Prevalence of chronic obstructive pulmonary disease (COPD) among rheumatoid arthritis: results from national inpatient database.

Rheumatoid arthritis (RA) is being increasingly recognized as an important contributor to chronic obstructive pulmonary disease (COPD). Although smoking is a major risk factor, other factors may play a role. We used National Inpatient Sample (NIS) from 2013 to explore this relationship. We used propensity matching with a 1:3 nearest-neighbor-matching algorithm to match 1 RA hospitalization to 3 age- and-sex-matched comparators. In the age- and-sex-matched population, RA had a higher odds of COPD (OR 1.20, 95% CI: 1.17-1.22

Machine learning for regional crop yield forecasting in Europe

Crop yield forecasting at national level relies on predictors aggregated from smaller spatial units to larger ones according to harvested crop areas. Such crop areas come from land cover maps or reported statistics, both of which can have errors and uncertainties. Sub-national or regional crop yield forecasting minimizes the propagation of these errors to some extent. In addition, regional forecasts provide added value and insights to stakeholders on regional differences within a country, which would otherwise compensate each other at national level. We propose a crop yield forecasting approach for multiple spatial levels based on regional crop yield forecasts from machine learning. Machine learning, with its data-driven approach, can leverage larger data sizes and capture nonlinear relationships between predictors and yield at regional level. We designed a generic machine learning workflow to demonstrate the benefits of regional crop yield forecasting in Europe. To evaluate the quality and usefulness of regional forecasts, we predicted crop yields for 35 case studies, including nine countries that are major producers of six crops (soft wheat, spring barley, sunflower, grain maize, sugar beets and potatoes). Machine learning models at regional level had lower normalized root mean squared errors (NRMSE) and uncertainty than a linear trend model, with Wilcoxon p-values of 3e-7 and 2e-7 for 60 days before harvest and end of season respectively. Similarly, regional machine learning forecasts aggregated to national level had lower NRMSEs than forecasts from an operational system in 18 out of 35 cases 60 days before harvest, with a Wilcoxon p-value of 0.95 indicating similar performance. Our models have room for improvement, especially during extreme years. Nevertheless, regional crop yield forecasts from machine learning and aggregated national forecasts provide a consistent forecasting method across spatial levels and insights from regional differences to support important policy decisions

Inventory of non-timber forest products in Western Nepal and strategies for sustainable management

Non-timber Forest Products (NTFPs) play an important role as traditional source for food, fiber, fodder, and medicine and offer income opportunities for poverty alleviation especially in rural households in Nepal who engage in a widespread trade of NTFPs. Adequate planning for sustainable use of NTFPs is imperative so we explored the inventory of multipurpose trees and herbs that are being used as NTFPs in Chitwan, Nepal. 70 households from Sukranagar and Mangalpur VDCs of Chitwan district were randomly selected and personal interviews were taken with them as well as focus group discussions were done. The community had been utilizing 49 plant species from the nearby community forest. Implementation of the policy of community forestry was found to have a positive impact on the sustainable production of NTFPs. More than 80% of the respondents believed that indigenous knowledge promoted sustainable NTFP production. Kurilo (Asparagus officinalis) was found to be the best NTFP for the study site. Various policy level reforms are proposed that will help in improving the sustainable production of NTFPs. Better utilization of NTFPs as well as their conservation is possible with proper trainings given to community forest users.International Journal of Environment Vol.5(3) 2016, pp.87-103</p

Trends in hospitalizations for vertebral compression fracture in ankylosing spondylitis: data from the National Inpatient Sample 2000-2014.

Ankylosing spondylitis (AS) patients are at increased risk of vertebral compression fractures (VCF). Our objective was to examine the yearly trend of VCF hospitalizations in AS patients as compared to rheumatoid arthritis (RA) and the general population. National Inpatient Sample (NIS) database (2000-2014) was used to identify adult (≥ 18 years) hospitalizations, based on validated ICD-9 diagnosis codes. The rate of VCF hospitalizations, as a primary diagnosis, was assessed in three mutually exclusive groups: AS, RA, and the general population. The prevalence of VCF hospitalization was highest in AS (2.70%), compared to 0.77% in RA and 0.35% in the general population. Over the 15-year period, VCF hospitalization in AS was noted to have an increasing trend (Annual Percent Change (APC) = 4.73, p \u3c 0.05) in contrast to the stable trend in the general population (APC = 0.34, p = NS) and a declining trend in RA (APC -3.61, p \u3c 0.05). VCF related to AS was also associated with a longer hospital stay as compared to the general population (8.1 days vs. 5.1 days, p \u3c 0.05) and higher inpatient mortality (3.4% vs. 1.0%, p \u3c 0.05). A higher rate of VCF hospitalization along with an increasing trend was noted in AS as compared to RA and compared to the general population. Better screening approaches and treatment strategies for AS patients with VCF risk are urgently needed to reduce hospitalizations and related complications. Key Points • An increasing trend of VCF hospitalization was noted in AS, in contrast to a declining trend in RA and a stable trend in the general population. • VCF in AS was associated with longer hospital stay and higher inpatient mortality than in RA and the general population

All-cause hospitalizations and mortality in systemic lupus erythematosus in the US: results from a national inpatient database

Systemic lupus erythematosus (SLE) is a multisystem disorder. While several studies have outlined risk factors for hospitalization and mortality in SLE; the frequency of hospitalizations from various causes has varied among studies and over the years. We aimed to assess the causes of SLE hospitalizations and inpatient mortality compared to those without SLE in the United States in a recent year (2016) using a large national inpatient database. We used National Inpatient Sample (NIS) to identify hospitalizations with SLE using the ICD-10 code M32. Among hospitalizations with SLE as secondary diagnosis, we used ICD-10 codes to assess the primary diagnoses associated with hospitalizations and mortality. Our study included 174,105 SLE hospitalizations matched to controls (similar age, sex, and NIS stratum) in the year 2016. Mean age of hospitalization with SLE was 51.82 years, and 89% of hospitalized SLE patients were females. Mean length of stay, cost and mortality for SLE were 5.6 ± 7.2 days, US $ 14,450 and 1.96%, respectively. SLE was the primary diagnosis in 10,185 (5.85%) of all SLE related hospitalizations. Among SLE hospitalizations, infection was the most common primary diagnosis (15.80%) followed by cardiac and renal manifestations (7.03% and 4.91% respectively). Infection was the leading cause of mortality (38.18%) followed by cardiac manifestations (12.04%). Infections and cardiac involvement were the leading causes of hospitalizations and in-hospital mortality in SLE. Whether this is related to the disease itself, its associated comorbidities or immunosuppressive agents would require further studies

Interpretability of deep learning models for crop yield forecasting

Machine learning models for crop yield forecasting often rely on expert-designed features or predictors. The effectiveness and interpretability of these handcrafted features depends on the expertise of the people designing them. Neural networks have the ability to learn features directly from input data and train the feature learning and prediction steps simultaneously. In this paper, we evaluate the performance and interpretability of neural network models for crop yield forecasting using data from the MARS Crop Yield Forecasting System of the European Commission's Joint Research Centre. The selected neural networks can handle sequential or time series data and include long short-term memory (LSTM) recurrent neural network and 1-dimensional convolutional neural network (1DCNN). Performance was compared with a linear trend model and a Gradient-Boosted Decision Trees (GBDT) model, trained using hand-designed features. Feature importance scores of input variables were computed using feature attribution methods and were analyzed by crop yield modeling and agronomy experts. Results showed that LSTM models perform statistically better than GBDT models for soft wheat in Germany and similar to GBDT models for all other case studies. In addition, LSTM models captured the effect of yield trend, static features (e.g. elevation, soil water holding capacity) and biomass features on crop yield well, but struggled to capture the impact of extreme temperature and moisture conditions. Our work shows the potential of deep learning to automatically learn features and produce reliable crop yield forecasts, and highlights the importance and challenges of involving human stakeholders in assessing model interpretability

Forest canopy resists plant invasions: a case study of Chromolaena odorata in Sal (Shorea robusta) forests of Nepal

Accepted manuscript version, licensed CC BY-NC-ND 4.0. Invasive alien species are a major threat to global biodiversity due to the tremendous ecological and economic damage they cause in forestry, agriculture, wetlands, and pastoral resources. Understanding the spatial pattern of invasive alien species and disentangling the biophysical drivers of invasion at the forest stand level is essential for managing forest ecosystems and the wider landscape. However, forest-level and species-specific information on Invasive Alien Plant Species (IAPS) abundance and their spatial extent are largely lacking. In this context, we analysed the cover of one of the world’s worst invasive plants, Chromolaena odorata, in Sal (Shorea robusta) forest in central Nepal. Vegetation was sampled in four community forests using 0.01 ha square quadrats, covering the forest edge to the interior. C. odorata cover, floral richness, tree density, forest canopy cover, shrub cover, tree basal area, and disturbances were measured in each plot. We also explored forest and IAPS management practices in community forests. C. odorata cover was negatively correlated with forest canopy cover, distance to the road, angle of slope, and shrub cover. Tree canopy cover had the largest effect on C. odorata cover. No pattern of C. odorata cover was seen along native species richness gradients. In conclusion, forest canopy cover is the overriding biotic covariate suppressing C. odorata cover in Sal forests