Miracle-dz/UniFAR-FTUs huggingface dataset:
Entomopathogens from Agricultural Soil and Their Potential to Control the Banded Fruit Weevil, Phlyctinus callosus (Schönherr) (Coleoptera: Curculionidae)

The banded fruit weevil (BFW), Phlyctinus callosus (Coleoptera: Curculionidae), is a major indigenous pest of deciduous fruits and grapevine in the Western Cape province of South Africa. Entomopathogens are effective in controlling soil-borne and above-ground stages of insect pests. Soil samples were collected from deciduous fruit orchards and vineyards in the Western Cape, baited with mealworms to isolate entomopathogenic nematodes (EPNs) and entomopathogenic fungi (EPF), which were identified using molecular techniques and assessed for their potential as biocontrol agents of BFW adults. A correlation analysis was performed to determine the effect of soil parameters on the presence of the entomopathogens. EPNs were trapped in 12 (17 %) samples, while Heterorhabditis bacteriophora and Heterorhabditis safricana were the only two species isolated. EPF were isolated from 26 (37 %) samples baited, with 14 fungal isolates identified as Beauveria bassiana (Cordycipitaceae) and 12 identified as Metarhizium anisopliae (Clavicipitaceae) complex. Heterorhabditis bacteriophora (53 %) gave significantly higher (P < 0.05) control of adult BFW than H. safricana (37 %). Metarhizium anisopliae (79 %) gave significantly higher (P < 0.05) control of BFW adults than B. bassiana (63 %). Organic matter, magnesium and phosphorus had a negative relationship with the occurrence of EPNs. A negative relationship was also reported between the occurrence of EPF, and organic matter and potassium. The results indicated that soils from the deciduous fruit orchards and vineyards from the Western Cape contain both EPNs and EPF with potential to be used in an integrated pest management system against soil-borne and above-ground stages of BFW.
Colony Foundation and the Development of Incipient Laboratory Colonies of Macrotermes natalensis (Haviland) (Termitidae: Macrotermitinae)

Alates of the fungus-growing termite Macrotermes natalensis (Haviland), a pest of building structures and forestry, were collected during swarming flights in Pretoria, South Africa (25°43′S 28°14′E), during the early summers of 1997 and 1998. Incipient colonies were reared in the laboratory with colony foundation and development and caste differentiation being observed over three months. Early colony mortality was high, with only a 4 % survival rate over three months. Oviposition began 4–12 days after pairing, with all females collected after a specific swarming flight starting to lay on the same day. The oviposition rate was initially high, with a mean of 17.85 (S.E. ± 1.314) eggs per individual being laid in the initial clutch; thereafter, the rate decreased to a mean of 1.7 (S.E. ± 0.078) eggs per day. Following hatching, the rate of oviposition decreased further to 0.9 eggs per day (S.E. ± 0.3049). Incubation of eggs varied between 20 and 34 days. As with oviposition, hatching started on the same day in all colonies set up after a specific swarming flight; however, not all the eggs laid on that specific date hatched simultaneously. Second (L2) and third (L3) instar larvae first appeared in the colonies a mean of 6.6 (S.E. ± 0.578) days and 11.5 (S.E. ± 0.3193) days, respectively, after the first eggs hatched. The first pre-soldier (SPS), which developed from a third instar larva, was differentiated a mean of 21.85 (S.E. ± 0.748) days after hatching, with the first soldier (SS) appearing 30.75 (S.E. ± 0.4787) days following hatching. The first major (LW) and minor (SW) workers were differentiated respectively 26 ± 0.660 and 28 ± 0.905 days after hatching. Approximately three months after alates had left the parent nest, workers began foraging. At three months, the single surviving colony contained 60 individuals. No large soldier (LS) had differentiation by 20 months.
Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies

Crop type classification using Earth Observation (EO) data is challenging, particularly for crop types with similar phenological growth stages. In this regard, the synergy of optical and Synthetic-Aperture Radar (SAR) data enables a broad representation of biophysical and structural information on target objects, enhancing crop type mapping. However, the fusion of multi-sensor dense time-series data often comes with the challenge of high dimensional feature space. In this study, we (1) evaluate how the usage of only optical, only SAR, and their fusion affect the classification accuracy; (2) identify the combination of which time-steps and feature-sets lead to peak accuracy; (3) analyze misclassifications based on the parcel size, optical data availability, and crops’ temporal profiles. Two fusion approaches were considered and compared in this study: feature stacking and decision fusion. To distinguish the most relevant feature subsets time- and variable-wise, grouped forward feature selection (gFFS) was used. gFFS allows focusing analysis and interpretation on feature sets of interest like spectral bands, vegetation indices (VIs), or data sensing time rather than on single features. This feature selection strategy leads to better interpretability of results while substantially reducing computational expenses. The results showed that, in contrast to most other studies, SAR datasets outperform optical datasets. Similar to most other studies, the optical-SAR combination outperformed single sensor predictions. No significant difference was recorded between feature stacking and decision fusion. Random Forest (RF) appears to be robust to high feature space dimensionality. The feature selection did not improve the accuracies even for the optical-SAR feature stack with 320 features. Nevertheless, the combination of RF feature importance and time- and variable-wise gFFS rankings in one visualization enhances interpretability and understanding of the features’ relevance for specific classification tasks. For example, by enabling the identification of features that have high RF feature importance values but are, in their information content, correlated with other features. This study contributes to the growing domain of interpretable machine learning.
Comparison of Empirical and Physical Modelling for Estimation of Biochemical and Biophysical Vegetation Properties: Field Scale Analysis across an Arctic Bioclimatic Gradient

To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.
Coral Reef Monitoring by Scuba Divers Using Underwater Photogrammetry and Geodetic Surveying

Underwater photogrammetry is increasingly being used by marine ecologists because of its ability to produce accurate, spatially detailed, non-destructive measurements of benthic communities, coupled with affordability and ease of use. However, independent quality control, rigorous imaging system set-up, optimal geometry design and a strict modeling of the imaging process are essential to achieving a high degree of measurable accuracy and resolution. If a proper photogrammetric approach that enables the formal description of the propagation of measurement error and modeling uncertainties is not undertaken, statements regarding the statistical significance of the results are limited. In this paper, we tackle these critical topics, based on the experience gained in the Moorea Island Digital Ecosystem Avatar (IDEA) project, where we have developed a rigorous underwater photogrammetric pipeline for coral reef monitoring and change detection. Here, we discuss the need for a permanent, underwater geodetic network, which serves to define a temporally stable reference datum and a check for the time series of photogrammetrically derived three-dimensional (3D) models of the reef structure. We present a methodology to evaluate the suitability of several underwater camera systems for photogrammetric and multi-temporal monitoring purposes and stress the importance of camera network geometry to minimize the deformations of photogrammetrically derived 3D reef models. Finally, we incorporate the measurement and modeling uncertainties of the full photogrammetric process into a simple and flexible framework for detecting statistically significant changes among a time series of models.
On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.
Mitochondrial Aspartyl-tRNA Synthetase (DARS2) Deficiency and Tissue-Specific Consequences of Defective Mitochondrial Translation

Cells try to counteract mitochondrial respiratory chain deficiencies via various kinds of largely unknown compensatory mechanisms, which play a central role in determining the extent of tissue-specific defects leading to disease phenotypes. In this study, we directly disrupted mitochondrial protein synthesis in mice by deleting the mitochondrial aspartyl-tRNA synthetase (Dars2) gene in a tissue- specific manner. We generated DARS2 deficiency in three different tissues (heart, skeletal muscle and forebrain neurons) and followed the dynamics and extent of pathological changes that occurred. Deficiency of this essential protein leads to severe deregulation of mitochondrial protein synthesis in both heart and skeletal muscle. Yet, mitochondrial stress responses, like increased biogenesis, decreased autophagy, upregulation of mitochondrial unfolded protein response and mitokine FGF21, are only observed in DARS2-deficient cardiomyocytes. Surprisingly, the initiation of these stress responses is stemming from perturbed mitochondrial proteostasis, rather than the respiratory deficiency. Skeletal muscle, on the other hand, has intrinsic protective mechanisms that make it better equipped for folding and turnover of mitochondrial proteins, as well as slow turnover of mitochondrial transcripts that is coupled with possible upregulation of muscle regeneration. As a result, skeletal muscle is able to cope with increased levels of unassembled proteins better. Although DARS2 depletion leads to very strong, deleterious respiratory deficiency in heart and skeletal muscle, causing animals to die within 7-8 weeks, its deficiency in forebrain neurons seems to have a milder effect that takes much longer time to develop. Defective mitochondrial translation in forebrain neurons caused abnormal behavior, and severe forebrain atrophy, which is caused by neuronal cell apoptosis and accompanied by activation of inflammatory responses such as microgliosis and reactive astrogliosis. Surprisingly, neurodegeneration occurred in an age-dependent manner and affected cortex and hippocampal regions differently.
A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

With the development of remote sensing technologies, change detection in heterogeneous images becomes much more necessary and significant. The main difficulty lies in how to make input heterogeneous images comparable so that the changes can be detected. In this paper, we propose an end-to-end heterogeneous change detection method based on the feature space constraint. First, considering that the input heterogeneous images are in two distinct feature spaces, two encoders with the same structure are used to extract features, respectively. A decoder is used to obtain the change map from the extracted features. Then, the Gram matrices, which include the correlations between features, are calculated to represent different feature spaces, respectively. The squared Euclidean distance between Gram matrices, termed as feature space loss, is used to constrain the extracted features. After that, a combined loss function consisting of the binary cross entropy loss and feature space loss is designed for training the model. Finally, the change detection results between heterogeneous images can be obtained when the model is trained well. The proposed method can constrain the features of two heterogeneous images to the same feature space while keeping their unique features so that the comparability between features can be enhanced and better detection results can be achieved. Experiments on two heterogeneous image datasets consisting of optical and SAR images demonstrate the effectiveness and superiority of the proposed method.
First Study of Diptera Seasonal Dynamics and their Community Structure in Moroccan Vineyards

The purpose of this study was to describe the structure and composition of Diptera in Moroccan vineyard, using wet-traps sampling technique throughout 2017. The study focused on five varieties within Loudaya vineyard (west region of Marrakech, Morocco). Eleven different fly species were identified belonging to 10 families, Ceratitis capitata, Drosophilla suzukii and Thaumatiya notata were the most abundant species and first time behaviourally dominant flies species. In addition, more flies were captured in the local varieties (10 579 individuals) than in the introduced ones (966). Moreover, a significant difference was observed in fly richness, evenness and diversity between varieties. Fly community structure in Loudaya showed a clear seasonal dynamics, which richness, abundance, diversity and evenness were significantly (P < 0.001) dissimilar among months. Furthermore, Diptera species were positively influenced by humidity and rainfall. Richness (expected by sample-based rarefaction) and diversity peaked in the middle of vineyard phenology, corresponding to mid-spring to late-summer in all varieties. Fly species occurrence and seasonal climatic variation in Loudaya vineyard might be among various ecological factors such as habitat distribution, climate and the availability of resources. Taking into account the periods of fly abundance, the survey could give relevant indications on new approaches to integrated pest management (IPM) in Moroccan vineyards, particularly in the Loudaya region.
CNN-based fusion and classification of SAR and Optical data

ABSTRACT Image fusion combines the images of different spectral, spatial, multi-date, as well as radiometric data to achieve a better quality image for improved classification results. Recently, Convolution Neural Network (CNN)-based classification algorithms are extensively used for remote sensing applications. Keeping this in view, present work proposes to use CNN-based fusion and classification of Sentinel 1 (VV and VH polarization) and Sentinel 2 datasets acquired over an agricultural area near Hisar (India). For image fusion, three CNN-based approaches are used to fuse Sentinel 2 (10 m) data with VV and VH bands of Sentinel 1 data. After fusion, classification was performed using 2D-CNN classifier to judge the performance of fused images in terms of classification accuracy. Results suggest that out of the three fusion approaches, only infrared image fusion (IVF) approach performed well with the considered dataset in terms of fusion indicators and classification accuracy. Keeping in view of its better performance, this study proposes a modified IVF approach by using different image pyramid methods. Comparison of results suggests an improved performance by modified IVF approach for the fusion of Sentinel 2 and Sentinel 1 data in comparison with the original IVF approach.
Broad SARS-CoV-2 cell tropism and immunopathology in lung tissues from fatal COVID-19

Background Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection in patients with Coronavirus Disease 2019 (COVID-19) prominently manifests with pulmonary symptoms histologically reflected by diffuse alveolar damage (DAD), excess inflammation, pneumocyte hyperplasia and proliferation, and formation of platelet aggregates or thromboemboli. However, the mechanisms mediating these processes remain unclear. Methods We performed multicolor staining for viral proteins, and lineage cell markers to identify SARS-CoV-2 tropism and to define the lung pathobiology in postmortem tissues from five patients with fatal SARS-CoV-2 infections. Findings The lung parenchyma showed severe DAD with thromboemboli in all cases. SARS-CoV-2 infection was found in an extensive range of cells including alveolar epithelial type II/pneumocyte type II (AT2) cells (HT2-280), ciliated cells (tyr--tubulin), goblet cells (MUC5AC), club-like cells (MUC5B) and endothelial cells (CD31 and CD34). Greater than 90% of infiltrating immune cells were positive for viral proteins including macrophages and monocytes (CD68 and CD163), neutrophils (ELA-2), natural killer (NK) cells (CD56), B-cells (CD19 and CD20), and T-cells (CD3{varepsilon}). Most but not all infected cells were positive for the viral entry receptor angiotensin-converting enzyme-2 (ACE2). The numbers of infected and ACE2-positive cells correlated with the extent of tissue damage. The infected tissues exhibited low numbers of B-cells and abundant CD3{varepsilon}+ T-cells consisting of mainly T helper cells (CD4), few cytotoxic T cells (CTL, CD8), and no T regulatory cell (FOXP3). Antigen presenting molecule HLA-DR of B and T cells was abundant in all cases. Robust interleukin-6 (IL-6) expression was present in most uninfected and infected cells, with higher expression levels observed in cases with more tissue damage. Interpretation In lung tissues from severely affected COVID-19 patients, there is evidence for broad SARS-CoV-2 cell tropisms, activation of immune cells, and clearance of immunosuppressive cells, which could contribute to severe tissue damage, thromboemboli, excess inflammation and compromised adaptive immune responses.
Distress relief and stabilising psychological well-being: spiritual coping among Sinhalese Buddhist in Sri Lanka

ABSTRACT The present study explores how personal experiential narratives influenced by culture affect psychological well-being among Sinhalese Buddhist in Sri Lanka, and how religious coping methods can be employed to refine negative narratives to reduce distress while obtaining psychological well-being. A narrative psychological approach was used to obtain autobiographical interviews from 27 participants. Empirical observations were carried out at two religious sites. The study reveals that the sense of uncertainty can form negative narratives about life-episodes, which in turn causes self-efficacy distortion while depreciating psychological well-being. The study thus highlights the importance of spirituality and mindfulness in refining those negative narratives of life-episodes that ultimately contribute to psychological well-being.
What Do Teachers Think They Want? A Comparative Study of In-Service Language Teachers’ Beliefs on LAL Training Needs

ABSTRACT The purpose of the study was to investigate English language teachers’ perceptions of assessment, their language assessment literacy (LAL) levels and their training needs. 113 teachers from Germany and 379 teachers from Greece completed a survey questionnaire. The data were analyzed through a series of RM ANOVAs, correlation analyses, and confirmatory factor analysis. Data from interviews with 25 German and 20 Greek teachers were used as supporting qualitative data. The results indicated that teachers use similar constructs in their assessment and in their conceptualisations of LAL but their perceived training needs differed depending on their educational contexts. The interviews helped identify deeper insights into contextual factors. The paper discusses the importance of context in assessment and offers recommendations for teacher education programmes in language assessment literacy that are context responsive.