Miracle-dz/UniFAR-FTUs huggingface dataset:
Temperature Effects on Mitochondria from Hydrothermal Vent Invertebrates: Evidence for Adaptation to Elevated and Variable Habitat Temperatures

The effects of elevated temperatures on mitochondrial respiration and the activities of selected mitochondrial enzymes were measured for several invertebrates endemic to the deep-sea hydrothermal vents to determine whether the differences in distribution patterns noted among these species are related to differences in temperature adaptation. Parallel studies were done with related (congeneric or confamilial) species from shallow marine habitats. Mitochondrial respiration of hydrothermal vent species living in zones with high water flux (high, variable temperatures), including the large tube worm Riftia pachyptila, the Pompeii worm Alvinella pompejana, and the brachyuran crab Bythograea thermydron, was more resistant to high temperatures than was mitochondrial respiration of hydrothermal vent species or shallow-living species in cooler waters, for example, the vent clam Calyptogena magnifica and mussel Bathymodiolus thermophilus. The temperatures at which Arrhenius plots of respiration rate exhibited sharp breaks were correlated with the apparent maximal habitat temperature of the species. The apparent Arrhenius activation energies of respiration also correlated with maximal habitat temperature, although not as closely as the Arrhenius break temperatures. Temperature inactivation of mitochondrial enzymes generally adhered to the pattern noted in the interspecific comparisons of mitochondrial respiration. These findings suggest that diferences in thermal adaptation exist among endemic vent species and that these differences are correlated with the microhabitat conditions the diferent species encounter at the vent sites.
Quantum phases of atomic Fermi gases with anisotropic spin-orbit coupling

We consider a general anisotropic spin-orbit coupling and analyze the phase diagrams of both balanced and imbalanced Fermi gases for the entire BCS-BEC evolution. In the first part, we use the self-consistent mean-field theory at zero temperature, and show that the topological structure of the ground-state phase diagrams is quite robust against the effects of anisotropy. In the second part, we go beyond the mean-field description, and investigate the effects of Gaussian fluctuations near the critical temperature. This allows us to derive the time-dependent Ginzburg-Landau theory, from which we extract the effective mass of the Cooper pairs and their critical condensation temperature in the molecular BEC limit.
Methodical characterization of rice ( Oryza sativa ) bran oil from Pakistan

The hexane-extracted oil content of four varieties of rice ( Oryza sativa ) viz. Super Kernel, 386, 385 and Basmati, bran was ranged 14.70-19.10 %. Other physical and chemical parameters of the extracted oils were as follow: Iodine value 112.40, 109.80, 105.1 and 103.70; refractive index ( 40 °C ) 1.4650, 1.4680, 1.4657 and 1.4660; density ( 40 °C ) 0.919, 0.913, 0.909 and 0.911; saponification value 183, 177, 186 and 190; unsaponifiable matter 6.15, 5.60, 4.98 and 5.40 % respectively. Tocopherols ( α, γ and δ) in the oils were: 284.00, 175.12, 180.42, 300.06; 83.40, 98.70, 120.70, 90.60; 75.16, 57.20, 39.32, 83.00 mg/kg respectively. The contents of tocotrienols ( α, γ and δ) in the oils were: 120.30, 106.00, 95.20, 135.74; 196.00, 125.00, 210.0, 276.41; 72.50, 20.00, 39.30, 64.00 mg/kg respectively. The amount of γ - Oryzanol in the investigated oils was ranged 415.12-802.05 The induction periods (Rancimat, 20 L/h, 120 °C ) of the crude oils were 6.81, 5.99, 6.39 and 7.40 h respectively. The major sterol fractions of the oils consisted of campesterol ranged (10.10-19.20%), stigmasterol (14.00-19.28 %), b -sitosterol (49.30-58.20 %), and D5 ,avenasterol (8.14-13.05 %). The investigated varieties ( Super Kernel, 386, 385 and Basmati) of rice bran oil were found to contain high levels of oleic acid 42.67, 38.59, 40.68 and 36.78 % followed by linoleic and palmitic acids 31.58, 33.80, 28.70, 30.51 and 17.00, 14.88, 19.63, 20.00 % respectively. The contents of myristic, stearic and arachidic acids was 1.50, 2.02, 4.28, 1.00; 2.64, 2.87, 4.02, 7.48; and 1.28, 3.00, 1.00, 1.00 % respectively. A number of parameters of the investigated rice bran oils indigenous to Pakistan were comparable to those of typical rice bran and some other vegetable oils, reported in the literature. The results of the present analysis as compared with those of different vegetable oils demonstrated rice bran to be a potential oil source and thus could be useful for the establishment of a globalized database of this valuable crop.
Animal Detection Using Thermal Images and Its Required Observation Conditions

: Information about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it is difﬁcult to apply such methods to large areas. To address this problem, we have developed several support systems for the automated detection of wild animals in remote sensing images. In this study, we applied one of the developed algorithms, the computer-aided detection of moving wild animals (DWA) algorithm, to thermal remote sensing images. We also performed several analyses to conﬁrm that the DWA algorithm is useful for thermal images and to clarify the optimal conditions for obtaining thermal images (during predawn hours and on overcast days). We developed a method based on the algorithm to extract moving wild animals from thermal remote sensing images. Then, accuracy was evaluated by applying the method to airborne thermal images in a wide area. We found that the producer’s accuracy of the method was approximately 77.3% and the user’s accuracy of the method was approximately 29.3%. This means that the proposed method can reduce the person-hours required to survey moving wild animals from large numbers of thermal remote sensing images. Furthermore, we conﬁrmed the extracted sika deer candidates in a pair of images and found 24 moving objects that were not identiﬁed by visual inspection by an expert. Therefore, the proposed method can also reduce oversight when identifying moving wild animals. The detection accuracy is expected to increase by setting suitable observation conditions for surveying moving wild animals. Accordingly, we also discuss the required observation conditions. The discussions about the required observation conditions would be extremely useful for people monitoring animal population changes using thermal remote sensing images.
Remote Sensing Images Stripe Noise Removal by Double Sparse Regulation and Region Separation

Stripe noise removal continues to be an active field of research for remote image processing. Most existing approaches are prone to generating artifacts in extreme areas and removing the stripe-like details. In this paper, a weighted double sparsity unidirectional variation (WDSUV) model is constructed to reduce this phenomenon. The WDSUV takes advantage of both the spatial domain and the gradient domain’s sparse property of stripe noise, and processes the heavy stripe area, extreme area and regular noise corrupted areas using different strategies. The proposed model consists of two variation terms and two sparsity terms that can well exploit the intrinsic properties of stripe noise. Then, the alternating direction method of multipliers (ADMM) optimal solver is employed to solve the optimization model in an alternating minimization scheme. Compared with the state-of-the-art approaches, the experimental results on both the synthetic and real remote sensing data demonstrate that the proposed model has a better destriping performance in terms of the preservation of small details, stripe noise estimation and in the mean time for artifacts’ reduction.
Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST

Afromontane forests are biodiversity hotspots and provide essential ecosystem services. However, they are under pressure as a result of an expanding human population and the impact of climate change. In many instances electric fencing has become a necessary management strategy to protect forest integrity and reduce human-wildlife conflict. The impact of confining hitherto migratory elephant populations within forests remains unknown, and monitoring largely inaccessible areas is challenging. We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests. Results indicated that BFAST was able to identify disturbances from anthropogenic, fire and elephant damage. Sequential monitoring enabled the detection of gradual changes in the forest canopy, with degradation and regeneration being observed in both sites. Annual rates of forest loss in both areas were significantly lower than reported in other studies on Afromontane forests, suggesting that installing fences has reduced land-use conversion from human-related disturbances. Negative changes in EVI were predominantly gradual degradation rather than large-scale, abrupt clearings of the forest. Results presented here demonstrate that BFAST can be used to monitor biotic and abiotic drivers of change in Afromontane forests.
Effect of Tree Phenology on LiDAR Measurement of Mediterranean Forest Structure

Retrieval of forest biophysical properties using airborne LiDAR is known to differ between leaf-on and leaf-off states of deciduous trees, but much less is understood about the within-season effects of leafing phenology. Here, we compare two LiDAR surveys separated by just six weeks in spring, in order to assess whether LiDAR variables were influenced by canopy changes in Mediterranean mixed-oak woodlands at this time of year. Maximum and, to a slightly lesser extent, mean heights were consistently measured, whether for the evergreen cork oak (Quercus suber) or semi-deciduous Algerian oak (Q. canariensis) woodlands. Estimates of the standard deviation and skewness of height differed more strongly, especially for Algerian oaks which experienced considerable leaf expansion in the time period covered. Our demonstration of which variables are more or less affected by spring-time leafing phenology has important implications for analyses of both canopy and sub-canopy vegetation layers from LiDAR surveys.
Changes in the Lake Area of Tonle Sap: Possible Linkage to Runoff Alterations in the Lancang River?

Tonle Sap Lake is the largest freshwater lake in Southeast Asia. Water development infrastructures are increasingly being constructed in the Lancang–Mekong River Basin, which is a major concern considering its potential impact on Tonle Sap Lake. This study aimed to investigate variations in the area of the lake and discuss their possible linkage to runoff alterations in the Lancang River (Upper Mekong) by comparing runoff at the Yunjinghong hydrological station before and after significant changes in runoff trends that occurred in 2008. First, four commonly used water body extraction methods (MNDWI, NDWI, NDVI, and EVI) were compared and MNDWI was found to provide a better and more stable performance. Based on MOD09A1 data, MNDWI was used to extract the water area of the lake from 2000 to 2014, and characteristics of variations in the area before and after 2008 were analyzed. The water area of Tonle Sap Lake displayed an overall decreasing trend, and specifically decreased by 8.3% during the flood season and by 1.5% on average during the dry season after 2008. Seasonal variations in the water area of Tonle Sap Lake were dominantly influenced by runoff from the Mekong River. Compared with the period 2000–2007, runoff at Yunjinghong station were increased during the dry season (20.74%) and decreased during the flood season (34.25%) between 2008 and 2014. Changes in upstream runoff contributed to runoff at the Stung Treng station in the lower Mekong River by 6.17% (dry season) and −2.41% (flood season). Evidently, the operation of dams in the Lancang River does not primarily account for the area decrease of Tonle Sap Lake during the flood season. In contrast, runoff increase during the dry season mitigates the area decrease of Tonle Sap Lake to a certain extent.
Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.
Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).
Impacts of Agricultural Expansion (1910s-2010s) on the Water Cycle in the Songneng Plain, Northeast China

Agricultural expansion is one of the primary land use changes on the Earth’s surface. The Songnen Plain in Northeast China is renowned for its Black Soil and is one of the most important agricultural regions of this country. In the last century, its population increased 20-fold and excessive areas of grassland were cultivated. Based on a series of decadal land use/land cover data sets in the plain (1910s–2010s), this study simulated the water balance in each decade using the Weather Research and Forecasting (WRF) model and assessed the water effects of centurial agricultural expansion. Six variables were simulated to explain the land-atmosphere interaction: precipitation, total evapotranspiration, canopy transpiration, canopy interception evaporation, land evaporation and land surface runoff and infiltration. Agreeing with historical climate reanalysis data, the simulated precipitation in the plain did not have a significant trend. However, the total evapotranspiration significantly increased in the study region. The canopy transpiration and interception evaporation increased and the runoff and infiltration decreased, both indicating a drought effect in soil. The drying trend varied spatially with the strongest pattern in the central plain where large areas of wetlands remain. As a consequence of agricultural expansion, the centurial drying process in the fertile Black Soil may put strong pressure on the crop productivity and food safety of this important agricultural region.
The Magnitude of Diurnal/Semidiurnal Atmospheric Tides (S1/S2) and Their Impacts on the Continuous GPS Coordinate Time Series

The site displacement due to diurnal and semidiurnal atmospheric tides (S1/S2) is often neglected in the routine precise GPS data processing. We recall the S1/S2 modeling method and show the magnitude of the S1/S2 tides under the Center of Mass (CM) frame. The results show that the annual amplitudes caused by both S1 and S2 tides exceed 1 mm for stations near the equator. The impact of S1/S2 on the 24-h Global Positioning System (GPS) solution is typically at the sub-mm level, and the scatter of the S1/S2 caused displacement difference increases with the decreasing latitude for northern hemisphere stations, among which the maximum Standard Deviation (STD) reaches up to 1.5 mm, 1 mm and 0.7 mm for the Up, East and North components, respectively, at low-latitude stations. We also find that 65% of the stations’ vertical velocity change caused by S1/S2 is larger than 1%, among which the maximum velocity variation rate reaches more than 40% for some coastal/island stations, while stations with the weighted root mean square reduced account for 65%, 39%, and 38% for the up, east, and north components respectively, in particular for most coastal/island stations. Furthermore, the S1/S2 correction could partly reduce the annual and the semi-annual signals of the global stacked vertical component together with the semi-annual amplitude of the east component. The power of some anomalous harmonics of 1.04 cycle per year also decreased a lot. These results further prove the benefits of S1/S2 correction in the precise GPS data processing.
An Exploration of Terrain Effects on Land Surface Phenology across the Qinghai-Tibet Plateau Using Landsat ETM+ and OLI Data

: Detecting spatial patterns of land surface phenology (LSP) with high spatial and temporal resolutions is crucial for accurately estimating phenological response and feedback to climate change and biogeochemical cycles. Numerous studies have revealed LSP across the Qinghai–Tibet Plateau (QTP) using a variety of coarse-resolution satellite data. However, detailed phenological spatial patterns along with changes of mountain topography remain poorly understood, which greatly limits efforts to predict the impacts of climate change on vegetation growth and ecosystem productivity in complex terrain regions. Combining Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8 OLI (Operational Land Imager) observations in overlapping zones of adjacent images, this study detected Normalized Difference Vegetation Index (NDVI)-based LSP metrics at a spatial resolution of 30 m, and explored how LSP varied with topographic factors along a 2600 km belt transect of the central QTP. The results show that the greenup onset date showed a delayed tendency with the increase of elevation at a mean rate of 1.52 days/100 m, while the dormancy onset date indicated an advanced tendency at a mean rate of − 0.59 days/100 m. In general, greenup onset date was later but dormancy onset date was earlier on shaded slopes than on sunlit slopes in the meadow area. By contrast, greenup onset date did not signiﬁcantly depend on aspect in the steppe area, while dormancy onset date indicated a similar response to aspect in the steppe area with that in the meadow area. With regard to the effect of slope on vegetation phenology in the meadow area, greenup onset date was signiﬁcantly delayed but dormancy onset date signiﬁcantly advanced with the increase of slope on both north and south slopes. In the steppe area, however, the inﬂuence pattern of slope on vegetation phenology was the opposite. Essentially, effects of topographical parameters on LSP were controlled by temperature and moisture combinations in complex terrain.
Quantitative Assessment of Digital Image Correlation Methods to Detect and Monitor Surface Displacements of Large Slope Instabilities

We evaluate the capability of three different digital image correlation (DIC) algorithms to measure long-term surface displacement caused by a large slope instability in the Swiss Alps. DIC was applied to high-resolution optical imagery taken by airborne sensors, and the accuracy of the displacements assessed against global navigation satellite system measurements. A dynamic radiometric correction of the input images prior to DIC application was shown to enhance both the correlation success and accuracy. Moreover, a newly developed spatial filter considering the displacement direction and magnitude proved to be an effective tool to enhance DIC performance and accuracy. Our results show that all algorithms are capable of quantifying slope instability displacements, with average errors ranging from 8 to 12% of the observed maximum displacement, depending on the DIC processing parameters, and the pre- and postprocessing of the in- and output. Among the tested approaches, the results based on a fast Fourier transform correlation approach provide a considerably better spatial coverage of the displacement field of the slope instability. The findings of this study are relevant for slope instability detection and monitoring via DIC, especially in the context of an ever-increasing availability of high-resolution air- and spaceborne imagery.