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To analyse the use, assessment, and measured outcomes of reflective writing (RW) in teaching communication to medical students.Systematic search of seven electronic databases, focused on using RW in teaching communication skills. Three reviewers selected and prepared the synthesis of the studies. The synthesis was based on thematic analysis using Braun and Clarke's approach.We identified 1325 studies, reviewed 101 full-text articles, and included 12 articles in the analysis. The four themes identified showed that RW is not a stand-alone practice. RW is blended with other teaching strategies. Through RW, students identified structural, emotional, and relational aspects and challenges of communication. Only a few studies found a positive correlation between reflective ability and communication skills CONCLUSION: RW can be integrated with various teaching methods, at all stages of learning, to stimulate discussion of interpersonal and intrapersonal topics. Through RW, students explore theirs and their patient's emotions, values, behaviours, and needs identifying challenges and practices relevant to communication.RW can address different structural, relational, and emotional issues that are relevant to communication learning. Further educational development and high-quality empirical research on the use of RW and unique outcomes are needed to support communication skills learning.

Controlled environment conditions inside protected agriculture (PA) structures can lead to the development of sustainable agriculture. In developed countries, the rapid growth of technology of sustainable, environmentally friendly agriculture via greenhouses or net-houses is due to the significant changes in climate and increasing demand for quality products such as vegetables, fruits, herbs, etc. Therefore, there is a need to map and classify different types of PAs to help understand the pattern of crop production. Using remote sensing, the mapping of PAs has gained significant consideration in recent decades. The main goal of this study is to develop a cost-effective, novel approach to create object-based image procedures for classifying and characterising different structures of PAs. To fulfil this goal, the project integrates high-resolution orthophoto and LiDAR data. Eleven distinctive major PA classes were identified, differing in size, height, construction, shape, materials and orientation. The research was conducted over a cluster of PAs, in the Arava Valley, Wadi Araba, Israel, and demonstrated an overall accuracy and Kappa index of agreement (KIA) 92% and 0.91, respectively. Remote information and discrimination of different types of structures within a PA cluster can provide important data to decision-makers, managers, environmental protection officers and others. Authorities might infer data about the number of farms, what is being cultivating and when, or, if the PA is abandoned. Such information can also be used for quantifying damage, for predicting the dispersion of virus and help strategic planning.

Objectives Patient-centered-care (PCC) attitudes are important to enhance PCC behavior, but research on perceptions and attitudes that contribute to PCC attitudes is limited. We tested a new model, assessing whether the association between self-efficacy and PCC attitudes is mediated by communication and empathy attitudes. Furthermore, we assessed medical-school-stage and gender differences in these variables. Methods Medical/dentistry students (N = 653) completed self-reported questionnaires. Analyses were performed using Pearson's-correlations, PROCESS macro, and independent samples t-tests. Results The association between communication self-efficacy and PCC attitudes was completely mediated by communication and empathy attitudes. Students in clinical years had more positive attitudes toward PCC and empathy, and higher communication self-efficacy. Females had more positive attitudes toward PCC, communication, and empathy. Conclusions The results emphasize the importance of communication self-efficacy and the mediating role of communication and empathy attitudes in enhancing PCC attitudes. This highlights the need to focus on learners' affective matter (what they feel/think) about their competencies and attitudes. Practice implications Educational interventions should improve understanding of the benefits of applying good communication skills, of learning the importance of empathy and its relevance to patient care, and strengthening students' confidence in applying these skills. Attention to these is important for enhancing students' PCC attitudes.

Enriching widely-consumed foods and beverages with nutraceuticals is an important disease-prevention strategy. (-)-Epigallocatechin-3-gallate (EGCG), the green-tea polyphenol, is a potent antioxidant with numerous attributed health benefits. However, it is bitter, astringent, and susceptible to oxidation. Herein we studied our recently developed beta-lactoglobulin (β-Lg)-based delivery system, assessing its efficacy in the prevention of metabolic syndrome effects in a high-fat-diet (HFD)-induced obesity mice model. Metabolic effects were evaluated in mice fed either HFD or normal diet (ND), which concomitantly consumed either milk (1% fat) fortified with EGCG-β-Lg complexes, or free EGCG, or water with EGCG or water only, for 12 weeks. HFD mice receiving EGCG-β-Lg complexes in milk, had significantly lower liver-triglycerides levels compared to control (HFD-water) group. Moreover, compared to free EGCG in milk, EGCG-β-Lg complexes significantly improved glucose tolerance in both diets. Thus, β-Lg delivery system may improve the efficacy of polyphenols like EGCG in preventing the metabolic syndrome.

Spatial resolution enhancement is a pre-requisite for integrating unmanned aerial vehicle (UAV) datasets with the data from other sources. However, the mobility of UAV platforms, along with radiometric and atmospheric distortions, makes the task difficult. In this paper, various convolutional neural network (CNN) architectures are explored for resolving the issues related to sub-pixel classification and super-resolution of drone-derived datasets. The main contributions of this work are: 1) network-inversion based architectures for super-resolution and sub-pixel mapping of drone-derived images taking into account their spectral-spatial characteristics and the distortions prevalent in them 2) a feature-guided transformation for regularizing the inversion problem 3) loss functions for improving the spectral fidelity and inter-label compatibility of coarser to finer-scale mapping 4) use of multi-size kernel units for avoiding over-fitting. The proposed approach is the first of its kind in using neural network inversion for super-resolution and sub-pixel mapping. Experiments indicate that the proposed super-resolution approach gives better results in comparison with the sparse-code based approaches which generally result in corrupted dictionaries and sparse codes for multispectral aerial images. Also, the proposed use of neural network inversion, for projecting spatial affinities to sub-pixel maps, facilitates the consideration of coarser-scale texture and color information in modeling the finer-scale spatial-correlation. The simultaneous consideration of spectral bands, as proposed in this study, gives better super-resolution results when compared to the individual band enhancements. The proposed use of different data-augmentation strategies, for emulating the distortions, improves the generalization capability of the framework. Sensitivity of the proposed super-resolution and sub-pixel mapping frameworks with regard to the network parameters is thoroughly analyzed. The experiments over various standard datasets as well as those collected from known locations indicate that the proposed frameworks perform better when compared to the prominent published approaches.

Ultraviolet (UV) light affects endocrinological and behavioral aspects of human sexuality via an unknown mechanism. Using a unique male-female comparative approach, we discovered that the sexual behavioral and hormonal features enhanced by UVB are mediated by the skin. In mice, UV exposure increases hypothalamus-pituitary-gonadal axis hormone levels, resulting in enhanced ovary size, extended estrus days, and anti-Mullerian hormone (AMH) expression. It likewise enhances sexual responsiveness and attractiveness of females and heterosexual interactions of both males and females. Conditional knockout of p53 specifically in skin keratinocytes abolished UV’s effects. In humans, UV exposure enhanced romantic passion in both genders, increased testosterone levels in men and correlated with heightened plasma AMH levels in women. Our data, revealing that UVB triggers a skin-brain-gonadal axis through skin p53 activation, offers therapeutic opportunities for sex-steroid-related dysfunctions. We speculate that during human furless skin evolution, the skin became the front-line regulator of the response to UVB.

The high spatial, spectral, and temporal resolutions of the Vegetation and Environment monitoring New Micro-Satellite (VENµS) satellite data facilitate field-level phenological analysis of crops. This study proposes deep learning (DL) based approaches to resolve the issues prevalent in crop phenology-based fingerprint estimation at field-level using VENµS satellite data. An encoder-decoder-based framework, called piece-wise kernel encoding network (PKNet), is proposed for missing data imputation of the vegetation index (VI) curves derived from time-series image data. PKNet adopts interpolation-based convolution, dynamic time wrapping (DTW) based layer formulation, and imputation-specific constraints for optimal smoothing of the irregularly sampled VI curves. Besides, PKNet learns kernel parameters dynamically. A variational encoding framework called a dynamic-projection-based generalization network (DPGNet), is proposed to generalize the pixel-level VI curves to synthesize a representative VI curve for a given field. DPGNet is more effective than the use of multiple moments as it is resilient to outliers and learns normally distributed latent space with a small number of samples. The current research also proposes a classifier, called dynamic time wrapping based capsule network (DTCapsNet), which learns a discriminative latent space and accurately models the VI curve features. The DTCapsNet considers the time-series nature of the input using DTW-based convolution layers. The feature characterization improves generalizability and gives good results, even with a limited number of training samples. Experiments using the ground truth information and satellite images, acquired over two farms in Israel, illustrate that the proposed frameworks give better results than the commonly-used existing approaches.

Satellite-based particulate matter (PM) models provide spatially and temporally resolved estimations, allowing greater spatial-temporal coverage compared to sparse ground monitoring stations. The spatio-temporal resolution of these models can be improved using aerosol optical depth (AOD) products from various satellite platforms with different overpass times which can capture possible changes in diurnal aerosol concentrations. Israel is characterized by diverse geo-climatic regions and it is subjected to frequent dust storms events. Our goal was to estimate PM10 and PM2.5 concentrations in Israel on daily and intra-daily (mean PM around the Aqua and Terra overpasses) temporal resolutions and to assess the differences between these time windows. A hybrid modeling approach that consists of three stages was used enabling spatially continuous PM estimations at 1 × 1 km spatial resolution. The model was calibrated on a daily basis applying a mixed modeling approach using MODIS-based MAIAC AOD and various spatial and temporal predictors. It was found that in certain urban areas the measured and estimated PM concentrations during the satellite overpass (Terra or Aqua) were higher than the mean daily PM. The models performed well showing cross-validated R2 that ranged between 0.82 and 0.92. Mean estimated PM for the study period (2005–2015) during days with no dust events showed different spatial patterns for the daily and intra-daily estimations and revealed areas in Israel that are affected by high PM concentrations (mainly industrial or dense urban areas). Estimations from these models are useful for epidemiological research and might contribute to environmental regulatory purposes by focusing the efforts of PM pollution reduction at the identified polluted areas.

Sunflower broomrape (Orobanche cumana) is a root holoparasitic plant causing major damage to sunflower (Helianthus annuus L.). Parasite infection initiates source-sink relations between the parasite (sink) and the host (source), allocating carbohydrates, water and nutrients to the parasite. The primary aim of the current study was to explore responses of sunflower to broomrape parasitism, specifically to examine alternations in leaf area, leaf mass per area (LMA), mesophyll structure and root hydraulic conductivity. Leaf changes revealed modifications similar to described previously in shade adapted plants, causing larger and thinner leaves. These traits were accompanied with significantly higher root hydraulics. These changes were caused by carbohydrate depletion due to source-sink relationships between the host and parasite. An Imazapic herbicide (ALS inhibitor) was used for controlling broomrape attachments and by to investigate the plasticity of the traits found. Broomrape infected plants which were treated with Imazapic had leaves similar to non-infected plants, including mesophyll structure and carbon assimilation rates. These results demonstrated source-sink effects of broomrape which cause a low-light-like acclimation behavior which is reversible.

Soil nutrients, including available nitrogen (N), phosphorous (P), and potassium (K), are critical properties for monitoring soil fertility and function. Spectroscopy analysis has proven to be a rapid and effective means for predicting soil properties, in general, and NPK, in particular. However, different calibration methods, including preprocessing transformations (PPTs) and regression algorithms (RAs), considerably affect the performance of prediction models. In this study, raw spectrum and 21 PPTs, combined with three RAs, for a total of 66 calibration methods, were investigated for modeling and predicting soil NPK using hyperspectral VNIR data (400–1000 nm). The ratio of performance to deviation (RPD) of validation set was selected to evaluate the prediction accuracy and the ratio between the interpretable sum squared deviation and the real sum squared deviation (SSR/SST) of the validation set was also used to evaluate the explanatory power of the models. It was found that there is a tradeoff between RPD and SSR/SST values; under this tradeoff, the multiplicative scatter correction, combined with the back-propagation neural network, was preferred for predicting P (RPD = 2.23, SSR/SST = 0.81). The Savitzky-Golay filtering + logarithmic transformation, combined with the partial least squares – regression, was preferred for predicting K (RPD = 1.47, SSR/SST = 0.95). However, with extremely low RPD and SSR/SST values, the prediction of N was unreliable in this study. The evaluation approach presented in this paper suggests a framework for choosing a calibration method for spectroscopy analysis for predicting soil NPK and perhaps some other properties.

Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size

Accurate monitoring of grassland aboveground fresh biomass (called AGB in the study) and its spatial-temporal dynamics is indispensable for sustainable grassland management. The most common method used in estimating AGB with remotely sensed data is based on the relationship between field AGB measurements and vegetation indices (VIs); however, the existing VIs do not deliver adequate results due to the soil background and spatial, temporal and sampling size variability. In this study, the AGB estimation model with the normalized difference phenology index (NDPI) was evaluated in terms of model robustness and spatial and temporal scalability based on comparisons with the widely used ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), and optimized soil-adjusted vegetation index (OSAVI). The field measurements of AGB of the natural grassland in Inner Mongolia, China, collected in 2013, 2016, and 2017 and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance products were used for analysis. The results based on training and independent validation data showed the following: (1) The R2 value between AGB and the NDPI was the highest (0.73) among all VIs, followed by soil-line-adjusted VIs, while the R2 values of the RVI and DVI were the lowest; (2) The NDPI-based model had the best robustness for different sampling sizes; (3) The NDPI-based model also had superior spatial and temporal scalability. The results from simulation experiments using the PROSAIL model also support the superiority of the NDPI in estimating AGB. The simulation analysis further reveals that the overall superiority of the NDPI originates from the fact that the NDPI overcomes the adverse impacts of the heterogeneity of the soil background and accounts for changes in the leaf water content that contribute substantially to AGB in grassland. These findings suggest that the NDPI-based AGB estimation model is advantageous for monitoring AGB in large grasslands with significant spatial-temporal heterogeneity.

Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns. To fulfill this goal, CI maps, derived from 31 annual Landsat images, were analyzed by applying advanced statistical and machine learning algorithms. A comprehensive overview of biocrusts' spatiotemporal patterns was achieved using an integrative approach, including a long-term analysis, using the Mann-Kendall (MK) statistical test, and a short-term analysis, using a rolling MK with a window size of five years. Additionally, temporal clustering, using the partition around medoids (PAM) algorithm, was applied to model the spatial multi-annual dynamics of the CI. A Granger Causality test was then applied to quantify the relations between CI dynamics and precipitation. The findings show that 88.7% of pixels experienced a significant negative change, and only 0.5% experienced a significant positive change. A strong association was found in temporal trends among all clusters (0.67 ≤ r ≤ 0.8), signifying a regional effect due to precipitation levels (p 0.05 for most clusters). The biocrust dynamics were also locally affected by anthropogenic factors (0.58 CI 0.64 and 0.64 CI 0.71 for strongly and weakly affected regions, respectively). A spatiotemporal analysis of a series of spaceborne images may improve conservation management by evaluating biocrust development in drylands. The suggested framework may also by applied to various disciplines related to quantifying spatial and temporal trends.

The objective of this study was to assess the relationship between the hyperspectral reflectance of soils and their albedo, measured under various roughness conditions. 108 soil surface measurements were conducted in Poland and Israel. Each surface was characterised by its diurnal albedo variation in the field as well as by its reflectance spectra obtained in the laboratory. The best fit to the model was achieved by post-processing manipulation of the spectra, namely second derivate transformation. Using a stepwise elimination process, four spectral wavelengths and the roughness index were selected for modelling. The resulting models allowed the albedo of a soil to be predicted for its different roughness states and any solar zenith angle, provided that hyperspectral reflectance data is available.

The two-spotted spider mite ( Tetranychus urticae Koch; TSSM) feeds on the under-surface of leaves, piercing the chloroplast-containing cells and affecting pigments as well as leaf structure. This damage could be spectrally detectable in the visible and near-infrared spectral regions. The aim was to spectrally explore the ability to assess TSSM damage levels in greenhouse-grown pepper ( Capsicum annuum ) and bean ( Phaseolus vulgaris ) leaves. Several vegetation indices (VIs) provided the ability to classify early TSSM damage using a one-way analysis of variance. Hyperspectral (400–1000 nm) and multispectral (five common bands) data were analysed and cross-validated independently by partial least squares-discriminant analysis models. These analyses resulted in 100% and 95% success in identifying early damage with hyperspectral data reflected from pepper and bean leaves, respectively, and in 92% with multispectral data reflected from pepper leaves. Although the TSSM activity occurred on the underside of leaves their damage can be spectrally detected by reflected data from the upper side. Early TSSM damage identification to greenhouse pepper and bean leaves, that their sole damage was by TSSM, can be obtained by VIs, hyperspectral data, and multispectral data. This study shows that by using sub leaf spatial resolution early damage by TSSM can be spectrally detected. It can be potentially applied for greenhouses as well as fields as an early detection method for TSSM management.

The techniques presented herein allow to directly determine certain crucial calibration parameters for the WRF-Hydro flood forecasting model. Typically, calibrations are chosen by an iterative, empirical, trial and error procedure. We suggest a more systematic methodology to arrive at a usable calibration. Our method is based on physical soil properties and does not depend on observed runoff from certain basins during specific storm events. Three specific calibration variables that most strongly affect the runoff predictions are addressed: topographic slope, saturated hydraulic conductivity, and infiltration. We outline a procedure for creating spatially distributed values for each of the three variables. Simulation runs are performed covering several storm events with calculated calibrations, with default values, and with an expert calibration. We show that our calibration, derived solely from soil physical properties, achieves forecast skill better than the default calibration and at least as good as an expert based calibration. This work presents a method for calibrating the WRF-Hydro flood forecasting model.The technique relies only on physical soil characteristics.By determining soil characteristics from remote sensing, the method avoids iterative reruns of the model to validate the calibration.We point out that the methods are applicable in any arid region.The WRF-Hydro results achieved with the calculated calibrations are in line with results from an expert calibration.

To use VRI systems, a field is divided into irrigation management zones (IMZs). While IMZs are dynamic in nature, most of IMZs prescription maps are static. High-resolution thermal images (TI) coupled with measured atmospheric conditions have been utilized to map the within-field water status variability and to delineate in-season IMZs. Unfortunately, spaceborne TIs have coarse spatial resolution and aerial platforms require substantial financial investments, which may inhibit their large-scale adoption. Three approaches are proposed to facilitate large-scale adoption of TI-based IMZs: 1) increase of the capacity of aerial TI by enhancing their spatial resolution; 2) sharpening the spatial resolution of satellite TI by fusing satellite multi-spectral images in the visible-near-infrared (VIS-NIR) range; 3) increase the capacity of aerial TI by fusing satellite multi-spectral images in the VIS-NIR range. The scientific and engineering basis of each of the approaches is described together with initial results.

Intensive grazing, such as that occurring in the vicinity of animals' shaded concentration points (CPs), causes the degradation of many soil properties, creating patterns as a function of distance from the CPs. The aim of this research is to characterize the spatial patterns of land degradation as a function of grazing intensity, in terms of soil quality, with respect to (1) the distance from CPs; (2) grazing density; and (3) additional environmental variables, in typical Mediterranean grassland, in the eastern Galilee region of Israel. To fulfill this aim, a soil quality index (SQI) was developed based on the integration of 12 physical, biological, and chemical soil properties. Soil properties and quality surface maps were developed by using the spline interpolation technique as a geostatistical analysis on a small spatial scale. In addition, a spatial statistics technique, namely the geographically weighted regression (GWR), was used to measure the effect of different explanatory variables (grazing density, distance from CPs, vegetation cover, aspect, slope, and landscape cover classification) on the SQI. The results show that near the CPs, there is a clear effect of grazing activity on all soil properties and nutrients that can be attributed to a low SQI. Significantly lower soil quality was found around the CPs with moderate and high stocking rates than in the pasture and the control area, where grazing was excluded. Applying the GWR model, using the landscape-cover classification as the explanatory variable for the SQI, provided the best results in the spatial analysis with R 2 adjusted = 0.97. The second and third most influential explanatory variables for the SQI were the distance from the CPs and grazing density, with R 2 adjusted = 0.94 and R 2 adjusted = 0.93, respectively. This outcome demonstrates grazing intensity effects on SQ and its complexity, mainly due to the impact of landscape cover and grazing density. In conclusion, the presented framework was found adequate and could potentially be made operational for assessing SQ under different grazing intensities and degradation stages in a spatial scale.

The intent of this article is to provide guidance and recommendations to cell therapy product sponsors (including developers and manufacturers) and their suppliers in the cell therapy industry regarding particulate source, testing, monitoring and methods for control. This information is intended to help all parties characterize the processes that generate particulates, understand product impact and provide recommendations to control particulates generated during manufacturing of cell therapy products.