Mediators of mechanotransduction between bone cells

Mechanical forces are known to regulate the function of tissues in the body, including bone. Bone adapts to its mechanical environment by altering its shape and increasing its size in response to increases in mechanical load associated with exercise, and by decreasing its size in response to decreases in mechanical load associated with microgravity or prolonged bed rest. Changes in bone size and shape are produced by a cooperative action of two main types of the bone cells - osteoclasts that destroy bone and osteoblasts that build bone. These cell types come from different developmental origins, and vary greatly in their characteristics, such as size, shape, and expression of receptor subtypes, which potentially may affect their responses to mechanical stimuli. The objective of this study is to compare the responses of osteoclasts and osteoblasts to mechanical stimulation. This study has allowed us to conclude the following: 1. A mediator is released from a single source cell. 2. The response to the mediator changes with distance. 3. The value of the apparent diffusion coeficient increases with distance. 4. A plausible proposed mechanism is that ATP is released and degrades to ADP. 5. Future experiments are required to confim that ATP is the mediator as suggested

What about lay counselors' experiences of task-shifting mental health interventions? Example from a family-based intervention in Kenya.

Background:A key focus of health systems strengthening in low- and middle-income countries is increasing reach and access through task-shifting. As such models become more common, it is critical to understand the experiences of lay providers because they are on the forefront for delivering care services. A greater understanding would improve lay provider support and help them provide high-quality care. This is especially the case for those providing mental health services, as providing psychological care may pose unique stressors. We sought to understand experiences of lay counselors, focusing on identity, motivation, self-efficacy, stress, and burnout. The goal was to understand how taking on a new provider role influences their lives beyond simply assuming a new task, which would in turn help identify actionable steps to improve interventions with task-shifting components. Methods:Semi-structured interviews (n = 20) and focus group discussions (n = 3) were conducted with three lay counselor groups with varying levels of experience delivering a community-based family therapy intervention in Eldoret, Kenya. Thematic analysis was conducted, including intercoder reliability checks. A Stress Map was created to visualize stress profiles using free-listing and pile-sorting data collected during interviews and focus group discussions. Results:Counselors described high intrinsic motivation to become counselors and high self-efficacy after training. They reported positive experiences in the counselor role, with new skills improving their counseling and personal lives. As challenges arose, including client engagement difficulties and balancing many responsibilities, stress and burnout increased, dampening motivation and self-efficacy. In response, counselors described coping strategies, including seeking peer and supervisor support, that restored their motivation to persevere. At case completion, they again experienced high self-efficacy and a desire to continue. Conclusions:Findings informed suggestions for ways to incorporate support for lay providers into task-shifting interventions at initiation, during training, and throughout implementation. These include acknowledging and preparing counselors for challenges during training, increasing explicit attention to counselor stress in supervision, fostering peer support among lay providers, and ensuring a fair balance between workload and compensation. Improving and building an evidence base around practices for supporting lay providers will improve the effectiveness and sustainability of lay provider-delivered interventions

Efficiency of the Incomplete Enumeration algorithm for Monte-Carlo simulation of linear and branched polymers

We study the efficiency of the incomplete enumeration algorithm for linear and branched polymers. There is a qualitative difference in the efficiency in these two cases. The average time to generate an independent sample of $n$ sites for large $n$ varies as $n^2$ for linear polymers, but as $exp(c n^{\alpha})$ for branched (undirected and directed) polymers, where $0<\alpha<1$. On the binary tree, our numerical studies for $n$ of order $10^4$ gives $\alpha = 0.333 \pm 0.005$. We argue that $\alpha=1/3$ exactly in this case.Comment: replaced with published versio

The Hyperfine Molecular Hubbard Hamiltonian

An ultracold gas of heteronuclear alkali dimer molecules with hyperfine structure loaded into a one-dimensional optical lattice is investigated. The \emph{Hyperfine Molecular Hubbard Hamiltonian} (HMHH), an effective low-energy lattice Hamiltonian, is derived from first principles. The large permanent electric dipole moment of these molecules gives rise to long range dipole-dipole forces in a DC electric field and allows for transitions between rotational states in an AC microwave field. Additionally, a strong magnetic field can be used to control the hyperfine degrees of freedom independently of the rotational degrees of freedom. By tuning the angle between the DC electric and magnetic fields and the strength of the AC field it is possible to control the number of internal states involved in the dynamics as well as the degree of correlation between the spatial and internal degrees of freedom. The HMHH's unique features have direct experimental consequences such as quantum dephasing, tunable complexity, and the dependence of the phase diagram on the molecular state

Radar-aeolian roughness project

The objective is to establish an empirical relationship between measurements of radar, aeolian, and surface roughness on a variety of natural surfaces and to understand the underlying physical causes. This relationship will form the basis for developing a predictive equation to derive aeolian roughness from radar backscatter. Results are given from investigations carried out in 1989 on the principal elements of the project, with separate sections on field studies, radar data analysis, laboratory simulations, and development of theory for planetary applications

An Experimental Analysis of Deep Learning Architectures for Supervised Speech Enhancement

Recent speech enhancement research has shown that deep learning techniques are very effective in removing background noise. Many deep neural networks are being proposed, showing promising results for improving overall speech perception. The Deep Multilayer Perceptron, Convolutional Neural Networks, and the Denoising Autoencoder are well-established architectures for speech enhancement; however, choosing between different deep learning models has been mainly empirical. Consequently, a comparative analysis is needed between these three architecture types in order to show the factors affecting their performance. In this paper, this analysis is presented by comparing seven deep learning models that belong to these three categories. The comparison includes evaluating the performance in terms of the overall quality of the output speech using five objective evaluation metrics and a subjective evaluation with 23 listeners; the ability to deal with challenging noise conditions; generalization ability; complexity; and, processing time. Further analysis is then provided while using two different approaches. The first approach investigates how the performance is affected by changing network hyperparameters and the structure of the data, including the Lombard effect. While the second approach interprets the results by visualizing the spectrogram of the output layer of all the investigated models, and the spectrograms of the hidden layers of the convolutional neural network architecture. Finally, a general evaluation is performed for supervised deep learning-based speech enhancement while using SWOC analysis, to discuss the technique’s Strengths, Weaknesses, Opportunities, and Challenges. The results of this paper contribute to the understanding of how different deep neural networks perform the speech enhancement task, highlight the strengths and weaknesses of each architecture, and provide recommendations for achieving better performance. This work facilitates the development of better deep neural networks for speech enhancement in the future

[OII] Emission, Eigenvector 1 and Orientation in Radio-quiet Quasars

We present supportive evidence that the Boroson and Green eigenvector 1 is not driven by source orientation. Until recently it was generally accepted that eigenvector 1 does not depend on orientation as it strongly correlates with [OIII]5007 emission, thought to be an isotropic property. However, recent studies of radio-loud AGN have questioned the isotropy of [OIII] emission and concluded that [OII]3727 emission is isotropic. In this paper we investigate the relation between eigenvector 1 and [OII] emission in radio-quiet BQS (Bright Quasar Survey) quasars, and readdress the issue of orientation as the driver of eigenvector 1. We find significant correlations between eigenvector 1 and orientation independent [OII] emission, which implies that orientation does not drive eigenvector 1. The luminosities and equivalent widths of [OIII] and [OII] correlate with one another, and the range in luminosities and equivalent widths is similar. This suggests that the radio-quiet BQS quasars are largely free of orientation dependent dust effects and ionization dependent effects in the narrow-line region. We also conclude that neither the [OIII] emission nor the [OII]/[OIII] ratio are dependent on orientation in our radio-quiet BQS quasar sample, contrary to recent results found for radio-loud quasars.Comment: 24 pages, 12 figures, accepted for publication in Ap

Arterial pathology in canine mucopolysaccharidosis-I and response to therapy.

Mucopolysaccharidosis-I (MPS-I) is an inherited deficiency of α-L-iduronidase (IdU) that causes lysosomal accumulation of glycosaminoglycans (GAG) in a variety of parenchymal cell types and connective tissues. The fundamental link between genetic mutation and tissue GAG accumulation is clear, but relatively little attention has been given to the morphology or pathogenesis of associated lesions, particularly those affecting the vascular system. The terminal parietal branches of the abdominal aorta were examined from a colony of dogs homozygous (MPS-I affected) or heterozygous (unaffected carrier) for an IdU mutation that eliminated all enzyme activity, and in affected animals treated with human recombinant IdU. High-resolution computed tomography showed that vascular wall thickenings occurred in affected animals near branch points, and associated with low endothelial shear stress. Histologically these asymmetric 'plaques' entailed extensive intimal thickening with disruption of the internal elastic lamina, occluding more than 50% of the vascular lumen in some cases. Immunohistochemistry was used to show that areas of sclerosis contained foamy (GAG laden) macrophages, fibroblasts and smooth muscle cells, with loss of overlying endothelial basement membrane and claudin-5 expression. Lesions contained scattered cells expressing nuclear factor-κβ (p65), increased fibronectin and transforming growth factor β-1 signaling (with nuclear Smad3 accumulation) in comparison to unaffected vessels. Intimal lesion development and morphology was improved by intravenous recombinant enzyme treatment, particularly with immune tolerance to this exogenous protein. The progressive sclerotic vasculopathy of MPS-I shares some morphological and molecular similarities to atherosclerosis, including formation in areas of low shear stress near branch points, and can be reduced or inhibited by intravenous administration of recombinant IdU

A Mixed Reality Approach for dealing with the Video Fatigue of Online Meetings

Much of the issue with video meetings is the lack of naturalistic cues, together with the feeling of being observed all the time. Video calls take away most body language cues, but because the person is still visible, your brain still tries to compute that non-verbal language. It means that you’re working harder, trying to achieve the impossible. This impacts data retention and can lead to participants feeling unnecessarily tired. This project aims to transform the way online meetings happen, by turning off the camera and simplifying the information that our brains need to compute, thus preventing ‘Zoom fatigue’. The immersive solution we are developing, iVXR, consists of cutting-edge augmented reality technology, natural language processing, speech to text technologies and sub-real-time hardware acceleration using high performance computing