Thermospheric winds around the cusp region

An equatorward wind has been observed first by the balloon‐borne Fabry‐Perot interferometer called High‐Altitude Interferometer Wind Observation on the equatorward side of the cusp near the local noon, which is opposite to the typical direction of neutral wind driven by the day‐night pressure gradient. However, this dayside equatorward wind was not reproduced by the standard Thermosphere Ionosphere Electrodynamics General Circulation Model under the resolution of 5° longitude by 5° latitude (5°×5°). In this study, the Global Ionosphere Thermosphere Model has been run in different cases and under different resolutions to investigate the neutral dynamics around the cusp region. First, we compare the simulations with and without additional cusp energy inputs to identify the influence of cusp heating. Both runs have a resolution of 5°×1° (longitude × latitude) in order to better resolve the cusp region. After adding in the cusp energy, the meridional wind in simulation turns to be equatorward on the dayside, which is consistent with the observation. It indicates that strong heating in the cusp region causes changes in the pressure gradient around the cusp and subsequent variations in the neutral winds. The simulations with the same cusp heating specifications are repeated, but with different horizontal resolutions to examine the influence of resolution on the simulation results. The comparisons show that the resolution of 5°×1° can resolve the cusp region much more stably and consistently than the 5°×5° resolution.Key PointsThe observed equatorward winds around the cusp have been reproduced by GITMThe impact of cusp energy on the horizontal neutral winds has been studiedThe influence of resolution on cusp simulation has also been investigatedPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110850/1/jgra51569.pd

Effects of Modified Atmosphere Packaging with Varied CO2 and O2 Concentrations on the Texture, Protein, and Odor Characteristics of Salmon during Cold Storage

The effect of gas ratio on the growth of bacteria has been well demonstrated, but some adverse effects of modified atmosphere packaging (MAP) on seafoods have also been found. To provide a better understanding of the effects of CO2 and O2 concentrations (CO2 from 40% to 100% and O2 from 0% to 30%) in MAP on the texture and protein contents and odor characteristics of salmon during cold storage, the physiochemical, microbial, and odor indicators were compared with those without treatment (CK). Generally, MAP treatments hindered the increase of microbial counts, total volatile basic nitrogen, and TCA-soluble peptides, and decreased the water-holding capacity, hardness, springiness, and sarcoplasmic and myofibrillar protein contents. The results also indicated that 60%CO2/10%O2/30%N2 was optimal and decreased the total mesophilic bacterial counts by 2.8 log cfu/g in comparison with CK on day 12. In agreement, the concentration of CO2 of 60% showed the lowest myofibrillar protein degradation, and less subsequent loss of hardness. The electronic nose characteristics analysis indicated that 60%CO2/20%O2/20%N2 and 60%CO2/10%O2/30%N2 had the best effect to maintain the original odor profiles of salmon. The correlation analysis demonstrated that microbial growth had a strong relationship with myofibrillar and sarcoplasmic protein content. It can be concluded that 60%CO2/10%O2/30%N2 displayed the best effect to achieve the goal of preventing protein degradation and odor changes in salmon fillets

Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis.

BACKGROUND: Staging diagnosis of liver fibrosis is a prerequisite for timely diagnosis and therapy in patients with chronic hepatitis B. In recent years, ultrasound elastography has become an important method for clinical noninvasive assessment of liver fibrosis stage, but its diagnostic value for early liver fibrosis still needs to be further improved. In this study, the texture analysis was carried out on the basis of two dimensional shear wave elastography (2D-SWE), and the feasibility of 2D-SWE plus texture analysis in the diagnosis of early liver fibrosis was discussed. AIM: To assess the diagnostic value of 2D-SWE combined with textural analysis in liver fibrosis staging. METHODS: This study recruited 46 patients with chronic hepatitis B. Patients underwent 2D-SWE and texture analysis; Young\u27s modulus values and textural patterns were obtained, respectively. Textural pattern was analyzed with regard to contrast, correlation, angular second moment (ASM), and homogeneity. Pathological results of biopsy specimens were the gold standard; comparison and assessment of the diagnosis efficiency were conducted for 2D-SWE, texture analysis and their combination. RESULTS: 2D-SWE displayed diagnosis efficiency in early fibrosis, significant fibrosis, severe fibrosis, and early cirrhosis (AUC \u3e 0.7, P \u3c 0.05) with respective AUC values of 0.823 (0.678-0.921), 0.808 (0.662-0.911), 0.920 (0.798-0.980), and 0.855 (0.716-0.943). Contrast and homogeneity displayed independent diagnosis efficiency in liver fibrosis stage (AUC \u3e 0.7, P \u3c 0.05), whereas correlation and ASM showed limited values. AUC of contrast and homogeneity were respectively 0.906 (0.779-0.973), 0.835 (0.693-0.930), 0.807 (0.660-0.910) and 0.925 (0.805-0.983), 0.789 (0.639-0.897), 0.736 (0.582-0.858), 0.705 (0.549-0.883) and 0.798 (0.650-0.904) in four liver fibrosis stages, which exhibited equivalence to 2D-SWE in diagnostic efficiency (P \u3e 0.05). Combined diagnosis (PRE) displayed diagnostic efficiency (AUC \u3e 0.7, P \u3c 0.01) for all fibrosis stages with respective AUC of 0.952 (0.841-0.994), 0.896 (0.766-0.967), 0.978 (0.881-0.999), 0.947 (0.835-0.992). The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis (P \u3c 0.05), whereas no significant differences were observed in other comparisons (P \u3e 0.05). CONCLUSION: Texture analysis was capable of diagnosing liver fibrosis stage, combined diagnosis had obvious advantages in early liver fibrosis, liver fibrosis stage might be related to the hepatic tissue hardness distribution

Anti-apoptotic and neuroprotective effects of Tetramethylpyrazine following spinal cord ischemia in rabbits

BACKGROUND: Tetramethylpyrazine (TMP) is one of the most important active ingredients of a Chinese herb Ligusticum wallichii Franchat, which is widely used in many ischemia disorders treatments. However, the exact mechanism by which TMP protects the spinal cord ischemia/reperfusion (I/R) injury is still unknown. For this purpose, rabbits were randomly divided into sham group, control group and TMP group. After the evaluation of neurologic function, the spinal cords were immediately removed for biochemical and histopathological analysis. Apoptosis was measured quantitatively by the terminal transferase UTP nick end-labeling (TUNEL) method and confirmed by electron microscopic examination, the expression of Bax and Bcl-2 was immunohistochemically evaluated and quantified by Western blot analysis. RESULTS: Neurologic outcomes in the TMP-group were significantly better than those in the control group (P < 0.05). TMP decreased spinal cord malondialdehyde (MDA) levels and ameliorated the down regulation of spinal cord superoxide dismutase (SOD) activity. TMP significantly reduced the loss of motoneurons and TUNEL-positive rate. Greater Bcl-2 and attenuated Bax expression was found in the TMP treating rabbits. CONCLUSION: These findings suggest that TMP has protective effects against spinal cord I/R injury by reducing apoptosis through regulating Bcl-2 and Bax expression

Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction

Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the highfrequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction