Allotransplanted Neurons Used to Repair Peripheral Nerve Injury Do Not Elicit Overt Immunogenicity

A major problem hindering the development of autograft alternatives for repairing peripheral nerve injuries is immunogenicity. We have previously shown successful regeneration in transected rat sciatic nerves using conduits filled with allogeneic dorsal root ganglion (DRG) cells without any immunosuppression. In this study, we re-examined the immunogenicity of our DRG neuron implanted conduits as a potential strategy to overcome transplant rejection. A biodegradable NeuraGen® tube was infused with pure DRG neurons or Schwann cells cultured from a rat strain differing from the host rats and used to repair 8 mm gaps in the sciatic nerve. We observed enhanced regeneration with allogeneic cells compared to empty conduits 16 weeks post-surgery, but morphological analyses suggest recovery comparable to the healthy nerves was not achieved. The degree of regeneration was indistinguishable between DRG and Schwann cell allografts although immunogenicity assessments revealed substantially increased presence of Interferon gamma (IFN-γ) in Schwann cell allografts compared to the DRG allografts by two weeks post-surgery. Macrophage infiltration of the regenerated nerve graft in the DRG group 16 weeks post-surgery was below the level of the empty conduit (0.56 fold change from NG; p<0.05) while the Schwann cell group revealed significantly higher counts (1.29 fold change from NG; p<0.001). Major histocompatibility complex I (MHC I) molecules were present in significantly increased levels in the DRG and Schwann cell allograft groups compared to the hollow NG conduit and the Sham healthy nerve. Our results confirmed previous studies that have reported Schwann cells as being immunogenic, likely due to MHC I expression. Nerve gap injuries are difficult to repair; our data suggest that DRG neurons are superior medium to implant inside conduit tubes due to reduced immunogenicity and represent a potential treatment strategy that could be preferable to the current gold standard of autologous nerve transplant

CESTA, a positive regulator of brassinosteroid biosynthesis

Brassinosteroids are important plant hormones involved in the regulation of cell elongation, division, differentiation and development. This study identifies CESTA as a basic helix-loop-helix transcription factor that positively regulates brassinosteroid homeostasis

nimbal/nimbaldetach: v1.0.3

&lt;p&gt;v1.0.3 changes&lt;/p&gt; &lt;ul&gt; &lt;li&gt;loosen dependency requirements to minimum version instead of minor version&lt;/li&gt; &lt;/ul&gt

nimbal/nimbalwear: v0.21.5

&lt;p&gt;v0.21.5&lt;/p&gt; &lt;ul&gt; &lt;li&gt;add collection report&lt;/li&gt; &lt;li&gt;change vertdetach references to nimbaldetach&lt;/li&gt; &lt;li&gt;bug fix: remove all Nonin file capabilities to resolve textract/six issues with Python 3.12&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;v0.21.4&lt;/p&gt; &lt;ul&gt; &lt;li&gt;add get_timestamps method to Device object&lt;/li&gt; &lt;li&gt;bug fix: error when dataframes not created if data does not exist&lt;/li&gt; &lt;li&gt;bug fix: error creating nonwear bouts dataframe if no nonwear detected&lt;/li&gt; &lt;li&gt;bug fix: dropping rejected syncs if none detected&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;v0.21.3&lt;/p&gt; &lt;ul&gt; &lt;li&gt;update vertdetach version&lt;/li&gt; &lt;li&gt;update compatibility with pyedflib v1.0.34 (sex header field and sample_frequency)&lt;/li&gt; &lt;li&gt;bug fix: datetime conversion when reading nonwear csv&lt;/li&gt; &lt;li&gt;bug fix: add flatten-dict to setup.cfg&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;v0.21.2&lt;/p&gt; &lt;ul&gt; &lt;li&gt;bug fix: indexing issue caused states to sometimes be skipped&lt;/li&gt; &lt;li&gt;bug fix: start date calculation for multiple gait devices&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;v0.21.1&lt;/p&gt; &lt;ul&gt; &lt;li&gt;fixed MANIFEST.in bug&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;v0.21.0&lt;/p&gt; &lt;ul&gt; &lt;li&gt;reorganized gait module code&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;v0.20.1&lt;/p&gt; &lt;ul&gt; &lt;li&gt;adjust start time moved to before sync&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;v0.20.0&lt;/p&gt; &lt;ul&gt; &lt;li&gt;add autocal offset and scale outputs&lt;/li&gt; &lt;li&gt;option to save separate sensor EDF files after data prep&lt;/li&gt; &lt;li&gt;move settings dump from log into separate file&lt;/li&gt; &lt;li&gt;rename Pipeline class to Study&lt;/li&gt; &lt;li&gt;separate default, study, and custom pipeline settings&lt;/li&gt; &lt;li&gt;some missing data handled and reported as warning instead of raising exception&lt;/li&gt; &lt;li&gt;bug fix: all filters now dual pass&lt;/li&gt; &lt;li&gt;separate sync event and segments into separate folders&lt;/li&gt; &lt;li&gt;syncs detected from any axis rather than choosing those from one axis&lt;/li&gt; &lt;li&gt;include config sync in sync list&lt;/li&gt; &lt;li&gt;add ref device type and location to sync output&lt;/li&gt; &lt;/ul&gt

nimbal/nimbalwear: v0.21.6

&lt;p&gt;v0.21.6&lt;/p&gt; &lt;pre&gt;&lt;code&gt;bug fix: resolve error in collection report when not all possible device locations collected&lt;/code&gt;&lt;/pre&gt

Deep UV photon-counting detectors and applications

Photon counting detectors are used in many diverse applications and are well-suited to situations in which a weak signal is present in a relatively benign background. Examples of successful system applications of photon-counting detectors include ladar, bio-aerosol detection, communication, and low-light imaging. A variety of practical photon-counting detectors have been developed employing materials and technologies that cover the waveband from deep ultraviolet (UV) to the near-infrared. However, until recently, photoemissive detectors (photomultiplier tubes (PMTs) and their variants) were the only viable technology for photon-counting in the deep UV region of the spectrum. While PMTs exhibit extremely low dark count rates and large active area, they have other characteristics which make them unsuitable for certain applications. The characteristics and performance limitations of PMTs that prevent their use in some applications include bandwidth limitations, high bias voltages, sensitivity to magnetic fields, low quantum efficiency, large volume and high cost. Recently, DARPA has initiated a program called Deep UV Avalanche Photodiode (DUVAP) to develop semiconductor alternatives to PMTs for use in the deep UV. The higher quantum efficiency of Geiger-mode avalanche photodiode (GM-APD) detectors and the ability to fabricate arrays of individually-addressable detectors will open up new applications in the deep UV. In this paper, we discuss the system design trades that must be considered in order to successfully replace low-dark count, large-area PMTs with high-dark count, small-area GM-APD detectors. We also discuss applications that will be enabled by the successful development of deep UV GM-APD arrays, and we present preliminary performance data for recently fabricated silicon carbide GM-APD arrays.Defence Advanced Research Projects Agency (contract FA8721-05-C-0002

NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour

Abstract Background Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status. Results & discussion NiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting. Conclusion The NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual’s daily health-related behavior. NiMBaLWear’s focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health