Bone morphogenetic protein 2/7 promotes repair of bone defect via induction of endochondral ossification

Purpose: To study the influence of bone morphogenetic protein 2/7 (BMP2/7) on repair of bone defect, and the mechanism involved.Methods: Bone marrow stromal cells (BMSCs) were randomly assigned to 2 groups: control and transfection groups. The cells were transfected with rBMP2/7 recombinant adenovirus. Cell growth and alkaline phosphatase (ALP) activity were determined in both groups. Rabbit model of femoral bone defect was prepared using standard methods. Male New Zealand white rabbits were randomly assigned to 3 groups, each of which had 10 rabbits: control, gelatin and BMP2/7 groups. Histopathological and xray examinations, and three-point bending flexural test were used to compare the potential of gelatin and BMP2/7 to repair bone defects.Results: Transfection of BMSCs with rBMP2/7 recombinant adenovirus significantly enhanced their growth (p < 0.05). Alkaline phosphatase (ALP) level was also markedly and time-dependently higher in transfection group than in control group (p < 0.05). Rabbits with grade 4 bone healing or above were more in BMP2/7-treated category than in control and gelatin groups. New bone hyperplasia with typical lamellar bone structure, irregular medullary cavity, as well as transition from osteoblast to osteocyte were observed in BMP2/7 group. Moreover, maximum flexural strength and repair were significantly higher in BMP2/7-transfected group than in control.Conclusion: These findings indicate that BMP2/7 promotes the repair of bone defect via induction of endochondral ossification in rabbits. Thus, this protein may be useful for the repair of bone defects in humans. Keywords: Alkaline phosphatase, Bone lesions, BMP2/7, Endochondral ossificatio

Recreation of the terminal events in physiological integrin activation.

Increased affinity of integrins for the extracellular matrix (activation) regulates cell adhesion and migration, extracellular matrix assembly, and mechanotransduction. Major uncertainties concern the sufficiency of talin for activation, whether conformational change without clustering leads to activation, and whether mechanical force is required for molecular extension. Here, we reconstructed physiological integrin activation in vitro and used cellular, biochemical, biophysical, and ultrastructural analyses to show that talin binding is sufficient to activate integrin alphaIIbbeta3. Furthermore, we synthesized nanodiscs, each bearing a single lipid-embedded integrin, and used them to show that talin activates unclustered integrins leading to molecular extension in the absence of force or other membrane proteins. Thus, we provide the first proof that talin binding is sufficient to activate and extend membrane-embedded integrin alphaIIbbeta3, thereby resolving numerous controversies and enabling molecular analysis of reconstructed integrin signaling

Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space

Diverse human motion prediction aims at predicting multiple possible future pose sequences from a sequence of observed poses. Previous approaches usually employ deep generative networks to model the conditional distribution of data, and then randomly sample outcomes from the distribution. While different results can be obtained, they are usually the most likely ones which are not diverse enough. Recent work explicitly learns multiple modes of the conditional distribution via a deterministic network, which however can only cover a fixed number of modes within a limited range. In this paper, we propose a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model. Our method works by generating an auxiliary space and smartly making randomly sampling from the auxiliary space equivalent to the diverse sampling from the target distribution. We propose a simple yet effective network architecture that implements this novel sampling strategy, which incorporates a Gumbel-Softmax coefficient matrix sampling method and an aggressive diversity promoting hinge loss function. Extensive experiments demonstrate that our method significantly improves both the diversity and accuracy of the samplings compared with previous state-of-the-art sampling approaches. Code and pre-trained models are available at https://github.com/Droliven/diverse_sampling.Comment: Paper and Supp of our work accepted by ACM MM 202

Door and window detection in 3D point cloud of indoor scenes.

This paper proposes a 3D-2D-3D algorithm for doors and windows detection in 3D indoor environment of point cloud data. Firstly, by setting up a virtual camera in the middle of this 3D environment, a set of pictures are taken from different angles by rotating the camera, so that corresponding 2D images can be generated. Next, these images are used to detect and identify the positions of doors and windows in the space. To obtain point cloud data containing the doors and windows position information, the 2D information are then mapped back to the origin 3D point cloud environment. Finally, by processing the contour lines and crossing points, the features of doors and windows through the position information are optimized. The experimental results show that this "global-local" approach is efficient when detecting and identifying the location of doors and windows in 3D point cloud environment

Patient factors influencing the prescribing of lipid lowering drugs for primary prevention of cardiovascular disease in UK general practice: a national retrospective cohort study

BACKGROUND: Guidelines indicate eligibility for lipid lowering drugs, but it is not known to what extent GPs' follow guidelines in routine clinical practice or whether additional clinical factors systematically influence their prescribing decisions. METHODS: A retrospective cohort analysis was undertaken using electronic primary care records from 421 UK general practices. At baseline (May 2008) patients were aged 30 to 74 years, free from cardiovascular disease and not taking lipid lowering drugs. The outcome was prescription of a lipid lowering drug within the next two years. The proportions of eligible and ineligible patients prescribed lipid lowering drugs were reported and multivariable logistic regression models were used to investigate associations between age, sex, cardiovascular risk factors and prescribing. RESULTS: Of 365,718 patients with complete data, 13.8% (50,558) were prescribed lipid lowering drugs: 28.5% (21,101/74,137) of those eligible and 10.1% (29,457/291,581) of those ineligible. Only 41.7% (21,101/50,558) of those prescribed lipid lowering drugs were eligible. In multivariable analysis prescribing was most strongly associated with increasing age (OR for age ≥65 years 4.21; 95% CI 4.05–4.39); diabetes (OR 4.49; 95% CI 4.35–4.64); total cholesterol level ≥7 mmol/L (OR 2.20; 95% CI 2.12–2.29); and ≥4 blood pressure measurements in the past year (OR 4.24; 95% CI 4.06–4.42). The predictors were similar in eligible and ineligible patients. CONCLUSIONS: Most lipid lowering drugs for primary prevention are prescribed to ineligible patients. There is underuse of lipid lowering drugs in eligible patients

Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions.

The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations. However, the pixel-level annotation process is very expensive and time-consuming. To reduce the cost, the paper proposes a semantic candidate regions trained extreme learning machine (ELM) method with image-level labels to achieve pixel-level labels mapping. In this work, the paper casts the pixel mapping problem into a candidate region semantic inference problem. Specifically, after segmenting each image into a set of superpixels, superpixels are automatically combined to achieve segmentation of candidate region according to the number of image-level labels. Semantic inference of candidate regions is realized based on the relationship and neighborhood rough set associated with semantic labels. Finally, the paper trains the ELM using the candidate regions of the inferred labels to classify the test candidate regions. The experiment is verified on the MSRC dataset and PASCAL VOC 2012, which are popularly used in semantic segmentation. The experimental results show that the proposed method outperforms several state-of-the-art approaches for deep semantic segmentation

A Core Genome Approach That Enables Prospective and Dynamic Monitoring of Infectious Outbreaks

Whole-genome sequencing is increasingly adopted in clinical settings to identify pathogen transmissions, though largely as a retrospective tool. Prospective monitoring, in which samples are continuously added and compared to previous samples, can generate more actionable information. To enable prospective pathogen comparison, genomic relatedness metrics based on single-nucleotide differences must be consistent across time, efficient to compute and reliable for a large variety of samples. The choice of genomic regions to compare, i.e., the core genome, is critical to obtain a good metric. We propose a novel core genome method that selects conserved sequences in the reference genome by comparing its k-mer content to that of publicly available genome assemblies. The conserved-sequence genome is sample set-independent, which enables prospective pathogen monitoring. Based on clinical data sets of 3436 S. aureus, 1362 K. pneumoniae and 348 E. faecium samples, ROC curves demonstrate that the conserved-sequence genome disambiguates same-patient samples better than a core genome consisting of conserved genes. The conserved-sequence genome confirms outbreak samples with high sensitivity: in a set of 2335 S. aureus samples, it correctly identifies 44 out of 44 known outbreak samples, whereas the conserved-gene method confirms 38 known outbreak samples