Bivariate Drought Analysis Using Streamflow Reconstruction with Tree Ring Indices in the Sacramento Basin, California, USA

Long-term streamflow data are vital for analysis of hydrological droughts. Using an artificial neural network (ANN) model and nine tree-ring indices, this study reconstructed the annual streamflow of the Sacramento River for the period from 1560 to 1871. Using the reconstructed streamflow data, the copula method was used for bivariate drought analysis, deriving a hydrological drought return period plot for the Sacramento River basin. Results showed strong correlation among drought characteristics, and the drought with a 20-year return period (17.2 million acre-feet (MAF) per year) in the Sacramento River basin could be considered a critical level of drought for water shortages

Enantioselective Synthesis of Homoisoflavanones by Asymmetric Transfer Hydrogenation and Their Biological Evaluation for Antiangiogenic Activity

Neovascular eye diseases are a major cause of blindness. Excessive angiogenesis is a feature of several conditions, including wet age-related macular degeneration, proliferative diabetic retinopathy, and retinopathy of prematurity. Development of novel anti-angiogenic small molecules for the treatment of neovascular eye disease is essential to provide new therapeutic leads for these diseases. We have previously reported the therapeutic potential of anti-angiogenic homoisoflavanone derivatives with efficacy in retinal and choroidal neovascularization models, although these are racemic compounds due to the C3-stereogenic center in the molecules. This work presents asymmetric synthesis and structural determination of anti-angiogenic homoisoflavanones and pharmacological characterization of the stereoisomers. We describe an enantioselective synthesis of homoisoflavanones by virtue of ruthenium-catalyzed asymmetric transfer hydrogenation accompanying dynamic kinetic resolution, providing a basis for the further development of these compounds into novel experimental therapeutics for neovascular eye diseases

Detect of Novel Alternative Form at Development Stages in Brain

this paper, we use the development information of ESTs to detect novel alternative form in Brain. We explain the expression pattern of gene for each development stage. Moreover, we expect that specific splice variants are associated with mouse disease of brain specifi

Evolution of a Strategy for Concise Enantioselective Total Synthesis of the Salinosporamide Family of Natural Products

Our first strategy for rapidly accessing pyrrolidinone cores of salinosporamides involved combined use of memory of chirality and dynamic kinetic resolution principles in aldol reactions of the serine-derived 5-oxazolidinone substrate, which was ultimately unsuccessful with respect to enantioselectivity. This failure led us to the revised strategy. The influence of the stereocenter in 5-oxazolidinone enabled selective installation of the C-2 stereocenter. The intramolecular aldol reaction of the C-2 stereodefined aldol substrate was successful. An unexpected hydrolytic dynamic kinetic resolution was observed in hydrolyses of the bicyclic aldol products. This unprecedented substrate-driven hydrolytic dynamic kinetic resolution was utilized in preparing the pyrrolidinone core with excellent efficiency. Through this strategy, the concise total syntheses of salinosporamides A and B as well as cinnabaramides A, E, and F were achieved with high selectivity.N

Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients

(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods—a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)—focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings