Fruit Tree Pollination Technology and Industrialization in China

This work investigates the bee pollination of fruit trees, especially apples and pears in the field. We first introduce research carried out into bee pollination of crops in China, and then our own pollination experiments with managed bees such as Apis mellifera in the field. We monitor the efficiency of bee pollination of fruit trees by regulating hive bees and tree arrangement. In addition, we develop some methods to attract bees to visit fruit trees. Our research shows that the number of beehives and the arrangement of trees greatly influence bee pollination. The results provide a comprehensive tutorial on the best practices of bee pollination of fruit trees

Template-dependent multiple displacement amplification for profiling human circulating RNA

Multiple displacement amplification (MDA) is widely used in whole-genome/transcriptome amplification. However, template-independent amplification (TIA) in MDA is a commonly observed phenomenon, particularly when using high concentrations of random hexamer primers and extended incubation times. Here, we demonstrate that the use of random pentamer primers with 5´ ends blocked by a C18 spacer results in MDA solely in a template-dependent manner, a technique we have named tdMDA. Together with an optimized procedure for the removal of residual genomic DNA during RNA extraction, tdMDA was used to profile circulating RNA from 0.2 mL of patient sera. In comparison to regular MDA, tdMDA demonstrated a lack of quantifiable DNA amplification in the negative control, a remarkable reduction of unmapped reads from Illumina sequencing (7 ± 10.9% versus 58.6 ± 39%, P = 0.006), and increased mapping rates of the serum transcriptome (26.9 ± 7.9% versus 5.8 ± 8.2%, P = 3.8 × 10-4). Transcriptome profiles could be used to separate patients with chronic hepatitis C virus (HCV) infection from those with HCV-associated hepatocellular carcinoma (HCC). We conclude that tdMDA should facilitate RNA-based liquid biopsy, as well as other genome studies with biological specimens having ultralow amounts of genetic material. </jats:p

Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty

Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again. However, it is not economical to train diverse teacher models for the disposable distillation. In this paper, we introduce a new concept dubbed Avatars for distillation, which are the inference ensemble models derived from the teacher. Concretely, (1) For each iteration of distillation training, various Avatars are generated by a perturbation transformation. We validate that Avatars own higher upper limit of working capacity and teaching ability, aiding the student model in learning diverse and receptive knowledge perspectives from the teacher model. (2) During the distillation, we propose an uncertainty-aware factor from the variance of statistical differences between the vanilla teacher and Avatars, to adjust Avatars' contribution on knowledge transfer adaptively. Avatar Knowledge Distillation AKD is fundamentally different from existing methods and refines with the innovative view of unequal training. Comprehensive experiments demonstrate the effectiveness of our Avatars mechanism, which polishes up the state-of-the-art distillation methods for dense prediction without more extra computational cost. The AKD brings at most 0.7 AP gains on COCO 2017 for Object Detection and 1.83 mIoU gains on Cityscapes for Semantic Segmentation, respectively.Comment: Accepted by ACM MM 202

Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles

Due to the energy-consumption efficiency between up-slope and down-slope is hugely different, a path with the shortest length on a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicles, realizing a good trade-off between distance and energy consumption on 2.5D path planning is significantly meaningful. In this paper, a deep reinforcement learning-based 2.5D multi-objective path planning method (DMOP) is proposed. The DMOP can efficiently find the desired path with three steps: (1) Transform the high-resolution 2.5D map into a small-size map. (2) Use a trained deep Q network (DQN) to find the desired path on the small-size map. (3) Build the planned path to the original high-resolution map using a path enhanced method. In addition, the imitation learning method and reward shaping theory are applied to train the DQN. The reward function is constructed with the information of terrain, distance, border. Simulation shows that the proposed method can finish the multi-objective 2.5D path planning task. Also, simulation proves that the method has powerful reasoning capability that enables it to perform arbitrary untrained planning tasks on the same map

DAMO-YOLO : A Report on Real-Time Object Detection Design

In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. In particular, we use MAE-NAS, a method guided by the principle of maximum entropy, to search our detection backbone under the constraints of low latency and high performance, producing ResNet/CSP-like structures with spatial pyramid pooling and focus modules. In the design of necks and heads, we follow the rule of ``large neck, small head''.We import Generalized-FPN with accelerated queen-fusion to build the detector neck and upgrade its CSPNet with efficient layer aggregation networks (ELAN) and reparameterization. Then we investigate how detector head size affects detection performance and find that a heavy neck with only one task projection layer would yield better results.In addition, AlignedOTA is proposed to solve the misalignment problem in label assignment. And a distillation schema is introduced to improve performance to a higher level. Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios. For general industry requirements, we propose DAMO-YOLO-T/S/M/L. They can achieve 43.6/47.7/50.2/51.9 mAPs on COCO with the latency of 2.78/3.83/5.62/7.95 ms on T4 GPUs respectively. Additionally, for edge devices with limited computing power, we have also proposed DAMO-YOLO-Ns/Nm/Nl lightweight models. They can achieve 32.3/38.2/40.5 mAPs on COCO with the latency of 4.08/5.05/6.69 ms on X86-CPU. Our proposed general and lightweight models have outperformed other YOLO series models in their respective application scenarios.Comment: Project Website: https://github.com/tinyvision/damo-yol

Stimulation of Type I Collagen Transcription in Human Skin Fibroblasts by TGF-β: Involvement of Smad 3

Transforming growth factor-β (TGF-β) stimulates the transcription of the α2(I) procollagen gene (COL1A2). The intracellular mediators involved in this response remain poorly understood. In this study, we demonstrate that primary human skin fibroblasts express Smads, a novel family of signaling molecules, in vitro in the absence of TGF-β. The levels of Smad 7 mRNA was rapidly and transiently increased by TGF-β. Transient overexpression of Smad 3 and Smad 4, but not Smad 1 or Smad 2, caused trans-activation of a CAT reporter gene driven by a 772 bp segment of the human COL1A2 promoter containing putative TGF-β response elements. Smad stimulation of promoter activity was ligand independent, but was further enhanced by TGF-β. Overexpression of a phosphorylation-deficient Smad 3 mutant or wild-type Smad 7, which lacks the carboxy-terminal phosphorylation motif, specifically inhibited TGF-β-induced activation of COL1A2 promoter. A CAGACA sequence shown to be a functional Smad-binding element in the plasminogen activator inhibitor-1 gene promoter was found within the TGF-β-response region of the proximal COL1A2 promoter. Gel mobility shift assays showed protein phosphorylation-dependent binding activity in fibroblast nuclear extracts specific for this sequence; TGF-β treatment strongly stimulated the formation of this DNA-protein complex. Smad was identified as a component of the CAGACA-binding transcription complex in TGF-β-treated fibroblasts by antibody supershifting. These results demonstrate that (i) Smad 3 transmits TGF-β signals from the receptor to the COL1A2 promoter in human fibroblasts, and is likely to play an important role in stimulation of COL1A2 promoter activity elicited by TGF-β; (ii) in fibroblasts, Smads appear to function as inducible DNA-binding transcription factors; and (iii) Smad 7 may be involved in autocrine negative feedback in the regulation of COL1A2 promoter activity by TGF-β

Alteration of Innate Immunity by Donor IL-6 Deficiency in a Presensitized Heart Transplant Model

Engraftment of IL-6 deficient donor into wild-type recipient could significantly improve allograft survival through T cell lineage particularly regulatory T cells (Tregs) in non-sensitized transplant host. However, its effect on innate immune responses remains uncertain. Our data revealed that donor IL-6 deficiency significantly increased infiltration of two subsets of MDSCs (CD11b+Gr1+myeloid-derived suppressor cells), CD11b+Gr1-low and CD11b+Gr1-int with strong immunosuppression activity in the transplanted graft. It resulted in a dramatic increase of CD11b+Gr1-low frequency and a significant decrease of the frequency of CD11b+Gr1-high and CD4-CD8-NK1.1+ cells in the recipient’s spleen. Unexpectedly, donor IL-6 deficiency could not significantly reduce macrophage frequency irrespective of in the host’s spleen or graft. Taken together, suppression of innate immune effector cells and enhanced activity of regulatory MDSCs contributed to tolerance induction by blockade of IL-6 signaling pathway. The unveiled novel mechanism of targeting IL-6 might shed light on clinical therapeutic application in preventing accelerated allograft rejection for those pre-sensitized transplant recipients