Single-molecule real-time sequencing of the full-length transcriptome of Portunus pelagicus

Reconstruction and annotation of transcripts, particularly for a species without reference genome, plays a critical role in gene discovery, investigation of genomic signatures, and genome annotation in the pre-genomic era. This is the first study to use Single-molecule real-time (SMRT) sequencing for reporting the full-length transcriptome of Portunus pelagicus. Overall, 16.26 Gb of raw reads were obtained, including 7,068,387 subreads, with average length of 2,300 bp and N50 length of 3,594 bp. In total, 351,870 circular consensus sequences (CCS) reads were extracted, including 255,378 full-length non-chimeric (FLNC) reads with mean length of 3,423 bp.70,407 genes were obtained after eliminating redundant sequences, and 56,557 (80.33%) genes were annotated in at least one database, 17,267 (24.52%) genes were annotated in all of the seven databases. Further, 68,797 coding sequences (CDS) were identified, including 36,848 complete CDS. A total of 1,730 unigenes were predicted to be transcription factors (TFs). Finally, 11,894 long noncoding RNA (lncRNA) transcripts were predicted by different computational approaches and 147,262 single sequence repeat (SSR)s were obtained. The transcriptome data reported herein are bound to serve as a basis for future studies on P. pelagicus

Methyl-CpG binding protein 2 is associated with the prognosis and mortality of elderly patients with hip fractures

Objectives: To investigate the expression level and clinical significance of Methyl-CpG binding Protein 2 (MECP2) in elderly patients with hip fractures. Methods: This prospective observational study included 367 elderly patients with hip fractures between April 2016 and December 2018. All the patients were treated with internal fixation or joint replacement. In addition, 50 healthy elderly individuals were enrolled as healthy controls. The serum levels of MECP2 and inflammatory factors Interleukin (IL)-1β, IL-6, IL-8, and Tumor Necrosis Factor (TNF)-α was determined by enzyme-linked immunosorbent assay. Data on patients' basic characteristics and postoperative complications were collected. The Harris score was used to assess hip function at 1-month, 3-months, and 6-months after surgery. Patient quality of life was measured using the Barthel Index (BI) score 3-months after surgery. The 1-year mortality was analyzed using the Kaplan-Meier curve, and logical regression was used to analyze the risk factors for mortality. Results: No significant differences were observed in the basic clinical characteristics of all patients. The serum MECP2 levels were remarkably high in patients with hip fractures and negatively correlated with serum IL-1β, IL-6, and TNF-α levels. Patients with higher MECP2 predicted higher dynamic Harris scores, lower postoperative complications, lower 1-year mortality, and higher BI scores. Logical regression showed that age was the only independent risk factor for postoperative 1-year mortality in elderly patients with hip fractures. Conclusion: Lower MECP2 predicted poor prognosis and higher 1-year mortality in elderly patients with hip fractures

Learning Controllable 3D Diffusion Models from Single-view Images

Diffusion models have recently become the de-facto approach for generative modeling in the 2D domain. However, extending diffusion models to 3D is challenging due to the difficulties in acquiring 3D ground truth data for training. On the other hand, 3D GANs that integrate implicit 3D representations into GANs have shown remarkable 3D-aware generation when trained only on single-view image datasets. However, 3D GANs do not provide straightforward ways to precisely control image synthesis. To address these challenges, We present Control3Diff, a 3D diffusion model that combines the strengths of diffusion models and 3D GANs for versatile, controllable 3D-aware image synthesis for single-view datasets. Control3Diff explicitly models the underlying latent distribution (optionally conditioned on external inputs), thus enabling direct control during the diffusion process. Moreover, our approach is general and applicable to any type of controlling input, allowing us to train it with the same diffusion objective without any auxiliary supervision. We validate the efficacy of Control3Diff on standard image generation benchmarks, including FFHQ, AFHQ, and ShapeNet, using various conditioning inputs such as images, sketches, and text prompts. Please see the project website (\url{https://jiataogu.me/control3diff}) for video comparisons.Comment: work in progres

Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors

We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons. The learned representation can be used to synthesize novel view images of an arbitrary person from a sparse set of cameras, and further animate them with the user's pose control. While existing methods can either generalize to new persons or synthesize animations with user control, none of them can achieve both at the same time. We attribute this accomplishment to the employment of a 3D proxy for a shared multi-person human model, and further the warping of the spaces of different poses to a shared canonical pose space, in which we learn a neural field and predict the person- and pose-dependent deformations, as well as appearance with the features extracted from input images. To cope with the complexity of the large variations in body shapes, poses, and clothing deformations, we design our neural human model with disentangled geometry and appearance. Furthermore, we utilize the image features both at the spatial point and on the surface points of the 3D proxy for predicting person- and pose-dependent properties. Experiments show that our method significantly outperforms the state-of-the-arts on both tasks. The video and code are available at https://talegqz.github.io/neural_novel_actor

Distributed Measurement of Temperature for PCC Energy Pile Using BOFDA

PCC energy pile is a new technology for sustainable development of urban areas. Learning and understanding the temperature variation of PCC energy pile are very important to its development and application. In this study, the Brillouin optical frequency domain analysis (BOFDA) technology is firstly used to measure the temperature variation of PCC energy pile from a model test. The aim is to provide an optical fiber sensing method for monitoring the temperature distribution of PCC energy pile. When the temperatures of circulating water are 70°C, 60°C, 50°C, and 40°C, the result shows that the temperatures of PCC energy pile under different conditions are measured well by the optical fiber sensor. It will help to master the temperature distribution and thermomechanical characteristic of PCC energy pile. It can also provide the important scientific and theoretical basis for the design and application of PCC energy pile

Distributed Measurement of Temperature for PCC Energy Pile Using BOFDA

PCC energy pile is a new technology for sustainable development of urban areas. Learning and understanding the temperature variation of PCC energy pile are very important to its development and application. In this study, the Brillouin optical frequency domain analysis (BOFDA) technology is firstly used to measure the temperature variation of PCC energy pile from a model test. The aim is to provide an optical fiber sensing method for monitoring the temperature distribution of PCC energy pile. When the temperatures of circulating water are 70 ∘ C, 60 ∘ C, 50 ∘ C, and 40 ∘ C, the result shows that the temperatures of PCC energy pile under different conditions are measured well by the optical fiber sensor. It will help to master the temperature distribution and thermomechanical characteristic of PCC energy pile. It can also provide the important scientific and theoretical basis for the design and application of PCC energy pile

Solution-processed perovskite light emitting diodes with efficiency exceeding 15% through additive-controlled nanostructure tailoring.

Organometal halide perovskites (OHP) are promising materials for low-cost, high-efficiency light-emitting diodes. In films with a distribution of two-dimensional OHP nanosheets and small three-dimensional nanocrystals, an energy funnel can be realized that concentrates the excitations in highly efficient radiative recombination centers. However, this energy funnel is likely to contain inefficient pathways as the size distribution of nanocrystals, the phase separation between the OHP and the organic phase. Here, we demonstrate that the OHP crystallite distribution and phase separation can be precisely controlled by adding a molecule that suppresses crystallization of the organic phase. We use these improved material properties to achieve OHP light-emitting diodes with an external quantum efficiency of 15.5%. Our results demonstrate that through the addition of judiciously selected molecular additives, sufficient carrier confinement with first-order recombination characteristics, and efficient suppression of non-radiative recombination can be achieved while retaining efficient charge transport characteristics