Machine learning predictive model for aspiration risk in early enteral nutrition patients with severe acute pancreatitis.

Background: The aim of this study was to build and validate a risk prediction model for aspiration in severe acute pancreatitis patients receiving early enteral nutrition (EN) by identifying risk factors for aspiration in these patients.

Methods: The risk factors for aspiration were analyzed to build a prediction model based on the data collected from 339 patients receiving enteral nutrition. Subsequently, we used six machine learning algorithms and the model was validated by the area under the curve.

A hybrid neural network approach for classifying diabetic retinopathy subtypes.

Objective: Diabetic retinopathy is a prevalent complication among diabetic patients that, if not predicted and treated promptly, can lead to blindness. This paper proposes a method for accurately and swiftly predicting the degree of diabetic retinopathy using a hybrid neural network model. Timely prediction of diabetic retinopathy is crucial in preventing blindness associated with this condition.

Carbon footprints: Uncovering multilevel spatiotemporal changes of ship emissions during 2019-2021 in the U.S.

Quantifying and understanding changes in carbon emissions is essential for the U.S. shipping industry to reduce carbon emissions, especially after its return to the Paris Agreement.

The purple acid phosphatase GmPAP17 predominantly enhances phosphorus use efficiency in soybean.

Purple acid phosphatase (PAP) is an important plant acid phosphatase, which can secrete to the rhizosphere to decompose organophosphorus, promote phosphorus use efficiency, plant growth and development. However, little is known about the functions of intracellular PAP in plants, especially for soybean. Our previous study integrating QTL mapping and transcriptome analysis identified an promising low phosphorus (LP)-induced gene GmPAP17.

QTL mapping of the genetic basis of stem diameter in soybean.

QTL mapping of stem diameter was carried out in three RIL populations using a high-density genetic map, and candidate genes related to stem diameter were predicted. Stem diameter is an important agronomic trait affecting soybean lodging and productivity. However, this trait is underexploited, and the underlying genetic mechanism in soybean remains unclear.

Genome-wide identification of low phosphorus responsive microRNAs in two soybean genotypes by high-throughput sequencing.

MicroRNAs (miRNAs) have been reported to be correlated with various stress responses in soybean, but only a few miRNAs have been demonstrated to respond to low phosphorus (LP) stress. To unravel the response mechanisms of miRNAs to low-P stress, the roots of two representative soybean genotypes with different P efficiency, Nannong94-156 (a LP-tolerant genotype) and Bogao (a LP-sensitive genotype), were used for the construction of RNA sequencing (RNA-seq) libraries under low/normal-P treatment by high-throughput sequencing. In total, 603 existing miRNAs and 1699 novel miRNAs belonging to 248 and 1582 families in all samples were identified, respectively.

Transcriptome-wide identification of novel circular RNAs in soybean in response to low-phosphorus stress.

Low-phosphorus (LP) stress is a major factor limiting the growth and yield of soybean. Circular RNAs (circRNAs) are novel noncoding RNAs that play a crucial role in plant responses to abiotic stress. However, how LP stress mediates the biogenesis of circRNAs in soybean remains unclear.