Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Dimensional Structure Inversion of Buildings with Nonparametric Iterative Adaptive Approach Using SAR Tomography

Synthetic aperture radar tomography (TomoSAR) is a useful tool for retrieving the three-dimensional structure of buildings in urban areas, especially for datasets with a high spatial resolution. However, among the previous TomoSAR estimators, some cannot retrieve the 3-D structure of objects with a high elevation resolution, some cannot maintain the spatial resolution, and some require the selection of a hyperparameter. To overcome these limitations, this paper proposes a new nonparametric iterative adaptive approach with a model selection tool based on the Bayesian information criterion (IAA-BIC) for the application of TomoSAR in urban areas. IAA-BIC employs weighted least squares to acquire a high elevation resolution and works well for both distributed and coherent scatterers, even with single-look. Concurrently, IAA-BIC does not require the user to make any difficult selection regarding a hyperparameter. The proposed IAA-BIC estimator was tested in simulated experiments, and the results confirmed the advantages of the IAA-BIC estimator. Moreover, the three-dimensional structure of the Hubei Science and Technology Venture building in Wuhan, China, was retrieved through the IAA-BIC method with nine very high spatial resolution TerraSAR-X images. The height estimation accuracy for this building was about 1% and 4% relative to its real height for single-look and multi-look, respectively. In addition, a comparison between the IAA-BIC estimator and some of the typical existing TomoSAR estimators (Capon, MUSIC, and compressed sensing (CS)) was also carried out. The results indicate that the IAA-BIC estimator obtains a better resolution for coherent sources than Capon and MUSIC; notably, the IAA-BIC estimator obtains a similar performance to CS, but in less computation time.
Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling

Forest biophysical variables derived from remote sensing observations are vital for climate research. The combination of structurally and radiometrically accurate 3D “virtual” forests with radiative transfer (RT) models creates a powerful tool to facilitate the calibration and validation of remote sensing data and derived biophysical products by helping us understand the assumptions made in data processing algorithms. We present a workflow that uses highly detailed 3D terrestrial laser scanning (TLS) data to generate virtual forests for RT model simulations. Our approach to forest stand reconstruction from a co-registered point cloud is unique as it models each tree individually. Our approach follows three steps: (1) tree segmentation; (2) tree structure modelling and (3) leaf addition. To demonstrate this approach, we present the measurement and construction of a one hectare model of the deciduous forest in Wytham Woods (Oxford, UK). The model contains 559 individual trees. We matched the TLS data with traditional census data to determine the species of each individual tree and allocate species-specific radiometric properties. Our modelling framework is generic, highly transferable and adjustable to data collected with other TLS instruments and different ecosystems. The Wytham Woods virtual forest is made publicly available through an online repository.
Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential

Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre- and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: The delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection but instead use a mean compositing approach in which all valid pixels (e.g., cloud-free) over a pre-specified date range (pre- and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared to the standard approach in which one pre-fire and one post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western United States. These validations are compared to Landsat-based fire severity datasets produced using only one pre- and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets generally show improved validation statistics compared to parallel versions in which only one pre-fire and one post-fire scene are used, though some of the improvements in some validations are more or less negligible. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e., fire perimeters) be produced independently and prior to applying our methods, we suggest that our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.
Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy.
TEC Forecasting Based on Manifold Trajectories

: In this paper, we present a method for forecasting the ionospheric Total Electron Content 1 (TEC) distribution from the International GNSS Service’s Global Ionospheric Maps. The forecasting 2 system gives an estimation of the value of the TEC distribution based on linear combination of 3 previous TEC maps (i.e. a set of 2D arrays indexed by time), and the computation of a tangent 4 subspace in a manifold associated to each map. The use of the tangent space to each map is justiﬁed 5 because it allows to model the possible distortions from one observation to the next as a trajectory on 6 the tangent manifold of the map. The coefﬁcients of the linear combination of the last observations 7 along with the tangent space are estimated at each time stamp in order to minimize the mean square 8 forecasting error with a regularization term. The the estimation is made at each time stamp to adapt 9 the forecast to short-term variations in solar activity. 10
Interdependent Dynamics of LAI-Albedo across the Roofing Landscapes: Mongolian and Tibetan Plateaus

The Mongolian Plateau (MP) and Tibetan Plateau (TP) have experienced higher-than-global average warming in recent decades, resulting in many significant changes in ecosystem structure and function. Among them are the leaf area index (LAI) and albedo, which play a fundamental role in understanding many causes and consequences of land surface processes and climate. Here, we focused on the spatiotemporal changes of LAI, albedo, and their spatiotemporal relationships on the two roofing landscapes in Eurasia. Based on the MODIS products, we investigated the spatiotemporal changes of albedo(VIS, NIR and SHO) and LAI from 2000 through 2016. We found that there existed a general negative logarithmic relationship between LAI and three measures of albedo on both plateaus. No significant relationship was found for LAI-albedoNIR on the TP, due to more complex land surface canopy characteristics affected by the NIR reflection there. During 2000–2016, overall, annual mean LAI increased significantly by 119.40 × 103 km2 on the MP and by 28.35 × 103 km2 on the TP, while the decreased areas for annual mean albedoVIS were 585.59 × 103 km2 and 235.73 × 103 km2 on the MP and TP, respectively. More importantly, the LAI-albedo relationships varied substantially across the space and over time, with mismatches found in some parts of the landscapes. Substantial additional efforts with observational and/or experimental investigations are needed to explore the underlying mechanisms responsible for these relationships, including the influences of vegetation characteristics and disturbances.
Designing NeuroSimVR: A Stereoscopic Virtual Reality Spine Surgery Simulator

This paper contributes NeuroSimVR, a stereoscopic virtual reality spine surgery simulator that allows novice surgeons to learn and practice a spinal pedicle screw insertion (PSI) procedure using simplified interaction capabilities and 3D haptic user interfaces. By collaborating with medical experts and following an iterative approach, we provide characterization of the PSI task, and derive requirements for applying this procedure in a 3D immersive interactive simulation system. We describe how these requirements were realized in our NeuroSimVR prototype, and outline the educational benefits of our 3D interactive system for training the PSI procedure. We conclude the paper with the results of a preliminary evaluation of NeuroSimVR and reflect on our interface benefits and limitations.
The Behavioral Level of Emotional Intelligence and Its Measurement

Emotional Intelligence (EI) is now widely used in organizations and graduate schools with an increase in published research supporting it. Discussion about EI whether based on measures or theory has given little distinction as to behavioral EI (i.e., how does EI appear in a person’s actions). This results in spurious conflicts about the validity of the different theories or measures which likely limit predicting managerial and leadership effectiveness, engagement, innovation and organizational citizenship. By adding a behavioral level, the concept of EI could relate to work and life outcomes beyond general mental ability and personality traits, avoid some of the criticisms while providing a more holistic theory of EI. As such, EI exists within personality as a performance trait or ability, and a self-schema self-image and trait, and a set of behaviors (i.e., competencies). The main contribution of this establishing the behavioral EI with a multi-level theory, while explaining how to assess it, the benefits of such a concept and its psychometric validity and challenges. The history and assortment of validation studies will illustrate that measures can rigorously and effectively assess the behavioral level of EI.
A Maximum-Likelihood Approach to Estimating the Insertion Frequencies of Transposable Elements from Population Sequencing Data

Abstract Transposable elements (TEs) contribute to a large fraction of the expansion of many eukaryotic genomes due to the capability of TEs duplicating themselves through transposition. A first step to understanding the roles of TEs in a eukaryotic genome is to characterize the population-wide variation of TE insertions in the species. Here, we present a maximum-likelihood (ML) method for estimating allele frequencies and detecting selection on TE insertions in a diploid population, based on the genotypes at TE insertion sites detected in multiple individuals sampled from the population using paired-end (PE) sequencing reads. Tests of the method on simulated data show that it can accurately estimate the allele frequencies of TE insertions even when the PE sequencing is conducted at a relatively low coverage (=5X). The method can also detect TE insertions under strong selection, and the detection ability increases with sample size in a population, although a substantial fraction of actual TE insertions under selection may be undetected. Application of the ML method to genomic sequencing data collected from a natural Daphnia pulex population shows that, on the one hand, most (>90%) TE insertions present in the reference D. pulex genome are either fixed or nearly fixed (with allele frequencies >0.95); on the other hand, among the nonreference TE insertions (i.e., those detected in some individuals in the population but absent from the reference genome), the majority (>70%) are still at low frequencies (<0.1). Finally, we detected a substantial fraction (∼9%) of nonreference TE insertions under selection.
An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH4: 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.
Functions of the C2H2 Transcription Factor Gene thmea1 in Trichoderma harzianum under Copper Stress Based on Transcriptome Analysis

Trichoderma spp. are important biocontrol filamentous fungi and have tremendous potential in soil bioremediation. In our previous studies, a C2H2 type transcription factor coding gene (thmea1) was cloned from a biocontrol agent T. harzianum Th-33; the encoded sequence of thmea1 contained 3 conserved C2H2 domains with Swi5 and Ace2 in Saccharomyces cerevisiae. The thmea1 knockout mutant Δthmea1 showed 12.9% higher copper tolerance than the wild-type Th33. To elucidate the function of thmea1 and its relationship with copper stress response, we conducted transcriptome sequencing and analysis of wild-type Th33 and Δthmea1 under 0.8 mM copper stress. A total of 1061 differentially expressed genes (DEGs) were identified between the two strains, all DEGs were assigned to KEGG pathway database, 383 DEGs were annotated in 191 individual pathways, and the categories of ribosomal protein synthesis and amino acid metabolism were the most highly enriched ones. Analysis of related DEGs showed that the expression levels of intracellular glutathione detoxification enzyme, heat shock proteins, and ribosomal proteins in Δthmea1 were higher than that of the wild-type Th33, and the expression of metallothionein (MT) gene did not change. In addition, the expression levels of genes coding for proteins associated with the Ccc2p-mediated copper chaperone Atx1p transport of copper ions into the Golgi secretory pathway increased, as well as the copper amine oxidase (CuAO). These findings suggest that Thmea1 is a negative regulated factor of copper tolerance ability in T. harzianum. It does not show metallothionein expression activator activities as that of Ace2 in S. cerevisiae. We hypothesize that after T. harzianum has lost its thmea1 gene, the ability of cells to scavenge reactive oxygen species, mainly through the glutathione antioxidant system, is enhanced, whereas protein synthesis and repair and copper secretion increase under copper stress, which increases the ability of the mutant strain to tolerate copper stress.
Confined states in graphene quantum blisters

Bilayer graphene samples may exhibit regions where the two layers are locally delaminated forming a so-called quantum blister in the graphene sheet. Electron and hole states can be confined in this graphene quantum blisters (GQB) by applying a global electrostatic bias. We scrutinize the electronic properties of these confined states under the variation of interlayer bias, coupling, and blister’s size. The spectra display strong anti-crossings due to the coupling of the confined states on upper and lower layers inside the blister. These spectra are layer localized where the respective confined states reside on either layer or equally distributed. For finite angular momentum, this layer localization can be at the edge of the blister and corresponds to degenerate modes of opposite momenta. Furthermore, the energy levels in GQB exhibit electron-hole symmetry that is sensitive to the electrostatic bias. Finally, we demonstrate that confinement in GQB persists even in the presence of a variation in the inter-layer coupling.
Weight Loss in Parkinson's Disease: The Relationship with Motor Symptoms and Disease Progression

Objectives To determine the prevalence of weight loss (WL) in PD patients, its relationship to the severity of motor manifestations and appetite changes. Methods 144 PD patients and 120 controls were evaluated in a single session. All subjects were asked about changes in body weight and appetite. PD patients were examined with the UPDRS-III and the Hoehn and Yahr (HY) scales. Subscores of tremor, bradykinesia /rigidity, and non-dopaminergic symptoms (NDS) were analyzed individually. Multivariable logistic regression analysis was used to determine an association between WL and PD motor manifestations. Results 48.6 % of PD patients presented WL compared to 20.8 % of controls (p < 0.001). Weight losers were significantly older and had longer disease duration, higher scores in HY stages, UPDRS-III, and NDS-subscore. Multivariable logistic regression analysis demonstrated that WL was associated with NDS-subscore (p= 0.002; OR: 1.33) and older age (p= 0.037; OR: 1.05). Appetite in PD cases losing weight was unchanged (35.7 %), decreased (31.4 %), or even increased (32.9). Conclusions Our results showed that WL occurs in almost half of PD patients and it is largely the consequence of disease progression rather than involuntary movements or a decrease in food intake.
Substituting gelatine with Pluronic F-127 matrix in 3D polymer gel dosimeters can improve nuclear magnetic resonance, thermal and optical properties

This work discusses the substitution of a gelatine physical gel matrix with a matrix made of poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide) (Pluronic F-127) in five 3D radiotherapy polymer gel dosimeters: MAGAT, PAGAT, NIPAM, VIPARnd (VIP) and VIPARCT (VIC). The current research outcomes showed that not each polymer gel dosimeter could be manufactured with Pluronic F-127. Two of the polymer gel dosimeters (PAGAT and VIP) containing the Pluronic F-127 matrix allowed for some proper dose response for radiotherapy dosimetry (a response to a dose range of e.g. 0‒50 Gy). The new best performing Pluronic-based polymer gel dosimeters were characterised by improved nuclear magnetic resonance properties, when being compared to gels with gelatine matrix at the same monomer content. These are: (i) a ~33% higher dose sensitivity; (ii) a comparable or slightly higher linear and dynamic dose range and (iii) a lower (new VIP composition, VIP3) or equivocal (new PAGAT composition, PAGAT2-Pluronic) dose threshold. However, there might be optimised gelatine based polymer dosimeters demonstrating even better sensitivity. UV–vis spectrophotometry measurements revealed that Pluronic matrices ensure six-times lower (VIP3–Pluronic) and eight-times lower (PAGAT2-Pluronic) absorbance (at 400 nm) of non-irradiated gels compared to gelatine matrices, which makes the new polymer gel dosimeters optically improved in comparison to their corresponding gelatine-based compositions. The differences in absorption reduce for higher wavelengths. Differential scanning calorimetry measurements revealed the following temperature stability ranges for the gels: (i) VIP with gelatine matrix: 0 °C‒26 °C, (ii) VIP3 with Pluronic matrix: 13.8 °C–55.2 °C, (iii) PAGAT2 with gelatine matrix: 0 °C–80 °C and (iv) PAGAT2 with Pluronic matrix: 21.4 °C–55.2 °C. In conclusion, Pluronic F-127 is an attractive co-polymer to serve as a substitute for the gelatine matrix in some 3D polymer gel dosimeters.
Biologically guided flap stability: the role of flap thickness including periosteum retention on the performance of the coronally advanced flap–A double‐blind randomized clinical trial

AIM To evaluate the possible benefit on wound healing and flap stability of periosteum inclusion, comparing a "split-full-split" thickness flap elevation versus a "split" thickness approach performed during CAF for the treatment of isolated-type gingival recessions in the upper jaw. MATERIAL AND METHODS Forty patients were randomized, 20 were treated with "split-full-split" (test group) and 20 with a "split" approach (control group). Analysed parameters at 1 year were CRC, percentage of recession coverage (RC), keratinized tissue (KT) gain and patient-related outcome measurements. RESULTS After 12 months, CRC was 80% in the test group and 35% in the control group. Percentages of RC and KT gain were higher in the test group, and a significant association between CRC and the thickness of the flap after elevation was found. Patient-related outcomes measurements were better for the test group. CONCLUSIONS Flap thickness preservation and the presence of the periosteum in part of the flap may play a fundamental role in obtaining CRC.