1,721,031 research outputs found
Timing neurogenesis by cell cycle
No full textAnimals; Anura; Cell Cycle; Hedgehog Proteins; Hedgehog Proteins: metabolism; Mice; MicroRNAs; MicroRNAs: metabolism; Models; Biological; Neurogenesis; Retinal Neurons; Retinal Neurons: cytology; Retinal Neurons: metabolism; Time Factor
Arabidopsis thaliana MBD7 protein interacts with the arginine methyltransferase AtPRMT11.
No full textCHARACTERIZATION OF CARROT NUCLEAR PROTEINS ABLE TO BIND DNA SEQUENCES WITH DIFFERENT MATHYLATION CONTEXTS
No full text
microRNA(interference) networks are embedded in the gene regulatory networks
No full textmicroRNAs (miRNAs) are a class of endogenous 22-25 nt single-stranded RNA molecules that regulate gene expression post-transcriptionally. They are highly conserved among species with distinct temporal and spatial patterns of expression, each of them potentially interacting with hundreds of messenger RNAs. Since miRNAs, like transcription factors (TFs), are trans-acting factors that interact with cis-regulatory elements, they potentially generate a complex combinatorial code. Moreover, as TFs and genes containing binding sites for TFs have a high probability of being targeted by miRNAs, the basic interplay miRNA/TF renders miRNAs key components of gene regulatory networks. Several biological processes, including diseases such as cancer, have been causatively associated to disturbances of miRNAs/TF interplay both in vitro and in vivo. These aspects, cumulatively, indicate that miRNAs and transcription factors have a crucial role in determining cellular behaviour, highlighting the role of small RNA molecules in regulatory mechanisms and indicating other routes in the evolutionary path of gene expression
Carrot DNA-methyltransferase is encoded by two classes of genes with differing patterns of expression
No full textIn the present study, the isolation and characterization of two distinct cDNAs that code for carrot DNA (cytosine-5)-methyltransferase (DNA-METase) are reported. The screening of a cDNA library with a carrot genomic DNA fragment, previously obtained by PCR using degenerate primers, has led to the isolation of clones that belong to two distinct classes of genes (Met1 and Met2) which differ in sequence and size. Met1-5 and Met2-21 derived amino acid sequences are more than 85% identical for most of the polypeptide and completely diverge at the N-terminus. The larger size of the Met2-21 cDNA is due to the presence of nearly perfect fivefold repeat of a 171bp sequence present only once in the Met1-5 cDNA. Northern and in situ hybridization analyses with young carrot plants and somatic embryos indicate that both genes are maximally expressed in proliferating cells (suspension cells, meristems and leaf primordial), but differ quantitatively and spatially in their mode of expression. Polyclonal antibodies were raised in rabbit using fusion proteins corresponding to the regulatory and catalytic regions of the most highly expressed gene (Met1-5). In nuclear carrot extracts, both antibodies were found to recognize a band of about 200 kDa along with some additional bands of lower size. These results provide the first direct demonstration that DNA-METases of a higher eukaryote are encoded by a gene family
How complex should lower-limb joint models be for subject-specific musculoskeletal modeling applications?
No full textMain topics: Musculoskeletal modelling; Mathematical simulation in human movement science; Orthopaedics
Introduction and aim: Subject-specific musculoskeletal modelling can be applied to study musculoskeletal disorders, providing a valuable approach to calculating muscle and joint forces during movement. The choice of the lower-limb joint models can significantly affect joint contact loads during walking [1]; however, the degree of joint model complexity according to the application has not been extensively studied. The aim of this study is twofold: first, to compare muscle and joint contact forces during common daily activities, using four subject-specific models including different joint models; second, to investigate how the uncertainties in the identification of subject-specific kinematic constraints affect the model outputs using a probabilistic modelling approach.
Patients/materials and methods: Experimental data were collected on a healthy volunteer subject, including lower-body MRI scans and gait data (marker trajectories, ground reaction forces and EMG recordings) during walking, chair rising, stair ascending and descending. A baseline musculoskeletal model (M1) was created, which included a 7-segment 3D articulated linkage with spherical joints at the hips, hinge joints at both knees and ankles [2], and 84 musculotendon actuators. More complex knee and ankle models were built upon the baseline model as follows: planar hinge and universal joint [3] (M2), modified spherical and universal joint [4] (M3), and subject-specific planar 4-bar-linkage knee and ankle [5,6] (M4). To test the sensitivity to parameter identification, five operators virtually palpated the anatomical landmarks necessary to create the M4 model. The joint parameters were statistically sampled according to inter-operator palpation variability, and a set of perturbed models was then created. Simulations of movements solving a typical inverse dynamics and static optimization problem were performed using OpenSim, to calculate muscle and joint contact forces. The predicted forces were compared between the different motor tasks and the robustness to the joint parameter identification was analyzed.
Results: M2 and M4 tended to predict lower knee forces (Table 1), as coupling planar translations to flexion increased knee extensor moment arms. Forces obtained using M4 were more robust to palpation uncertainties at the knee than at the ankle, where, for high dorsiflexion angles (such as in stair descending), M4 predicted unrealistically high contact forces with high variability (Table 1). Hip contact forces were slightly affected by the degree of knee and ankle joint complexity, except for the M3 model, in all simulated motor tasks.
Table 1. peak joint contact loads (BW). M4: mean ± SD.
Gait Chair rising Stair ascending Stair descending
Hip Knee Ankle Hip Knee Ankle Hip Knee Ankle Hip Knee Ankle
M1 3.63 3.40 5.44 3.15 2.96 1.21 3.68 5.30 4.88 4.08 5.13 7.04
M2 3.77 3.80 5.82 3.05 2.54 1.46 3.61 4.61 4.99 4.07 4.80 7.57
M3 4.47 5.80 5.58 3.23 2.59 1.50 3.88 5.87 5.05 3.83 4.39 7.18
M4 4.00 ± 0.14 4.45 ± 0.25 4.96 ± 0.26 3.14 ± 0.02 2.26 ± 0.07 1.12 ± 0.07 3.75 ± 0.01 4.14 ± 0.14 4.61 ± 0.32 4.11 ± 0.01 4.91 ± 0.99 9.28 ± 2.77
Discussion and conclusions: The models predicted different joint loads, and behaved differently in different motor tasks. In the absence of direct validation methods of the predicted forces, the choice of implementing complex subject-specific joints should be justified only when the predictions are robust to the uncertainties in parameter identification. Therefore, before implementing subject-specific kinematic constraints, it is advisable to analyze the sensitivity of predictions over the joint range of motion and, if this choice appears weak (as in the stair descending task), opting for less complex, but more robust and reproducible joint models
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