Safe storage guidelines for soybeans at different temperatures and moisture contents: Poster

Poor storage capacity of soybean makes it prone to fungal spoilage and heating during storage, resulting in lower quality. Early prediction of the fungal spoilage in stored soybeans is very difficult because fungi are often too small to be seen with the naked eye. Here a new method for fungus to early detection is adopted: it is called counting fungal spores. Soybeans with moisture contents of 11.4, 12.1, 13.0, 13.9, 14.3 and 14.7%, were held at 6 temperatures 10, 15, 20, 25, 30 and 35? for180d. Samples were taken at regular intervals and the fungal spores counted. The safe storage conditions (temperature, moisture content, duration) were estimated by means of a curve fitted using the power function fitting. It can predict of soybean spoilage by fungus before there is visible damage.Poor storage capacity of soybean makes it prone to fungal spoilage and heating during storage, resulting in lower quality. Early prediction of the fungal spoilage in stored soybeans is very difficult because fungi are often too small to be seen with the naked eye. Here a new method for fungus to early detection is adopted: it is called counting fungal spores. Soybeans with moisture contents of 11.4, 12.1, 13.0, 13.9, 14.3 and 14.7%, were held at 6 temperatures 10, 15, 20, 25, 30 and 35? for180d. Samples were taken at regular intervals and the fungal spores counted. The safe storage conditions (temperature, moisture content, duration) were estimated by means of a curve fitted using the power function fitting. It can predict of soybean spoilage by fungus before there is visible damage

Similarities and differences of functional connectivity in drug-naïve, first-episode adolescent and young adult with major depressive disorder and schizophrenia

Major depressive disorder (MDD) and schizophrenia (SZ) are considered two distinct psychiatric disorders. Yet, they have considerable overlap in symptomatology and clinical features, particularly in the initial phases of illness. The amygdala and prefrontal cortex (PFC) appear to have critical roles in these disorders; however, abnormalities appear to manifest differently. In our study forty-nine drug-naïve, first-episode MDD, 45 drug-naïve, first-episode SZ, and 50 healthy control (HC) participants from 13 to 30 years old underwent resting-state functional magnetic resonance imaging. Functional connectivity (FC) between the amygdala and PFC was compared among the three groups. Significant differences in FC were observed between the amygdala and ventral PFC (VPFC), dorsolateral PFC (DLPFC), and dorsal anterior cingulated cortex (dACC) among the three groups. Further analyses demonstrated that MDD showed decreased amygdala-VPFC FC and SZ had reductions in amygdala-dACC FC. Both the diagnostic groups had significantly decreased amygdala-DLPFC FC. These indicate abnormalities in amygdala-PFC FC and further support the importance of the interaction between the amygdala and PFC in adolescents and young adults with these disorders. Additionally, the alterations in amygdala-PFC FC may underlie the initial similarities observed between MDD and SZ and suggest potential markers of differentiation between the disorders at first onset

Calcium-Sensing Receptor Regulates Cytosolic [Ca 2+ ] and Plays a Major Role in the Development of Pulmonary Hypertension.

Pulmonary arterial hypertension (PAH) is a progressive disease characterized by elevated pulmonary vascular resistance (PVR) leading to right heart failure and premature death. The increased PVR results in part from pulmonary vascular remodeling and sustained pulmonary vasoconstriction. Excessive pulmonary vascular remodeling stems from increased pulmonary arterial smooth muscle cell (PASMC) proliferation and decreased PASMC apoptosis. A rise in cytosolic free Ca2+ concentration ([Ca2+]cyt) in PASMC is a major trigger for pulmonary vasoconstriction and a key stimulus for PASMC proliferation and migration, both contributing to the development of pulmonary vascular remodeling. PASMC from patients with idiopathic PAH (IPAH) have increased resting [Ca2+]cyt and enhanced Ca2+ influx. Enhanced Ca2+ entry into PASMC due to upregulation of membrane receptors and/or Ca2+ channels may contribute to PASMC contraction and proliferation and to pulmonary vasoconstriction and pulmonary vascular remodeling. We have shown that the extracellular Ca2+-sensing receptor (CaSR), which is a member of G protein-coupled receptor (GPCR) subfamily C, is upregulated, and the extracellular Ca2+-induced increase in [Ca2+]cyt is enhanced in PASMC from patients with IPAH in comparison to PASMC from normal subjects. Pharmacologically blockade of CaSR significantly attenuate the development and progression of experimental pulmonary hypertension in animals. Additionally, we have demonstrated that dihydropyridine Ca2+ channel blockers (e.g., nifedipine), which are used to treat PAH patients but are only effective in 15-20% of patients, activate CaSR resulting in an increase in [Ca2+]cyt in IPAH-PASMC, but not normal PASMC. Our data indicate that CaSR functionally couples with transient receptor potential canonical (TRPC) channels to mediate extracellular Ca2+-induced Ca2+ influx and increase in [Ca2+]cyt in IPAH-PASMC. Upregulated CaSR is necessary for the enhanced extracellular Ca2+-induced increase in [Ca2+]cyt and the augmented proliferation of PASMC in patients with IPAH. This review will highlight the pathogenic role of CaSR in the development and progression of PAH

Development of a Portable Single Photon Ionization-Photoelectron Ionization Time-of-Flight Mass Spectrometer

A vacuum ultraviolet lamp based single photon ionization- (SPI-) photoelectron ionization (PEI) portable reflecting time-of-flight mass spectrometer (TOFMS) was designed for online monitoring gas samples. It has a dual mode ionization source: SPI for analyte with ionization energy (IE) below 10.6 eV and PEI for IE higher than 10.6 eV. Two kinds of sampling inlets, a capillary inlet and a membrane inlet, are utilized for high concentration and trace volatile organic compounds, respectively. A mass resolution of 1100 at m/z 64 has been obtained with a total size of 40 × 31 × 29 cm, the weight is 27 kg, and the power consumption is only 70 W. A mixture of benzene, toluene, and xylene (BTX), SO2, and discharging products of SF6 were used to test its performance, and the result showed that the limit of quantitation for BTX is as low as 5 ppbv (S/N = 10 : 1) with linear dynamic ranges greater than four orders of magnitude. The portable TOFMS was also evaluated by analyzing volatile organic compounds from wine and decomposition products of SF6 inside of a gas-insulated switchgear

Experimental investigation on plugging performance of nanospheres in low-permeability reservoir with bottom water

The oil production rate decreases rapidly after a short period of high yield from acidizing or fracturing in low-permeability reservoirs. In this paper, nanospheres are applied before the fracturing step, which possess the ability to absorb water and expand in the water layer, reducing the flow capacity of bottom water and finally enhancing the oil recovery. The plugging performance is investigated by nanosphere displacement  experiments in cores and sand-packs, which explores the plugging effect in the oil layer, the oil-water transition zones, the water layer and the fracturing zones. In addition, a nuclear magnetic resonance experiment is conducted to study the flow mechanism of nanospheres and determine the plugging rates, which can characterize the plugging performance of nanospheres in porous media. The results show that the plugging rate is 85.84% and 78.65% on the water layer and oil-water transition zone, respectively, and 94.36% in the fracturing zone. Meanwhile, the nanospheres cannot plug the oil layer. The formation pressure has a less considerable effect on the plugging performance of nanospheres. The nanospheres have good injectivity, and the intensity variations in small, medium and large pores account for 34.46%, 13.22% and 52.32%, respectively. Overall, this paper explores the feasibility of applying nanospheres for water plugging and enhanced oil recovery.Cited as: Tang, M., Wang, C., Deng, X., Yang, H., Lu, J., Yu, H. Experimental investigation on plugging performance of nanospheres in low-permeability reservoir with bottom water. Advances in Geo-Energy Research, 2022, 6(2): 95-103. https://doi.org/10.46690/ager.2022.02.0

UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation

Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems. However, current 3D perception research follows a modality-specific paradigm, leading to additional computation overheads and inefficient collaboration between different sensor data. In this paper, we present an efficient multi-modal backbone for outdoor 3D perception named UniTR, which processes a variety of modalities with unified modeling and shared parameters. Unlike previous works, UniTR introduces a modality-agnostic transformer encoder to handle these view-discrepant sensor data for parallel modal-wise representation learning and automatic cross-modal interaction without additional fusion steps. More importantly, to make full use of these complementary sensor types, we present a novel multi-modal integration strategy by both considering semantic-abundant 2D perspective and geometry-aware 3D sparse neighborhood relations. UniTR is also a fundamentally task-agnostic backbone that naturally supports different 3D perception tasks. It sets a new state-of-the-art performance on the nuScenes benchmark, achieving +1.1 NDS higher for 3D object detection and +12.0 higher mIoU for BEV map segmentation with lower inference latency. Code will be available at https://github.com/Haiyang-W/UniTR .Comment: Accepted by ICCV202

Label-free Node Classification on Graphs with Large Language Models (LLMS)

In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost? To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with a cost less than 1 dollar.Comment: The code will be available soon via https://github.com/CurryTang/LLMGN