Abstract:The Siberian elm (Ulmus pumila L.) is one of the most commonly found tree species in arid areas of northeast Asia. To understand the morphological and physiological characteristics of Siberian elms in arid regions, we analyzed leaves from seven study sites (five arid or semi-arid and two mesic) in China, Mongolia and the Republic of Korea, which covered a wide range of average annual precipitation (232 mm·year −1 to 1304 mm·year −1 ) under various aridity indexes (AI) and four different microenvironments: sand dune, steppe, riverside and forest. The traits of Siberian elms varied widely along different annual precipitation (P) and AI gradients. Tree height (H), leaf size (LS) and stomatal area per unit leaf area (A S /A L ) decreased with increasing AI, whereas leaf mass per unit leaf area (LMA) and water-use efficiency (WUE) increased significantly. In addition, trees at the five arid sites showed significant differences in LS, LMA and A S /A L but not in H and WUE. Thus, our study indicated that indigenous Siberian elm trees in arid areas have substantially altered their morphological and physiological characteristics to avoid heat stress and increase water conservation in comparison to mesic areas. However, their changes differed depending on the surrounding microenvironment even in arid areas. Trees in sand dunes had a smaller LS, higher LMA, thicker leaf cuticle layer and higher stomatal density and A S than those in steppes and near a riverside.

Objective: This study aimed to create an ideal machine learning model to predict mechanical complications in adult spinal deformity (ASD) surgery based on GAPB (modified global alignment and proportion scoring with body mass index and bone mineral density) factors.Methods: Between January 2009 and December 2018, 238 consecutive patients with ASD, who received at least 4-level fusions and were followed-up for ≥ 2 years, were included in the study. The data were stratified into training (n = 167, 70%) and test (n = 71, 30%) sets and input to machine learning algorithms, including logistic regression, random forest gradient boosting system, and deep neural network.Results: Body mass index, bone mineral density, the relative pelvic version score, the relative lumbar lordosis score, and the relative sagittal alignment score of the global alignment and proportion score were significantly different in the training and test sets (p < 0.05) between the complication and no complication groups. In the training set, the area under receiver operating characteristics (AUROCs) for logistic regression, gradient boosting, random forest, and deep neural network were 0.871 (0.817–0.925), 0.942 (0.911–0.974), 1.000 (1.000–1.000), and 0.947 (0.915–0.980), respectively, and the accuracies were 0.784 (0.722–0.847), 0.868 (0.817–0.920), 1.000 (1.000–1.000), and 0.856 (0.803–0.909), respectively. In the test set, the AUROCs were 0.785 (0.678–0.893), 0.808 (0.702–0.914), 0.810 (0.710–0.910), and 0.730 (0.610–0.850), respectively, and the accuracies were 0.732 (0.629–0.835), 0.718 (0.614–0.823), 0.732 (0.629–0.835), and 0.620 (0.507–0.733), respectively. The random forest achieved the best predictive performance on the training and test dataset.Conclusion: This study created a comprehensive model to predict mechanical complications after ASD surgery. The best prediction accuracy was 73.2% for predicting mechanical complications after ASD surgery.

Background and AimsDespite accumulating evidence on the benefits of dietary fiber in the general population, there is a lack of representative data on mortality in patients with chronic kidney disease (CKD). This study examined the role of dietary fiber intake on all-cause and cardiovascular mortality in patients with CKD using representative Korean cohort data.MethodsThe study included 3,892 participants with estimated glomerular filtration rates &lt;60 mL/min/1.73 m2 from the Korean Genome and Epidemiology Study. Mortality status was followed by data linkage with national data sources. Nutritional status was assessed using a validated food frequency questionnaire. Dietary fiber was categorized into quintiles (Q). A multivariable Cox proportional hazards regression model was used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause and cardiovascular mortality.ResultsThe average daily fiber intake of patients with CKD was 5.1 g/day. During the 10.1-year follow-up period, 602 (149 cardiovascular) deaths were documented. The HR (95% CI) for all-cause mortality in the highest quintile compared with that in the lowest quintile was 0.63 (0.46–0.87) after adjusting for age, sex, BMI, smoking, alcohol intake, exercise, total calorie intake, hypertension, diabetes, and dyslipidemia (P = 0.005). The HR (95% CI) for cardiovascular mortality in the highest quintile compared with that in the lowest quintile was 0.56 (0.29–1.08) after adjusting for same confounders (P = 0.082).ConclusionIn conclusion, we observed an inverse association between dietary fiber intake and all-cause mortality in CKD patients. Small increments in fiber intake reduced the risk of all-cause mortality by 37%. This finding highlights the need for inexpensive but important dietary modification strategies for encouraging fiber intake in the Korean CKD population.

This study was conducted to examine the characteristics of mammal abundance that are related to habitat variables in natural deciduous forest and Japanese larch (Larix leptolepis) plantation in the national forest of Mt. Gariwang, Pyeongchang, Korea. Habitat variables differed between forest types. We counted the mammal trails of Korean hares (Lepus coreanus), raccoon dogs (Nyctereutes procyonoides), Siberian weasels (Mustela sibirica), Eurasian badgers (Meles meles), water deer (Hydropotes inermis) and wild boars (Sus scrofa) during the study period. Eurasian badgers, water deer, and wild boars exhibited one or two significant correlations with coverage of understory, downed trees, and coverage of overstory, as determined using a stepwise approach. Habitat variables could be used as predictors of mammal abundance, and thus forest managers should consider such variables in mammal conservation and management activities.

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