Millimeter line observations toward four local galaxies

We present results of millimeter line observations toward four local gas-rich galaxies (NGC 3079, NGC 4258, NGC 6240 and VII Zw 31) with the IRAM 30 meter millimeter telescope. More than 33 lines in these four sources were detected, including normal dense gas tracers (HCN 1-0, HCO$^+$ 1-0, and C$_2$H 1-0, etc) and their isotopic species. H$^{13}$CN (1-0) and H$^{13}$CO$^+$ (1-0) are detected for the first time in NGC 4258. Optical depths of HCN 1-0 and HCO$^{+}$ 1-0 were estimated with detected isotopic lines in NGC 4258, which were 4.1 and 2.6, respectively. HC$_3$N $J=29-28$, which requires high volume density and high temperature to excite, was detected in NGC 6240. High ratios of HCO$^+$/HCN in NGC 4258 and NGC 6240 imply that this ratio might not be a perfect diagnostic tool between AGN and starburst environments, due to contamination/combination of both processes. The low HC$_3$N/HCN line ratios with less than 0.15 in NGC 4258, NGC 6240 and the non-detection of HC$_3$N line in NGC 3079 and VII Zw 31 indicates that these four galaxies are HC$_3$N-poor galaxies. The variation of fractional abundance of CN in different types of galaxies is large.Comment: 15pages, 13 figures; accepted for publication in MNRA

Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification algorithms for light curves. Among them, periodic variable stars are frequently studied. Different categories of periodic variable stars have a high degree of class imbalance and pose a challenge to algorithms including deep learning methods. We design two kinds of architectures of neural networks for the classification of periodic variable stars in the Catalina Survey's Data Release 2: a multi-input recurrent neural network (RNN) and a compound network combing the RNN and the convolutional neural network (CNN). To deal with class imbalance, we apply Gaussian Process to generate synthetic light curves with artificial uncertainties for data augmentation. For better performance, we organize the augmentation and training process in a "bagging-like" ensemble learning scheme. The experimental results show that the better approach is the compound network combing RNN and CNN, which reaches the best result of 86.2% on the overall balanced accuracy and 0.75 on the macro F1 score. We develop the ensemble augmentation method to solve the data imbalance when classifying variable stars and prove the effectiveness of combining different representations of light curves in a single model. The proposed methods would help build better classification algorithms of periodic time series data for future sky surveys (e.g., LSST).Comment: 10 pages, 8 figures, accepte

Lipophilic M(α,α′-OC5H11)8phthalocyanines (M = H2 and Ni(II)): synthesis, electronic structure, and their utility for highly efficient carbonyl reductions

A lipophilic and electron-rich phthalocyanine (α,α′-n-OC5H11)8-H2Pc and its nickel(II) complex (α,α′-n-OC5H11)8-Ni(II)Pc have been synthesized and characterized. Detailed analyses of the electronic structure were carried out by spectroscopy, electrochemistry, spectroelectrochemistry, and TD-DFT calculations. A series of experiments demonstrate that the (α,α′-n-OC5H11)8-Ni(II)Pc complex can be used as a catalyst for highly efficient carbonyl reductions.Original publication is available at http://dx.doi.org/10.1039/C5DT03256

CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation

Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance. Given recent developments in Large Language Models (LLMs), models such as ChatGPT demonstrate zero-shot capability on many text-annotation tasks, comparable with or even exceeding human annotators. Such LLMs can serve as alternatives for manual annotation, due to lower costs and higher scalability. However, limited work has leveraged LLMs as complementary annotators, nor explored how annotation work is best allocated among humans and LLMs to achieve both quality and cost objectives. We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Under this framework, we utilize uncertainty to estimate LLMs' annotation capability. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline. For code implementation, see https://github.com/SALT-NLP/CoAnnotating

Comprehensive analysis of metastasis-related genes reveals a gene signature predicting the survival of colon cancer patients

Objective The mechanism underlying colon cancer metastasis remain unclear. This study aimed to elucidate the genes alteration during the metastasis of colon cancer and identify genes that crucial to the metastasis and survival of colon cancer patients. Methods The dataset of primary and metastasis tissue of colon cancer, and dataset of high and low metastasis capability of colon cancer cells were selected as training cohort, and the overlapped differentially expressed genes (DEGs) were screened from the training cohort. The functional enrichment analysis for the overlapped DEGs was performed. The prognostic value of overlapped DEGs were analyzed in The Cancer Genome Atlas dataset, and a gene signature was developed using genes that related to the overall survival (OS). The prognostic value of the gene signature was further confirmed in a validation cohort. Results A total of 184 overlapped DEGs were screened from the training cohort. Functional enrichment analysis revealed the significant gene functions and pathways of the overlapped DEGs. Four hub genes (3-oxoacid CoA-transferase 1, actinin alpha 4, interleukin 8, integrin subunit alpha 3) were identified using protein–protein network analysis. Six genes (aldehyde dehydrogenase 2, neural precursor cell expressed, developmentally down-regulated 9, filamin A, lamin B receptor, twinfilin actin binding protein 1, serine and arginine rich splicing factor 1) were closely related to the OS of colon cancer patients. A gene signature was developed using these six genes based on their risk score, and the validation cohort indicated that the prognostic value of this gene signature was high in the prediction of colon cancer patients. Conclusions Our study demonstrates a gene profiles related to the metastasis of colon cancer, and identify a six-gene signature that acts as an independent biomarker on the prognosis of colon cancer

Spectroscopic investigations and theoretical calculations of DABCO induced xanthene bridged self-assembled zinc (II) porphyrin dimer

An in-depth study of the electronic structure of a 1,4-diazabicyclo[2.2.2]octane (DABCO) induced molecular self-assembled xanthene-bridged and amide-bonded porphyrin dimer is reported. Density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations are used to identify trends in the optical spectroscopic properties. B3LYP geometry optimization predicts the formation of an almost perfectly eclipsed structure with respect to the two porphyrin rings with the analogous pyrrole nitrogens separated by 7.7–8.1 Å. The observed distinctive derivative-shaped band morphology of the pseudo-Faraday-A11 terms in the MCD spectra has been used to identify the main electronic Q and B-bands and to validate the TD-DFT calculations. The absence of a discernible splitting of the redox steps or a quenching of the fluorescence demonstrates that there is no significant exciton coupling between the two porphyrin rings

Highly efficient CCl bond cleavage and unprecedented CC bond cleavage of environmentally toxic DDT through molecular electrochemical catalysis

The electrocatalytic properties of a Co(II)octaalkoxyphthalocyanine complex (Co(II)Pc) with eight strongly electron-donating substituents provide the first example of the complete dechlorination of DDT through molecular electrocatalysis, rather than the use of metal electrodes which had been achieved previously. Interaction with a highly nucleophilic [Co(I)Pc]2− species results in rapid cleavage of the C(sp3) Cl, C(sp2) Cl and aromatic C(sp2) Cl bonds. Bis(p-chlorophenyl)methanone (BPCl2) is detected in high yield along with its full dechlorination product, diphenylmethanone (BP) and the conventional C Cl bond cleavage products, due to an unprecedented C C bond cleavage reaction that is followed by the formation of a C−O bond. Theoretical calculations are used to analyze trends in the electronic structure of the Co(II)octaalkoxyphthalocyanine complex that account for the efficiency of the C Cl bond cleavage reactions, and the reaction process and mechanism are analyzed in depth