Exploring the potential effect and mechanisms of protocatechuic acid on human hair follicle melanocytes

This study aims to evaluate the effect of Protocatechuic acid (PCA) on human hair follicle melanocytes (HFM). Normal primary HFM were isolated and cultured till logarithmic period of second passage, then treated with different concentrations of PCA (0.1~200 μmol L–1) to study the cell proliferation, melanin contents, tyrosinase activity and protein and mRNA expression of melanogenic genes (tyrosinase-related protein 1 (TRP-1), tyrosinase-related protein 2 (TRP-2), and microphthalmia-associated transcription factor (MITF)) in the cultured HFM. In addition, we have also measured the contents of superoxide dismutase (SOD) and glutathione (GSH) in PCA treated HFM. Vitamin C was used as a positive control. The result showed that PCA can decrease the synthesis of melanin and the tyrosinase activity with IC50 = 8.9 μmol L–1 and IC50 = 6.4 μmol L–1, respectively, at the treatment time of 24 hours, without inducing any cytotoxicity in HFM cells. In addition, the mRNA transcription and protein expression levels of TRP-1, TRP-2 and MITF significantly decreased with a dose-dependent manner after 24-hour PCA treated in HFM cells. Furthermore, PCA has significantly increased the SOD and GSH activity in a dose-dependent manner for 24-hour PCA treatment. This study suggested that PCA has an inhibitory effect on the production of melanin through down-regulation of the expression of melanogenesis-related protein and the effect of anti-oxidation, which could be useful for the therapy of melanin overproduction or skin whitening

Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines

Although large conversational AI models such as OpenAI's ChatGPT have demonstrated great potential, we question whether such models can guarantee factual accuracy. Recently, technology companies such as Microsoft and Google have announced new services which aim to combine search engines with conversational AI. However, we have found numerous mistakes in the public demonstrations that suggest we should not easily trust the factual claims of the AI models. Rather than criticizing specific models or companies, we hope to call on researchers and developers to improve AI models' transparency and factual correctness

Republican personality cults in wartime China: contradistinction and collaboration

This paper explores the development of the Wang Jingwei personality cult during the Japanese occupation of China (1937–1945). It examines how the collaborationist Chinese state led by Wang sought to distinguish its figurehead from the person he had replaced, Nationalist leader Chiang Kai-shek. Drawing on visual, archival, and published sources, it traces the development of the Wang cult from the early years of the war, and argues that the unusual context in which the cult evolved ultimately undermined its coherence. The case of Wang Jingwei illustrates how the Chinese case more broadly can enhance our understandings of personality cults that develop under occupation. To this end, I compare the Wang regime with various European “collaborationist” governments that sought to promote their leaders in similar ways

3D reconstruction identifies loci linked to variation in angle of individual sorghum leaves

Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quan- tification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

Time-free solution to independent set problem using P systems with active membranes

Membrane computing is a branch of natural computingwhich abstracts fromthe structure and the functioning of living cells. The computation models obtained in the field of membrane computing are usually called P systems. P systems have been used to solve computationally hard problems efficiently on the assumption that the execution of each rule is completed in exactly one time-unit (a global clock is assumed for timing and synchronizing the execution of rules). However, in biological reality, different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, with this biological reality, we give a time-free solution to independent set problemusing P systems with active membranes, which solve the problem independent of the execution time of the involved rules

P Systems with Proteins on Active Membranes

P systems with active membranes, as a sort of basic P system, include in communication rules and out communication rules, where communication rules are controlled by polarizations. However, the communication of objects among living cells may be controlled by several factors, such as proteins, polarizations, etc. Based on this biological fact, in this article, a new class of P systems, named P systems with proteins on active membranes (known as PAM P systems) is considered, where the movement of objects is controlled by both proteins and polarizations. The computational theory of PAM P systems is discussed. More specifically, we show that PAM P systems achieve Turing universality when the systems use two membranes, one protein and one polarization. Moreover, the PAM P systems, with the help of membrane division rules, make the SAT problem solvable. These results indicate that PAM P systems are also a sort of powerful system

P Systems with Proteins on Active Membranes

P systems with active membranes, as a sort of basic P system, include in communication rules and out communication rules, where communication rules are controlled by polarizations. However, the communication of objects among living cells may be controlled by several factors, such as proteins, polarizations, etc. Based on this biological fact, in this article, a new class of P systems, named P systems with proteins on active membranes (known as PAM P systems) is considered, where the movement of objects is controlled by both proteins and polarizations. The computational theory of PAM P systems is discussed. More specifically, we show that PAM P systems achieve Turing universality when the systems use two membranes, one protein and one polarization. Moreover, the PAM P systems, with the help of membrane division rules, make the SAT problem solvable. These results indicate that PAM P systems are also a sort of powerful system