First Report of Polygonatum kingianum Leaf Spot Caused by Alternaria alternata in Kunming, China.

Polygonatum kingianum Coll. et Hemsl., a Polygonatum species in the Asparagaceae family, plays an important role in Chinese herbal medicine (Zhao et al. 2018). P. kingianum is widely planted in the Southwestern China. In September 2023, we observed a leaf spot of P. kingianum with disease incidence of 100%, and disease index reached 60 in commercial plantings in Kunming, Yunnan province, China (24.3610°N, 102.3740°E). In the initial stage of infection, symptoms manifested as a small circular brown spot. As the spots gradually expanded, they formed oval to irregular shaped lesions with grayish-white or dark-brown borders. Progressively the entire leaf withered and died. For identification of the causal agent of the leaf spot, leaf sections (5×5 mm2) were cut from the margin of the lesion and soaked in 75% ethanol for 10 s, 1% sodium hypochlorite for 3 min, washed with sterile distilled water, dried on sterilized tissue paper and placed on potato dextrose agar (PDA). The Petri dishes were then incubated at 28℃ for 3 days with a 12-h photoperiod. A predominant fungus was isolated from 95% of the samples. Three monosporic isolates were screened using a single-spore isolation method. After 4 days of incubation the colonies were white, after 7 days turned yellow-white. Conidia were black-brown, oblong or fusiform, with 3-7 transverse septa and 0-3 longitudinal septa, with dimensions of 19.5 to 49.5 × 8.7 to 17.6 µm (n = 30). Total genomic DNA of these three isolates was extracted from mycelia by the cetyltrimethylammonium bromide (CTAB) protocol. The nucleotide sequences of the elongation factor 1-alpha (EF1α), nuclear ribosomal internal transcribed spacer (ITS), 28S nuclear ribosomal large subunit rRNA gene (LSU), 18S nuclear ribosomal small subunit rRNA gene (SSU), and the second largest subunit of nuclear DNA-directed RNA polymerase II (RPB2) gene regions were amplified using the primer pairs EF1-728F/EF1-986R (Carbone and Kohn 1999), ITS1/ITS4 (White et al. 1990), LR0R/LR5 (Schoch et al. 2012), NS1/NS4 (Schoch et al. 2012), and fRPB2-5F/fRPB2-7Cr (Liu et al. 1999), respectively. Amplicons were cloned in a pMDTM19-T vector (code no. 6013, Takara, Kusatsu, Japan) and bidirectionally sequenced. All three isolates had identical nucleotide sequences. Sequences from one isolate (PkF03) were deposited in GenBank. BLASTn analyses showed that sequences of EF1α (GenBank accession no. PP695240), ITS (PP694046), LSU (PP683406), SSU (PP683407), and RPB2 (PP695241) of isolate PkF03 were 99.6 (KP125134), 100 (KP124358), 100 (KP124510), 99.9 (KP124980), and 100% (KP124826), respectively, identical with Alternaria alternata (Fr.) Keissl. strain CBS 118815. Based on the nucleotide sequences of EF1α, ITS, LSU, SSU, and RPB2, a maximum likelihood phylogenetic tree was constructed using MEGAX with Tamura-Nei model. Isolate PkF03 was grouped in the same clade as A. alternata. According to the morphology and sequence analyses isolate PkF03 was identified as A. alternata (Woudenberg et al. 2013). To determine pathogenicity of isolate PkF03, a spore suspension (106 spores/mL) was sprayed on 1-year-old healthy leaves of P. kingianum. The control leaves were sprayed with sterile water. All plants were incubated at 28℃, 70% relative humidity, and a 12-h photoperiod. The pathogenicity tests were repeated three times with six plants in each treatment. Fifteen days post-inoculation, the inoculated leaves showed brown-yellow lesions, whereas the control leaves remained symptomless. A. alternata was reisolated from infected leaves. To our knowledge, this is the first report of A. alternata causing leaf spot on P. kingianum in Kunming, China. The results provide a scientific basis for prevention and control of the disease.

Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning.

Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA's appropriate applications. Near-infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi-strategy improved Walrus Optimization Algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.

Implementation of near-infrared spectroscopy and convolutional neural networks for predicting particle size distribution in fluidized bed granulation.

Monitoring the particle size distribution (PSD) is crucial for controlling product quality during fluidized bed granulation. This paper proposed a rapid analytical method that quantifies the D10, D50, and D90 values using a Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) framework tailored for deep learning with near-infrared (NIR) spectroscopy. This innovative framework, which fuses CBAM with CNN, excels at extracting intricate features while prioritizing crucial ones, thereby facilitating the creation of a robust multi-output regression model. To expand the training dataset, we incorporated the C-Mixup algorithm, ensuring that the deep learning model was trained comprehensively. Additionally, the Bayesian optimization algorithm was introduced to optimize the hyperparameters, improving the prediction performance of the deep learning model. Compared with the commonly used Partial Least Squares (PLS), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models, the CBAM-CNN model yielded higher prediction accuracy. Furthermore, the CBAM-CNN model avoided spectral preprocessing, preserved the spectral information to the maximum extent, and returned multiple predicted values at one time without degrading the prediction accuracy. Therefore, the CBAM-CNN model showed better prediction performance and modeling convenience for analyzing PSD values in fluidized bed granulation.

Study on the preparation and biological activities of low molecular weight squid ink polysaccharide from Sepiella maindroni.

Sepiella maindroni ink polysaccharide (SIP) from the ink of cuttlefish Sepiella maindroni and its sulfated derivative (SIP-SII) have been demonstrated to possess diverse biological activities. But little is known about low molecular weight squid ink polysaccharides (LMWSIPs). In this study, LMWSIPs were prepared by acidolysis, and the fragments with molecular weight (Mw) distribution in the ranges of 7 kDa to 9 kDa, 5 kDa to 7 kDa and 3 kDa to 5 kDa were grouped and named as LMWSIP-1, LMWSIP-2 and LMWSIP-3, respectively. The structural features of LMWSIPs were elucidated, and their anti-tumor, antioxidant and immunomodulatory activities were also studied. The results showed that with the exception of LMWSIP-3, the main structures of LMWSIP-1 and LMWSIP-2 did not change compared with SIP. Though there were no significant differences in the antioxidant capacity between LMWSIPs and SIP, the anti-tumor and immunomodulatory activities of SIP were enhanced to a certain extent after degradation. It is particularly noteworthy that the activities of LMWSIP-2 in anti-proliferation, promoting apoptosis and inhibiting migration of tumor cells as well as promoting the proliferation of spleen lymphocytes were significantly higher than those of SIP and the other degradation products, which is promising in the anti-tumor pharmaceutical field.

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning.

Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA-A and LMWHA-E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural differences between LMWHA-A and LMWHA-E, and then achieve a fast and accurate classification based on near-infrared (NIR) spectroscopy and machine learning. First, we combined nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR) spectroscopy, two-dimensional correlated NIR spectroscopy (2DCOS), and aquaphotomics to analyze the structural differences between LMWHA-A and LMWHA-E. Second, we compared the dimensionality reduction methods including principal component analysis (PCA), kernel PCA (KPCA), and t-distributed stochastic neighbor embedding (t-SNE). Finally, the differences in classification effect of traditional machine learning methods including partial least squares-discriminant analysis (PLS-DA), support vector classification (SVC), and random forest (RF) as well as deep learning methods including one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were compared. The results showed that genetic algorithm (GA)-SVC and RF were the best performers in traditional machine learning, but their highest accuracy in the test dataset was 90%, while the accuracy of 1D-CNN and LSTM models in the training dataset and test dataset classification was 100%. The results of this study show that compared with traditional machine learning, the deep learning models were better for the classification of LMWHA-A and LMWHA-E. Our research provides a new methodological reference for the rapid and accurate classification of biological macromolecules.

Novel Similarity Methods Evaluation and Feasible Application for Pharmaceutical Raw Material Identification with Near-Infrared Spectroscopy.

Raw material identification (RMID) is necessary and important to fulfill the quality and safety requirements in the pharmaceutical industry. Near-infrared (NIR) spectroscopy is a rapid, nondestructive, and commonly used analytical technique that could offer great advantages for RMID. In this study, two brand new similarity methods S1 and S2, which could reflect the similarity from the perspective of the inner product of the two vectors and the closeness with the cosine of the vectorial angle or correlation coefficient, were proposed. The ability of u and v factors to distinguish the difference between small peaks was investigated with the spectra of NIR. The results showed that the distinguishing ability of u is greater than v, and the distinguishing ability of S2 is greater than S1. Adjusting exponents u and v in these methods, which are variable and configurable parameters greater than 0 and less than infinity, could identify small peaks in different situations. Meanwhile, S1 and S2 could rapidly identify raw materials, suggesting that the on-site and in situ pharmaceutical RMID for large-volume applications can be highly achievable. The methods provided in this study are accurate and easier to use than traditional chemometric methods, which are important for the pharmaceutical RMID or other analysis.

Leishmania lipophosphoglycan components: A potent target for synthetic neoglycoproteins as a vaccine candidate for leishmaniasis.

Leishmania is an obligate intracellular pathogen that invades phagocytic host cells. Due to its high morbidity and mortality rates, leishmaniasis attracts significant attention. The disease, which is caused by Leishmania parasites, is distributed worldwide, particularly among developing communities, and causes fatal complications if not treated expediently. Unfortunately, the existing treatments are not preventive and do not impede Leishmania infection. Many drugs available for leishmaniasis are becoming less effective due to emerging resistance in some Leishmania species. Other drugs have drawbacks such as low cost-effectiveness, toxicity, and side effects. The World Health Organization (WHO) considers leishmaniasis to be a major public health problem and suggests that the best prevention is to develop a vaccine for this dangerous disease. In this review, we focus on the unique components of lipophosphoglycan (LPG), a component of the Leishmania cell wall, particularly [Galp(1 → 4)-ß-[Manp-(1 → 2)-α-Manp-(1 → 2)-α]-Manp] in the cryptic tetrasaccharide cap, and on synthetic approaches as a potent candidate for a leishmaniasis vaccine.

Understanding hyaluronic acid induced variation of water structure by near-infrared spectroscopy.

In order to understand the hydration effect of hyaluronic acid (HA) in aqueous solution, near-infrared (NIR) spectroscopy was used to investigate the HA aqueous solutions at different concentrations and temperature. As HA concentration was raised, there was a nonlinear change in absorption value in the first overtone region of OH, indicating the changes of hydration water. A reconstructed spectrum based on principal component analysis (PCA) was established and analyzed with the concept of aquaphotomics. The results showed that HA acted as a structure maker to make water molecules arranged in order. Water species with two hydrogen bonds (S2) and three hydrogen bonds (S3) showed the decrease at low concentration range of 0-40 mg/mL, but increased at higher concentration, indicating the difference in water species at different HA concentration. Meanwhile, HA had the ability to improve the thermal stability of water structure, suggesting a potential bio-protective function. This study provides a unique perspective on the molecular interactions between HA and water molecules, which is helpful for understanding the role of HA in life process and may serve as the basis for HA applications.

Inhibition of EGF-induced migration and invasion by sulfated polysaccharide of Sepiella maindroni ink via the suppression of EGFR/Akt/p38 MAPK/MMP-2 signaling pathway in KB cells.

SIP-SII, the sulfated Sepiella maindroni ink polysaccharide (SIP), has been manifested to possess anti-tumor and anti-metastasis activity in vivo and in vitro. In the present study, we evaluated its inhibitory effect on the epidermal growth factor (EGF)-induced migration and invasion of human epidermoid carcinoma cell (KB cell line) as well as the related signaling pathways. The results of MTT assay indicated that SIP-SII inhibited the proliferation of KB cells in a concentration and time dependent manner. Notably, the attenuation of cell growth by SIP-SII was enlarged in the presence of EGF. The wound healing assay and transwell invasion assay were used to evaluate the effect of SIP-SII on the EGF-induced migration and invasion of KB cells and the results showed that SIP-SII markedly attenuated the EGF-induced migration and invasion. Besides, the EGF-induced matrix metalloproteinase-2 (MMP-2) expression was also suppressed by SIP-SII. However, SIP-SII showed no significant inhibition of the EGF-induced matrix metalloproteinase-9 (MMP-9) expression. Further research revealed that SIP-SII decreased the EGF-induced phosphorylation of epidermal growth factor receptor (EGFR), Akt and p38, but no significant suppression on EGF-induced phosphorylation of extracellular signal-regulated kinase 1 and 2 (Erk1/2) and c-Jun N-terminal kinases (JNK) by SIP-SII treatment was observed. The involvement of EGFR/Akt/p38 pathway was confirmed by evidence that SIP-SII would enlarge the inhibitory effect of the specific signal pathway inhibitors. These results indicate that SIP-SII has the potential to be used as the inhibitor of tumor metastasis especially for cancers characterized by over-activation of EGF/EGFR signaling.