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MEIOB is a vital protein in meiotic homologous recombination and plays an indispensable role in human gametogenesis. In mammals, MEIOB and its partner SPATA22 form a heterodimer, ensuring their effective localization on single-strand DNA (ssDNA) and proper synapsis processes. Mutations in human MEIOB (hMEIOB) cause human infertility attributed to the failure of its interaction with human SPATA22 (hSPATA22) and ssDNA binding. However, the detailed mechanism is still unclear. In our study, truncated or full-length hMEIOB and hSPATA22 are traced by fused expression with fluorescent proteins (i.e., copGFP or mCherry), and the live cell imaging system is used to observe the expression and localization of the proteins. When transfected alone, hMEIOB accumulates in the cytoplasm. Interestingly, a covered NLS in the OB domain of hMEIOB is identified, which can be exposed by hSPATA22 and is necessary for the nuclear localization of hMEIOB. When hSPATA22 loses its hMEIOB interacting domain or NLS, the nuclear localization of hMEIOB is aborted. Collectively, our results prove that the NLS in the OB domain of hMEIOB and interaction with hSPATA22 are required for hMEIOB nuclear localization.

The injection performance of a small volume of needle-free injection (up to 0.3 mL) has proven to be controllable and satisfactory in transdermal drug delivery. However, no comprehensive research on the behavior of larger volume injections with different requirements for clinical applications exists. This study aims to present the penetration characteristics of larger volume injections, including dynamic properties, dispersion pattern, and percent delivery. The researchers conduct impact and injection experiments with injection volumes from 0.1–1.0 mL, driving pressures of 0.5–1.75 MPa, and orifice diameters of 0.17–0.5 mm. This study uses high-speed photography and impact experiments to capture the dynamic properties of the liquid jet. The researchers observe the dispersion patterns of liquid penetration into the gels in the gel injection experiments and investigate the percentages of liquid delivered to the skin tissues in porcine tissues injection experiments. Moreover, this study uses the response surface methodology (RSM) to analyze the interactive effect between various injection parameters on the injection performance. Results describe the differences in penetration performance between larger volume injections and small volume injections, and the critical volume of the mentioned injections is approximately 0.6 mL.

Hypodermic needles and syringes are currently the main route of the transdermal administration. Many complaints associated with needle-stick injuries, needle phobia, and needle abuse have motivated the development of alternative drug delivery strategies. Pneumatic needle-free injections stand out from various alternative strategies because of the convenience in adjusting the driving pressure and the injection volume. This study proposed a novel control method of the pneumatic needle-free injection for delivering larger volume drugs to the target sites at desired rates and time by controlling the driving pressure. The dynamic characteristics of this injection mode were evaluated in the impact experiments and the results confirmed the reliability of this controllable system in controlling the outlet pressure and jet velocity. The injection performances of it were compared with that of other injection modes through in vivo and in vitro experiments. This controllable system was able to control the injection depth and it was more conducive to the lateral diffusion of the liquid jets in gelatin. Moreover, it could not only enlarge the diffusion of diclofenac solution in rats, but also accelerate the metabolism of diclofenac solution in rats. All experimental results confirmed the reliability of this controllable injection system.

Vascular smooth muscle cells (VSMCs) are an important source of foam cells in atherosclerosis. The mechanism for VSMC-derived foam cell formation is, however, poorly understood. Here, we demonstrate that the P2RY12/P2Y12 receptor is important in regulating macroautophagy/autophagy and VSMC-derived foam cell formation in advanced atherosclerosis. Inhibition of the P2RY12 receptor ameliorated lipid accumulation and VSMC-derived foam cell formation in high-fat diet-fed apoe(-/-) mice (atherosclerosis model) independent of LDL-c levels. Activation of the P2RY12 receptor blocked cholesterol efflux via PI3K-AKT, while genetic knockdown or pharmacological inhibition of the P2RY12 receptor inhibited this effect in VSMCs. Phosphoproteomic analysis showed that the P2RY12 receptor regulated the autophagy pathway in VSMCs. Additionally, activation of the P2RY12 receptor inhibited MAP1LC3/LC3 maturation, SQSTM1 degradation, and autophagosome formation in VSMCs. Genetic knockdown of the essential autophagy gene Atg5 significantly attenuated P2RY12 receptor inhibitor-induced cholesterol efflux in VSMCs. Furthermore, activation of the P2RY12 receptor led to the activation of MTOR through PI3K-AKT in VSMCs, whereas blocking MTOR activity (rapamycin) or reducing MTOR expression reversed the inhibition of cholesterol efflux mediated by the P2RY12 receptor in VSMCs. In vivo, inhibition of the P2RY12 receptor promoted autophagy of VSMCs through PI3K-AKT-MTOR in advanced atherosclerosis in apoe(-/-) mice, which could be impeded by an autophagy inhibitor (chloroquine). Therefore, we conclude that activation of the P2RY12 receptor decreases cholesterol efflux and promotes VSMC-derived foam cell formation by blocking autophagy in advanced atherosclerosis. Our study thus suggests that the P2RY12 receptor is a therapeutic target for treating atherosclerosis. Abbreviations: 2-MeSAMP: 2-methylthioadenosine 5′-monophosphate; 8-CPT-cAMP: 8-(4-chlorophenylthio)-adenosine-3ʹ,5ʹ-cyclic-monophosphate; ABCA1: ATP binding cassette subfamily A member 1; ABCG1: ATP binding cassette subfamily G member 1; ACTB: actin beta; ADPβs: adenosine 5′-(alpha, beta-methylene) diphosphate; ALs: autolysosomes; AMPK: AMP-activated protein kinase; APOA1: apolipoprotein A1; APs: autophagosomes; ATG5: autophagy related 5; ATV: atorvastatin; AVs: autophagic vacuoles; CD: chow diet; CDL: clopidogrel; CQ: chloroquine; DAPI: 4ʹ,6-diamidino-2-phenylindole; dbcAMP: dibutyryl-cAMP; DIL-oxLDL: dioctadecyl-3,3,3,3-tetramethylin docarbocyanine-oxLDL; EIF4EBP1/4E-BP1: eukaryotic translation initiation factor 4E binding protein 1; EVG: elastic van gieson; HE: hematoxylin-eosin; HDL: high-density lipoprotein; HFD: high-fat diet; KEGG: Kyoto Encyclopedia of Genes and Genomes; LDL-c: low-density lipoprotein cholesterol; LDs: lipid droplets; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; Masson: masson trichrome; MCPT: maximal carotid plaque thickness; MK2206: MK-2206 2HCL; NBD-cholesterol: 22-(N-[7-nitrobenz-2-oxa-1,3-diazol-4-yl] amino)-23,24-bisnor-5-cholen-3β-ol; OLR1/LOX-1: oxidized low density lipoprotein receptor 1; ORO: oil Red O; ox-LDL: oxidized low-density lipoprotein; SQSTM1/p62: sequestosome 1; TEM: transmission electron microscopy; TIC: ticagrelor; ULK1: unc-51 like autophagy activating kinase 1; VSMCs: vascular smooth muscle cells

Evaporation duct is a kind of chaotic phenomenon over the ocean. In this paper, a new nonlinear prediction algorithm, the Darwinian evolutionary algorithm (DEA), is introduced to obtain the specific nonlinear formula $P(\cdot)$ of the chaotic phenomenon. Based on Darwinian natural selection and survival theory, the method first selects a suitable training set of samples, and then produces an initial population before going through an evolutionary process of selection, reproduction and mutation until the optimal individual is found. Finally, a specific expression for a nonlinear chaotic time series is obtained, which can realize the short-term prediction of evaporation duct height (EDH) quickly and accurately. After that, the DEA, the support vector regression (SVR), and the back propagation (BP) neural network were applied to predict the EDH which were formed over the ocean by using sounding data. After interpolation and smoothing of the original data, we selected the first 250 data as training samples and the last 115 data as test samples to test the effect of the EDA algorithm. The results showed that the root mean squared error (RMSE) for the DEA was about 7% less than that of the SVR and 10% less than that of BP neural network; the mean absolute percent error (MAPE) for the DEA was about 9% less than that of the SVR and 15% less than that of BP neural network. In addition, the DEA obtained, for the first time, a nonlinear expression for EDH, which provides an important reference for future research on the evaporation ducts.

Objective: Recent studies suggest that the P2Y12 (P2Y purinoceptor 12) receptor of vascular smooth muscle cells in atherosclerotic plaques aggravates atherosclerosis, and P2Y12 receptor inhibitors such as CDL (clopidogrel) may effectively treat atherosclerosis. It is imperative to identify an effective biomarker for reflecting the P2Y12 receptor expression on vascular smooth muscle cells in plaques. Approach and Results: We found that there was a positive correlation between the level of circulating sLRP1 (soluble low-density lipoprotein receptor-related protein 1) and the number of LRP1 + α-SMA + (α-smooth muscle actin), P2Y12 + , or P2Y12 + LRP1 + cells in plaques from apoE −/− mice fed a high-fat diet. Furthermore, activation of the P2Y12 receptor increased the expression and shedding of LRP1 in vascular smooth muscle cells by inhibiting cAMP (3'-5'-cyclic adenosine monophosphate)/PKA (protein kinase A)/SREBP-2 (sterol regulatory element binding transcription factor 2). Conversely, genetic knockdown or pharmacological inhibition of the P2Y12 receptor had the opposite effects. Additionally, CDL decreased the number of lesional LRP1 + α-SMA + cells and the levels of circulating sLRP1 by activating cAMP/PKA/SREBP-2 in apoE −/− mice fed a high-fat diet. Conclusions: Our study suggests that sLRP1 may be a biomarker that reflects the P2Y12 receptor level in plaques and has the potential to be an indicator for administering P2Y12 receptor inhibitors for patients with atherosclerosis.

This paper develops a new method for the diagnosis and prediction of the evaporation duct heights on the sea, which has certain reference significance for the study of the evaporation ducts. Based on traditional diagnostic and predictive models of evaporation duct heights, a new diagnostic model is proposed. By determining the overall Richardson number Rib, the Monin-Obukhov (M-O) length L and the wind speed characteristic parameter u∗, temperature characteristic parameter θ∗ and humidity characteristic parameters q∗ are calculated, and then the evaporation duct height is diagnosed. Taking the diagnosed heights as a time series, and using the support vector regression (SVR) algorithm improved by a simulated annealing operator, then the time series is analyzed by taking three consecutive sample steps as input and the next sample step as output in order to develop an algorithm for predicting future heights. Finally, the prediction results are compared with those from the traditional auto-regressive (AR) algorithm and classical SVR algorithm to identify the advantages and disadvantages of the improved SVR algorithm. The results show that the root-mean-square error (RMSE) of the traditional AR, the classical SVR and the improved SVR algorithms is 0.60, 0.45, and 0.38, and the mean absolute percentage error (MAPE) of the three algorithms is 7.79%, 6.10% and 4.78%, respectively. The prediction error of the improved SVR algorithm is 37% less than that of the traditional AR algorithm and 15% less than that of the classical SVR algorithm, signifying an improvement in its prediction capability.

An idealized model is used to simulate radio occultation bending angles and residual ionospheric errors. The test results of the proposed simulation method agree with those of previous studies that use end-to-end simulation tools. Also, a new residual ionospheric error model proposed by Healy and Culverwell (2015) is verified in this letter by characterizing the key parameter, κ. A simple model, κ(a) = A − B × (a − 20)/60, is used to estimate the values of κ, where A and B are constants that indicate the magnitude and variation of the values of κ, respectively, and a represents the impact height. When the modelled values of κ are applied in performing ionospheric corrections, the residual ionospheric errors decrease from approximately 5 × 10−8 rad to 1 × 10−9 rad at a latitude of 40°N during the daytime and at a solar activity level of F10.7 = 210. Though the proposed model does not assess other error terms, such as those associated with asymmetry and noise, it will likely prove to be an effective tool for describing idealized residual ionospheric errors in radio occultation, and the features of the κ values identified in this study may be helpful in improving ionospheric correction methods.

Using temperature data measured by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument from February 2002 to March 2020, the temperature linear trend and temperature responses to the solar cycle (SC), Quasi-Biennial Oscillation (QBO), and El Niño-Southern Oscillation (ENSO) were investigated from 20 km to 110 km for the latitude range of 50°S-50°N. A four-component harmonic fit was used to remove the seasonal variation from the observed monthly temperature series. Multiple linear regression (MLR) was applied to analyze the linear trend, SC, QBO, and ENSO terms. In this study, the near-global mean temperature shows consistent cooling trends throughout the entire middle atmosphere, ranging from -0.28 to -0.97 K/decade. Additionally, it shows positive responses to the solar cycle, varying from -0.05 to 4.53 K/100sfu. A solar temperature response boundary between 50°S and 50°N is given, above which the atmospheric temperature is strongly affected by solar activity. The boundary penetrates deep below the stratopause to ~ 42 km over the tropical region and rises to higher altitudes with latitude. Temperature responses to the QBO and ENSO can be observed up to the upper mesosphere and lower thermosphere. In the equatorial region, 40%-70% of the total variance is explained by QBO signals in the stratosphere and 30%-50% is explained by the solar signal in the upper middle atmosphere. Our results, obtained from 18-year SABER observations, are expected to be an updated reliable estimation of the middle atmosphere temperature variability for the stratospheric ozone recovery period.

New satellite-derived latent and sensible heat fluxes are performed by using WindSat wind speed, WindSat sea surface temperature, the European Centre for Medium-range Weather Forecasting (ECMWF) air humidity, and ECMWF air temperature from 2004 to 2014. The 55 moored buoys are used to validate them by using the 30 min and 25 km collocation window. Furthermore, the objectively analyzed air-sea heat fluxes (OAFlux) products and the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis 2 (NCEP2) products are also used for global comparisons. The mean biases of sensible and latent heat fluxes between WindSat flux results and buoy flux data are–0.39 and–8.09 W/m2, respectively. In addition, the root-mean-square (RMS) errors of the sensible and latent heat fluxes between them are 5.53 and 24.69 W/m2, respectively. The RMS errors of sensible and latent heat fluxes are observed to gradually increase with an increasing buoy wind speed. The difference shows different characteristics with an increasing sea surface temperature, air humidity, and air temperature. The zonal average latent fluxes have some high regions which are mainly located in the trade wind zones where strong winds carry dry air in January, and the maximum value centers are found in the eastern waters of Japan and on the US east coast. Overall, the seasonal variability is pronounced in the Indian Ocean, the Pacific Ocean, and the Atlantic Ocean. The three sensible and latent heat fluxes have similar latitudinal dependencies; however, some differences are found in some local regions.

The extraction of auroral ovals from images acquired by the Ultraviolet Imager is notoriously difficult due to low contrast, inhomogeneity and the presence of dayglow. In this paper, to address these issues, we propose an improved level set segmentation algorithm by incorporating the shape feature and intensity distribution of the auroral oval into the variational framework. The shape term is proposed to keep the annular ring appearance of the auroral oval and avoid the boundary leak by imposing a distance constraint on the inner and outer boundaries. The local information term tackles the difficulty of intensity inhomogeneity by utilizing the statistical distribution in the local window. Experimental results demonstrate that the proposed method obtains more accurate inner and outer boundaries of the auroral oval, comparing to the existing pixel based and level set based methods.

The problem of atmospheric duct inversion is usually solved as a single objective optimization problem. Based on ground-based Global Positioning System (GPS) phase delay and propagation loss, this paper develops a multi-objective method including the effect of source frequency and receiving antenna height. The diversity and convergence of solution sets are evaluated for seven multi-objective evolutionary algorithms with three performance metrics: Hypervolume (HV), Inverted Generational Distance (IGD), and the averaged Hausdorff distance ( Δ 2 ). The inversion results are compared with the simulation results, and the experimental comparison is conducted on three groups of test situations. The results demonstrate that the ranking of algorithm performance varies because of the different methods used to calculate performance metrics. Moreover, when the algorithms show overwhelming performance using performance metrics, the inversion result is not more close to the real value. In the comparison of computational experiments, it was found that, as the retrieved parameter dimension increases, the inversion result becomes more unstable. When the observed data are sufficient, the inversion result seems to be improved.

This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s−1 and 0.88°C, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the QuikSCAT measurements, and the RMS error was 13.3°. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.

Nearshore wind speeds retrieved by WindSat are validated by a comparison with the moored buoy observations near the U.S. west and east coasts. A 30 min and 25 km collection window is used for the WindSat wind data and buoy measurements from January 2004 to December 2014. Comparisons show that the overall root-mean-square error is better than 1.44 m/s near the U.S. coasts, and the result for the east coast is better than that for the west coast. The retrieval accuracy of the descending portions is slightly better than that of the ascending portions. Most buoy-to-buoy variations are not significantly correlated with the coastal topography, the longitude and the distance from the shore or satellite-buoy separation distance. In addition, comparisons between a polarimetric microwave radiometer and a microwave scatterometer are accomplished with the nearshore buoy observations from 2007 to 2008. The WindSat-derived winds tend to be lower than the buoy observations near the U.S. coasts. In contrast, the QuikSCAT-derived winds tend to be higher than the buoy observations. Overall, the retrieval accuracy of WindSat is slightly better than that of QuikSCAT, and these satellite-derived winds are sufficiently accurate for scientific studies.

To evaluate the ocean surface wind vector and the sea surface temperature obtained from WindSat, we compare these quantities over the time period from January 2004 to December 2013 with moored buoy measurements. The mean bias between the WindSat wind speed and the buoy wind speed is low for the low frequency wind speed product (WSPD_LF), ranging from–0.07 to 0.08 m/s in different selected areas. The overall RMS error is 0.98 m/s for WSPD_LF, ranging from 0.82 to 1.16 m/s in different selected regions. The wind speed retrieval result in the tropical Ocean is better than that of the coastal and offshore waters of the United States. In addition, the wind speed retrieval accuracy of WSPD_LF is better than that of the medium frequency wind speed product. The crosstalk analysis indicates that the WindSat wind speed retrieval contains some cross influences from the other geophysical parameters, such as sea surface temperature, water vapor and cloud liquid water. The mean bias between the WindSat wind direction and the buoy wind direction ranges from–0.46° to 1.19° in different selected regions. The overall RMS error is 19.59° when the wind speed is greater than 6 m/s. Measurements of the tropical ocean region have a better accuracy than those of the US west and east coasts. Very good agreement is obtained between sea surface temperatures of WindSat and buoy measurements in the tropical Pacific Ocean; the overall RMS error is only 0.36°C, and the retrieval accuracy of the low latitudes is better than that of the middle and high latitudes.

The traditional method of retrieving atmospheric ducts is to use the special sensor of weather balloons or rocket soundings to obtain information intelligently, and it is very expensive. Today, with the development of technology, it is very convenient to retrieve the atmospheric ducts from Global Navigation Satellite System (GNSS) phase delay and propagation loss observation data, and then the GNSS receiver on the ground forms an automatic receiving sensor. This paper proposes a hybrid decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes (EN-MOEA/ACD-NS), which dynamically imposes some constraints on the objectives. The decomposition-based multi-objective evolutionary algorithm (MOEA/D) updates the solutions through neighboring objectives, the number of which affects the quality of the optimal solution. Properly constraining the optimization objectives can effectively balance the diversity and convergence of the population. The experimental results from the Congress on Evolutionary Computation (CEC) 2009 on test instances with hypervolume (HV), inverted generational distance (IGD), and average Hausdorff distance ∆2 metrics show that the new method performs similarly to the evolutionary algorithm MOEA/ACD-NS, which considers only the dynamic change of the neighborhood sizes. The improved algorithm is applied to the practical problem of jointly retrieving atmospheric ducts with GNSS signals, and its performance further demonstrates its feasibility and practicability.

The propagation of Global Positioning System (GPS) signals at low-elevation angles is significantly affected by a surface duct. In this paper, we present an improved algorithm known as NSSAGA, in which simulated annealing (SA) is combined with the non-dominated sorting genetic algorithm II (NSGA-II). Matched-field processing was used to remotely sense the refractivity structure by using the data of ground-based GPS phase delay and propagation loss from multiple antenna heights. The performance was checked by simulation data with and without noise. In comparison with NSGA-II, the new hybrid algorithm retrieved the refractivity structure more efficiently under various noise conditions. We then modified the objective function and found that matched-field processing is more effective than the conventional least-squares method for inferring the refractivity parameters. Comparing the inversion results and in situ sounding data suggests that the improved method presented herein can capture refractivity characteristics in realistic environments.