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The lockdown that was imposed by governments as part of the attempt to contain the COVID-19 pandemic included extreme measures, such as home confinement and the shutting down of special education systems. Our aim was to learn about the core experiences of parents of children with autism during this significant life disturbance. Thirty-one parents of 25 children with autism participated in semi-structured telephone interviews which were transcribed verbatim and underwent a qualitative, immersion/crystallization analysis. The analysis was conducted in an iterative consensus-building process to identify parents' experiences, concerns, challenges, coping strategies, and perceived needs during the lockdown. The main themes that emerged related to the various parental concerns; the major difficulties encountered during this unique time, the functional, social, and behavioral implications the lockdown had on these children; and the manner in which the parents coped as an indication of their resourcefulness and outlook. Our findings broaden the insight into the underlying elements of the hardships and gains experienced by children with autism and their parents in times of significant life adversity. Programs in such times should be directed at supporting and guiding parents on how to better accommodate to the situation, thereby optimizing their coping strategies and resilience.

Despite the harsh climatic conditions in the Central Negev Desert, Israel, thousands of dry stonewalls were built across ephemeral streams between the fourth and seventh centuries CE to sustain productive agricultural activity. Since 640 CE, many of these ancient terraces have remained untouched but buried by sediments, covered by natural vegetation, and partially destroyed. The main goal of the current research is to develop a procedure for the automatic recognition of ancient water harvesting systems by incorporating two remote sensing datasets (a high-resolution color orthophoto and LiDAR-derived topographic variables) and two advanced processing methods (an object-based image analysis (OBIA) and a deep convolutional neural networks (DCNN) model). A confusion matrix of object-based classification revealed an overall accuracy of 86% and a Kappa coefficient of 0.79. The DCNN model achieved a Mean Intersection over Union (MIoU) value for testing datasets of 53. The individual IoU values of terraces and sidewalls were 33.2 and 30.1, respectively. The current study demonstrates how incorporating OBIA, aerial photographs, and LiDAR in the context of DCNN improves the identification and mapping of archaeological structures.

Burnout is a work-related stress syndrome with substantial consequences for patients, physicians, and medical students. Personal resilience, i.e., the ability to bounce back and thrive despite challenging circumstances, and certain practices, such as self-care, may protect individuals from burnout. However, limited information exists on the complex relationships between resilience, different self-care practices, and burnout. Understanding these associations is important for designing efficient interventions within medical schools. Therefore, the present study examined the direct and indirect associations through a cross-sectional study among 95 fourth-year medical students. Self-reported questionnaires measured resilience, self-care dimensions (stress management, spiritual growth, interpersonal relations, health responsibility), and burnout dimensions (emotional exhaustion, depersonalization, personal accomplishment). Data were analyzed via IBM-SPSS and PROCESS-macro. The main results demonstrated that self-care mediated the associations between resilience and burnout: stress management and interpersonal relations mediated the associations with emotional exhaustion, while spiritual growth mediated the association with personal accomplishment. These results highlight that medical students' resilience may encourage self-care behaviors, thus decreasing levels of the burnout dimensions of emotional exhaustion and personal accomplishment. Developing curricula that enhance students' resilience through applying self-care techniques in stressful situations may reduce the negative impact of burnout in healthcare. [ABSTRACT FROM AUTHOR]

Spontaneous light emission is central to a vast range of physical systems and is a founding pillar for the theory of light–matter interactions. In the presence of complex photonic media, the description of spontaneous light emission usually requires advanced theoretical quantum optics tools such as macroscopic quantum electrodynamics, involving quantized electromagnetic fields. Although rigorous and comprehensive, the complexity of such models can obscure the intuitive understanding of many quantum-optical phenomena. Here, we review a method for calculating spontaneous emission and other quantum-optical processes without making explicit use of quantized electromagnetic fields. Instead, we introduce the concept of transition currents, comprising charges in matter that undergo transitions between initial and final quantum states. We show how predictions that usually demand advanced methods in quantum electrodynamics or quantum optics can be reproduced by feeding these transition currents as sources to the classical Maxwell equations. One then obtains the relevant quantum observables from the resulting classical field amplitudes, without washing out quantum optical effects. We show that this procedure allows for a straightforward description of quantum phenomena, even when going beyond the dipole approximation and single emitters. As illustrative examples, we calculate emission patterns and Purcell-enhanced emission rates in both bound-electron and free-electron systems. For the latter, we derive cathodoluminescence emission and energy-loss probabilities of free electrons interacting with nanostructured samples. In addition, we calculate quantum-beat phenomena in bound-electron systems and wave function-dependent optical coherence in free-electron systems. Remarkably, the transition-current formalism captures more complex phenomena, such as many-body interference effects and super-radiance of both bound- and free-electron systems, second-order processes such as two-photon emission, and quantum recoil corrections to free-electron radiation. We review a variety of light–matter interactions in fields ranging from electron microscopy to nanophotonics and quantum optics, for which the transition-current theoretical formalism facilitates practical simulations and a deeper understanding of novel applications. [ABSTRACT FROM AUTHOR]

Abstract A recent paper has focused on residents' poor lifestyle habits and their potential negative impact on patients' lifestyles. This commentary argues that there are even greater reasons to be concerned about the health and well-being of residents and medical students and the resultant effects on patients throughout the careers of these learners. There is a “hidden curriculum”, i.e., customs, rituals and norms of behavior, in medical education and in the training at the healthcare setting, often contradicts the formal curriculum and include messages that neglect the basic needs of the learners as well as the patients. Due to the impact of these messages on the professional identity formation of learners, including a deterioration in their own wellbeing as well as impairment of their ability to empathize with and care for patients, we must align our formal and hidden curricula to show dignity and caring for learners, colleagues, and patients. To do this well, we need to change our approach. We need to add processes for learners to support them in dealing with the stresses of their education and training and allow them to build their families and commit fully to medicine as a vocation, not just a job. We also must add faculty development processes to help align the formal and hidden curricula and help faculty empower and constructively assist their learners to handle challenging situations, e.g., where they see a resident struggling with patient care and day-to-day workload, through empathic feedback. When our learners are treated with kindness and respect they will lead more fulfilling lives and be better able to provide the high-quality care and caring all patients deserve.

The state-of-the-art crop phenological classifiers use vegetation index (VI) time-series data and deep learning (DL) techniques. However, the scarcity of training samples limits the performance of these approaches. Unlike the conventional augmentation techniques, the data augmentation of VI curves should preserve the crop-specific phenological events. The DL-based augmentation approaches do not give good results when the training samples are limited. Also, the conventional approaches such as translation, rotation, scaling, and wrapping do not preserve the characteristic features of the index curves, thereby making them inappropriate for the VI-curve-based augmentations. This article proposes a non-DL-based data augmentation strategy that requires only a minimal number of actual training samples. In the proposed approach, the periodic phenological events and the underlying trend for each crop class are modeled to improve the augmentation. The trends of different crop classes are estimated by jointly maximizing the autocorrelation and variance, while the optimal subsequences are generalized as the phenological events. The proposed augmentation strategy of using Maximal overlap discrete wavelet transform for obtaining the surrogates that retain the crop-specific features and periodicities significantly improves the results. It may be noted that the proposed approach does not alter the wavelet coefficients that are characteristics of a given crop class. The experiments using time series VI data, covering 90 fields of wheat, and 60 fields of barley, confirm better accuracy of the proposed augmentation approaches as compared to the prominent approaches.

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.

Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) was also demonstrated. We demonstrated that hyperspectral reflectance analyses can provide reliable information about seed germination and levels of susceptibility in A. palmeri. The use of reflectance-based analyses can help to better understand the invasiveness of A. palmeri, and thus facilitate the development of targeted control methods. It also has enormous potential for impacting environmental management in that it can be used to prevent ineffective herbicide applications. It also has potential for use in mapping tempo-spatial population dynamics in agro-ecological landscapes.

Control of effective magnetization textures like skyrmions is limited by the thermodynamic stability in current materials. Here, the authors propose a 3D nonlinear photonic crystal to emulate 2D spin transport phenomena with excellent controllability.

PurposeThe global mobility of medical student and trainee populations has drawn researchers' attention to consider internationalization in medical education. Recently, researchers have focused on cultural diversity, predominately drawing on Hofstede's cross-cultural analysis of cultural dimensions from general population data to explain their findings. However, to date no research has been specifically undertaken to examine cultural dimensions within a medical student or trainee population. This is problematic as within-country differences between gender and professional groups have been identified within these dimensions. We address this gap by drawing on the theoretical concept of national context effects: specifically Hofstede's six-dimensional perspective. In doing so we examine medical students' and trainees' country profiles across dimensions, country-by-gender clustering, and differences between our data and Hofstede's general population data.MethodsWe undertook a cross-cultural online questionnaire study (eight languages) containing Hofstede's 2013 Values Survey. Our questionnaire was live between 1st March to 19th Aug 2018, and December 2018 to mitigate country holiday periods. We recruited undergraduate medical students and trainees with at least 6-months' clinical training using school-specific methods including emails, announcements, and snowballing.ResultsWe received 2,529 responses. Sixteen countries were retained for analyses (n = 2,307, 91%): Australia, Chile, China, Hong Kong, India, Indonesia, Ireland, Israel, Japan, Malaysia, New Zealand, Pakistan, South Africa, South Korea, Sri-Lanka, Taiwan. Power distance and masculinity are homogenous across countries. Uncertainty avoidance shows the greatest diversity. We identified four country clusters. Masculinity and uncertainty are uncorrelated with Hofstede's general population data.ConclusionsOur medical student and trainee data provides medical education researchers with more appropriate cultural dimension profiles than those from general population data. Country cluster profiles stimulate useful hypotheses for further research, especially as patterning between clusters cuts across traditional Eastern-Western divides with national culture being stronger than gendered influences. The Uncertainty dimension with its complex pattern across clusters is a particularly fruitful avenue for further investigation.

Tall buildings have become a prevalent typology around the world. They carry benefits for the entrepreneur (profits) and the local authority (municipal taxes), the architect and the tenants (prestige). Yet little is known about the actual needs of the building itself, the fact that its different levels are exposed to different climatic conditions, thus have different energy needs, or the contingencies involved in living high up in, or down around them. This paper presents results of ongoing research, including tall buildings’ microclimatic peculiarities with height and ensuant energy needs; building envelope design and energy efficiency; microclimatic peculiarities created on the pedestrian level; and environmental quality. Specific modules results have been published in several papers, while additional work is ongoing, since this building prototype and its implications are still mostly poorly understood. Such work ties into climate change and the built environment, public health, survivability and resilience under extreme environmental events, which seem to become the norm. Results of parts of this research certainly tie into Covid-related contingencies and the need for usable public open spaces, efficient building ventilation to ensure good Indoor Air Quality (IAQ), and a whole array of other issues.

In most parts of the electromagnetic spectrum, solar radiation cannot penetrate clouds. Therefore, cloud detection and masking are essential in image preprocessing for observing the Earth and analyzing its properties. Because clouds vary in size, shape, and structure, an accurate algorithm is required for removing them from the area of interest. This task is usually more challenging over bright surfaces such as exposed sunny deserts or snow than over water bodies or vegetated surfaces. The overarching goal of the current study is to explore and compare the performance of three Convolutional Neural Network architectures (U-Net, SegNet, and DeepLab) for detecting clouds in the VENμS satellite images. To fulfil this goal, three VENμS tiles in Israel were selected. The tiles represent different land-use and cover categories, including vegetated, urban, agricultural, and arid areas, as well as water bodies, with a special focus on bright desert surfaces. Additionally, the study examines the effect of various channel inputs, exploring possibilities of broader usage of these architectures for different data sources. It was found that among the tested architectures, U-Net performs the best in most settings. Its results on a simple RGB-based dataset indicate its potential value for any satellite system screening, at least in the visible spectrum. It is concluded that all of the tested architectures outperform the current VENμS cloud-masking algorithm by lowering the false positive detection ratio by tens of percents, and should be considered an alternative by any user dealing with cloud-corrupted scenes.

Summary: Ultraviolet (UV) light affects endocrinological and behavioral aspects of sexuality via an unknown mechanism. Here we discover that ultraviolet B (UVB) exposure enhances the levels of sex-steroid hormones and sexual behavior, which are mediated by the skin. In female mice, UVB exposure increases hypothalamus-pituitary-gonadal axis hormone levels, resulting in larger ovaries; extends estrus days; and increases anti-Mullerian hormone (AMH) expression. UVB exposure also enhances the sexual responsiveness and attractiveness of females and male-female interactions. Conditional knockout of p53 specifically in skin keratinocytes abolishes the effects of UVB. Thus, UVB triggers a skin-brain-gonadal axis through skin p53 activation. In humans, solar exposure enhances romantic passion in both genders and aggressiveness in men, as seen in analysis of individual questionaries, and positively correlates with testosterone level. Our findings suggest opportunities for treatment of sex-steroid-related dysfunctions.

Mining contributes significantly to economic development, but it also entails extensive environmental damage, such as soil degradation and water and air pollution. Mining activity impacts the soil quality, often making it unable to support ecosystem function and structure. The current study aims to apply the soil quality index (SQI) as a methodology for quantifying soil restoration status in an open-pit phosphate mine in Israel’s hyper-arid environment. In this regard, we evaluated an ecological restoration practice that includes topsoil refilling compared to the adjacent undisturbed natural system, using transformed and standardized scorings of 11 physical, biological, and chemical soil properties that were further statistically integrated into overall SQI values. Our results revealed significant differences between the restoration practice areas and the nearby natural areas, with a higher soil quality value in the latter. It is proposed that the topsoil restoration method is mainly affected by soil biological indicators, such as soil organic matter, soil proteins, and polysaccharides related to micro-organic growth, and in a lesser extent, by physical properties (primarily infiltration rate, followed by AWC). The former properties encourage the biocrust establishment, which is essential for soil surface stabilization and affects the water infiltration rate and nutrient availability. The chemical indicators showed no significant differences between most of the sites for the overall soil quality. In conclusion, soil properties, primarily physio-biological ones, should be selected to quantify and evaluate restoration practices in hyper-arid ecosystems.

Purpose: Good relationships between physicians, patients, families and the healthcare team are essential for high‐quality care. Medical encounters are sometimes challenging. They may include conflicts, requiring physicians to be assertive: that is to share and protect their needs, rights and values while preserving those of others. Whereas assertiveness has been studied in patients and nursing staff (those with less power in healthcare), physicians' assertiveness, which must be mindful of these power differences, lacks a comprehensive review. Thus, this scoping review focuses on assertive communication in physicians' encounters. Methods: A literature search of four online databases: MEDLINE, Embase, PsycINFO and WoS, seeking articles on physicians' assertiveness as a communication style published until February 2022. The Joanna Briggs Institute approach and the Preferred Reporting Items for Systematic Reviews checklist underpinned the review protocol. Results: We identified 1513 articles relating to assertiveness, reviewed 153 full‐text articles and selected 22 for full review, 68% from the last decade. The articles focused mainly on assertive communication and relationships with medical staff, with 54% focusing on bottom‐up power relations. In 40% of the articles, no clear definition of assertiveness was included. Definitions included had varied focus: on self, on the other or both. Overall, assertiveness measures varied widely, precluding a methodical comparison. Conclusions: Despite the growing interest in physicians' assertiveness, a clearer definition and in‐depth exploration of assertiveness are needed alongside development of valid measures of assertiveness appropriate to physicians. Based on the review, we offer a relational definition of assertiveness as the capacity to communicate one's views, concerns, rights and needs while respecting others and preserving therapeutic, collegial and educational professional alliances. This definition may serve to expand research in the field while offering a professional alternative to problematic communication styles—passive and self‐denying or paternalistic and aggressive —that obfuscate and thus undermine physician–patient relationships. Gutgeld‐Dror et al. provide evidence that assertiveness is important to physicians' interactions as long as it takes into account the needs of both parties and is engaged while valuing maintainance of professional relationships. [ABSTRACT FROM AUTHOR]

Recent climatic changes have profoundly impacted the urban microclimate, exposing city dwellers to harsh living conditions. One effective approach to mitigating these events involves incorporating more green infrastructure into the cityscape. The ecological services provided by urban vegetation play a crucial role in enhancing the sustainability and livability of cities. However, monitoring urban vegetation and accurately estimating its status pose challenges due to the heterogeneous nature of the urban environment. In response to this, the current study proposes utilizing a remote sensing-based classification framework to enhance data availability, thereby improving practices related to urban vegetation management. The aim of the current research is to explore the spatial pattern of vegetation and enhance the classification of tree species within diverse and complex urban environments. This study combines various remote sensing observations to enhance classification capabilities. High-resolution colored rectified aerial photographs, LiDAR-derived products, and hyperspectral data are merged and analyzed using advanced classifier methods, specifically partial least squares-discriminant analysis (PLS-DA) and object-based image analysis (OBIA). The OBIA method demonstrates an impressive overall accuracy of 95.30%, while the PLS-DA model excels with a remarkable overall accuracy of 100%. The findings validate the efficacy of incorporating OBIA, aerial photographs, LiDAR, and hyperspectral data in improving tree species classification and mapping within the context of PLS-DA. This classification framework holds significant potential for enhancing management practices and tools, thereby optimizing the ecological services provided by urban vegetation and fostering the development of sustainable cities. [ABSTRACT FROM AUTHOR]

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.

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.