either explicitly or implicitly. Explicit methods provide fine-grained expression control but cannot handle topological changes caused by hair and accessories, while implicit ones can model varied topologies but have limited generalization caused by the unconstrained deformation fields. We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images. To achieve both deformation accuracy and topological flexibility, we propose a 3D representation called Generative Texture-Rasterized Tri-planes. The proposed representation learns Generative Neural Textures on top of parametric mesh templates and then projects them into three orthogonal-viewed feature planes through rasterization, forming a tri-plane feature representation for volume rendering. In this way, we combine both fine-grained expression control of mesh-guided explicit deformation and the flexibility of implicit volumetric representation. We further propose specific modules for modeling mouth interior which is not taken into account by 3DMM. Our method demonstrates state-of-the-art 3D-aware synthesis quality and animation ability through extensive experiments. Furthermore, serving as 3D prior, our animatable 3D representation boosts multiple applications including one-shot facial avatars and 3D-aware stylization.Comment: Project page: https://mrtornado24.github.io/Next3D

Learning Object-Language Alignments for Open-Vocabulary Object Detection

Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts. However, learning open-vocabulary object detection from language is challenging since image-text pairs do not contain fine-grained object-language alignments. Previous solutions rely on either expensive grounding annotations or distilling classification-oriented vision models. In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data. We formulate object-language alignment as a set matching problem between a set of image region features and a set of word embeddings. It enables us to train an open-vocabulary object detector on image-text pairs in a much simple and effective way. Extensive experiments on two benchmark datasets, COCO and LVIS, demonstrate our superior performance over the competing approaches on novel categories, e.g. achieving 32.0% mAP on COCO and 21.7% mask mAP on LVIS. Code is available at: https://github.com/clin1223/VLDet.Comment: Technical Repor

Feasibility study on a robot-assisted procedure for tumor localization using needle-rotation force signals

Accurate tumor localization is critical to early-stage cancer diagnosis and therapy. The recent force-guided technique allows to determine the depth of a suspicious tumor on the insertion path, while the spatial localization is still a great challenge. In this paper, a novel force-guided procedure was proposed to identify spatial tumor location using force signals during needle rotation. When there is a harder tumorous tissue around the needle rotation, an abnormal force signal will point to the location of the suspicious tissue. Finite element simulation and phantom experiment were conducted to test the feasibility of the procedure for the tumor localization. The simulation results showed that the harder tumorous tissue made a significant difference on the stress and deformation distributions for the surroundings, changing the needle-rotation force signals when the needle rotated towards the harder tissue. The experimental results indicated that the direction of the tumor location can be identified by the rotation-needle force signals. The intersection point of the two identified directions, derived from force signals of twice needle rotations, determined the tumor location ultimately. Also, parametric sensitivity tests were performed to examine the effective distance of the tumor location centre and the needle insertion point for the tumor localization. This procedure is expected to be used in robot-assisted system for cancer biopsy and brachytherapy

Determinants of depression, problem behavior, and cognitive level of adolescents in China: Findings from a national, population-based cross-sectional study

IntroductionWe aimed to assess the associated factors for adolescent depression, problem behavior and cognitive level in China.MethodsA total of 2,584 adolescents aged from 10 to 15 years old in 2018 were included for analyses. Information on a comprehensive set of potential determinants was collected by the questionnaire, including demographic, health-, school- and family-related factors. Differences in average scores of depression, problem behavior, and cognitive level across subgroups were assessed by two independent sample t-tests and one-way analysis of variance (ANOVA). The clinical relevance among subgroups was assessed by the effect size. Multivariate linear regression models were applied to identify the statistically significant determinants.ResultsSchool-related factors and parental depressive status were strongly associated with depression. Low maternal education, poor/bad health of adolescents, high academic pressure, and parental depression were significantly associated with behavior problems. The socioeconomic factors, poor academic performance and father’s depression were significantly associated with adolescent cognitive level.DiscussionMultiple associated factors were identified for depression, problem behavior, and cognition of Chinese adolescents, which will provide insights into developing more targeted public health policies and interventions to improve their mental health

CCND1 as a Predictive Biomarker of Neoadjuvant Chemotherapy in Patients with Locally Advanced Head and Neck Squamous Cell Carcinoma

BACKGROUND: Cyclin D1 (CCND1) has been associated with chemotherapy resistance and poor prognosis. In this study, we tested the hypothesis that CCND1 expression determines response and clinical outcomes in locally advanced head and neck squamous cell carcinoma (HNSCC) patients treated with neoadjuvant chemotherapy followed by surgery and radiotherapy. METHODOLOGY AND FINDINGS: 224 patients with HNSCC were treated with either cisplatin-based chemotherapy followed by surgery and radiotherapy (neoadjuvant group, n = 100) or surgery and radiotherapy (non-neoadjuvant group, n = 124). CCND1 expression was assessed by immunohistochemistry. CCND1 levels were analyzed with chemotherapy response, disease-free survival (DFS) and overall survival (OS). There was no significant difference between the neoadjuvant group and non-neoadjuvant group in DFS and OS (p = 0.929 and p = 0.760) when patients treated with the indiscriminate administration of cisplatin-based chemotherapy. However, in the neoadjuvant group, patients whose tumors showed a low CCND1 expression more likely respond to chemotherapy (p<0.001) and had a significantly better OS and DFS than those whose tumors showed a high CCND1 expression (73% vs 8%, p<0.001; 63% vs 6%, p<0.001). Importantly, patients with a low CCND1 expression in neoadjuvant group received more survival benefits than those in non-neoadjuvant group (p = 0.016), however patients with a high CCND1 expression and treated with neoadjuvant chemotherapy had a significantly poor OS compared to those treated with surgery and radiotherapy (p = 0.032). A multivariate survival analysis also showed CCND1 expression was an independent predictive factor (p<0.001). CONCLUSIONS: This study suggests that some but not all patients with HNSCC may benefit from neoadjuvant chemotherapy with cisplatin-based regimen and CCND1 expression may serve as a predictive biomarker in selecting patients undergo less than two cycles of neoadjuvant chemotherapy