Human midbrain dopaminergic progenitors.

Human midbrain dopaminergic progenitors (mDAPs) are one of the most representative cell types in both basic research and clinical applications. However, there are still many challenges for the preparation and quality control of mDAPs, such as the lack of standards. Therefore, the establishment of critical quality attributes and technical specifications for mDAPs is largely needed. "Human midbrain dopaminergic progenitor" jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human mDAPs in China. This standard specifies the technical requirements, test methods, inspection rules, instructions for usage, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for human mDAPs, which is applicable to the quality control for human mDAPs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that the publication of this guideline will facilitate the institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of human mDAPs for clinical development and therapeutic applications.

Human neural stem cells.

'Human neural stem cells' jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human neural stem cells (hNSCs) in China. This standard specifies the technical requirements, test methods, test regulations, instructions for use, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for hNSCs, which is applicable to the quality control for hNSCs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that publication of the guideline will facilitate institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of hNSCs for clinical development and therapeutic applications.

Single-molecule study reveals Hmo1, not Hho1, promotes chromatin assembly in budding yeast.

Linker histone H1 plays a crucial role in various biological processes, including nucleosome stabilization, high-order chromatin structure organization, gene expression, and epigenetic regulation in eukaryotic cells. Unlike higher eukaryotes, little about the linker histone in Saccharomyces cerevisiae is known. Hho1 and Hmo1 are two long-standing controversial histone H1 candidates in budding yeast. In this study, we directly observed at the single-molecule level that Hmo1, but not Hho1, is involved in chromatin assembly in the yeast nucleoplasmic extracts (YNPE), which can replicate the physiological condition of the yeast nucleus. The presence of Hmo1 facilitates the assembly of nucleosomes on DNA in YNPE, as revealed by single-molecule force spectroscopy. Further single-molecule analysis showed that the lysine-rich C-terminal domain (CTD) of Hmo1 is essential for the function of chromatin compaction, while the second globular domain at the C-terminus of Hho1 impairs its ability. In addition, Hmo1, but not Hho1, forms condensates with double-stranded DNA via reversible phase separation. The phosphorylation fluctuation of Hmo1 coincides with metazoan H1 during the cell cycle. Our data suggest that Hmo1, but not Hho1, possesses some functionality similar to that of linker histone in Saccharomyces cerevisiae, even though some properties of Hmo1 differ from those of a canonical linker histone H1. Our study provides clues for the linker histone H1 in budding yeast and provides insights into the evolution and diversity of histone H1 across eukaryotes. IMPORTANCE There has been a long-standing debate regarding the identity of linker histone H1 in budding yeast. To address this issue, we utilized YNPE, which accurately replicate the physiological conditions in yeast nuclei, in combination with total internal reflection fluorescence microscopy and magnetic tweezers. Our findings demonstrated that Hmo1, rather than Hho1, is responsible for chromatin assembly in budding yeast. Additionally, we found that Hmo1 shares certain characteristics with histone H1, including phase separation and phosphorylation fluctuations throughout the cell cycle. Furthermore, we discovered that the lysine-rich domain of Hho1 is buried by its second globular domain at the C-terminus, resulting in the loss of function that is similar to histone H1. Our study provides compelling evidence to suggest that Hmo1 shares linker histone H1 function in budding yeast and contributes to our understanding of the evolution of linker histone H1 across eukaryotes.

Dynamically Quantifying Intracellular Elemental Sulfur and Predicting Pertinent Gene Transcription by Raman Spectroscopy in Living Cells.

The ability to monitor changes in metabolites and corresponding gene transcription within living cells is highly desirable. However, most current assays for quantification of metabolites or for gene transcription are destructive, precluding tracking the real-time dynamics of living cells. Here, we used the intracellular elemental sulfur in a Thiophaeococcus mangrovi cell as a proof-of-concept to link the quantity of metabolites and relevant gene transcription in living cells by a nondestructive Raman approach. Raman spectroscopy was utilized to quantify intracellular elemental sulfur noninvasively, and a computational mRR (mRNA and Raman) model was developed to infer the transcription of genes relevant to elemental sulfur. The results showed a significant linear correlation between the exponentially transformed Raman spectral intensity of intracellular elemental sulfur and the mRNA levels of genes encoding sulfur globule proteins in T. mangrovi. The mRR model was verified independently in two genera of Thiocapsa and Thiorhodococcus, and the mRNA levels predicted by mRR showed high consistency with actual gene expression detected by real-time polymerase chain reaction (PCR). This approach could enable noninvasive assessment of the quantity of metabolites and link the pertinent gene expression profiles in living cells, providing useful baseline data to spectroscopically map various omics in real time.

Inactivation of porcine epidemic diarrhea virus with electron beam irradiation under cold chain conditions.

The many instances of COVID-19 outbreaks suggest that cold chains are a possible route for the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, owing to the low temperatures of cold chains, which are normally below 0 °C, there are limited options for virus inactivation. Here, high-energy electron beam (E-beam) irradiation was used to inactivate porcine epidemic diarrhea virus (PEDV) under simulated cold chain conditions. This coronavirus was used as a surrogate for SARS-CoV-2. The possible mechanism by which high-energy E-beam irradiation inactivates PEDV was also explored. An irradiation dose of 10 kGy reduced the PEDV infectious viral titer by 1.68-1.76 log10TCID 50 / 100 µ L in the cold chain environment, suggesting that greater than 98.1% of PEDV was inactivated. E-beam irradiation at 5-30 kGy damaged the viral genomic RNA with an efficiency of 46.25%-92.11%. The integrity of the viral capsid was disrupted at 20 kGy. The rapid and effective inactivation of PEDV at temperatures below freezing indicates high-energy E-beam irradiation as a promising technology for disinfecting SARS-CoV-2 in cold chain logistics to limit the transmission of COVID-19.

Active meta-learning for predicting and selecting perovskite crystallization experiments.

Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.

High-Throughput Screen for Inhibitors of Klebsiella pneumoniae Virulence Using a Tetrahymena pyriformis Co-Culture Surrogate Host Model.

The continuing emergence of antibacterial resistance reduces the effectiveness of antibiotics and drives an ongoing search for effective replacements. Screening compound libraries for antibacterial activity in standard growth media has been extensively explored and may be showing diminishing returns. Inhibition of bacterial targets that are selectively important under in vivo (infection) conditions and, therefore, would be missed by conventional in vitro screens might be an alternative. Surrogate host models of infection, however, are often not suitable for high-throughput screens. Here, we adapted a medium-throughput Tetrahymena pyriformis surrogate host model that was successfully used to identify inhibitors of a hyperviscous Klebsiella pneumoniae strain to a high-throughput format and screened circa 1.2 million compounds. The screen was robust and identified confirmed hits from different chemical classes with potent inhibition of K. pneumoniae growth in the presence of T. pyriformis that lacked any appreciable direct antibacterial activity. Several of these appeared to inhibit capsule/mucoidy, which are key virulence factors in hypervirulent K. pneumoniae. A weakly antibacterial inhibitor of LpxC (essential for the synthesis of the lipid A moiety of lipopolysaccharides) also appeared to be more active in the presence of T. pyriformis, which is consistent with the role of LPS in virulence as well as viability in K. pneumoniae.

Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning.

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.

Structural insights into multifunctionality of human FACT complex subunit hSSRP1.

Human structure-specific recognition protein 1 (hSSRP1) is an essential component of the facilitates chromatin transcription complex, which participates in nucleosome disassembly and reassembly during gene transcription and DNA replication and repair. Many functions, including nuclear localization, histone chaperone activity, DNA binding, and interaction with cellular proteins, are attributed to hSSRP1, which contains multiple well-defined domains, including four pleckstrin homology (PH) domains and a high-mobility group domain with two flanking disordered regions. However, little is known about the mechanisms by which these domains cooperate to carry out hSSRP1's functions. Here, we report the biochemical characterization and structure of each functional domain of hSSRP1, including the N-terminal PH1, PH2, PH3/4 tandem PH, and DNA-binding high-mobility group domains. Furthermore, two casein kinase II binding sites in hSSRP1 were identified in the PH3/4 domain and in a disordered region (Gly617-Glu709) located in the C-terminus of hSSRP1. In addition, a histone H2A-H2B binding motif and a nuclear localization signal (Lys677‒Asp687) of hSSRP1 are reported for the first time. Taken together, these studies provide novel insights into the structural basis for hSSRP1 functionality.

Deactivation of SARS-CoV-2 surrogate porcine epidemic diarrhea virus with electron beam irradiation under the cold chain transportation condition

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has prevailed all over the world and emerged as a significant public health emergency. The rapid outbreak of SARS-CoV-2 is largely due to its high transmission capacity. Studies implied that the cold chain logistics would be a potential route for the spread of SARS-CoV-2. The low temperature condition of the cold chain is conducive to survival and transmission of virus. Thus, the virus disinfection in cold chain should not be neglected for controlling COVID-19. However, due to the low temperature feature of the cold-chain, the virus disinfecting methods suitable in cold chain are limited. Here the high-energy electron beam irradiation is proposed to disinfect the SARS-CoV-2 in cold chain logistics. We evaluated the impact of high-energy electron beam irradiation on porcine epidemic diarrhea virus (PEDV), an enveloped virus surrogate for SARS-CoV-2, and explored the possible mechanism of the action of high-energy electron beam irradiation on PEDV. The irradiation dose of 10 kGy inactivated 98.1 % PEDV on the both top and bottom surfaces of various packaging materials under cold chain frozen condition. High-energy electron beam inactivated PEDV by inducing damages on viral genome or even capsid.

Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens.

The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.

Pharmacokinetics and safety of liposomal bupivacaine after local infiltration in healthy Chinese adults: a phase 1 study.

BACKGROUND: Liposomal bupivacaine (LB) is a long-acting formulation of bupivacaine. The safety and efficacy of LB has been demonstrated across surgical procedures. However, pharmacokinetic (PK) parameters and safety of LB in the Chinese population have not been assessed. METHODS: In this single-arm, single center, phase 1, open-label study, PK and safety of local infiltration with LB 266 mg were assessed in healthy Chinese adults. Eligible participants were aged 18 to 55 years with biologic parents and grandparents of Chinese ethnicity, in generally good health (i.e., no clinically significant abnormalities), and with a body mass index (BMI) 19.0 to 24.0 kg/m2 (inclusive) and body weight ≥ 50 kg. RESULTS: Participants (N = 20) were predominantly men (80 %); mean age was 32 years; and mean BMI was 21.8 kg/m2. After LB administration, mean plasma levels of bupivacaine rapidly increased during the first hour and continued to increase through 24 h; plasma levels then gradually decreased through 108 h followed by a monoexponential decrease through 312 h. Geometric mean maximum plasma concentration was 170.9 ng/mL; the highest plasma bupivacaine concentration detected in any participant was 374.0 ng/mL. Twenty-two treatment-emergent adverse events were reported (mild, n = 21; moderate, n = 1). CONCLUSIONS: After single-dose administration of LB, PK measures were similar to a previously reported profile in US adults. The highest observed peak plasma concentration of bupivacaine was several-fold below the plasma concentration threshold accepted as being associated with neurotoxicity or cardiotoxicity (2000-4000 ng/mL). These data support that LB is well tolerated and safe in individuals of Chinese descent. TRIAL REGISTRATION: NCT04158102 (ClinicalTrials.gov identifier), Date of registration: November 5, 2019.

Correction: A dynamic generalized fuzzy multi-criteria group decision making approach for green supplier segmentation.

[This corrects the article DOI: 10.1371/journal.pone.0245187.].

A dynamic generalized fuzzy multi-criteria croup decision making approach for green supplier segmentation.

Supplier selection and segmentation are crucial tasks of companies in order to reduce costs and increase the competitiveness of their goods. To handle uncertainty and dynamicity in the supplier segmentation problem, this research thus proposes a new dynamic generalized fuzzy multi-criteria group decision making (MCGDM) approach from the aspects of capability and willingness and with respect to environmental issues. The proposed approach defines the aggregated ratings of alternatives, the aggregated weights of criteria, and the weighted ratings by using generalized fuzzy numbers with the effect of time weight. Next, we determine the ranking order of alternatives via a popular centroid-index ranking approach. Finally, two case studies demonstrate the efficiency of the proposed dynamic approach. The applications show that the proposed appoach is effective in solving the MCGDM in vague environment.

Blood identification at the single-cell level based on a combination of laser tweezers Raman spectroscopy and machine learning.

Laser tweezers Raman spectroscopy (LTRS) combines optical tweezers technology and Raman spectroscopy to obtain biomolecular compositional information from a single cell without invasion or destruction, so it can be used to "fingerprint" substances to characterize numerous types of biological cell samples. In the current study, LTRS was combined with two machine learning algorithms, principal component analysis (PCA)-linear discriminant analysis (LDA) and random forest, to achieve high-precision multi-species blood classification at the single-cell level. The accuracies of the two classification models were 96.60% and 96.84%, respectively. Meanwhile, compared with PCA-LDA and other classification algorithms, the random forest algorithm is proved to have significant advantages, which can directly explain the importance of spectral features at the molecular level.

Classification of pathogens by Raman spectroscopy combined with generative adversarial networks.

Rapid identification of marine pathogens is very important in marine ecology. Artificial intelligence combined with Raman spectroscopy is a promising choice for identifying marine pathogens due to its rapidity and efficiency. However, considering the cost of sample collection and the challenging nature of the experimental environment, only limited spectra are typically available to build a classification model, which hinders qualitative analysis. In this paper, we propose a novel method to classify marine pathogens by means of Raman spectroscopy combined with generative adversarial networks (GANs). Three marine strains, namely, Staphylococcus hominis, Vibrio alginolyticus, and Bacillus licheniformis, were cultured. Using Raman spectroscopy, we acquired 100 spectra of each strain, and we fitted them into GAN models for training. After 30,000 training iterations, the spectra generated by G were similar to the actual spectra, and D was used to test the accuracy of the spectra. Our results demonstrate that our method not only improves the accuracy of machine learning classification but also solves the problem of requiring a large amount of training data. Moreover, we have attempted to find potential identifying regions in the Raman spectra that can be used for reference in subsequent related work in this field. Therefore, this method has tremendous potential to be developed as a tool for pathogen identification.

Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification.

Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a novel method that uses artificial intelligence to analyze biological Raman spectra and identify the microbes at a single-cell level. The combination of a framework of convolutional neural network (ConvNet) and Raman spectroscopy allows the extraction of the Raman spectral features of a single microbial cell and then categorizes cells according to their spectral features. As the proof of concept, we measured Raman spectra of 14 microbial species at a single-cell level and constructed an optimal ConvNet model using the Raman data. The average accuracy of classification by ConvNet is 95.64 ± 5.46%. Meanwhile, we introduced an occlusion-based Raman spectra feature extraction to visualize the weights of Raman features for distinguishing different species.