The influence of bile acids homeostasis by cryptotanshinone-containing herbs

Background: Herbs might affect the homeostasis of bile acids through influence of multiple metabolic pathways of bile acids. Aim: The present study aims to investigate the inhibition of cryptotanshinone towards the glucuronidation of LCA, trying to indicate the possible influence of cryptotanshinone-containing herbs towards the homeostasis of bile acids. Methods: The LCA-3-glucuronidation and LCA-24-glucuronidation reaction was monitored by LC-MS. Results: Initial screening showed that 100 μM of cryptotanshinone inhibited LCA-24-glucuronidation and LCA-3-glucuronidation reaction activity by 82.6% and 79.1%, respectively. This kind of inhibition behaviour exerted cryptotanshinone concentrations-dependent and LCA concentrations-independent inhibition behaviour. Conclusion: All these data indicated the possibility of cryptotanshinone’s influence towards the bile acids metabolism and homeostasis of bile acids.Keywords: herbs, lithocholic acid (LCA), homeostasisAfrican Health sciences Vol 14 No. 1 March 201

Highly sensitive electrochemical sensing platform for the detection of L-dopa based on electropolymerizing glutathione disulfide and multi-walled carbon nanotube-modified electrodes

Afacile sensing platformfor the detection of L-dopa has been developed by electropolymerizing glutathione disulfide (PGSSG) on the surface of glass carbon electrodes (GCE) which were modified by multi-walled carbon nanotubes (MWCNTs). The electrochemical behaviour of the proposed electrodes were investigated via cyclic voltammetry (CV) and differential pulse voltammetry DPV). The morphology of the PGSSG and PGSSG/MWCNTs were characterized by scanning electron microscopy (SEM). Under the optimized experimental conditions, the sensing platform showed the linear response to L-Dopa in a range from 1.0 × 10–6 to 1.2 × 10–3 M with a detection limit of 3.3 × 10–7M (S/N = 3). Moreover, with the merits of high sensitivity and selectivity, good stability and reproducibility, the sensor was successfully applied for the determination of L-dopa in a real sample.Keywords: L-dopa, glutathione disulfide, multi-walled carbon nanotubes, electropolymerization, electrochemical determinatio

Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models

Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing prompts manually, Context Optimization (CoOp) has been recently proposed to learn continuous prompts using taskspecific training data. Despite the performance improvements on downstream tasks, several studies have reported that CoOp suffers from the overfitting issue in two aspects: (i) the test accuracy on base classes first improves and then worsens during training;(ii) the test accuracy on novel classes keeps decreasing. However, none of the existing studies can understand and mitigate such overfitting problems. In this study, we first explore the cause of overfitting by analyzing the gradient flow. Comparative experiments reveal that CoOp favors generalizable and spurious features in the early and later training stages, respectively, leading to the non-overfitting and overfitting phenomena. Given those observations, we propose Subspace Prompt Tuning (SubPT) to project the gradients in back-propagation onto the low-rank subspace spanned by the early-stage gradient flow eigenvectors during the entire training process and successfully eliminate the overfitting problem. In addition, we equip CoOp with a Novel Feature Learner (NFL) to enhance the generalization ability of the learned prompts onto novel categories beyond the training set, needless of image training data. Extensive experiments on 11 classification datasets demonstrate that SubPT+NFL consistently boost the performance of CoOp and outperform the state-of-the-art CoCoOp approach. Experiments on more challenging vision downstream tasks, including open-vocabulary object detection and zero-shot semantic segmentation, also verify the effectiveness of the proposed method. Codes can be found at https://tinyurl.com/mpe64f89

Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients

Adversarial examples have been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples, based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD), but with different parameters (i.e., shape factor, mean, and variance). GGD is a general distribution family to cover many popular distributions (e.g., Laplacian, Gaussian, or uniform). It is more likely to approximate the intrinsic distributions of internal responses than any specific distribution. Besides, since the shape factor is more robust to different databases rather than the other two parameters, we propose to construct discriminative features via the shape factor for adversarial detection, employing the magnitude of Benford-Fourier coefficients (MBF), which can be easily estimated using responses. Finally, a support vector machine is trained as the adversarial detector through leveraging the MBF features. Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust on detecting adversarial examples of different crafting methods and different sources, compared to state-of-the-art adversarial detection methods

Ferromagnetic and insulating behavior in both half magnetic levitation and non-levitation LK-99 like samples

Finding materials exhibiting superconductivity at room temperature has long been one of the ultimate goals in physics and material science. Recently, room-temperature superconducting properties have been claimed in a copper substituted lead phosphate apatite (Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O, or called LK-99) [1-3]. Using a similar approach, we have prepared LK-99 like samples and confirmed the half-levitation behaviors in some small specimens under the influence of a magnet at room temperature. To examine the magnetic properties of our samples, we have performed systematic magnetization measurements on the as-grown LK-99-like samples, including the half-levitated and non-levitated samples. The magnetization measurements show the coexistence of soft-ferromagnetic and diamagnetic signals in both half-levitated and non-levitated samples. The electrical transport measurements on the as-grown LK-99-like samples including both half-levitated and non-levitated samples show an insulating behavior characterized by the increasing resistivity with the decreasing temperature

A series of lanthanide(iii) metal-organic frameworks derived from a pyridyl-dicarboxylate ligand: single-molecule magnet behaviour and luminescence properties

The reactions of LnIII ions with a versatile pyridyl-decorated dicarboxylic acid ligand lead to a series of novel three-dimensional (3D) Ln-MOFs, [Ln3(pta)4(Hpta)(H2O)]·xH2O (Ln = Dy (1), Eu (2), Gd (3), Tb (4), H2pta = 2-(4-pyridyl)-terephthalic acid, x = 6 for 1, 2.5 for 2, 1.5 for 3 and 2 for 4). The Ln3+ ions act as the nine-coordinated Muffin spheres, linking to each other to generate trinuclear {Ln3(OOC)6N2} SBUs, which are further extended to be interesting 3D topology architectures. To the best of our knowledge, the Dy-MOF exhibits a zero-field single-molecule magnet (SMM) behaviour with the largest effective energy barrier among the previously reported 3D MOF-based Dy-SMMs. The combined analyses of a dilution sample (1@Y) and ab initio calculation demonstrate that the thermally assisted slow relaxation is mainly attributed to the single-ion magnetism. Furthermore, fluorescence measurements reveal that H2pta can sensitize EuIII and TbIII characteristic luminescence