3-[(Hydr­oxy)(4-isoprop­oxy-2-methoxy­phen­yl)methyl­ene]-1-isopropyl­pyrrol­idine-2,4-dione

The title compound, C18H23NO5, a potential herbicide, has an enol group that is intra­molecularly hydrogen bonded to a keto O atom. The dihedral angle between the six-membered ring formed by the enol group and the aromatic benzene ring is 53.35 (6)°

MaNGA DynPop -- V. The dark-matter fraction versus stellar velocity dispersion relation and initial mass function variations: dynamical models and full spectrum fitting of integral-field spectroscopy

Using the final MaNGA sample (DR17) of 10K galaxies, we investigate the dark matter fraction $f_{\rm DM}$ within one half-light radius $R_{\rm e}$ for about 6K galaxies with good kinematics spanning a wide range of morphologies and stellar velocity dispersion ($1.6\lesssim \lg\,\sigma_{\rm e}/\mathrm{km\,s^{-1}}\lesssim 2.6$). We employ two techniques to estimate $f_{\rm DM}$: (i) Jeans Anisotropic Modelling (JAM), which performs dark matter decomposition based on the stellar kinematics and (ii) comparing the total dynamical mass-to-light ratios $(M/L)_{\rm JAM}$ and the $(M_{\ast}/L)_{\rm SPS}$ from Stellar Population Synthesis (SPS). We find that both methods consistently show a significant trend of increasing $f_{\rm DM}$ with decreasing $\sigma_{\rm e}$, for $\lg(\sigma_{\rm e}/\mathrm{km\,s^{-1}})\lesssim2.1$ and very low $f_{\rm DM}$ at larger $\sigma_{\rm e}$. For the 235 early-type galaxies with the best dynamical models, we explore the variation of the stellar initial mass function (IMF) by comparing the stellar mass-to-light ratios $(M_{\ast}/L)_{\rm JAM}$ from JAM and SPS. We confirm that the stellar mass excess $\alpha_{\rm IMF}\equiv (M_{\ast}/L)_{\rm JAM}/(M_{\ast}/L)_{\rm SPS}$, which reflects the IMF shape, increases with $\sigma_{\rm e}$, in agreement with previous studies that reported a transition from Chabrier-like to Salpeter IMF among galaxies. We also detect weak positive correlations between $\alpha_{\rm IMF}$ and age, but no correlations with metallicity ($[Z/H]$). Finally, we stack galaxy spectra according to their $\alpha_{\rm IMF}$ to search for differences in IMF-sensitive spectral features (e.g. the $\rm Na_{\rm I}$ doublet). We only find marginal evidence for such differences, which casts doubt on the validity of one or both methods to measure the IMF.Comment: 17 pages, 9 figures, 2 tables; Submitted to MNRAS on 21 September 202

Intrinsic Lithiophilicity of Li–Garnet Electrolytes Enabling High‐Rate Lithium Cycling

Solid‐state lithium batteries are widely considered as next‐generation lithium‐ion battery technology due to the potential advantages in safety and performance. Among the various solid electrolyte materials, Li–garnet electrolytes are promising due to their high ionic conductivity and good chemical and electrochemical stabilities. However, the high electrode/electrolyte interfacial impedance is one of the major challenges. Moreover, short circuiting caused by lithium dendrite formation is reported when using Li–garnet electrolytes. Here, it is demonstrated that Li–garnet electrolytes wet well with lithium metal by removing the intrinsic impurity layer on the surface of the lithium metal. The Li/garnet interfacial impedance is determined to be 6.95 Ω cm2 at room temperature. Lithium symmetric cells based on the Li–garnet electrolytes are cycled at room temperature for 950 h and current density as high as 13.3 mA cm−2 without showing signs of short circuiting. Experimental and computational results reveal that it is the surface oxide layer on the lithium metal together with the garnet surface that majorly determines the Li/garnet interfacial property. These findings suggest that removing the superficial impurity layer on the lithium metal can enhance the wettability, which may impact the manufacturing process of future high energy density garnet‐based solid‐state lithium batteries.By removing the impurity layer on the surface of the lithium metal, Li–garnet electrolytes are demonstrated to well wet the lithium metal, rendering a Li/garnet interfacial impedance of 6.95 Ω cm2, stable galvanostatic cycling for 950 h, and a current density as high as 13.3 mA cm−2 without showing any sign of short circuiting at room temperature.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154451/1/adfm201906189-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154451/2/adfm201906189.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154451/3/adfm201906189_am.pd

Neighborhood-Regularized Self-Training for Learning with Few Labels

Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar representations, we develop a neighborhood-based sample selection approach to tackle the issue of noisy pseudo labels. We further stabilize self-training via aggregating the predictions from different rounds during sample selection. Experiments on eight tasks show that our proposed method outperforms the strongest self-training baseline with 1.83% and 2.51% performance gain for text and graph datasets on average. Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36.8% and saves 57.3% of the time when compared with the best baseline. Our code and appendices will be uploaded to https://github.com/ritaranx/NeST.Comment: Accepted to AAAI 202

Applications of linear regression models in exploring the relationship between media attention, economic policy uncertainty and corporate green innovation

The media plays a dual role of "supervision" and "collusion" in governance mechanisms. This study investigates the impact of media attention and economic policy uncertainty on green innovation by analyzing A-share industrial listed enterprises data between 2011 and 2020. The results show that media attention can effectively promote green innovation and that this impact is significantly heterogeneous. Media attention significantly affects green innovation in non-state-owned enterprises and manufacturing companies positively, but it is insignificant for state-owned enterprises and mining and energy supply industries. Moreover, the results indicate that external economic policy uncertainty can lead enterprises to take early measures to hedge risks, thereby positively regulating the promotion effect of media attention on green innovation during economic fluctuations. Finally, media attention can promote green innovation by increasing environmental regulation intensity, reducing corporate financing constraints, and enhancing corporate social responsibility. Therefore, paying full attention to the media as an institutional subject outside of laws and regulations, gradually forming a pressure-driven mechanism for corporate green innovation, and reducing information opacity, is a pivotal way to promote enterprises' green innovation

Loss to Follow-Up from HIV Screening to ART Initiation in Rural China.

BackgroundPatients who are newly screened HIV positive by EIA are lost to follow-up due to complicated HIV testing procedures. Because this is the first step in care, it affects the entire continuum of care. This is a particular concern in rural China.Objective(s)To assess the routine HIV testing completeness and treatment initiation rates at 18 county-level general hospitals in rural Guangxi.MethodsWe reviewed original hospital HIV screening records. Investigators also engaged with hospital leaders and key personnel involved in HIV prevention activities to characterize in detail the routine care practices in place at each county.Results699 newly screened HIV-positive patients between January 1 and June 30, 2013 across the 18 hospitals were included in the study. The proportion of confirmatory testing across the 18 hospitals ranged from 14% to 87% (mean of 43%), and the proportion of newly diagnosed individuals successfully initiated antiretroviral treatment across the hospitals ranged from 3% to 67% (mean of 23%). The average interval within hospitals for individuals to receive the Western Blot (WB) and CD4 test results from HIV positive screening (i.e. achieving testing completion) ranged from 14-116 days (mean of 41.7 days) across the hospitals. The shortest interval from receiving a positive EIA screening test result to receiving WB and CD4 testing and counseling was 0 day and the longest was 260 days.ConclusionThe proportion of patients newly screened HIV positive that completed the necessary testing procedures for HIV confirmation and received ART was very low. Interventions are urgently needed to remove barriers so that HIV patients can have timely access to HIV/AIDS treatment and care in rural China

MicroRNA 506 regulates expression of PPAR alpha in hydroxycamptothecin-resistant human colon cancer cells

AbstractChemotherapeutic drug resistance remains a major obstacle to the successful treatment of colon cancer. Here, we show that 77 differentially expressed miRNAs were identified in SW1116/HCPT versus SW1116, and over-expressed miR-506 in SW1116/HCPT cells was validated. Then it was indicated that PPARα is a common target of miR-506 by using a luciferase reporter assay. Our results also demonstrated that cytotoxic ability of HCPT requires the concomitant presence of PPARα, and that loss of PPARα expression imparts resistance to HCPTs anti-tumor effects. All together, our studies indicate that miR-506 over-expression in established HCPT-resistant colon cancer cell line confers resistance to HCPT by inhibiting PPARα expression, then providing a rationale for the development of miRNA-based strategies for reversing resistance in HCPT-resistant colon cancer cells

Lentivirus Display: Stable Expression of Human Antibodies on the Surface of Human Cells and Virus Particles

Background: Isolation of human antibodies using current display technologies can be limited by constraints on protein expression, folding and post-translational modifications. Here we describe a discovery platform that utilizes self-inactivating (SIN) lentiviral vectors for the surface display of high-affinity single-chain variable region (scFv) antibody fragments on human cells and lentivirus particles. Methodology/Principal Findings: Bivalent scFvFc human antibodies were fused in frame with different transmembrane (TM) anchoring moieties to allow efficient high-level expression on human cells and the optimal TM was identified. The addition of an eight amino acid HIV-1 gp41 envelope incorporation motif further increased scFvFc expression on human cells and incorporation into lentiviral particles. Both antibody-displaying human cells and virus particles bound antigen specifically. Sulfation of CDR tyrosine residues, a property recently shown to broaden antibody binding affinity and antigen recognition was also demonstrated. High level scFvFc expression and stable integration was achieved in human cells following transduction with IRES containing bicistronic SIN lentivectors encoding ZsGreen when scFvFc fusion proteins were expressed from the first cassette. Up to 10[super]6-fold enrichment of antibody expressing cells was achieved with one round of antigen coupled magnetic bead pre-selection followed by FACS sorting. Finally, the scFvFc displaying human cells could be used directly in functional biological screens with remarkable sensitivity. Conclusions/Significance: This antibody display platform will complement existing technologies by virtue of providing properties unique to lentiviruses and antibody expression in human cells, which, in turn, may aid the discovery of novel therapeutic human mAbs

EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records

Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solving. We propose EHRAgent, an LLM agent empowered with a code interface, to autonomously generate and execute code for multi-tabular reasoning within electronic health records (EHRs). First, we formulate an EHR question-answering task into a tool-use planning process, efficiently decomposing a complicated task into a sequence of manageable actions. By integrating interactive coding and execution feedback, EHRAgent learns from error messages and improves the originally generated code through iterations. Furthermore, we enhance the LLM agent by incorporating long-term memory, which allows EHRAgent to effectively select and build upon the most relevant successful cases from past experiences. Experiments on three real-world multi-tabular EHR datasets show that EHRAgent outperforms the strongest baseline by up to 29.6% in success rate. EHRAgent leverages the emerging few-shot learning capabilities of LLMs, enabling autonomous code generation and execution to tackle complex clinical tasks with minimal demonstrations.Comment: Work in Progres