Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy

Field spectroscopy has been suggested to be an efficient method for predicting soil properties using quantitative mathematical models in a rapid and non-destructive manner. Traditional multivariate regression algorithms usually regard the modeling of each soil property as a single task, which means only one response variable is considered as the output during modeling. Therefore, these algorithms are less suitable for the prediction of several key soil properties with low concentrations or unobvious spectral absorption signals. In the current study, we investigated the performance of a linear multi-task learning (LMTL) algorithm based on a regularized dirty model for modeling and predicting several key soil properties using field spectroscopy (350–2500 nm) as an integrated approach. We tested seven key soil properties including available nitrogen (N), phosphorus (P) and potassium (K), pH, water content (WC), organic matter (OM), and electrical conductivity (EC) in drylands. The model performances of LMTL models were compared with the commonly used single-task algorithm of the partial least squares regression (PLS-R). Our results show that the LMTL models outperformed the PLS-R models with the advantage of shared features; the ratio of performance to deviation (RPD) values in the validation set improved by 10.24%, 4.93%, 25.77%, 11.76%, 6.74%, 53.13%, and 3.15% for N, P, K, pH, WC, OM, and EC, respectively. The best prediction was obtained for OM with RPD = 2.29, indicating high accuracy (RPD > 2). The prediction results of N, P, WC, and pH were categorized as of moderate accuracy (1.4 < RPD < 2), while K and EC were categorized as of poor accuracy (RPD < 1.4). However, the explanatory power of the LMTL models was moderate due to fewer features being selected by the regularization algorithm of the LMTL approach, which should be further studied in the soil spectral analysis. Our results highlight the use of LMTL in field spectroscopy analysis that can improve the generalization performance of regression models for predicting soil properties

Multispectral Approach for Identifying Invasive Plant Species Based on Flowering Phenology Characteristics

Invasive plant species (IPS) are the second biggest threat to biodiversity after habitat loss. Since the spatial extent of IPS is essential for managing the invaded ecosystem, the current study aims at identifying and mapping the aggressive IPS of Acacia salicina and Acacia saligna, to understand better the key factors influencing their distribution in the coastal plain of Israel. This goal was achieved by integrating airborne-derived hyperspectral imaging and multispectral earth observation for creating species distribution maps. Hyperspectral data, in conjunction with high spatial resolution species distribution maps, were used to train the multispectral images at the species level. We incorporated a series of statistical models to classify the IPS location and to recognize their distribution and density. We took advantage of the phenological flowering stages of Acacia trees, as obtained by the multispectral images, for the support vector machine classification procedure. The classification yielded an overall Kappa coefficient accuracy of 0.89. We studied the effect of various environmental and human factors on IPS density by using a random forest machine learning model, to understand the mechanisms underlying successful invasions, and to assess where IPS have a higher likelihood of occurring. This algorithm revealed that the high density of Acacia most closely related to elevation, temperature pattern, and distances from rivers, settlements, and roads. Our results demonstrate how the integration of remote-sensing data with different data sources can assist in determining IPS proliferation and provide detailed geographic information for conservation and management efforts to prevent their future spread

Mental health consequences of the COVID-19 pandemic: Results from a large-scale population-based study in Israel

Introduction: Contemporary evidence notes the COVID-19 pandemic greatly impacted the utilization of physical and mental health services worldwide. The present study was therefore designed to evaluate the changes in the utilization of mental health services during the first year of the COVID-19 pandemic compared to previous years as well as to estimate the moderating role age had on these changes. Materials and methods: Psychiatric data was collected from n = 928,044 individuals living in Israel. Rates of receipt of psychiatric diagnoses and purchases of psychotropic medication were extracted for the first year of the COVID-19 pandemic and for two comparison years. The odds of receiving a diagnosis or of purchasing a psychotropic medication during the pandemic were compared to control years using uncontrolled logistic regression models and controlled and logistic regression that accounted for differences between ages. Results: There was a general reduction of about 3–17% in the odds of receiving a psychiatric diagnosis or purchasing psychotropic medications during the pandemic year compared to control years. The bulk of tests conducted showed that reduction in the rates of receiving diagnoses and purchasing medications during the pandemic were evident or more profound in the older age groups. An analysis of a combined measure conclusive of all other measures revealed decreased rates of utilizing any service examined during 2020, with rates decreasing as age increases up to a decrease of 25% in the oldest age group (80–96). Discussion and conclusion: Changes in utilization of mental health services reveal the interplay between psychological distress that has been documented to increase during the pandemic and people's reluctance to seek professional assistance. This appears to be especially prominent among the vulnerable elderly, who may have received even less professional help for their emerging distress. The results obtained in Israel are likely to be replicated in other countries as well, given the global impact of the pandemic on adults' mental health and individuals' readiness to utilize mental healthcare services. Future research on the long-term impact of the pandemic on utilization of mental healthcare services is warranted, with an emphasis on the response of different populations to emergency situations

Structural Changes of Desertified and Managed Shrubland Landscapes in Response to Drought: Spectral, Spatial and Temporal Analyses

Drought events cause changes in ecosystem function and structure by reducing the shrub abundance and expanding the biological soil crusts (biocrusts). This change increases the leakage of nutrient resources and water into the river streams in semi-arid areas. A common management solution for decreasing this loss of resources is to create a runoff-harvesting system (RHS). The objective of the current research is to apply geo-information techniques, including remote sensing and geographic information systems (GIS), on the watershed scale, to monitor and analyze the spatial and temporal changes in response to drought of two source-sink systems, the natural shrubland and the human-made RHSs in the semi-arid area of the northern Negev Desert, Israel. This was done by evaluating the changes in soil, vegetation and landscape cover. The spatial changes were evaluated by three spectral indices: Normalized Difference Vegetation Index (NDVI), Crust Index (CI) and landscape classification change between 2003 and 2010. In addition, we examined the effects of environmental factors on NDVI, CI and their clustering after successive drought years. The results show that vegetation cover indicates a negative ∆NDVI change due to a reduction in the abundance of woody vegetation. On the other hand, the soil cover change data indicate a positive ∆CI change due to the expansion of the biocrusts. These two trends are evidence for degradation processes in terms of resource conservation and bio-production. A considerable part of the changed area (39%) represents transitions between redistribution processes of resources, such as water, sediments, nutrients and seeds, on the watershed scale. In the pre-drought period, resource redistribution mainly occurred on the slope scale, while in the post-drought period, resource redistribution occurred on the whole watershed scale. However, the RHS management is effective in reducing leakage, since these systems are located on the slopes where the magnitude of runoff pulses is low

Assessment of plant species distribution and diversity along a climatic gradient from Mediterranean woodlands to semi-arid shrublands

Climate and land-use change profoundly affect plant species distribution (SD) and composition, and the impact of these processes is expected to increase in the coming years. As a proxy of global changes, knowledge of SD and diversity along climatic gradients is essential to determine the efforts needed for species conservation. Plant spectral diversity is an emerging approach used as a proxy for species diversity based on remote sensing. Thus, the research aim was to develop a comprehensive methodology based on spectral diversity for SD and richness mapping and to study their relations with environmental and human-derived factors, demonstrated along Mediterranean to semi-arid climatic gradient. The study addresses two main knowledge gaps regarding spectral diversity: (1) improving the accuracy of woody species classification by features extraction and selection, and by using texture analysis in an ecosystem characterized by high spatial variability and relatively small-sized and sparse woody vegetation; and (2) developing a better estimate of the local species ‎richness and their response to environmental and human-derived factors (i.e. climate, topography, substrate, and land cover factors) across a transition zone between Mediterranean woodlands and semi-arid dwarf shrublands. A hyperspectral image was acquired for a 43-km strip along the study area using an airborne flight of AISA-FENIX (380–2500 nm, 420 bands) at the end of the 2017 rainy season. The dominant species were surveyed, with a total number of 247 trees and shrubs, to train a machine learning support vector machine (SVM) classification for species distribution mapping, which yielded an overall accuracy of 86.1%. A feature extraction and selection methodology was developed, combining principal component analysis and neighborhood component analysis techniques, facilitating the identification of 33 spectral diagnostic bands out of 330 spectral bands. The classification accuracy was decreased by about 2% to 84.2% using only 33 spectral bands. The classification accuracy improved by about 7.1% for the seven large crown species (93.3%) by adding texture information. Later, the local species richness was calculated by utilizing the alpha diversity index (i.e. the Shannon Index) for 30-m grid cells and was tested in response to environmental (i.e. climate, substrate, and topography) and human-derived factors (i.e. land cover). The highest sensitivity to alpha diversity factors was mean annual precipitation, slope, and land surface temperature. The alpha diversity showed higher richness in the natural Mediterranean shrubland and the guarrigue located in the northern part of the climate gradient. We suggest that the approach presented here significantly improves the estimation of woody species distribution and diversity in areas characterized by high spatial heterogeneity along steep climatic gradients

The transcriptome landscape of the carcinogenic treatment response in the blind mole rat: insights into cancer resistance mechanisms

Abstract Background Spalax, the blind mole rat, developed an extraordinary cancer resistance during 40 million years of evolution in a subterranean, hypoxic, thus DNA damaging, habitat. In 50 years of Spalax research, no spontaneous cancer development has been observed. The mechanisms underlying this resistance are still not clarified. We investigated the genetic difference between Spalax and mice that might enable the Spalax relative resistance to cancer development. We compared Spalax and mice responses to a treatment with the carcinogen 3-Methylcholantrene, as a model to assess Spalax’ cancer-resistance. Results We compared RNA-Seq data of untreated Spalax to Spalax with a tumor and identified a high number of differentially expressed genes. We filtered these genes by their expression in tolerant Spalax that resisted the 3MCA, and in mice, and found 25 genes with a consistent expression pattern in the samples susceptible to cancer among species. Contrasting the expressed genes in Spalax with benign granulomas to those in Spalax with malignant fibrosarcomas elucidated significant differences in several pathways, mainly related to the extracellular matrix and the immune system. We found a central cluster of ECM genes that differ greatly between conditions. Further analysis of these genes revealed potential microRNA targets. We also found higher levels of gene expression of some DNA repair pathways in Spalax than in other murines, like the majority of Fanconi Anemia pathway. Conclusion The comparison of the treated with the untreated tissue revealed a regulatory complex that might give an answer how Spalax is able to restrict the tumor growth. By remodeling the extracellular matrix, the possible growth is limited, and the proliferation of cancer cells was potentially prevented. We hypothesize that this regulatory cluster plays a major role in the cancer resistance of Spalax. Furthermore, we identified 25 additional candidate genes that showed a distinct expression pattern in untreated or tolerant Spalax compared to animals that developed a developed either a benign or malignant tumor. While further study is necessary, we believe that these genes may serve as candidate markers in cancer detection

Time series analysis of vegetation-cover response to environmental factors and residential development in a dryland region

Land-use changes as a result of residential development often lead to degradation and alter vegetation cover (VC). Although these are worldwide phenomena, sufficient knowledge about anthropogenic effects caused by various populated areas in dryland ecosystems is lacking. This study explored anthropogenic development in rural areas and its effects on the conservation of protected areas in drylands, focusing on the change in VC, the reasons, extent, and the drivers of change. We propose a novel framework for exploring VC change (VCC) as a function of environmental and human-driven factors including different types of populated areas in drylands. As a case study, we used a 30-year time series of Landsat satellite images over the arid region of Israel to analyze spatiotemporal VCC. The temporal analysis involved the Contextual Mann-Kendall significance test and spatial analysis to model clustering of VCC. A Gradient Boosted Regression machine learning algorithm was applied to study the relative influence of environmental and human-driven factors on VCC. In addition, we used ANOVA to examine differences between the effects of three types of populated areas on the spatiotemporal trends of VC. The results show that the most influential environmental variable on VCC was elevation (relative contribution of 17%), followed by slope (14.8%) and distance from populated areas (14.6%). Moreover, different types of populated areas affected VC differently with varying distances from residential centroids. The nature reserves increased VC positively and significantly, while livestock settlements had a negative effect. Change in vegetation was mostly confined to the stream network and occurred in lower elevations. The study demonstrates how different land-use practices alter the landscape in terms of VC and differ in their extents, patterns, and effects. With the expected growth in population and residential development worldwide, the proposed framework may assist conservation managements and policy makers in minimizing environmental degradation in drylands