Transforming growth factor-beta (TGF-β) and bone morphogenic protein (BMP) signaling has fundamental roles in both embryonic skeletal development and postnatal bone homeostasis. TGF-βs and BMPs, acting on a tetrameric receptor complex, transduce signals to both the canonical Smad-dependent signaling pathway (that is, TGF-β/BMP ligands, receptors, and Smads) and the non-canonical-Smad-independent signaling pathway (that is, p38 mitogen-activated protein kinase/p38 MAPK) to regulate mesenchymal stem cell differentiation during skeletal development, bone formation and bone homeostasis. Both the Smad and p38 MAPK signaling pathways converge at transcription factors, for example, Runx2 to promote osteoblast differentiation and chondrocyte differentiation from mesenchymal precursor cells. TGF-β and BMP signaling is controlled by multiple factors, including the ubiquitin-proteasome system, epigenetic factors, and microRNA. Dysregulated TGF-β and BMP signaling result in a number of bone disorders in humans. Knockout or mutation of TGF-β and BMP signaling-related genes in mice leads to bone abnormalities of varying severity, which enable a better understanding of TGF-β/BMP signaling in bone and the signaling networks underlying osteoblast differentiation and bone formation. There is also crosstalk between TGF-β/BMP signaling and several critical cytokines' signaling pathways (for example, Wnt, Hedgehog, Notch, PTHrP, and FGF) to coordinate osteogenesis, skeletal development, and bone homeostasis. This review summarizes the recent advances in our understanding of TGF-β/BMP signaling in osteoblast differentiation, chondrocyte differentiation, skeletal development, cartilage formation, bone formation, bone homeostasis, and related human bone diseases caused by the disruption of TGF-β/BMP signaling.

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TGF-β and BMP signaling in osteoblast, skeletal development, and bone formation, homeostasis and disease

Internet of Things in agriculture, recent advances and future challenges

Motivation It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement. Results This paper presents a novel model of Inductive Matrix Completion for MiRNA-Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA-disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave-one-out-cross-validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms-related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms-related miRNAs are verified. Availability and implementation The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA. Supplementary information Supplementary data are available at Bioinformatics online.

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Predicting miRNA-disease association based on inductive matrix completion.

Graphene-based nanomaterials (GBNs) have attracted increasing interests of the scientific community due to their unique physicochemical properties and their applications in biotechnology, biomedicine, bioengineering, disease diagnosis and therapy. Although a large amount of researches have been conducted on these novel nanomaterials, limited comprehensive reviews are published on their biomedical applications and potential environmental and human health effects. The present research aimed at addressing this knowledge gap by examining and discussing: (1) the history, synthesis, structural properties and recent developments of GBNs for biomedical applications; (2) GBNs uses as therapeutics, drug/gene delivery and antibacterial materials; (3) GBNs applications in tissue engineering and in research as biosensors and bioimaging materials; and (4) GBNs potential environmental effects and human health risks. It also discussed the perspectives and challenges associated with the biomedical applications of GBNs.

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A Review on Graphene-Based Nanomaterials in Biomedical Applications and Risks in Environment and Health

Fatty acids play a key role in metabolism: as a metabolic fuel, as a necessary component of all membranes, and as a gene regulator. In addition, fatty acids have a number of industrial uses.


Keywords:

biological effects;
eicosanoids;
requirements;
polyunsaturated fatty acids;
diet;
peroxidation

Fatty Acids: Structures and Properties

With modern technologies such as microarray, deep sequencing, and liquid chromatography-mass spectrometry (LC-MS), it is possible to measure the expression levels of thousands of genes/proteins simultaneously to unravel important biological processes. A very first step towards elucidating hidden patterns and understanding the massive data is the application of clustering techniques. Nonlinear relations, which were mostly unutilized in contrast to linear correlations, are prevalent in high-throughput data. In many cases, nonlinear relations can model the biological relationship more precisely and reflect critical patterns in the biological systems. Using the general dependency measure, Distance Based on Conditional Ordered List (DCOL) that we introduced before, we designed the nonlinear K-profiles clustering method, which can be seen as the nonlinear counterpart of the K-means clustering algorithm. The method has a built-in statistical testing procedure that ensures genes not belonging to any cluster do not impact the estimation of cluster profiles. Results from extensive simulation studies showed that K-profiles clustering not only outperformed traditional linear K-means algorithm, but also presented significantly better performance over our previous General Dependency Hierarchical Clustering (GDHC) algorithm. We further analyzed a gene expression dataset, on which K-profile clustering generated biologically meaningful results.

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Jianwei Lu

Papers

K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data.