LIN28 Is Involved in Glioma Carcinogenesis and Predicts Outcomes of Glioblastoma Multiforme Patients

LIN28, an evolutionarily conversed RNA binding protein which can bind to the terminal loops of let-7 family microRNA precursors and block their processing to maturation, is highly expressed in several subsets of tumors that carry poor prognoses, such as ovarian carcinoma, hepatocellular carcinoma, colon carcinoma and germ cell carcinoma. However, there has been no study on the expression of LIN28 in glioma tissues or their importance as a prognostic predictor of glioma patients. This study aimed to examine the expression of LIN28 in glioma and correlate the results to patient outcome. We found that LIN28 expression was significantly higher in the group of patients with a poor prognosis compared to patients with a good prognosis by gene microarray. Log-rank analysis showed patients with higher LIN28 expression level in tumor had a shorter progression-free survival and overall survival times compared to those with lower LIN28 expression level. Similar results were also obtained from the tissue microarray analysis. Univariate and multivariate analyses showed high LIN28 expression was an independent prognostic factor for a shorter progression-free survival and overall survival in GBM patients. Furthermore in vitro experiments showed that down-regulation of LIN28 in U251 and U373 cells caused cell cycle arrest in the G1 phase, delayed cell proliferation, increased apoptosis, and resulted in fewer colonies compared to controls. Summarily, our data provides a potential target for cancer therapy as an approach to overcome the poor options currently available for GBM patients

For Your Predicament or Your Success? Understanding Lenders\u27 Decisions in Lending-based Prosocial Crowdfunding

Prosocial lending is a specific form of crowdfunding where lenders’ decisions involve considerations related to both charitable donations and financial investment. While prior literature has explored how these considerations are reflected in lenders\u27 assessments of borrowers\u27 narrative orientations and their characteristics, the impact of the interplay between these different assessment bases on crowdfunding outcomes remains unclear. To address this gap, our study has developed a model by integrating expectancy violation theory and stereotype content theory. It suggests that emotional variability could enhance the positive impact of socially oriented narratives on crowdfunding speed, while it could amplify the negative impact of financially oriented narratives on crowdfunding speed. A preliminary survival analysis of 173,646 loan requests from Kiva, a leading prosocial lending platform, corroborates these hypotheses. Our findings are expected to contribute to both academic understanding and practical approaches to optimizing prosocial crowdfunding performance

Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction

Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networks (GNNs) for modeling spatial correlations and temporal dependencies of traffic speed evolving patterns, regularized by graph topology.While achieving promising results, current traffic speed prediction methods still suffer from ignoring topology-free patterns, which cannot be captured by GNNs. To tackle this challenge, we propose a generic model for enabling the current GNN-based methods to preserve topology-free patterns. Specifically, we first develop a Dual Cross-Scale Transformer (DCST) architecture, including a Spatial Transformer and a Temporal Transformer, to preserve the cross-scale topology-free patterns and associated dynamics, respectively. Then, to further integrate both topology-regularized/-free patterns, we propose a distillation-style learning framework, in which the existing GNN-based methods are considered as the teacher model, and the proposed DCST architecture is considered as the student model. The teacher model would inject the learned topology-regularized patterns into the student model for integrating topology-free patterns. The extensive experimental results demonstrated the effectiveness of our methods.Comment: Accepted to IJCAI 202

Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense

Visual commonsense understanding requires Vision Language (VL) models to not only understand image and text but also cross-reference in-between to fully integrate and achieve comprehension of the visual scene described. Recently, various approaches have been developed and have achieved high performance on visual commonsense benchmarks. However, it is unclear whether the models really understand the visual scene and underlying commonsense knowledge due to limited evaluation data resources. To provide an in-depth analysis, we present a Multimodal Evaluation (ME) pipeline to automatically generate question-answer pairs to test models' understanding of the visual scene, text, and related knowledge. We then take a step further to show that training with the ME data boosts the model's performance in standard VCR evaluation. Lastly, our in-depth analysis and comparison reveal interesting findings: (1) semantically low-level information can assist the learning of high-level information but not the opposite; (2) visual information is generally under utilization compared with text.Comment: Accepted to EMNLP 2022 Long Pape

Multicarrier Modulation-Based Digital Radio-over-Fibre System Achieving Unequal Bit Protection with Over 10 dB SNR Gain

We propose a multicarrier modulation-based digital radio-over-fibre system achieving unequal bit protection by bit and power allocation for subcarriers. A theoretical SNR gain of 16.1 dB is obtained in the AWGN channel and the simulation results show a 13.5 dB gain in the bandwidth-limited case

Lipid biomarkers of GVHD in allogeneic stem hematopoietic cell transplantation patients

ObjectiveWhile dyslipidemia is established as a key modulator of innate and adaptive immune responses, its role in hematopoietic reconstitution remains unclear. This study aimed to characterize lipid profiles in patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT) and evaluate the associations between dyslipidemia and clinical outcomes.MethodsA retrospective analysis was conducted in a cohort of 106 adult patients (≥18 years) who underwent allogeneic HSCT between January 2019 and December 2023 and had complete lipid records.ResultsProfound dyslipidemia was observed post-transplantation, with over 60% of patients developing significantly decreased high-density lipoprotein cholesterol (HDL-C) and elevated triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) compared to baseline. HDL-C reached its nadir around day 14 and recovered slowly thereafter. Patients with grade III–IV acute graft-versus-host disease (GVHD) exhibited significantly lower HDL-C levels compared to those without GVHD. Lower HDL-C levels were correlated with delayed neutrophil engraftment and inferior GVHD-free/relapse-free survival (GRFS), though not with overall survival (OS). An HDL-C threshold of ≤0.84 mmol/L was identified as an independent predictor of GVHD.ConclusionEarly post-transplant HDL-C dynamics represent a promising biomarker for GVHD risk stratification. These findings support the incorporation of protocolized lipid monitoring into HSCT management to guide preemptive therapeutic interventions

SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks

We introduce SwiftSage, a novel agent framework inspired by the dual-process theory of human cognition, designed to excel in action planning for complex interactive reasoning tasks. SwiftSage integrates the strengths of behavior cloning and prompting large language models (LLMs) to enhance task completion performance. The framework comprises two primary modules: the Swift module, representing fast and intuitive thinking, and the Sage module, emulating deliberate thought processes. The Swift module is a small encoder-decoder LM fine-tuned on the oracle agent's action trajectories, while the Sage module employs LLMs such as GPT-4 for subgoal planning and grounding. We develop a heuristic method to harmoniously integrate the two modules, resulting in a more efficient and robust problem-solving process. In 30 tasks from the ScienceWorld benchmark, SwiftSage significantly outperforms other methods such as SayCan, ReAct, and Reflexion, demonstrating its effectiveness in solving complex real-world tasks.Comment: Project website: https://yuchenlin.xyz/swiftsage

Nonapotassium trialuminium hexa­phosphate

In the title compound, K9Al3(PO4)6, the anionic substructure is built of inter­linked [PO4] and [AlO4] tetra­hedra. Each O atom of the [AlO4] tetra­hedron is common to a positionally different [PO4] tetra­hedron; thus, each [AlO4] tetra­hedron is surrounded by four positionally different [PO4] tetra­hedra. On the other hand, each [PO4] tetra­hedron shares its two O atoms with two positionally different [AlO4] tetra­hedra; the other two phosphate O atoms are terminal ones coordinated by K atoms. The terminal O atoms are usually closer to the K atoms than the bridging O atoms between the [AlO4] and [PO4] tetra­hedra. There are nine symmetry-independent K atoms in the structure. The coordination numbers of the K atoms are 6 or 7 or 8 up to a distance of 3.31 Å. There are channels in the anionic substructure oriented along the [10] direction that are filled by K atoms