An Optimized System to Solve Text-Based Captcha

CAPTCHA(Completely Automated Public Turing test to Tell Computers and Humans Apart) can be used to protect data from auto bots. Countless kinds of CAPTCHAs are thus designed, while we most frequently utilize text-based scheme because of most convenience and user-friendly way [1]. Currently, various types of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different segmentation ways. Our goal is to defeat the CAPTCHA,thus rstly the CAPTCHAs need to be split into character by character. There isn't a regular segmentation algorithm to obtain the divided characters in all kinds of examples, which means that we have to treat the segmentation individually. In this paper, we build a whole system todefeat the CAPTCHAs as well as achieve state-of-the-art performance.In detail, we present our self-adaptive algorithm to segment different kinds of characters optimally, and then utilize both the existing methods and our own constructed convolutional neural network as an extra classfier. Results are provided showing how our system work well towards defeating these CAPTCHAs

CO Dissociation Mechanism on Pd-Doped Fe(100): Comparison with Cu/Fe(100)

Spin-polarized density functional theory computations have been used to investigate the CO dissociation mechanisms and the different catalytic activities of the reaction on Fe(100) surfaces with different Pd coverages. CO can dissociate on Pd/Fe surfaces via three different mechanisms: direct and H-assisted mechanisms via HCO intermediate or COH intermediate. In our calculation, it was found that the activation barriers of direct CO and COH dissociation mechanisms on pure and Pd-doped Fe(100) surfaces were higher than that of the HCO dissociation mechanism. Besides, energy barriers for the identical reaction pathway on Fe-rich Fe(100) surfaces were lower than those on Pd-rich Fe(100) surfaces, namely, CO dissociation mainly occurs via the HCO intermediate pathway and the catalytic activity becomes lower with Pd coverage increasing toward CO dissociation in both direct CO and H-assisted CO dissociation mechanisms. As a result, CO dissociation mainly occurs on Fe-rich Pd/Fe surfaces, leading to the formation of CH_<i>x</i>, and Pd-rich Pd/Fe surfaces can stabilize CO, which may afford the high selectivity to oxygenate. The bimetallic catalysts will provide two different active sites that are synergetic for the formation of higher alcohols. Moreover, the difference between Pd-doped and Cu-doped Fe(100) systems was compared and analyzed based on the d-band model, and it was found that the d-bandwidth of Cu/Fe(100) was more narrow compared to that of Pd/Fe(100); this was agreement with the calculation results that the energy barrier for C–O bond scission on Cu/Fe(100) was lower than that on Pd/Fe(100). We predicted that methane content decreases and methanol content increases with Pd coverage increases on Pd/Fe(100), and the selectivity of methanol on Pd/Fe(100) is higher than that on Cu/Fe(100). Importantly, a typical “ volcano curve” between ethanol synthesis and the HCO dissociation barrier was gained, in which the selectivity for the ethanol synthesis is highest on the Fe₂Cu₂/Fe­(100) system among these studied bimetallic model catalysts due to its moderate catalytic activity for HCO dissociation

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening

MiR-124 is significantly upregulated during neurogenic transdifferentiation of ADMSCs.

<p>(A) A protocol outlined describing the strategy to induce neurogenic transdifferentiation of ADMSCs. (B) Representative morphological images of ADMSCs before induction (up) and on day 1 post-induction (down). (C) Representative immunofluorescent images of neuronal markers (NSE and Tuj-1) and glial cell marker (GFAP) expressed in differentiated ADMSCs on day 1 post-induction. Red: neurogenic markers. Blue: DAPI. (D) A total of 12 differential expressed miRNAs, including 6 up-regulated and 6 down-regulated, were identified in the ADMSCs on day 0, 5 and 10 of the induction. The criteria was fold change ≥3 from day 0 to day 10, <i>p</i><0.05. Columns represent samples and rows represent miRNAs (black, green, and red correspond to unchanged, down-regulated and upregulated, respectively). D0: day 0, D5: day 5, D10: day 10. (E) QRT-PCR analysis of miR-124 expression of the ADMSCs on day 0, day 5 and day 10 of induction. Transdiff. = transdifferentiated. * <i>p</i><0.05, ** <i>p</i><0.01, *** <i>p</i><0.001.</p

The schematic diagram of miR-124’s regulation over neurogenic transdifferentiation of ADMSCs.

<p>The schematic diagram of miR-124’s regulation over neurogenic transdifferentiation of ADMSCs.</p

MiR-124 deficiency can affect neurogenic transdifferentiation of ADMSCs.

<p>(A) QRT-PCR analysis of miR-124 expression in ADMSCs infected with the pLV-miR-124 locker lentiviral particles or the vector negative control (Vector NC). (B) QRT-PCR analysis of NSE, Tuj-1 and GFAP mRNA expression in ADMSCs (with or without miR-124 knockdown) on day 1 post-induction. (C) Representative images of flow cytometry analysis of ADMSCs (with or without miR-124 knockdown) with positive neurogenic markers on day 1 post-induction. (D) Quantification of the proportion of ADMSCs with positive neurogenic markers showed in (C). * <i>p</i><0.05, ** <i>p</i><0.01, *** <i>p</i><0.001.</p

MiR-124 knockdown hampers the acquired electrophysiological properties due to transdifferentiation.

<p>(A) Representative traces of intracellular calcium dynamics (ratiometric acquisition) showing lack of response of untransdifferentiated ADMSCs at biochemical depolarization with 50 nM KCI while still preserving their functional response following exposure to 1 mM ATP. (B) Representative traces of intracellular calcium dynamics (ratiometric acquisition) showing functional response of transdiff. ADMSCs (without miR-124 knockdown: up; with miR-124 knockdown: down) both at biochemical depolarization with KCI as well as the electrical field potential stimulation. However, the responses of the transdiff. ADMSCs/miR-124(-) were significantly weaker. (C) Representative traces of intracellular calcium dynamics (ratiometric acquisition) of mature hippocampal neurons (HN) at biochemical depolarization with 50 nM KCI. (D) Quantification of the peak of intensity of response of transdiff. ADMSC and neurons following exposure to 50 mM KCI (Δ340/380; ADMSCs naive = 0.002±0.001, n = 6; transdiff. ADMSCs = 0.052±0.006, n = 6; transdiff. ADMSCs/miR-124(-) = 0.023±0.008, n = 6; HN = 0.133±0.021, n = 6). (E) Quantitative evaluation of membrane potential values in ADMSCs, transdiff. ADMSCs (with or without miR-124 knockdown) and in hippocampal neurons (ADMSCs naive: 10.13±4.22 mV, n = 6; transdiff. ADMSCs = -42.52±5.31 mV, n = 6; transdiff. ADMSCs/miR-124(-) = -16.37±4.42 mV, n = 6; HN = -52.75±7.48, n = 6). *: comparison with ADMSCs; #: comparison with transdiff. ADMSCs. * and # <i>p</i><0.05, ** and ## <i>p</i><0.01, *** and ### <i>p</i><0.001.</p

MiR-124 enhances neuronal transdifferentiation of ADMSCs partly through RhoA/ROCK1 signaling pathway.

<p>(A) QRT-PCR analysis of RhoA, ROCK1 and ROCK2 mRNA expression in ADMSCs infected with the RhoA shRNA lentiviral particles, ROCK1 shRNA lentiviral particles, ROCK2 shRNA lentiviral particles or the negative control. (B) QRT-PCR analysis of NSE, Tuj-1 and GFAP mRNA expression in ADMSCs infected with the pLV-miR-124 locker lentiviral particles, co-infected with the pLV-miR-124 locker lentiviral particles and the RhoA shRNA lentiviral particles, ROCK1 shRNA lentiviral particles or ROCK2 shRNA lentiviral particles on day 1 after transdifferentiation induction. (C, E and G) Representative images of flow cytometry analysis of neurogenic markers (NSE, Tuj-1 and GFAP) positive ADMSCs infected with the pLV-miR-124 locker lentiviral particles and co-infected with the pLV-miR-124 locker lentiviral particles and the RhoA shRNA lentiviral particles (C), ROCK1 shRNA lentiviral particles (E) or ROCK2 shRNA lentiviral particles (G) on day 1 after transdifferentiation induction. (D, F and H) Quantification of the proportion of ADMSCs with positive neurogenic markers showed in C, E and G. *: comparison with shRNA NC or vector NC; #: comparison with transdiff. ADMSCs/miR-124(-). * and # <i>p</i><0.05, ** and ## <i>p</i><0.01, *** and ### <i>p</i><0.001.</p

MiR-124 can directly target RHOA mRNA and regulate its expression.

<p>(A) QRT-PCR (up) and western blot (down) analysis of RhoA expression in ADMSCs on day 0, 5 and 10 during the transdifferentiation induction. (B) Predicted binding site between 3’UTR of RhoA mRNA and miR-124 and the designed mutant sequence. (C) Dual luciferase assay of relative luciferase activity of pGL3-RhoA-WT (RhoA-WT) and pGL3-RhoA-MUT (RhoA-WT) transfected with 50 nM miR-124 mimics or the negative control in HEK 293T cells. Firefly luciferase activity was normalized to that of Renilla luciferase. (D) QRT-PCR (up) and western blot (down) analysis of RhoA expression in ADMSCs infected with the pLV-miR-124 locker lentiviral particles or the negative control. * <i>p</i><0.05, ** <i>p</i><0.01, *** <i>p</i><0.001.</p

Illustrating the relationship between three measures of temporal instability.

<p>Two permutations of the same set of IBeIs are presented; both have identical central tendency and PDL_max statistics. The IBeI series in (A) exhibits temporal dependency, with gradual transitions from IBeI to IBeI. The IBeI series in (B) exhibits a more stochastic pattern of IBeI transitions. These differences in temporal structure are reflected in the SPC_max and PTD_max statistics.</p