Maximal information component analysis: a novel non-linear network analysis method.

BackgroundNetwork construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.ResultsWe have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.ConclusionsIn making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions

Therapeutic targeting of ALS pathways: refocusing an incomplete picture

© 2023 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Numerous potential amyotrophic lateral sclerosis (ALS)-relevant pathways have been hypothesized and studied preclinically, with subsequent translation to clinical trial. However, few successes have been observed with only modest effects. Along with an improved but incomplete understanding of ALS as a neurodegenerative disease is the evolution of more sophisticated and diverse in vitro and in vivo preclinical modeling platforms, as well as clinical trial designs. We highlight proposed pathological pathways that have been major therapeutic targets for investigational compounds. It is likely that the failures of so many of these therapeutic compounds may not have occurred because of lack of efficacy but rather because of a lack of preclinical modeling that would help define an appropriate disease pathway, as well as a failure to establish target engagement. These challenges are compounded by shortcomings in clinical trial design, including lack of biomarkers that could predict clinical success and studies that are underpowered. Although research investments have provided abundant insights into new ALS-relevant pathways, most have not yet been developed more fully to result in clinical study. In this review, we detail some of the important, well-established pathways, the therapeutics targeting them, and the subsequent clinical design. With an understanding of some of the shortcomings in translational efforts over the last three decades of ALS investigation, we propose that scientists and clinicians may choose to revisit some of these therapeutic pathways reviewed here with an eye toward improving preclinical modeling, biomarker development, and the investment in more sophisticated clinical trial designs.Research funding: Cytokinetics National Institutes of Health, USA. Grant Number: 5R01NS117604-03info:eu-repo/semantics/publishedVersio

South Carolina energy efficiency roadmap

Energy efficiency (EE) is widely considered a least-cost option for meeting energy demand while reducing energy costs and carbon emissions. While EE has experienced slow and steady growth in South Carolina, much more can be done to maximize the full potential of this least cost resource. This Energy Efficiency Roadmap report collects the expertise and ideas from over 70 EE stakeholders in the region and maps out the shared objectives and strategies that can help the state implement new solutions, overcome challenges, and achieve its EE potential

Autologous bone plugs in unilateral total knee arthroplasty.

BACKGROUND: The purpose of this study was to compare blood loss, declines in hemoglobin (HgB) and hematocrit (HcT) levels, and required homologous transfusions for patients who either had the femoral intramedullary defect left open or filled with an autologous bone plug during total knee arthroplasty (TKA). We hereby present our results of autologous bone plugs in unilateral TKA. MATERIALS AND METHODS: A retrospective chart review was performed on 55 patients diagnosed with osteoarthritis (OA) who had undergone unilateral TKA. Twenty six patients had the femoral defect filled with an autologous bone plug and 29 did not. Lateral releases and patella replacements were not performed. Drained blood was reinfused when appropriate. RESULTS: MEAN BLOOD LOSS AND MEAN BLOOD REINFUSED WERE SIMILAR FOR THE PLUGGED (LOSS: 960.8 ± 417.3 ml; reinfused: 466.7 ± 435.9 mL) and unplugged groups (loss: 1065.9 ± 633.5 ml, P = 0.38; reinfused: 528.4 ± 464.8 ml, P = 0.61). Preoperative HgB (14.3 ± 1.4 g/dL, P = 0.93) and HcT levels (42.2 ± 4.6%, P = 0.85) were similar across plug conditions. HgB and HcT levels declined similarly for the plugged (2.7 ± 1.2 g/dl and 7.9 ± 4.0%) and unplugged groups (3.0 ± 0.9 g/dl, P = 0.16 and 9.0 ± 2.6%, P = 0.16), respectively. Of patients, one in the plugged group and none in the unplugged group required homologous transfusions (P = 0.5). CONCLUSION: The autologous bone plug does not appear to reduce the need for homologous blood transfusions following unilateral TKA

Using MOST to reveal the secrets of the mischievous Wolf-Rayet binary CV Ser

The WR binary CV Serpentis (= WR113, WC8d + O8-9IV) has been a source of mystery since it was shown that its atmospheric eclipses change with time over decades, in addition to its sporadic dust production. The first high-precision time-dependent photometric observations obtained with the MOST space telescope in 2009 show two consecutive eclipses over the 29d orbit, with varying depths. A subsequent MOST run in 2010 showed a seemingly asymmetric eclipse profile. In order to help make sense of these observations, parallel optical spectroscopy was obtained from the Mont Megantic Observatory (2009, 2010) and from the Dominion Astrophysical Observatory (2009). Assuming these depth variations are entirely due to electron scattering in a beta-law wind, an unprecedented 62% increase in mass-loss rate is observed over one orbital period. Alternatively, no change in mass-loss rate would be required if a relatively small fraction of the carbon ions in the wind globally recombined and coaggulated to form carbon dust grains. However, it remains a mystery as to how this could occur. There also seems to be evidence for the presence of corotating interaction regions (CIR) in the WR wind: a CIR-like signature is found in the light curves, implying a potential rotation period for the WR star of 1.6 d. Finally, a new circular orbit is derived, along with constraints for the wind collision.Comment: 11 pages, 11 figures, 5 table

Social media, surveillance and news work: On the apps promising journalists a “crystal ball”

Social media platforms are becoming an indispensable resource for journalists. Their use involves both direct interaction with the platforms themselves and, increasingly, the use of specialist third-party apps to find, filter, and follow content and contributors. This article explores some of the ways social media platforms, and their technological ecosystems, are infusing news work. A range of platforms and apps—including Geofeedia, Spike, and Twitter—were critically examined, and their use by trainee journalists (N=81) analysed. The results reveal how journalists can—and do—surveil social network users and their content via sophisticated, professional apps that are also utilised by the police and security forces. While journalists recognise the value of such apps in news work, they also have concerns, including about privacy and popularism. And although the participants in this study thought the apps they used could help with verification, there were warning signs that an over-reliance on the technology could develop, dulling journalists’ critical faculties