Implications of microRNAs in Colorectal Cancer Development, Diagnosis, Prognosis, and Therapeutics

MicroRNAs (miRNAs) are a class of non-coding small RNAs with critical regulatory functions as post-transcriptional regulators. Due to the fundamental importance and broad impact of miRNAs on multiple genes and pathways, dysregulated miRNAs have been associated with human diseases, including cancer. Colorectal cancer (CRC) is among the most deadly diseases, and miRNAs offer a new frontier for target discovery and novel biomarkers for both diagnosis and prognosis. In this review, we summarize the recent advancement of miRNA research in CRC, in particular, the roles of miRNAs in CRC stem cells, epithelial-to-mesenchymal transition, chemoresistance, therapeutics, diagnosis, and prognosis

Investigation on data fusion of sun-induced chlorophyll fluorescence and reflectance for photosynthetic capacity of rice

Studying crop photosynthesis is crucial for improving yield, but current methods are labor-intensive. This research aims to enhance accuracy by combining leaf reflectance and sun-induced chlorophyll fluorescence (SIF) signals to estimate key photosynthetic traits in rice. The study analyzes 149 leaf samples from two rice cultivars, considering reflectance, SIF, chlorophyll, carotenoids, and CO2 response curves. After noise removal, SIF and reflectance spectra are used for data fusion at different levels (raw, feature, and decision). Competitive adaptive reweighted sampling (CARS) extracts features, and partial least squares regression (PLSR) builds regression models. Results indicate that using either reflectance or SIF alone provides modest estimations for photosynthetic traits. However, combining these data sources through measurement-level data fusion significantly improves accuracy, with mid-level and decision-level fusion also showing positive outcomes. In particular, decision-level fusion enhances predictive capabilities, suggesting the potential for efficient crop phenotyping. Overall, sun-induced chlorophyll fluorescence spectra effectively predict rice's photosynthetic capacity, and data fusion methods contribute to increased accuracy, paving the way for high-throughput crop phenotyping

Predicting Berth Stay for Tanker Terminals: A Systematic and Dynamic Approach

Given the trend of digitization and increasing number of maritime transport, prediction of vessel berth stay has been triggered for requirements of operation research and scheduling optimization problem in the era of maritime big data, which takes a significant part in port efficiency and maritime logistics enhancement. This study proposes a systematic and dynamic approach of predicting berth stay for tanker terminals. The approach covers three innovative aspects: 1) Data source employed is multi-faceted, including cargo operation data from tanker terminals, time-series data from automatic identification system (AIS), etc. 2) The process of berth stay is decomposed into multiple blocks according to data analysis and information extraction innovatively, and practical operation scenarios are also developed accordingly. 3) The predictive models of berth stay are developed on the basis of prior data analysis and information extraction under two methods, including regression and decomposed distribution. The models are evaluated under four dynamic scenarios with certain designated cargoes among two different terminals. The evaluation results show that the proposed approach can predict berth stay with the accuracy up to 98.81% validated by historical baselines, and also demonstrate the proposed approach has dynamic capability of predicting berth stay among the scenarios. The model may be potentially applied for short-term pilot-booking or scheduling optimizations within a reasonable time frame for advancement of port intelligence and logistics efficiency

An Asymmetric Proximal Decomposition Method for Convex Programming with Linearly Coupling Constraints

The problems studied are the separable variational inequalities with linearly coupling constraints. Some existing decomposition methods are very problem specific, and the computation load is quite costly. Combining the ideas of proximal point algorithm (PPA) and augmented Lagrangian method (ALM), we propose an asymmetric proximal decomposition method (AsPDM) to solve a wide variety separable problems. By adding an auxiliary quadratic term to the general Lagrangian function, our method can take advantage of the separable feature. We also present an inexact version of AsPDM to reduce the computation load of each iteration. In the computation process, the inexact version only uses the function values. Moreover, the inexact criterion and the step size can be implemented in parallel. The convergence of the proposed method is proved, and numerical experiments are employed to show the advantage of AsPDM

Variations of surface marine heatwaves in the Northwest Pacific during 1993–2019

Parameters of surface marine heatwaves (MHWs) in the Northwest Pacific during 1993–2019 are derived from two sea surface temperature (SST) products: the Optimum Interpolation SST based on satellite remote sensing (OISST V2.1) and the Global Ocean Physics Reanalysis based on data-assimilative global ocean model (GLORYS12V1). Similarities and differences between the MHW parameters derived from the two datasets are identified. The spatial distributions of the mean annual MHW total days, frequency, duration, mean intensity and cumulative intensity, and interannual variations of these parameters are generally similar, while the MHW total days and duration from GLORYS12V1 are usually higher than that from OISST V2.1. Based on seasonal-mean values from GLORYS12V1, longer MHW total days (>7) have the largest spatial coverage in both the shelf and deep waters in summer, while the smallest coverage in spring. In selected representative regions, interannual variations of the MHW total days are positively correlated with the SST anomalies. In summer, the MHW total days have positive correlations with the Western Pacific Subtropical High intensity, and negative correlations with the East Asia Monsoon intensity, over nearly the whole South China Sea (SCS) and the low-latitude Pacific. In winter, positive correlations with both the Subtropical High and Monsoon intensities present over the western part of SCS. Strong El Niño is followed by longer MHW total days over the western half of SCS in winter, and over the whole SCS and low-latitude Pacific in summer of the next year. These correlation relationships are valuable for developing forecasts of MHWs in the region

(E)-2-[2-(4-Chloro­benzyl­idene)hydrazin-1-yl]-4-{[3-(dimethyl­aza­nium­yl)prop­yl]amino}­quinazolin-1-ium bis­(perchlorate)

In the title compound, C20H25ClN6 2+·2ClO4 −, the organic cation is roughly planar, as shown by the dihedral angle of 3.78 (3)° between the quinazoline and chloro­phenyl rings. The quinazoline ring is itself approximately planar, with an average deviation of 0.018 (4) Å. The organic cation adopts an E configuration with respect to the C= N double bond of the hyrazinyl group. The (dimethyl­aza­nium­yl)propyl­amino side chain is disordered over two sets of sites with occupancies of 0.768 (10) and 0.232 (10). In the crystal, two cations and four anions are linked by strong N—H⋯O hydrogen bonds. Weak C—H⋯O hydrogen bonds exist among these aggregates

Maternal face processing in Mosuo preschool children

Instinctively responding to maternal face is an evolutionary function of enhancing survival and development. However, because of the confounding nature of familiarity, little is known concerning the neural mechanism involved in maternal face recognition. We had a rare opportunity to examine Mosuo preschool children who were raised in a matrilineal society in which mothers and aunts represent equally familiar faces to the children. The participants were exposed to photographs of their mother's face, aunt's face, and an unfamiliar female's faces during electroencephalography (EEG) recording. The EEG results showed that the mother's face elicited a more negative N1 component, a larger left N170 component, and a larger P300 component; both the mother's and aunt's faces elicited a larger right N170 component. These results suggest that the emotional attachment between mother and child has neural ramifications across three successive face processing stages that are distinguished from the neural effects of facial familiarity. (C) 2014 Elsevier B.V. All rights reserved