Metabolomic analysis reveals spermatozoa and seminal plasma differences between Duroc and Liang guang Small-spotted pig

The Liang guang Small-spotted pig is a well-known Chinese indigenous pig that is valued for its exceptional meat quality. However, the Liang guang Small-spotted pig has a lower semen storage capacity, shorter storage time and worse semen quality compared to Duroc. Pig sperm used for artificial insemination (AI) loses part of vitality and quality when being stored in commercial solutions. Serious vitality losses and short shelf life of the semen are particularly prominent in Liang guang Small-spotted pig. In this study, the metabolites in seminal plasma and spermatozoa of Duroc and Liang guang Small-spotted pigs were identified using UHPLC–Q-TOF/MS technology. The findings indicated forty distinct metabolites concentrating on energy metabolic substrates and antioxidant capacity in Liang guang Small-spotted pig and Duroc seminal plasma, including D-Fructose, succinate, 2-dehydro-3-deoxy-d-gluconate, alanine betaine, citrate, carnitine, acetylcarnitine and so on. Seventeen different metabolites were explored, with a focus on glycerophospholipid metabolism in Liang guang Small-spotted pig and Duroc spermatozoa, primarily including glycerol 3-phosphate, acetylcarnitine, phosphatidylcholine (PC) 16:0/16:0, palmitoyl sphingomyelin, acetylcholine, choline, glycerophosphocholine, betaine, L-carnitine, creatinine and others. This study reveals the metabolite profile of spermatozoa and seminal plasma among different pig breeds and might be valuable for understanding the mechanisms that lead to sperm storage capacity. Metabolites involved in energy metabolism, antioxidant capacity and glycerophospholipid metabolism might be key to the poor sperm storage capacity in Liang guang Small-spotted pig

A patch filling method for thematic map refinement: a case study on forest cover mapping in the Greater Mekong Subregion and Malaysia

Accurate forest cover mapping is essential for monitoring the status of forest extent in Southeast Asia. However, tropical areas frequently experience cloud cover, resulting in invalid or missing data in thematic maps. The initial 2005 and 2010 forest cover maps produced by the collaboration of the Greater Mekong Subregion and Malaysia (GMS+) economies contain unclassified pixels in the areas affected by cloud or cloud shadow. To enhance the usability and effectiveness of the 2005 and 2010 GMS+ forest cover maps for further analysis and applications, we present a novel method for accurately mapping forest cover in the presence of cloud cover. We employed a pixel-based algorithm to create clear view composites and automatically generated land cover training labels from the existing forest cover maps. We then reclassified the invalid areas and produced updated maps. The land cover types for all previously missing pixels have been successfully reclassified. The accuracy of this method was assessed at both the pixel and region level, with an overall accuracy of 94.2% at the forest/non-forest level and 86.6% at the finer classification level by pixel level assessment across all reclassified patches, and 93.2% at the forest/non-forest level and 89.9% at the finer level by region level for the selected site. There are 2.6% of forest and 0.7% of non-forest areas in the 2005 map, as well as 2.7% of forest and 0.6% of non-forest in the 2010 map have been reclassified from invalid pixels. This approach provides a framework for filling invalid areas in the existing thematic map toward improving its spatial continuity. The updated outputs provide more accurate and reliable information than the initial maps on the status of forest extent in the GMS+, which is critical for effective forest management and sustainable use in the region

A study of ignition of timber by incandescent lamp

In this article, a study is reported on the ignition of timber board by an incandescent light. A set of experimental tests were conducted to demonstrate the possibility of such an event. Inference was then made to the possible cause of a fire incident that occurred at a building construction site a few years ago. Theoretical analysis was also conducted based on an ignition model. The model is able to correlate the time to ignition with the distance between the light bulb with a given power output and the timber board

Fermented Feed Modulates Meat Quality and Promotes the Growth of Longissimus Thoracis of Late-Finishing Pigs

This study investigated the effect of fermented diet on growth performance, carcass traits, meat quality and growth of longissimus thoracis (LT) of finishing pigs. A total of 48 finishing pigs [Duroc × (Landrace × Large White), male, 126 ± 5-d-old] weighing 98.76 ± 1.27 kg were randomly assigned to two treatments (eight pens per treatment and three pigs per pen) for a 28-d feeding trial, including control diet and fermented diet. Fermented diet significantly increased the loin eye area and lean mass percentage, decreased backfat thickness and improved meat quality of LT by decreasing the shear force and drip loss at 48 h post slaughter and improving meat sensory characteristics compared with control diet. A fermented diet also significantly increased the abundance of insulin, insulin receptor (IR), myoblast determination protein (MyoD) and myosin heavy chain-I (MyHC-I) transcripts, and the phosphorylation levels of AKT, mTORC1, 4EBP1 and S6K1 in LT, while decreasing the expression of muscle atrophy F-box (MAFbx) and forkhead Box O1 (Foxo1) mRNA transcripts. Moreover, proteomic analysis revealed that differentially expressed proteins predominantly involved in protein synthesis and muscle development were modulated by fermented diet. Our results indicated that a fermented diet improved meat quality and enhanced LT growth of finishing pigs by increasing insulin/AKT/mTORC1 protein synthesis cascade and activating the Foxo1/MAFbx pathway, along with the regulation of ribosomal protein and proteins involved in muscle contraction and muscle hypertrophy

Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China

A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China

A Split Window Algorithm for Retrieving Land Surface Temperature from FY-3D MERSI-2 Data

The thermal infrared (TIR) data from the Medium Resolution Spectral Imager II (MERSI-2) on the Chinese meteorological satellite FY-3D have high spatiotemporal resolution. Although the MERSI-2 land surface temperature (LST) products have good application prospects, there are some deviations in the TIR band radiance from MERSI-2. To accurately retrieve LSTs from MERSI-2, a method based on a cross-calibration model and split window (SW) algorithm is proposed. The method is divided into two parts: cross-calibration and LST retrieval. First, the MODTRAN program is used to simulate the radiation transfer process to obtain MERSI-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data, establish a cross-calibration model, and then calculate the actual brightness temperature (BT) of the MERSI-2 image. Second, according to the characteristics of the near-infrared (NIR) bands, the atmospheric water vapor content (WVC) is retrieved, and the atmospheric transmittance is calculated. The land surface emissivity is estimated by the NDVI-based threshold method, which ensures that both parameters (transmittance and emissivity) can be acquired simultaneously. The validation shows the following: 1) The average accuracy of our algorithm is 0.42 K when using simulation data; 2) the relative error of our algorithm is 1.37 K when compared with the MODIS LST product (MYD11A1); 3) when compared with ground-measured data, the accuracy of our algorithm is 1.23 K. Sensitivity analysis shows that the SW algorithm is not sensitive to the two main parameters (WVC and emissivity), which also proves that the estimation of LST from MERSI-2 data is feasible. In general, our algorithm exhibits good accuracy and applicability, but it still requires further improvement

A neural network technique for separating land surface emissivity and temperature from ASTER imagery

Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11,12,13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations can be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11,12,13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.005 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11,12,13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated. © 2007 IEEE

Driving forces of land surface temperature anomalous changes in North America in 2002-2018.

The land surface temperature (LST) changes in North America are very abnormal recently, but few studies have systematically researched these anomalies from several aspects, especially the influencing forces. After reconstructing higher quality MODIS monthly LST data (0.05° * 0.05°) in 2002-2018, we analyzed the LST changes especially anomalous changes and their driving forces in North America. Here we show that North America warmed at the rate of 0.02 °C/y. The LST changes in three regions, including frigid region in the northwestern (0.12 °C/y), the west coast from 20°N-40°N (0.07 °C/y), and the tropics south of 20°N (0.04 °C/y), were extremely abnormal. The El Nino and La Nina were the main drivers for the periodical highest and lowest LST, respectively. The North Atlantic Oscillation was closed related to the opposite change of LST in the northeastern North America and the southeastern United States, and the warming trend of the Florida peninsula in winter was closely related to enhancement of the North Atlantic Oscillation index. The Pacific Decadal Oscillation index showed a positive correlation with the LST in most Alaska. Vegetation and atmospheric water vapor also had a profound influence on the LST changes, but it had obvious difference in latitude