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Amyotrophic lateral sclerosis (ALS) is a progressively paralytic neurodegenerative disease that can be caused by mutations in Cu/Zn-superoxide dismutase 1 (SOD1). Transgenic mice that overexpress mutant SOD1 develop paralysis and accumulate aggregates of mutant protein in the brainstem and spinal cord. Bee venom (BV), which is also known as apitoxin, is extracted from honeybees and is commonly used in oriental medicine for the treatment of chronic rheumatoid arthritis and osteoarthritis. The purpose of the present study was to determine whether BV affects misfolded protein aggregates such as alpha-synuclein, which is a known pathological marker in Parkinson disease, and ubiquitin-proteasomal activity in hSOD1G93A mutant mice. BV was bilaterally administered into a 98-day-old hSOD1G93A animal model. We found that BV-treated hSOD1G93A transgenic mice showed reduced detergent-insoluble polymerization and phosphorylation of α-synuclein. Furthermore, phosphorylated or nitrated α-synuclein was significantly reduced in the spinal cords and brainstems of BV-treated hSOD1G93A mice and reduced proteasomal activity was revealed in the brainstems of BV-treated symptomatic hSOD1G93A. From these findings, we suggest that BV treatment attenuates the dysfunction of the ubiquitin-proteasomal system in a symptomatic hSOD1G93A ALS model and may help to slow motor neuron loss caused by misfolded protein aggregates in ALS models

Effects of Acupuncture on Alzheimer’s Disease in Animal-Based Research

Alzheimer’s disease (AD) is a chronic neurodegenerative disease characterized by the accumulation of amyloid beta (Aβ) plaques, neurofibrillary tangles, and severe functional deficits in the brain. The pathogenesis and treatment of AD remain topics of investigation and significant global socioeconomic issues. The effect of complementary medicine has been investigated in managing AD. Acupuncture, a form of therapy practiced for more than 3000 years, has shown positive effects on several neurological disorders including AD. Animal studies have evaluated the specific utility and neuropathological mechanisms addressed by acupoint manipulation; however, no study has summarized the relationships among different acupoints and their therapeutic effects in the context of AD. Therefore, we reviewed the effects of acupuncture at different acupoints in animal models of AD. In general, acupuncture produced therapeutic benefits in rodent models of AD. Studies demonstrate the utility of GV20 as a valuable acupoint for electroacupuncture and manual acupuncture. GV20 stimulation suppresses Aβ generation, improves glucose metabolism, and attenuates neuropathological features in various disease models. However, a lack of sufficient evidence in preclinical and clinical studies makes these results controversial. Additional studies are required to confirm the exact utility of specific acupoints in clinically managing AD

Melittin restores proteasome function in an animal model of ALS

Amyotrophic lateral sclerosis (ALS) is a paralyzing disorder characterized by the progressive degeneration and death of motor neurons and occurs both as a sporadic and familial disease. Mutant SOD1 (mtSOD1) in motor neurons induces vulnerability to the disease through protein misfolding, mitochondrial dysfunction, oxidative damage, cytoskeletal abnormalities, defective axonal transport- and growth factor signaling, excitotoxicity, and neuro-inflammation

Distinct vertical behavior of key Arctic copepods following the midnight sun period in the East Siberian continental margin region, Arctic Ocean

Diel vertical migration (DVM) of zooplankton plays a vital role in biological carbon pump and food web interactions. However, there is considerable debate about the DVM of zooplankton in response to environmental changes in the Arctic Ocean. We investigated DVM behavior in the key Arctic copepods Calanus glacialis, Calanus hyperboreus, and Metridia longa following the midnight sun period in the East Siberian continental margin region. The two Calanus species showed non-DVM behaviors, whereas M. longa showed a typical DVM pattern consistent with the solar radiation cycle. Additionally, these species showed different vertical distributions. Calanus glacialis was distributed at depths above 20 m in the warm fresh water, where the highest density gradient was observed. Calanus hyperboreus was distributed at depths between 30 and 55 m in the cold salty water, where a high contribution of micro phytoplankton and the subsurface chlorophyll maximum (SCM) layer were observed. M. longa was found across a broader range of temperature and salinity than both Calanus species, and it was distributed in the upper water column, where the SCM layer was observed at night and at depths between 100 and 135 m in the daytime. These results imply that M. longa can be well adapted to the changing Arctic Ocean environment, where sea ice loss and ocean warming are ongoing, whereas C. hyperboreus can be the most vulnerable to these changes. These findings provide important information for understanding variations in the vertical distributions of key copepod species in the rapidly changing Arctic marine environment

Strain-gradient-induced magnetic anisotropy in straight-stripe mixed-phase bismuth ferrites: An insight into flexomagnetic phenomenon

Implementation of antiferromagnetic compounds as active elements in spintronics has been hindered by their insensitive nature against external perturbations which causes difficulties in switching among different antiferromagnetic spin configurations. Electrically-controllable strain gradient can become a key parameter to tune the antiferromagnetic states of multiferroic materials. We have discovered a correlation between an electrically-written straight-stripe mixed-phase boundary and an in-plane antiferromagnetic spin axis in highly-elongated La-5%-doped BiFeO$_{3}$ thin films by performing polarization-dependent photoemission electron microscopy in conjunction with cluster model calculations. Model Hamiltonian calculation for the single-ion anisotropy including the spin-orbit interaction has been performed to figure out the physical origin of the link between the strain gradient present in the mixed phase area and its antiferromagnetic spin axis. Our findings enable estimation of the strain-gradient-induced magnetic anisotropy energy per Fe ion at around 5$\times$10$^{-12}$ eV m, and provide a new pathway towards an electric-field-induced 90$^{\circ}$ rotation of antiferromagnetic spin axis at room temperature by flexomagnetism.Comment: 32 pages, 5 figure

Cross-domain Sound Recognition for Efficient Underwater Data Analysis

This paper presents a novel deep learning approach for analyzing massive underwater acoustic data by leveraging a model trained on a broad spectrum of non-underwater (aerial) sounds. Recognizing the challenge in labeling vast amounts of underwater data, we propose a two-fold methodology to accelerate this labor-intensive procedure. The first part of our approach involves PCA and UMAP visualization of the underwater data using the feature vectors of an aerial sound recognition model. This enables us to cluster the data in a two dimensional space and listen to points within these clusters to understand their defining characteristics. This innovative method simplifies the process of selecting candidate labels for further training. In the second part, we train a neural network model using both the selected underwater data and the non-underwater dataset. We conducted a quantitative analysis to measure the precision, recall, and F1 score of our model for recognizing airgun sounds, a common type of underwater sound. The F1 score achieved by our model exceeded 84.3%, demonstrating the effectiveness of our approach in analyzing underwater acoustic data. The methodology presented in this paper holds significant potential to reduce the amount of labor required in underwater data analysis and opens up new possibilities for further research in the field of cross-domain data analysis.Comment: Accepted to APSIPA 202

Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here for personal use, not for redistribution. The definitive version was published in Remote Sensing of Environment 179 (2016): 1-12, doi:10.1016/j.rse.2016.03.026.Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon and water fluxes. However, robust and efficient ways to monitor the temporal dynamics of leaf traits are lacking. Here we assessed the potential of using leaf spectroscopy to predict leaf traits across their entire life cycle, forest sites, and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. The dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, mass-based nitrogen concentration (Nmass), mass-based carbon concentration (Cmass), and leaf mass per area (LMA)]. All leaf properties, including leaf traits and spectra, varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) analysis to estimate leaf traits from spectra, and found a significant capability of PLSR to capture the variability across time, sites, and light environment of all leaf traits investigated (R2=0.6~0.8 for temporal variability; R2=0.3~0.7 for cross-site variability; R2=0.4~0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the leaf trait seasonal patterns. Increasing the sampling frequency improved in the estimation of Nmass, Cmass and LMA comparing with foliar pigments. Our results, based on the comprehensive analysis of spectra-trait relationships across time, sites and light environments, highlight the capacity and potential limitations to use leaf spectra to estimate leaf traits with strong seasonal variability, as an alternative to time-consuming traditional wet lab approaches.This research was supported by the Brown University–Marine Biological Laboratory graduate program in Biological and Environmental Sciences, and Marine Biological Laboratory start-up funding for JT. JT was also partially supported by the U.S. Department of Energy (U.S. DOE) Office of Biological and Environmental Research grant DE-SC0006951 and the National Science Foundation grants DBI-959333 and AGS-1005663. SPS was supported in part by the U.S. DOE contract No. DE-SC00112704 to Brookhaven National Laboratory. JW was supported by the NASA Earth and Space Science Fellowship (NESSF2014)