Unveiling spatial inequalities: Exploring county-level disaster damages and social vulnerability on public disaster assistance in contiguous US.

Understanding the dynamics between public disaster assistance, disaster damages, and social vulnerability at county-level is crucial for designing effective disaster mitigation strategies. This study utilized the Local Bivariate Moran Index (LBMI) and geographically weighted regression (GWR) models to examine spatial patterns and relationships between disaster damages, social vulnerability, and public disaster assistance in contiguous US counties from 2001 to 2021. LBMI results reveal that public disaster assistance has predominantly been directed towards post-disaster recovery efforts, with a particular focus on coastal communities affected by major declared disasters. However, the distributions of public assistance and individual housing assistance, which are the two primary sources of public disaster assistance, do not adequately cover physically and socially vulnerable communities. The distribution of pre-disaster risk mitigation also falls short of sufficiently covering vulnerable communities. Results further indicate the complex interactions between different categories of natural disasters and public assistances. The GWR model results demonstrate spatial variations in predicting each category of public disaster assistance. These findings indicate the need to address disparities in accessing public disaster assistance in the US, and advocate for more equitable disaster mitigation strategies.

How attachment style, mentalization and preparedness for death are associated with pre-loss grief symptoms' severity: A network analysis study in caregivers of terminally ill cancer patients.

Several psychological dimensions influence the psychological adjustment of terminally ill cancer patients' caregivers, during the end-of-life phase. The present study explored the associations between attachment styles, mentalization, preparedness for death, and the severity of pre-loss grief symptoms in 102 caregivers of terminal cancer patients. The results of the network analysis showed that insecure attachment dimensions were positively associated with several central pre-loss grief symptoms. Mentalization and preparedness for death showed negative associations with several pre-loss grief symptoms. Interestingly, bitterness showed a negative association with need for approval and a positive association with mentalization. The results provided insight into the grieving process for palliative care providers to implement effective caregiver support interventions.

Impact-based probabilistic modeling of hydro-morphological processes in China (1985-2015).

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) pose a relevant threat to infrastructure, urban and rural settlements and to lives in general. This has been widely observed in recent years and will likely become worse as climate change will influence the spatio-temporal pattern of precipitation events. The modelling of where HMP-driven hazards may occur can help define the appropriate course of actions before and during a crisis, reducing the potential losses that HMPs cause in their wake. However, the probabilistic information on locations prone to experience a given hazard is not sufficient to depict the risk our society may incur. To cover this aspect, modeling the loss information could open up to better territorial management strategies. In this work, we made use of the HMP catalogue of China from 1985 to 2015. Specifically, we implemented the Light Gradient Boosting (LGB) classifier to model the impact level that locations across China have suffered from HMPs over the thirty-year record. We obtained six impact levels as a combination of financial and life losses, whose classes we used as separate target variables for our LGB. In doing so, we estimated spatial probabilities of certain HMP impact, something that has yet to be tested in the natural hazard community, especially over such a large spatial domain. The results we obtained are encouraging, with each of the six impact categories being separately classified with excellent to outstanding performance (the worst case corresponds to a mean AUC = 0.862, whereas the best case corresponds to a mean AUC of 0.915). The good predictive performance our model produced suggest that the cartographic output could be useful to inform authorities of locations prone to human and infrastructural losses of specific magnitudes.

The role of family functioning, attachment style, and care setting on pre-loss grief symptoms and burden in caregivers of terminally cancer patients.

OBJECTIVES: The aim of the present cross-sectional study was to investigate the associations between caregivers' attachment styles, family functioning, the care setting and pre-loss grief symptoms, the burden, and the caregivers' belief of patients' awareness of the terminal cancer diagnosis. METHODS: A total of 101 caregivers of patients with terminal cancer in residential hospice care and home care were interviewed and completed self-report questionnaires. RESULTS: Insecure attachment style and home care setting were associated with worse psychological effects in caregivers of patients with terminal cancer. Moreover, family cohesion can promote low social burden and the patient's awareness of their terminal condition. SIGNIFICANCE OF RESULTS: This study highlighted the importance of evaluating the caregiver's attachment style, family functioning, and the setting of care during the terminal phase of the patient's life. These findings will be useful to planning interventions to prevent burden and the pre-loss grief symptoms in the caregivers.

Will I or my loved one die? Concordant awareness between terminal cancer patients and their caregivers is associated with lower patient anxiety and caregiver burden.

OBJECTIVE: The aims of this study were to investigate the association between patients' awareness of their terminal illness and the levels of anxiety and depression, whether the concordance between the patients' and caregivers' belief about the patient's terminal illness was associated with patient's anxiety and depression, and with the caregiver burden. METHOD: The study recruited 31 terminally ill patients with cancer along with their caregivers from a Palliative Care Unit. All data about patients and caregivers' awareness of the illness, patients' depression and anxiety, and caregiver burden were collected. RESULTS: Patients aware of their short-term prognosis of death showed lower levels of anxiety than the unaware ones, especially women. Aware patients with concordant caregivers showed lower levels of anxiety but not of depression. Caregivers concordant with the patients' awareness presented lower levels of strain and burden. Finally, terminal patients who had an adult child caregiver were less likely to be aware of their terminal condition. CONCLUSIONS: It appears that illness awareness and the caregiver's concordance with the patient's belief on the terminal condition are associated with lower anxiety, especially in women, and a reduced burden for caregivers.

Soil erosion modelling: A bibliometric analysis.

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

Persistent complex bereavement disorder in caregivers of terminally ill patients undergoing supportive-expressive treatment: a pilot study.

BACKGROUND: The proposal of persistent complex bereavement disorder (PCBD) in the DSM-V increased the interest on the impact of grief on the psychological health. AIMS: Investigating the time course of psychological symptoms, emotional and social abilities in caregivers (undergoing or not to supportive-expressive treatment) of terminally ill cancer patients from 1 months before loss to 14 months after it. METHOD: Thirty-three of 60 caregivers were assessed by PG-12, HAM-A, HAM-D, TAS-20 and ASQ, at the admission in Hospice, and after 3, 10 and 14 months from the loss. Twelve caregivers adhered to follow a supportive-expressive treatment and 21 caregivers did not. RESULTS: PG-12, anxiety, and depression scores decreased in both groups over time. The score of difficulty in identifying emotions and confidence with closeness decreased significantly only in the treated-group. PG-12 score at T0 was able to predict the DSM V diagnosis of PCBD at T3. CONCLUSIONS: Findings showed a decrease of the anxiety, depression, security in the attachment style and an increase of the ability to identify emotions during the first year after loss in caregivers of terminally ill cancer patients. Pre-loss assessment of prolonged grief risk seems useful to predict the diagnosis of PCBD 1 year after loss.

Spiritual well-being, dignity-related distress and demoralisation at the end of life-effects of dignity therapy: a randomised controlled trial.

OBJECTIVES: This single-centre prospective randomised controlled study aimed to investigate the effectiveness of dignity therapy on spiritual well-being, demoralisation and dignity-related distress compared with standard palliative care. METHODS: A total of 111 terminally ill hospice patients were randomly allocated to one of two groups: dignity therapy plus standard palliative care (intervention group) or standard palliative care alone (control group). The main outcomes were meaning, peace, faith, loss of meaning and purpose, distress and coping ability, existential distress, psychological distress and physical distress. Assessments were conducted at baseline, 7-10 and 15-20 days. RESULTS: Following randomisation, 11 dropped out before baseline assessment and 33 after post-treatment assessment. A total of 67 patients completed the study, 35 in the experimental group and 32 in the control group. Repeated measures general linear model showed significant differences between groups on peace and psychological distress over time, but not on existential distress, physical distress, meaning and purpose, distress and coping ability, meaning and faith. Specifically, patients in the dignity therapy intervention maintained similar levels of peace from baseline to follow-up, whereas patients in the control group significantly declined in peace during the same time period. Moreover, psychological distress significantly decreased from pretreatment to post-treatment in the intervention group and increased in the control group. CONCLUSIONS: Dignity therapy may be an effective intervention in maintaining sense of peace for terminally ill patients. The findings of our study are of relevance in palliative care and suggest the potential clinical utility of this psychological intervention.

Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy.

Shallow landslides represent potentially damaging processes in mountain areas worldwide. These geomorphic processes are usually caused by an interplay of predisposing, preparatory, and triggering environmental factors. At regional scales, data-driven methods have been used to model shallow landslides by addressing the spatial and temporal components separately. So far, few studies have explored the integration of space and time for landslide prediction. This research leverages generalized additive mixed models to develop an integrated approach to model shallow landslides in space and time. We built upon data on precipitation-induced landslide records from 2000 to 2020 in South Tyrol, Italy (7400 km2). The slope unit-based model predicts landslide occurrence as a function of static and dynamic factors while seasonal effects are incorporated. The model also accounts for spatial and temporal biases inherent in the underlying landslide data. We validated the resulting predictions through a suite of cross-validation techniques, obtaining consistent performance scores above 0.85. The analyses revealed that the best-performing model combines static ground conditions and two precipitation time windows: a short-term cumulative precipitation of 2 days before the landslide event and a medium-term cumulative precipitation of 14 days. We demonstrated the model's predictive capabilities by predicting the dynamic landslide probabilities over historical data associated with a heavy precipitation event on August 4th and August 5th, 2016, and hypothetical non-spatially explicit precipitation (what-if) scenarios. The novel approach shows the potential to integrate static and dynamic landslide factors for large areas, accounting for the underlying data structure and data limitations.

Assessing multi-hazard susceptibility to cryospheric hazards: Lesson learnt from an Alaskan example.

Classifying a given landscape on the basis of its susceptibility to surface processes is a standard procedure in low to mid-latitudes. Conversely, these procedures have hardly been explored in periglacial regions. However, global warming is radically changing this situation and will change it even more in the future. For this reason, understanding the spatial and temporal dynamics of geomorphological processes in peri-arctic environments can be crucial to make informed decisions in such unstable environments and shed light on what changes may follow at lower latitudes. For this reason, here we explored the use of data-driven models capable of recognizing locations prone to develop retrogressive thaw slumps (RTSs) and/or active layer detachments (ALDs). These are cryospheric hazards induced by permafrost degradation, and their development can negatively affect human settlements or infrastructure, change the sediment budget and release greenhouse gases. Specifically, we test a binomial Generalized Additive Modeling structure to estimate the probability of RST and ALD occurrences in the North sector of the Alaskan territory. The results we obtain show that our binary classifiers can accurately recognize locations prone to RTS and ALD, in a number of goodness-of-fit (AUCRTS = 0.83; AUCALD = 0.86), random cross-validation (mean AUCRTS = 0.82; mean AUCALD = 0.86), and spatial cross-validation (mean AUCRTS = 0.74; mean AUCALD = 0.80) routines. Overall, our analytical protocol has been implemented to build an open-source tool scripted in Python where all the operational steps are automatized for anyone to replicate the same experiment. Our protocol allows one to access cloud-stored information, pre-process it, and download it locally to be integrated for spatial predictive purposes.

Surface temperature controls the pattern of post-earthquake landslide activity.

The patterns and controls of the transient enhanced landsliding that follows strong earthquakes remain elusive. Geostatistical models can provide clues on the underlying processes by identifying relationships with a number of physical variables. These models do not typically consider thermal information, even though temperature is known to affect the hydro-mechanical behavior of geomaterials, which, in turn, controls slope stability. Here, we develop a slope unit-based multitemporal susceptibility model for the epicentral region of the 2008 Wenchuan earthquake to explore how land surface temperature (LST) relates to landslide patterns over time. We find that LST can explain post-earthquake landsliding while it has no visible effect on the coseismic scene, which is dominated by the strong shaking. Specifically, as the landscape progressively recovers and landslide rates decay to pre-earthquake levels, a positive relationship between LST and landslide persistence emerges. This seems consistent with the action of healing processes, capable of restoring the thermal sensitivity of the slope material after the seismic disturbance. Although analyses in other contexts (not necessarily seismic) are warranted, we advocate for the inclusion of thermal information in geostatistical modeling as it can help form a more physically consistent picture of slope stability controls.

New pedotransfer approaches to predict soil bulk density using WoSIS soil data and environmental covariates in Mediterranean agro-ecosystems.

For the estimation of the soil organic carbon stocks, bulk density (BD) is a fundamental parameter but measured data are usually not available especially when dealing with legacy soil data. It is possible to estimate BD by applying pedotransfer function (PTF). We applied different estimation methods with the aim to define a suitable PTF for BD of arable land for the Mediterranean Basin, which has peculiar climate features that may influence the soil carbon sequestration. To improve the existing BD estimation methods, we used a set of public climatic and topographic data along with the soil texture and organic carbon data. The present work consisted of the following steps: i) development of three PTFs models separately for top (0-0.4 m) and subsoil (0.4-1.2 m), ii) a 10-fold cross-validation, iii) model transferability using an external dataset derived from published data. The development of the new PTFs was based on the training dataset consisting of World Soil Information Service (WoSIS) soil profile data, climatic data from WorldClim at 1 km spatial resolution and Shuttle Radar Topography Mission (SRTM) digital elevation model at 30 m spatial resolution. The three PTFs models were developed using: Multiple Linear Regression stepwise (MLR-S), Multiple Linear Regression backward stepwise (MLR-BS), and Artificial Neural Network (ANN). The predictions of the newly developed PTFs were compared with the BD calculated using the PTF proposed by Manrique and Jones (MJ) and the modelled BD derived from the global SoilGrids dataset. For the topsoil training dataset (N = 129), MLR-S, MLR-BS and ANN had a R2 0.35, 0.58 and 0.86, respectively. For the model transferability, the three PTFs applied to the external topsoil dataset (N = 59), achieved R2 values of 0.06, 0.03 and 0.41. For the subsoil training dataset (N = 180), MLR-S, MLR-BS and ANN the R2 values were 0.36, 0.46 and 0.83, respectively. When applied to the external subsoil dataset (N = 29), the R2 values were 0.05, 0.06 and 0.41. The cross-validation for both top and subsoil dataset, resulted in an intermediate performance compared to calibration and validation with the external dataset. The new ANN PTF outperformed MLR-S, MLR-BS, MJ and SoilGrids approaches for estimating BD. Further improvements may be achieved by additionally considering the time of sampling, agricultural soil management and cultivation practices in predictive models.

Soil erosion modelling: A global review and statistical analysis.

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named 'Global Applications of Soil Erosion Modelling Tracker (GASEMT)', includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

Dignity Therapy Helps Terminally Ill Patients Maintain a Sense of Peace: Early Results of a Randomized Controlled Trial.

Introduction: Dignity Therapy (DT) is a brief, individualized, narrative psychotherapy developed to reduce psychosocial and existential distress, and promote dignity, meaning, and hope in end of life patients. Previous studies have shown that DT was effective in reducing anxiety and depression, and improving dignity-related distress. However, less is known about its efficacy on spiritual well-being. The aim of this study is to contribute to the existing literature by investigating the effects of DT on specific dimensions of spiritual well-being, demoralization and dignity-related distress in a sample of terminally ill patients. Methods: A randomized, controlled trial was conducted with 64 terminally ill patients who were randomly assigned to the intervention group (DT + standard palliative care) or the control group (standard palliative care alone). The primary outcome measures were Meaning, Peace, and Faith whereas the secondary outcome measures were (loss of) Meaning and purpose, Distress and coping ability, Existential distress, Psychological distress, and Physical distress. All measures were assessed at baseline (before the intervention), 7-10 and 15-20 days after the baseline assessment. The trial was registered with ClinicalTrials.gov (Protocol Record NCT04256239). Results: The MANOVA yielded a significant effect for the Group X Time interaction. ANOVA with repeated measures showed a significant effect of time on peace and a significant Group X Time interaction effect on peace. Post hoc comparisons revealed that, while there was a decrease in peace from pre-treatment to follow-up and from post-treatment to follow-up in the control group, there was no such trend in the intervention group. Discussion: This study provides initial evidence that patients in the DT intervention maintained similar levels of peace from pre-test to follow-up, whereas patients in the control group showed a decrease in peace during the same time period. We did not find significant longitudinal changes in measures of meaning, faith, loss of meaning and purpose, distress and coping ability, existential, psychological and physical distress. The findings of our study are of relevance in palliative care and suggest the potential clinical utility of DT, since they offer evidence for the importance of this intervention in maintaining peace of mind for terminally ill patients.

GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches.

In arid and semi-arid areas, groundwater resource is one of the most important water sources by the humankind. Knowledge of groundwater distribution over space, associated flow and basic exploitation measures can play a significant role in planning sustainable development, especially in arid and semi-arid areas. Groundwater potential mapping (GWPM) fits in this context as the tool used to predict the spatial distribution of groundwater. In this research we tested four GIS-based models for GWPM, consisting of: i) random forest (RF); ii) weight of evidence (WoE); iii) binary logistic regression (BLR); and iv) technique for order preference by similarity to ideal solution (TOPSIS) multi-criteria. The Shahroud plain located in Iran, was selected to research the water scarcity and over-exploitation of groundwater resources over the past 20 years. In this research, using Iranian Department of Water Resources Management data, and extensive field surveys, 122 groundwater well data with high potential yield of ≥11 m3 h-1 were selected for GWPM. Specifically, we generated four different models selecting 70% (n = 85) of the wells and validated the resulting GWP maps upon the complementary 30% (n = 37).A total of fifteen ground water conditioning factors to explain the groundwater well distribution over the Shahroud plain were selected. From the Advanced Land Observing Satellite (ALOS), a DEM (30 m resolution) was extracted to calculate a set of morphometric properties which were combined with thematic ones such as land use/land cover (LU/LC) and Soil Type (ST). Results show that in RF (LU/LC), LR (ST), and AHP (Slope) are the most relevant contributors to groundwater occurrence. After that, using the natural break method, final maps were divided into five susceptibility classes of very low, low, moderate, high, and very high. The accuracy of models was ultimately tested using prediction rate (validation data), success rate (training data) and the seed cell area index (SCAI) indicators. Results of validation show that BLR with prediction rate of 0.905 (90.5%) and success rate of 0.918 (91.8%) had higher accuracy than WoE, RF and TOPSIS models with respective prediction rates of 0.885, 0.873 and 0.870 (88.5%, 87.3%, and 87%) and success rate of 0.900, 0.889, and 0.881 (90%, 88.9%, and 88.1%). SCAI results show that all models have acceptable classification accuracy although BLR outperformed the other models in terms of accuracy. Results show that the combination of remote sensing (RS) data and geographic information system (GIS) with new approaches can be used as a powerful tool in GWPM in arid and semi-arid areas. The results of this investigation introduced a potential novel methodology that could be used by decision-makers for the sustainable management of ground water resources.

PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches.

Geospatial computation, data transformation to a relevant statistical software, and step-wise quantitative performance assessment can be cumbersome, especially when considering that the entire modelling procedure is repeatedly interrupted by several input/output steps, and the self-consistency and self-adaptive response to the modelled data and the features therein are lost while handling the data from different kinds of working environments. To date, an automated and a comprehensive validation system, which includes both the cutoff-dependent and -independent evaluation criteria for spatial modelling approaches, has not yet been developed for GIS based methodologies. This study, for the first time, aims to fill this gap by designing and evaluating a user-friendly model validation approach, denoted as Performance Measure Tool (PMT), and developed using freely available Python programming platform. The considered cutoff-dependent criteria include receiver operating characteristic (ROC) curve, success-rate curve (SRC) and prediction-rate curve (PRC), whereas cutoff-independent consist of twenty-one performance metrics such as efficiency, misclassification rate, false omission rate, F-score, threat score, odds ratio, etc. To test the robustness of the developed tool, we applied it to a wide variety of geo-environmental modelling approaches, especially in different countries, data, and spatial contexts around the world including, the USA (soil digital modelling), Australia (drought risk evaluation), Vietnam (landslide studies), Iran (flood studies), and Italy (gully erosion studies). The newly proposed PMT is demonstrated to be capable of analyzing a wide range of environmental modelling results, and provides inclusive performance evaluation metrics in a relatively short time and user-convenient framework whilst each of the metrics is used to address a particular aspect of the predictive model. Drawing on the inferences, a scenario-based protocol for model performance evaluation is suggested.

The Effects of the Perception of Being Recognized by Patients With Alzheimer Disease on a Caregiver's Burden and Psychophysical Health.

Caring for a patient with Alzheimer disease (AD) represents a real challenge that can have considerable long-term psychological and physical consequences. The aim of this study was to evaluate the impact of the perception of being recognized on both the psychophysical health and the level of burden reported in caregivers of patients with AD. The secondary aim was to evaluate the association between the use of a home care assistance service and the burden and psychophysical health in caregivers. The Caregiver Burden Inventory (CBI), the 36-item Short-Form Health Survey (SF-36), and a visual analog scale (VAS) to evaluate the level of perception of being recognized were administered to 31 caregivers of patients with AD. Data were also collected from patients with AD using the Mini-Mental State Examination (MMSE). The level of perception of being recognized was significantly and negatively correlated with total burden ( r = -0.36; P = .045) and objective burden ( r = -0.53; P = .002). It was also significantly and positively correlated with the MMSE score ( r = 41; P = .02). Regression models showed that only the perception of being recognized, and not MMSE, significantly predicted lower caregiver objective burden scores. Furthermore, the use of a home care assistance service significantly predicted higher caregiver physical functioning. The perception of being recognized by an AD relative significantly predicted the caregiver's objective burden associated with a shortage of time. The use of a home care assistance service also resulted in a promotion in the caregiver's social functioning. Further studies are needed to confirm these results.

Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling.

SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0-0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R2 (0.63-0.69) and low uncertainty (s.d.<0.76gCkg-1 with RS, and <1.25gCkg-1 without RS). These outputs allowed depicting a time variation of SOC at 1arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. RS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-RS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of RS covariates enhanced the modelling performance.

Spirituality and Awareness of Diagnoses in Terminally Ill Patients With Cancer.

OBJECTIVE: Aims of the present study were to investigate the association between awareness of own illness condition and psychological outcome in end-of-life phase and to test the association between the spirituality and the awareness of own illness condition. METHODS: Three hundred and ninety-nine terminally ill patients with cancer were enrolled in a hospice in central Italy. One hundred patients satisfied the inclusion criteria. The Systems of Belief Inventory, the Hospital Anxiety and Depression Scale, and a psychological interview to determine the level of awareness of the illness diagnosis (aware; partially aware; and not aware) were administered to terminally ill patients. RESULTS: The main finding was that the awareness of one's own illness condition was positively associated with the extrinsic spirituality and negatively associated with intrinsic spirituality (regression model R = .26; R2 = .07; adjusted R2 = .05; F2, 97 = 3.45; P = .036). The aware group showed lower anxiety and depression ( F2, 97 = 1.9; P = . 075; F2, 97 = 2.6; P = .04) scores than partially aware and not aware groups. The psychological outcome was not associated with the spirituality level. CONCLUSION: In terminally ill patients with cancer, the levels of depression and anxiety were lower in patients aware of their own illness state. Moreover, higher levels of extrinsic and lower levels of intrinsic spirituality predicted the awareness of one's own illness state.