Electrotunable liquid sulfur microdroplets.

Manipulating liquids with tunable shape and optical functionalities in real time is important for electroactive flow devices and optoelectronic devices, but remains a great challenge. Here, we demonstrate electrotunable liquid sulfur microdroplets in an electrochemical cell. We observe electrowetting and merging of sulfur droplets under different potentiostatic conditions, and successfully control these processes via selective design of sulfiphilic/sulfiphobic substrates. Moreover, we employ the electrowetting phenomena to create a microlens based on the liquid sulfur microdroplets and tune its characteristics in real time through changing the shape of the liquid microdroplets in a fast, repeatable, and controlled manner. These studies demonstrate a powerful in situ optical battery platform for unraveling the complex reaction mechanism of sulfur chemistries and for exploring the rich material properties of the liquid sulfur, which shed light on the applications of liquid sulfur droplets in devices such as microlenses, and potentially other electrotunable and optoelectronic devices

Exploring the Code of Rural Talent Attraction: A Configurational Study on the Influencing Factors of College Graduates' Willingness to Work in Rural Areas—Based on the Survey in City S and Analysis Using the fsQCA Method

In light of the current situation where college graduates face employment difficulties and rural areas suffer from a shortage of talent, leading to slow development, and by integrating social cognitive theory, a ternary framework and research model for college graduates' willingness to work in rural areas have been constructed. Using fuzzy-set qualitative comparative analysis, the differential impact of various factors on the willingness of college graduates from City S to work in rural areas was studied. The research indicates that the willingness of college graduates to work in rural areas is influenced by multiple concurrent factors, yet these factors converge in different ways, and there exists an asymmetric nature of causality. The antecedent conditions leading to high willingness outcomes are not consistent with those leading to low willingness. Furthermore, the research reveals the sufficient and necessary conditions influencing the willingness of college graduates to work in rural areas. In addition, the study identifies two configurations each for high and low willingness groups, namely, high ability + resource-driven type, policy-led type, external deficiency type, and internal-external linkage deficiency type

High-precision calculation of gas saturation in organic shale pores using an intelligent fusion algorithm and a multi-mineral model

 Shale gas reservoirs have been the subject of intensifying research in recent years. In particular, gas saturation has received considerable attention as a key parameter reflecting the gas-bearing properties of reservoirs. However, no mature model exists for calculating the saturation of shale gas reservoirs due to the difficulty in calculating the gas saturation. This paper proposes a new gas saturation prediction method that combines model-driven and data-driven approaches. A multi-mineral petrophysical model is applied to derive the apparent saturation model. Using the calculated apparent saturation, matrix parameters and porosity curve as inputs, an intelligent fusion algorithm composed of five regression algorithms is employed to predict the gas saturation. The gas saturation prediction results in the Yongchuan block, Sichuan Basin, reveal that the model proposed in this paper boasts good reliability and a greatly improved prediction accuracy. The proposed model can greatly assist in calculating the gas saturation of shale gas reservoirs.Cited as: Zhu, L., Zhang, C., Zhang, Z., Zhou, X. High-precision calculation of gas saturation in organic shale pores using an intelligent fusion algorithm and a multi-mineral model. Advances in Geo-Energy Research, 2020, 4(2): 135-151, doi: 10.26804/ager.2020.02.0

Validation of the SCID-hu Thy/Liv mouse model with four classes of licensed antiretrovirals.

BackgroundThe SCID-hu Thy/Liv mouse model of HIV-1 infection is a useful platform for the preclinical evaluation of antiviral efficacy in vivo. We performed this study to validate the model with representatives of all four classes of licensed antiretrovirals.Methodology/principal findingsEndpoint analyses for quantification of Thy/Liv implant viral load included ELISA for cell-associated p24, branched DNA assay for HIV-1 RNA, and detection of infected thymocytes by intracellular staining for Gag-p24. Antiviral protection from HIV-1-mediated thymocyte depletion was assessed by multicolor flow cytometric analysis of thymocyte subpopulations based on surface expression of CD3, CD4, and CD8. These mice can be productively infected with molecular clones of HIV-1 (e.g., the X4 clone NL4-3) as well as with primary R5 and R5X4 isolates. To determine whether results in this model are concordant with those found in humans, we performed direct comparisons of two drugs in the same class, each of which has known potency and dosing levels in humans. Here we show that second-generation antiretrovirals were, as expected, more potent than their first-generation predecessors: emtricitabine was more potent than lamivudine, efavirenz was more potent than nevirapine, and atazanavir was more potent than indinavir. After interspecies pharmacodynamic scaling, the dose ranges found to inhibit viral replication in the SCID-hu Thy/Liv mouse were similar to those used in humans. Moreover, HIV-1 replication in these mice was genetically stable; treatment of the mice with lamivudine did not result in the M184V substitution in reverse transcriptase, and the multidrug-resistant NY index case HIV-1 retained its drug-resistance substitutions.ConclusionGiven the fidelity of such comparisons, we conclude that this highly reproducible mouse model is likely to predict clinical antiviral efficacy in humans

A Swin-Transformer-based Model for Efficient Compression of Turbulent Flow Data

This study proposes a novel deep-learning-based method for generating reduced representations of turbulent flows that ensures efficient storage and transfer while maintaining high accuracy during decompression. A Swin-Transformer network combined with a physical constraints-based loss function is utilized to compress the turbulent flows with high compression ratios and then restore the data with the underlying physical properties. The forced isotropic turbulent flow is used to demonstrate the ability of the Swin-Transformer-based (ST) model, where the instantaneous and statistical results show the excellent ability of the model to recover the flow data with remarkable accuracy. Furthermore, the capability of the ST model is compared with a typical Convolutional Neural Network-based auto-encoder (CNN-AE) by using the turbulent channel flow at two friction Reynolds numbers $Re_\tau$ = 180 and 550. The results generated by the ST model are significantly more consistent with the DNS data than those recovered by the CNN-AE, indicating the superior ability of the ST model to compress and restore the turbulent flow. This study also compares the compression performance of the ST model at different compression ratios (CR) and finds that the model has low enough error even at very high CR. Additionally, the effect of transfer learning (TL) is investigated, showing that TL reduces the training time by 64\% while maintaining high accuracy. The results illustrate for the first time that the Swin-Transformer-based model incorporating a physically constrained loss function can compress and restore turbulent flows with the correct physics.Comment: 21 page, 16 figure

Utilization of a deoxynucleoside diphosphate substrate by HIV reverse transcriptase

Background: Deoxynucleoside triphosphates (dNTPs) are the normal substrates for DNA sysnthesis is catalyzed by polymerases such as HIV-1 reverse transcriptase (RT). However, substantial amounts of deoxynucleoside diphosphates (dNDPs) are also present in the cell. Use of dNDPs in HIV-1 DNA sysnthesis could have significant implications for the efficacy of nucleoside RT inhibitors such as AZT which are first line therapeutics fro treatment of HIV infection. Our earlier work on HIV-1 reverse transcriptase (RT) suggested that the interaction between the γ phosphate of the incoming dNTP and RT residue K65 in the active site is not essential for dNTP insertion, implying that this polymerase may be able to insert dNPs in addition to dNTPs. Methodology/Principal Findings: We examined the ability of recombinant wild type (wt) and mutant RTs with substitutions at residue K65 to utilize a dNDP substrate in primer extension reactions. We found that wild type HIV-1 RT indeed catalyzes incorporation of dNDP substrates whereas RT with mutations of residue K645 were unable to catalyze this reaction. Wild type HIV-1 RT also catalyzed the reverse reaction, inorganic phosphate-dependent phosphorolysis. Nucleotide-mediated phosphorolytic removal of chain-terminating 3′-terminal nucleoside inhibitors such as AZT forms the basis of HIV-1 resistance to such drugs, and this removal is enhanced by thymidine analog mutations (TAMs). We found that both wt and TAM-containing RTs were able to catalyze Pi-mediated phosphorolysis of 3′-terminal AZT at physiological levels of Pi with an efficacy similar to that for ATP-dependent AZT-excision. Conclusion: We have identified two new catalytic function of HIV-1 RT, the use of dNDPs as substrates for DNA synthesis, and the use of Pi as substrate for phosphorolytic removal of primer 3′-terminal nucleotides. The ability to insert dNDPs has been documented for only one other DNA polymerase The RB69 DNA polymerase and the reverse reaction employing inorganic phosphate has not been documented for any DNA polymerase. Importantly, our results show that Pi-mediated phosphorolysis can contribute to AZT resistance and indicates that factors that influence HIV resistance to AZT are more complex than previously appreciated. © 2008 Garforth et al