Biomimetic Upconversion Nanoplatform Synergizes Photodynamic Therapy and Enhanced Radiotherapy against Tumor Metastasis.

The easy recurrence and high metastasis of fatal tumors require the development of a combination therapy, which is able to overcome the drawbacks of monomodal strategies such as surgery, photodynamic therapy (PDT), and radiotherapy (RT). Taking the complementary advantages of PDT and RT, we present herein the integration of lanthanide-doped upconversion nanoparticles (UCNPs) with chlorin e6 (Ce6)-imbedded RBC membrane vesicles as a near-infrared-induced PDT agent for achieving synchronous depth PDT and RT with reduced radiation exposure. In such a nanoagent, gadolinium-doped UCNPs with strong X-ray attenuation ability act not only as a light transductor to activate the loaded photosensitizer Ce6 to allow PDT but also as a radiosensitizer to enhance RT. PDT with enhanced low-dose RT can achieve synergistic inhibition of tumor growth by producing reactive oxygen species to destroy local tumor cells and inducing strong T-cell-dependent immunogenic cell death to arrest systemic cancer metastasis. This combination of PDT and RT might be a potential appealing strategy for tumor eradication.

The ability of critical care nurses to identify pressure injury and incontinence-associated dermatitis: A multicentre cross-sectional survey.

AIM: To investigate the ability of critical care nurses to identify pressure injury and incontinence-associated dermatitis and analyse the possible influencing factors. DESIGN: Cross-sectional survey. METHODS: This study was conducted at 24 hospitals across 12 provinces in China. A self-made electronic questionnaire was used. Nurses identified and judged injuries according to the information provided. RESULTS: The average identification score for pressure injury and incontinence-associated dermatitis was 9.00 ± 3.51 points, and only 2.16% of nurses scored ≥16 points. The average correct identification rate for pressure injury and incontinence-associated dermatitis was 45%. The correct identification rate for stage 1 pressure injury was the highest, while those for stage 3, stage 4, deep tissue pressure injury and unstageable pressure injury were all lower than 50%; incontinence-associated dermatitis was also easily misjudged. Nurses' educational backgrounds, professional titles, job positions, hospital levels and learning frequency were the factors that affected their ability to identify pressure injury and incontinence-associated dermatitis.

Multidimensional soil salinity data mining and evaluation from different satellites.

Soil salinization, a common land degradation mode, restricts the ecological environment and is a global issue due to climate change. Accurately, quickly and effectively monitoring soil salinity is critical for governmental institutions that develop hazard prevention and mitigation strategies. Remote sensing (RS) technology provides a viable alternative to traditional field work due to its large area coverage, abundant spectral information and nearly constant observations. Key issues in RS-based soil salinity monitoring include the lack of both data-mining techniques for obtaining spectral band information and comprehensive considerations of synergies among different spectra. The main objective of this study was to provide in-depth explorations of data mining and integration algorithms from different satellites to multidimensionally evaluate soil salinity models. The Ebinur Lake Wetland Reserve (Xinjiang Province, China) was selected as a case study. First, ground-measured visible and near infrared (VIS-NIR) spectral data were combined with the RS band to simulate Landsat 8 (L8) and Sentinel 2 (S2) and 3 (S3) data. Second, one-dimensional RS bands and 15 soil salinity and vegetation indices were selected, and 15 spectral data transformations (reciprocal, differential, absorbance, etc.) were obtained. Two- and three-dimensional spectral indices were constructed, and the response relationships between different spectral indices and soil electrical conductivity (EC) were comprehensively explored. Finally, an integrated multidimensional algorithm was used to estimate soil salinity in high-performance models for the three satellites. The results showed that all data-mining-based model combinations performed well for all satellites (R2 > 0.80). However, with multidimensional model combinations, S3 presented the highest predictive capability (R2 = 0.89, RMSE = 2.57 mS·cm-1, RPD = 2.05), followed by S2 (R2 = 0.86, RMSE = 2.71 mS·cm-1, RPD = 1.90) and L8 (R2 = 0.85, RMSE = 2.84 mS·cm-1, RPD = 1.87). Therefore, data mining with integration algorithms in model combinations performs significantly better than previous models and could be considered a promising method for obtaining improved results from soil salinity susceptibility models in similar cases.

Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China.

Purpose: In the urban region of Shenzhen, the changes in the concentration of Black carbon (BC) have been evaluated throughout the dry season, and apportioned the BC sources, including in the form of fossil fuel (e.g., vehicle emissions) and biomass fuel (e.g., industrial emissions). Methods: The new seven-channel aethalometer model (AE-33), PM2.5, and meteorological data were collected in the dry season (October-May) from 2019 to 2020, to quantify BC emissions in urban Shenzhen. Explored the source allocation of BC based on Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) model. Results: We revealed that the mean BC concentration was 2672 ± 1506 ng/m3 in the dry season, with values of 4062 ± 1182 ng/m3, 2519 ± 1568 ng/m3, and 1900 ± 776 ng/m3 in autumn, winter, and spring, respectively. Additionally, we found that fossil fuels have higher contributions to BC concentrations (86.3% to 86.8% in autumn and spring) in the dry season than biomass fuels (16% to 20% in autumn, spring and winter), which is different from Beijing, Nanjing and other large economic zones in China. The diurnal variation in BC and the contribution of fossil fuels indicate that there is a significantly greater increase in BC during peak traffic hours in urban Shenzhen than in other cities. Finally, meteorological parameters and PM2.5 data provided supporting evidence that BC is sourced mainly from local vehicle emissions and industry-related combustion in the western and northeastern/southeastern parts of the study area. Conclusion: This study showed that the concentration of BC is lower than other regions, and the source allocation is mainly local fossil fuels (vehicle emission, etc.). Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-022-00793-3.

Associations among professional quality of life dimensions, burnout, nursing practice environment, and turnover intention in newly graduated nurses.

BACKGROUND: Professional quality of life affects nurses' well-being and the quality of care. However, little attention is paid to the relationships among professional quality of life dimensions, burnout, nursing practice environment, and intention to leave this job in newly graduated nurses. AIMS: To assess the levels of professional quality of life dimensions and turnover intention, to examine the predictors for turnover intention, and to explore the mediating roles of professional quality of life dimensions on the associations between these predictors and turnover intention in Chinese newly graduated nurses. METHODS: This was a cross-sectional study with 315 newly graduated nurses selected from five tertiary hospitals and five secondary hospitals in Sichuan province, China. Multiple regression analysis was used to examine the effects of demographic characteristics and work-related factors on intention to leave this job. Structural equation modeling technique was performed to explore the mediating effect of each domain of professional quality of life on the relationships between the predictors and turnover intention. RESULTS: The prevalence of average levels of burnout, secondary traumatic stress, and compassion satisfaction was 43.2%, 57.1%, and 81.3%, respectively. Moreover, 43.8% and 0.6% of the participants reported high and exceptionally high intention to leave this job. Nursing practice environment, social support, and empathy indirectly and significantly affected turnover intention via the mediating roles of burnout and compassion satisfaction, respectively. However, no significant mediating effect of secondary traumatic stress was found between these predictors and turnover intention. LINKING EVIDENCE TO ACTION: Perceptions of greater nursing practice environment, social support, and empathy result in lower turnover intention via reducing burnout and facilitating compassion satisfaction. Strategies such as developing a supportive work and family environment, and cultivating empathic capacity can be effective methods to mitigate against intention to leave this job in newly graduated nurses.

Effects of resilience, social support, and work environment on turnover intention in newly graduated nurses: The mediating role of transition shock.

AIM: The aim of this work was to examine the mediating role of transition shock on the relationships between resilience, social support, work environment, and turnover intention in newly graduated nurses. BACKGROUND: Reports assessing the associations between nurses' resilience, social support, work environment, and turnover intention, both directly and through the mediating effect of transition shock, are scarce. METHODS: This was a cross-sectional study, which recruited 361 Chinese newly graduated nurses with <1 year of work experience. RESULTS: Resilience, social support, and work environment directly and significantly predicted transition shock (direct effect -0.158 to -0.350, p < .01). Resilience, work environment, and transition shock directly and significantly affected turnover intention (direct effect -0.118 to -0.257, p < .05). Transition shock mediated the relationships between resilience, social support, work environment, and turnover intention indirectly and significantly (indirect effect -0.019 to -0.041, p < .05). CONCLUSIONS: The results suggest that nurse managers could enhance resilience and social support and establish a positive work environment to reduce transition shock and turnover intention. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse managers could continuously provide resilience training and develop a supportive workplace climate for newly graduated nurses to facilitate smooth transition into clinical practice, could alleviate their transition shock and turnover intention.

Strategies and criteria targeting the reform and development for the teaching resource of nursing undergraduate: A multi-center cross-sectional study.

ABSTRACT: Teaching resource of nursing students play an important role in improving clinical performance, while there is a little know and dearth of the access and development criteria or guidance of teaching resources for nursing undergraduates.To develop the admission and development criteria of education resources for nursing undergraduates, and to explore and determine its composition and connotation.A cross-sectional survey has been used.A total of 22 Chinese nursing schools and affiliated teaching bases (hospitals and community health centers).A total of 20 nursing experts were consulted to develop the questionnaire of admission and development criteria of teaching resource for nursing undergraduates in China, and then 285 valid experts from 22 Chinese nursing schools and affiliated teaching bases (hospitals and community health centers) conducted the questionnaire survey to evaluate experts' consensus rate and view on the composition and connotation of the developed criteria.There were 6 domains and 43 items included in the admission and development criteria of teaching resource for nursing undergraduates, which domains included educational budget and allocation of resources, infrastructure, clinical teaching bases, books and information services, educational experts, and educational exchanges. The experts' consensus rate was more than 90.2%.The standard is helpful to guide the future admission and development of teaching resource for nursing undergraduates, and favor the education quality improvement of nursing undergraduates.

The relationships of both transition shock, empathy, resilience and coping strategies with professional quality of life in newly graduated nurses.

BACKGROUND: Data on professional quality of life in newly graduated nurses are scarce. This study aimed to describe the levels of professional quality of life, and to explore the relationships of transition shock, empathy, resilience and coping strategies with professional quality of life in newly graduated nurses. METHODS: This was a cross-sectional study, which used a two-stage sampling method to recruit 393 newly graduated nurses in Sichuan province of China. Multiple regression analysis was used to explore the effects of transition shock, empathy, resilience and coping strategies on professional quality of life. Data were collected using standardized scales. RESULTS: The prevalence of average levels of compassion satisfaction, burnout and secondary traumatic stress in newly graduated nurses were 80.2, 38.2 and 57.5%, respectively. Transition shock was a significant negative predictor, and empathy, resilience and adaptive coping were significant positive predictors for compassion satisfaction. Transition shock and passive coping were significant positive predictors, and empathy was a significant negative predictor for burnout and secondary traumatic stress. Resilience and adaptive coping contributed to burnout significantly and negatively. CONCLUSION: Higher transition shock and lower empathy cause lower compassion satisfaction and higher compassion fatigue. More resilience and adaptive coping cause more compassion satisfaction and less burnout. More passive coping contributes to higher compassion fatigue. Strategies such as transition or preceptorship programmes, and empathy, resilience and coping training are effective methods to reduce transition shock, facilitate empathy, resilience and coping, and consequently, enhance professional quality of life in newly graduated nurses.

The impact of resilience on turnover intention in dialysis nurses: The mediating effects of work engagement and compassion fatigue.

AIM: To describe the levels of turnover intention, and to explore the mediating effects of work engagement and compassion fatigue on the relationship between resilience and turnover intention in dialysis nurses. METHODS: A descriptive cross-sectional study was conducted to recruit 496 dialysis nurses in 25 tertiary hospitals in Sichuan province, China. Structural equation modeling technique was used to examine the mediating roles of work engagement and compassion fatigue on the association between resilience and turnover intention. RESULTS: The prevalence of high and exceptionally high levels of turnover intention in dialysis nurses were 56.8% (282 nurses) and 8.7% (43 nurses), respectively. Resilience was a significant and direct contributor to work engagement (standardized direct effect = 0.62, p < .001) and compassion fatigue (standardized direct effect = -0.35, p < .001), respectively. However, resilience had no direct and significant effect on turnover intention (standardized direct effect = 0.15, p > .05). Work engagement and compassion fatigue had direct and significant effects on turnover intention (standardized direct effect = -0.40, p < .001; standard direct effect = 0.31, p < .001). Resilience affected turnover intention indirectly and significantly via the whole mediating effects of work engagement and compassion fatigue (standardized indirect effect = -0.36, p < .001). CONCLUSION: Higher resilience leads to lower turnover intention via enhancing work engagement and reducing compassion fatigue in dialysis nurses. Nursing policies should be established to promote resilience training, enhance work engagement and reduce compassion fatigue in order to alleviate turnover intention in dialysis nurses.

Prevalence and predictors for compassion fatigue and compassion satisfaction in nursing students during clinical placement.

BACKGROUND: Compassion fatigue and compassion satisfaction affect clients' care satisfaction and nurses' well-being. However, little attention is paid to compassion fatigue and satisfaction in nursing students during clinical placement. OBJECTIVES: To explore the relationships between social support, empathy, resilience, coping strategies, and compassion fatigue and satisfaction in nursing students during clinical placement in China. DESIGN: A cross-sectional study was performed from May 1 to May 31, 2020. SETTINGS: and participants: A total of 972 nursing students from 15 universities or colleges in Sichuan province, who received clinical training for at least 10 months were investigated. METHODS: The instruments included Perceived Social Support Scale, Jefferson Scale of Empathy, Connor-Davidson Resilience Scale, Simple Coping Style Questionnaire and Professional Quality of Life Scale. Descriptive, correlation and regression analyses of these variables were conducted. RESULTS: The prevalence of low, moderate and high risk of burnout were 1.3%, 97.8% and 0.9%, respectively. The prevalence of low, average and high levels of secondary traumatic stress were 43.6%, 55.3% and 1.1%. Moreover, 9 (0.9%), 316 (32.5%) and 647 (66.6%) respondents reported low, moderate and high levels of compassion satisfaction. Cognitive empathy and resilience were significant protectors from compassion fatigue, and significant contributors to compassion satisfaction. Less compassionate care and more passive coping were significant risk factors for compassion fatigue. Adaptive coping predicted burnout significantly and negatively, and predicted compassion satisfaction significantly and positively. Family support was a significant contributor to compassion satisfaction. CONCLUSION: It is essential to develop strategies to increase empathy and resilience, avoid passive coping and enhance adaptive coping, and improve family support in order to reduce compassion fatigue and facilitate compassion satisfaction in nursing students during clinical clerkship.

Possibility of using multiscale normalized difference vegetation index data for the assessment of total suspended solids (TSS) concentrations in surface water: A specific case of scale issues in remote sensing.

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R2 = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions.

Relationships between resilience, empathy, compassion fatigue, work engagement and turnover intention in haemodialysis nurses: A cross-sectional study.

AIM: To explore the relationships between resilience, empathy, compassion fatigue, work engagement and turnover intention in Chinese haemodialysis nurses. BACKGROUND: Although several studies explored nurses' turnover intention in multiple hospital wards, fewer studies focused on turnover intention and its predictors among nurses in dialysis care. METHODS: We conducted a cross-sectional study and adopted a two-stage sampling method to recruit 528 Chinese haemodialysis nurses. Multiple regression analysis was performed to explore the effects of resilience, empathy, compassion fatigue and work engagement on turnover intention. RESULTS: The prevalence of high and exceptionally high levels of turnover intention was 59.1% and 9.0%. Compassion fatigue had the strongest significant effect on turnover intention (ß = 0.276), followed by work engagement (ß = -0.256) and resilience (ß = 0.193). Haemodialysis nurses in tertiary hospitals reported significant higher levels of turnover intention than those in secondary hospitals (ß = 0.127). CONCLUSIONS: Higher levels of compassion fatigue and lower levels of resilience and work engagement can result in higher turnover intention in haemodialysis nurses. IMPLICATIONS FOR NURSING MANAGEMENT: Strategies such as resilience training programme, mindfulness-based intervention and establishing a positive work environment may be effective methods to improve resilience, reduce compassion fatigue, promote work engagement and decrease turnover intention.

Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China.

Studies on fine particulate matter with an aerodynamic diameter of 2.5 µm or smaller (PM2.5) are closely related to the atmospheric environment and human activities but are often limited by ground-level in situ observations. Satellite remote sensing techniques have been widely used to estimate the PM2.5 concentration over large areas where ground-monitoring sites are unavailable. However, satellite-retrieved aerosol optical depth (AOD) products usually feature a coarse resolution, which is insufficient for the estimation of the urban-scale PM2.5 concentration. We developed a new improved random forest (IRF) model based on machine learning and a newly released AOD product with a high resolution of 1-km, which could more effectively and accurately estimate the PM2.5 concentration over Shenzhen in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. Daily PM2.5 concentrations from 2016 to 2018 were estimated from ground-level PM2.5 and meteorological variable data. The popular linear regression model, geographically and temporally weighted regression (GTWR) model and random forest (RF) model without spatiotemporal information were employed for comparison and validation purposes through the 10-fold cross-validation (CV) approach. The IRF model attained an overall R2 value of 0.915 and a root mean square error (RMSE) value of 3.66 µg m-3. This suggests that the IRF model can estimate the urban PM2.5 concentration with a high spatial resolution at the daily, seasonal and annual scales, and the improved machine learning method is better than the linear model proposed by previous studies in terms of the estimation accuracy of the PM2.5 concentration. Generally, the IRF model coupled with AOD data with a 1-km resolution can significantly improve the calculation accuracy of the atmospheric PM2.5 concentration over coastal urban areas in the future.

The effects of a transitional care program on discharge readiness, transitional care quality, health services utilization and satisfaction among Chinese kidney transplant recipients: A randomized controlled trial.

BACKGROUND: Kidney transplantation is the major treatment for end-stage renal disease (ESRD). However, kidney transplant recipients (KTRs) face severe challenges during the transition period from hospital discharge to home, increasing the risk of early hospital readmission (EHR) and affecting patient safety. Nevertheless, knowledge of effective transitional care for KTRs is limited in China. OBJECTIVE: To evaluate the effectiveness of an innovative transitional care program in improving discharge readiness, transitional care quality, health services utilization and patient satisfaction among KTRs in China. DESIGN: A prospective randomized controlled trial. SETTINGS AND PARTICIPANTS: Patients admitted to undergo kidney transplantation were recruited in a general tertiary hospital in Chengdu, China. METHODS: A total of 220 eligible patients were recruited and randomly assigned to the intervention and control groups. Participants in the intervention group received a transitional care intervention developed by the research team, including a risk assessment for early readmission, health education from admission to predischarge, individualized discharge planning, and a telephone follow-up once per week for one month and WeChat follow-up postdischarge. The control group received routine care of comparable length and follow-up contact. A trained research assistant collected all patients' baseline data on admission (T0), evaluated the discharge readiness (by the Readiness for Hospital Discharge Scale) on the day of discharge (T1), collected data on transitional care quality (by the Care Transition Measure-15) and patients' satisfaction with transitional care services (by a self-developed patient satisfaction scale) on the 30th day postdischarge (T2), and collected data on hospital readmission, unscheduled outpatient department visits, and emergency room visits on the 30th and 90th days (by a self-developed health services utilization record table) (T3) postdischarge. Intervention effects were analyzed using independent samples t-tests, Wilcoxon-Mann-Whitney U tests, Chi-square tests or Fisher's exact test. RESULTS: Compared with the control group, the intervention group showed significantly better discharge readiness (personal status, P<0.001; knowledge, P = 0.010; coping ability, P<0.001; expected support, P = 0.007; total score, P<0.001), better transitional care quality (importance of preferences, P<0.001; management preparation, P<0.001; critical understanding, P = 0.003; written and understandable care plan, P = 0.012; total score, P<0.001), lower readmission rate at T2 (P = 0.033) and at T3 (P = 0.013), lower emergency room visit rate at T3 (P = 0.014), and better satisfaction with transitional care services (P<0.001). CONCLUSIONS: This study provides evidence that an innovative transitional care program is effective in promoting KTRs' discharge readiness, transitional care quality, reducing hospital readmission and emergency room visits, and improving their satisfaction with transitional care services. TRIAL REGISTRATION: Clinical Trials ChiCTR1800014971.

Assessing arid Inland Lake Watershed Area and Vegetation Response to Multiple Temporal Scales of Drought Across the Ebinur Lake Watershed.

The Ebinur Lake watershed is an important ecological barrier for environmental changes in the Junggar Basin in Xinjiang Uygur Autonomous Region (XUAR). Due to the tremendous changes in the underlying surface environment of the watershed in the past few decades, the watershed has become a typical region of ecological degradation. Drought affects the surface dynamics and characterizes the regional dry and wet environments, while the dynamic variation in lakes and vegetation are indicators of dynamic changes in land surfaces. Thus, a quantitative assessment of the response of lakes and vegetation to drought conditions at multiple temporal scales is critical for assessing the potential impacts of regional climate change on terrestrial ecosystems and ecological restoration. The standardized precipitation evapotranspiration index (SPEI), the spectral water index (NDWI) and the normalized difference vegetation index (NDVI) were used to analyse the evolution of drought, the variation in lake surface area and the sustainable variation in vegetation. Furthermore, we quantitatively evaluated the response patterns of vegetation to droughts of multiple temporal scales (1-, 3-, 6-, 12-, 24-month). The conclusions showed that (1) overall, the area of Ebinur Lake experienced drastic fluctuations, and the lake area has decreased significantly since 2003, with a dynamic area of 817.63 km2 in 2003 and 384.60 km2 in 2015, and the lake area had shrank severely. (2) The interannual variation of wet and dry changed alternately during the observation period, and persistent drought events occurred from 2006 to 2010 across the Ebinur Lake watershed. (3) The vegetation area of cultivated land expanded continuously across the watershed, and the grassland degraded severely. (4) The changes in lake surface area are significantly correlated with the inflow water volume (correlation coefficient = 0.64, P < 0.01). (5) The vegetation of different terrestrial ecosystems exhibited heterogeneous responses to multiple temporal scales of drought in different seasons. The percentage was 72.78% of the total area, which showed a correlation between vegetation and drought conditions during the growing season period, and there were more impacts of drought on vegetation, with values as high as 64.33% of the area in summer, than those in other seasons.

The impact of empathy on work engagement in hemodialysis nurses: The mediating role of resilience.

AIM: To examine the reciprocal associations between empathy, resilience and work engagement, and to explore the mediating effect of resilience on the empathy and work engagement relationship among hemodialysis nurses in China. METHODS: This was a cross-sectional quantitative study. A convenience sampling was used to investigate 582 hemodialysis nurses in Chengdu, China. Structural equation modeling technique was conducted to analyze the mediating effect of resilience on the association between empathy and work engagement. RESULTS: Empathy and resilience were direct, positive and significant predictors for work engagement. Empathy also had a direct, positive and significant predictive effect on resilience. Empathy indirectly and significantly affected work engagement via the partial mediating effect of resilience. CONCLUSION: Higher empathic ability may lead to greater work engagement by enhancing resilience. Attention should be paid to the development of empathic capacity and resilience to foster work engagement in hemodialysis nurses.

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring.

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model's SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0-10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R 2 val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R 2 val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

EpiAlignment: alignment with both DNA sequence and epigenomic data.

Comparative epigenomics, which subjects both epigenome and genome to interspecies comparison, has become a powerful approach to reveal regulatory features of the genome. Thus elucidated regulatory features surpass the information derived from comparison of genomic sequences alone. Here, we present EpiAlignment, a web-based tool to align genomic regions with both DNA sequence and epigenomic data. EpiAlignment takes DNA sequence and epigenomic profiles derived by ChIP-seq from two species as input data, and outputs the best semi-global alignments. These alignments are based on EpiAlignment scores, computed by a dynamic programming algorithm that accounts for both sequence alignment and epigenome similarity. For timely response, the EpiAlignment web server automatically initiates up to 140 computing threads depending on the size of user input data. For users' convenience, we have pre-compiled the comparable human and mouse epigenome datasets in matched cell types and tissues from the Roadmap Epigenomics and ENCODE consortia. Users can either upload their own data or select pre-compiled datasets as inputs for EpiAlignment analyses. Results are presented in graphical and tabular formats where the entries can be interactively expanded to visualize additional features of these aligned regions. EpiAlignment is available at https://epialign.ucsd.edu/.

Evaluation of psychometric properties of needs assessment tools in cancer patients: A systematic literature review.

BACKGROUND: Although a wide range of needs assessment tools for cancer patients have been developed, no standardized and commonly accepted instruments were recommended to use in clinical care. This systematic review was conducted to assess the quality of psychometric properties of needs assessment tools among cancer patients in order to help oncology healthcare professionals select the most appropriate needs assessment tools in routine clinical practice. METHODS: Searches were conducted in the electronic databases of PUBMED from 1966, CINAHL from 1960, EMBASE from 1980 and PsychINFO from 1967 as well as additional sources. The quality of psychometric properties of the recruited needs assessment tools was evaluated using the agreed quality criteria for measurement properties of health status questionnaires. RESULTS: Thirty-seven studies which evaluated the psychometric properties of 20 needs assessment tools were identified. Internal consistency was tested in 32 studies with 9 studies indicating negative rating and 4 studies intermediate rating. Less than half of the studies (13 studies) assessed test-retest reliability, and only 4 studies reported positive rating. Content validity was the most tested psychometric property appraised in 33 studies and indicated positive rating in all the evaluated studies. Structural validity was adequately evaluated in 28 studies with 23 studies reporting intermediate rating. More than half of the studies (29 studies) tested hypothesis testing and 13 studies were rated positive. Cross-cultural validity results were obtained in 13 studies with 7 studies showing negative rating. No data was available on measurement error and criterion validity. Only one study appraised responsiveness and showed intermediate rating. The Supportive Care Needs Survey-Short Form (SCNS-SF) is the most widely used instrument for needs assessment in cancer patients. It had strong evidence for internal consistency, content validity, structural validity and hypothesis testing, and moderate evidence for reliability and cross-cultural validity. Cancer Survivors' Unmet Needs Measure (CaSUN) reported strong or moderate evidence for internal consistency, reliability, content and structural validity, and hypothesis testing. Furthermore, Supportive Cancer Care Needs Assessment Tool for Indigenous People (SCNAT-IP) had strong evidence for content validity, and moderate evidence for internal consistency, structural validity and hypothesis testing. CONCLUSIONS: Despite several needs assessment tools exist to assess care needs in cancer patients, further improvement of already existing and promising instruments is recommended.