Predicting drug-target binding affinity with cross-scale graph contrastive learning.

Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug-target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel cross-scale graph contrastive learning approach for drug-target binding affinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experiments on two benchmark datasets, and the proposed model outperformed existing state-of-art methods. The ablation experiment demonstrated the significance and efficacy of multi-scale features and cross-scale contrastive learning modules in improving the prediction performance. Moreover, we applied the CSCo-DTA to predict the novel potential targets for Erlotinib and validated the predicted targets with the molecular docking analysis.

Dihydrochalcone glycoside biosynthesis in Malus is regulated by two MYB-like transcription factors and is required for seed development.

Dihydrochalcones (DHCs) including phlorizin (phloretin 2'-O-glucoside) and its positional isomer trilobatin (phloretin 4'-O-glucoside) are the most abundant phenylpropanoids in apple (Malus spp.). Transcriptional regulation of DHC production is poorly understood despite their importance in insect- and pathogen-plant interactions in human physiology research and in pharmaceuticals. In this study, segregation in hybrid populations and bulked segregant analysis showed that the synthesis of phlorizin and trilobatin in Malus leaves are both single-gene-controlled traits. Promoter sequences of PGT1 and PGT2, two glycosyltransferase genes involved in DHC glycoside synthesis, were shown to discriminate Malus with different DHC glycoside patterns. Differential PGT1 and PGT2 promoter activities determined DHC glycoside accumulation patterns between genotypes. Two transcription factors containing MYB-like DNA-binding domains were then shown to control DHC glycoside patterns in different tissues, with PRR2L mainly expressed in leaf, fruit, flower, stem, and seed while MYB8L mainly expressed in stem and root. Further hybridizations between specific genotypes demonstrated an absolute requirement for DHC glycoside production in Malus during seed development which explains why no Malus spp. with a null DHC chemotype have been reported.

Anaplastic Lymphoma Kinase (ALK) Inhibitors Show Activity in Colorectal Cancer With ALK Rearrangements: Case Series and Literature Review.

Anaplastic lymphoma kinase (ALK) rearrangement is a well-known driver oncogene detected in approximately 5% of non-small cell lung cancer. However, ALK rearrangement is much less frequent in other solid tumors outside the lungs, such as colorectal cancer (CRC); thus, the optimal management of CRC with ALK rearrangements has yet to be established. In this report, we describe 2 cases of ALK-positive CRC, both of which benefited from ALK tyrosine kinase inhibitor (ALK-TKI) therapy. Case 1 was a postoperative patient with poorly differentiated colon adenocarcinoma, who was diagnosed with metastatic relapse shortly after surgery. Both fluorouracil, leucovorin, and oxaliplatin (FOLFOX) and bevacizumab combined with 5-fluorouracil, l-leucovorin, and irinotecan (FOLFIRI) proved ineffective against the disease. The patient was then treated with ensartinib, as the CAD-ALK fusion gene was detected by genomic analysis. The patient was initially treated with ensartinib monotherapy for 9 months, then with ensartinib combined with local radiotherapy and fruquintinib for another 4 months for isolated hilar hepatic lymph node metastasis. The patient experienced disease progression with an acquired ALK G1202R resistance mutation that responded well to lorlatinib. Case 2 involved a 72-year-old man with advanced colon cancer (pT4bN2aM1b, stage IV) harboring an EML4-ALK fusion. The patient underwent resection of the right colon tumor due to intestinal obstruction, but the disease continued to progress after 12 courses of FOLFIRI and bevacizumab chemotherapy. However, the patient responded remarkably well to alectinib. Our report emphasizes the importance of gene detection in the treatment of malignant tumors, and the significance of ALK mutations in CRC.

DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions.

MOTIVATION: Drug-food interactions (DFIs) occur when some constituents of food affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic and/or pharmacokinetic processes. Many computational methods have achieved remarkable results in link prediction tasks between biological entities, which show the potential of computational methods in discovering novel DFIs. However, there are few computational approaches that pay attention to DFI identification. This is mainly due to the lack of DFI data. In addition, food is generally made up of a variety of chemical substances. The complexity of food makes it difficult to generate accurate feature representations for food. Therefore, it is urgent to develop effective computational approaches for learning the food feature representation and predicting DFIs. RESULTS: In this article, we first collect DFI data from DrugBank and PubMed, respectively, to construct two datasets, named DrugBank-DFI and PubMed-DFI. Based on these two datasets, two DFI networks are constructed. Then, we propose a novel end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents. In topology space, we adopt a simplified graph convolution network-based method to learn the topological structure features. In feature space, we use a deep neural network to extract attribute features from the original node attributes. The evaluation results indicate that DFinder performs better than other baseline methods. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/23AIBox/23AIBox-DFinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Epinecidin-1 and lactic acid synergistically inhibit Aeromonas hydrophila through membrane disruption.

Epinecidin-1 (Epi-1) is an antimicrobial peptide originated from fish with various pharmacological activities but carries the risk of acquiring resistance with long-term use. In the present study, we use L-lactic acid to enhance the antibacterial activity of synthesized Epi-1 against the aquaculture and food pathogen Aeromonas hydrophila. The results showed that 5.5 mmol/L lactic acid increased the inhibitory and bactericidal activity of 25 µmol/L Epi-1 against two strains of A. hydrophila. The laser confocal images proved that lactic acid pre-treatment improved the attachment efficiency of Epi-1 in A.hydrophila cells. In addition, lactic acid enhanced the damaging effect of Epi-1 on the cell membrane of A. hydrophila, evidenced by releasing more nucleic acids, proteins, and transmembrane pH ingredients decrease and electromotive force dissipation. SEM images showed that compared with the single Epi-1 treatment, the co-treatment of Epi-1 and lactic acid caused more outer membrane vesicles (OMVs) and more severe cell deformation. These findings proved that lactic acid could enhance the efficiency of Epi-1 against A. hydrophila and shed light on new aspects to avoid resistance of pathogens against Epi-1.

Customizable polarization-selective narrowband meta-filters using a printable UV-curing nanocomposite.

Low-cost nanocomposite metasurfaces have demonstrated attractive potential to replace the equivalent dielectric metasurfaces for light engineering. However, the resonance characteristics of embedded structures in nanocomposite metasurfaces have not been further analyzed beyond the effective refractive index. Herein, we have proposed customizable polarization-selective narrowband meta-filters using ultraviolet-curable (UV) nanocomposites. As an additional degree of freedom, near-field effects between highly concentrated doped nanoparticles can enhance the Mie resonance of the low aspect ratio (AR = 0.2) meta-units. The surface lattice resonances (SLRs) of meta-filters can be coupled with enhanced Mie resonances of individual meta-units to realize tunable narrowband (FWHM ∼0.007λ) reflections with intensities near unity. Meanwhile, the polarization-selective properties of the reflection peaks can be tuned by optimizing the asymmetric lattice. Such proposed new-generation customizable meta-filters will offer, to our knowledge, novel strategies for filtering specific near-infrared polarized fluorescence in the integrated imaging systems.

Disease trajectory of high neuroticism and the relevance to psychiatric disorders: A retro-prospective cohort study.

BACKGROUND: Neuroticism is a psychological personality trait that has a significant impact on public health and is also a potential predisposing factor for adverse disease outcomes; however, comprehensive studies of the subsequently developed conditions are lacking. The starting point of disease trajectory in terms of genetic variation remains unclear. METHOD: Our study included 344,609 adult participants from the UK Biobank cohort who were virtually followed up from January 1, 1997. Neuroticism levels were assessed using 12 items from the Eysenck Personality Questionnaire. We performed a phenome-wide association analysis of neuroticism and subsequent diseases. Binomial tests and logistic regression models were used to test the temporal directionality and association between disease pairs to construct disease trajectories. We also investigated the association between polygenic risk scores (PRSs) for five psychiatric traits and high neuroticism. RESULTS: The risk for 59 diseases was significantly associated with high neuroticism. Depression, anxiety, irritable bowel syndrome, migraine, spondylosis, and sleep disorders were the most likely to develop, with hazard ratios of 6.13, 3.66, 2.28, 1.74, 1.74, and 1.71, respectively. The disease trajectory network revealed two major disease clusters: cardiometabolic and chronic inflammatory diseases. Medium/high genetic risk groups stratified by the PRSs of four psychiatric traits were associated with an elevated risk of high neuroticism. We further identified eight complete phenotypic trajectory clusters of medium or high genetic risk for psychotic, anxiety-, depression-, and stress-related disorders. CONCLUSION: Neuroticism plays an important role in the development of somatic and mental disorders. The full picture of disease trajectories from the genetic risk of psychiatric traits and neuroticism in early life to a series of diseases later provides evidence for future research to explore the etiological mechanisms and precision management.

Nitrogen addition affected the root competition in Cunninghamia lanceolata-Phoebe chekiangensis mixed plantation.

Little is known about below-ground competition in mixed-species plantations under increasing nitrogen (N) deposition. This study aims to determine the effects of N addition on root competition in coniferous and broad-leaved species mixed plantations. A pot experiment was conducted using the coniferous species Cunninghamia lanceolata and the broad-leaved species Phoebe chekiangensis planted in mixed plantations with different competition intensities under N addition (0 or 45 kg N ha-1 yr-1). Biomass allocation, root morphology, root growth level, and competitive ability were determined after five months of treatment. Our findings indicated that root interactions in mixed plantations did not influence biomass allocation in either C. lanceolata or P. chekiangensis but promoted growth in C. lanceolata when no N was added. However, N addition decreased biomass accumulation in both species in the mixed plantation and had a negative effect on the root growth of C. lanceolata due to intensified competition. Addition of N increased the relative importance of root predatory competition in P. chekiangensis, and increased the allelopathic competitive advantage in C. lanceolata. This suggests that N addition causes a shift in the root competitive strategy from tolerance to competition. Overall, these findings highlight the significant impact that the addition of N can have on plant interactions in mixed plantations. Our results provide implications for the mechanisms of root competition in response to increasing atmospheric N deposition in mixed plantations.

Domiciliary monitoring of exhaled nitric oxide in the management of asthma: a pilot study.

BACKGROUND: Whether asthma patients could benefit from home monitoring for fractional exhaled nitric oxide (flow of 50 mL/s, FeNO50) is unknown. We explore the application value of home monitoring FeNO50 in daily asthma management. METHODS: Twenty-two untreated, uncontrolled asthma patients were selected. Medical history, blood and sputum samples, pulmonary function, Asthma Control Test (ACT), and other clinical data of the subjects were collected. All subjects underwent daily monitoring for four weeks using a FeNO50 monitor and mobile spirometry (mSpirometry). The diurnal differences and dynamic changes were described. Compare the effect-acting time and the relative plateau of treatment between FeNO50 and mSpirometry monitoring. RESULTS: In the first two weeks, the morning median (IQR) level of FeNO50 was 44 (35, 56) ppb, which was significantly higher than the evening median level [41 (32, 53) ppb, P = 0.028]. The median (IQR) effect-acting time assessed by FeNO50 was 4 (3, 5) days, which was significantly earlier than each measure of mSpirometry (P < 0.05). FeNO50 reached the relative plateau significantly earlier than FEV1 (15 ± 2 days vs. 21 ± 3 days, P < 0.001). After treatment, the daily and weekly variation rates of FeNO50 showed a gradually decreasing trend (P < 0.05). The ACT score, sputum eosinophils, and blood eosinophils also significantly improved (P ≤ 0.01). CONCLUSIONS: The daily home monitoring of FeNO50 in asthmatic patients showed significant circadian rhythm, and the sensitivity of FeNO50 in evaluating the response to treatment was higher than mSpirometry. The daily and weekly variation rates of FeNO50 change dynamically with time, which may be used to assess the condition of asthma.

New targets of TetR-type regulator SLCG_2919 for controlling lincomycin biosynthesis in Streptomyces lincolnensis.

The transcription factor (TF)-mediated regulatory network controlling lincomycin production in Streptomyces lincolnensis is yet to be fully elucidated despite several types of associated TFs having been reported. SLCG_2919, a tetracycline repressor (TetR)-type regulator, was the first TF to be characterized outside the lincomycin biosynthetic cluster to directly suppress the lincomycin biosynthesis in S. lincolnensis. In this study, improved genomic systematic evolution of ligands by exponential enrichment (gSELEX), an in vitro technique, was adopted to capture additional SLCG_2919-targeted sequences harboring the promoter regions of SLCG_6675, SLCG_4123-4124, SLCG_6579, and SLCG_0139-0140. The four DNA fragments were confirmed by electrophoretic mobility shift assays (EMSAs). Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) showed that the corresponding target genes SLCG_6675 (anthranilate synthase), SLCG_0139 (LysR family transcriptional regulator), SLCG_0140 (beta-lactamase), SLCG_6579 (cytochrome P450), SLCG_4123 (bifunctional DNA primase/polymerase), and SLCG_4124 (magnesium or magnesium-dependent protein phosphatase) in ΔSLCGL_2919 were differentially increased by 3.3-, 4.2-, 3.2-, 2.5-, 4.6-, and 2.2-fold relative to those in the parental strain S. lincolnensis LCGL. Furthermore, the individual inactivation of these target genes in LCGL reduced the lincomycin yield to varying degrees. This investigation expands on the known DNA targets of SLCG_2919 to control lincomycin production and lays the foundation for improving industrial lincomycin yields via genetic engineering of this regulatory network.

Analysis of the mediating effect between ehealth literacy and health self-management of undergraduate nursing students' mental health literacy.

BACKGROUND: Good health self-management positively affects the health of healthcare providers and their ability to manage their patients' health. This study explored the relationship between ehealth literacy, health self-management skills, and mental health literacy among undergraduate nursing students. Some studies have confirmed the correlation between e-health literacy and health self-management skills, while mental health literacy may be correlated with both, and this study aims to explore the relationship between the three. METHODS: A descriptive cross-sectional survey was conducted at a medical university in northwestern China among 385 Chinese undergraduate nursing students. Participants completed the General Information Questionnaire, the Adult Health Self-Management Skills Rating Scale, the Mental Health Literacy Rating Scale, and the eHealth Literacy Scale, and provided valid responses. The IBM SPSS 27.0 statistical software was used for data entry and descriptive analysis, t-test, ANOVA, and Pearson correlation analysis. The IBM Amos 26.0 was used to construct the mediation effect model, and the Bootstrap method was employed to test mediating effects. RESULTS: Mental health literacy, ehealth literacy, and health self-management skills of undergraduate nursing students were at a moderate to high level. Mental health literacy, ehealth literacy, and health self-management were positively correlated. Mental health literacy, particularly, played a partial mediating role of 31.1% ( 95% CI [0.307-1.418] ) between ehealth literacy and health self-management. CONCLUSIONS: Undergraduate nursing students' mental health literacy partially mediates the link between eHealth literacy and health self-management skills. Schools should emphasize the development of nursing students' e-health literacy and mental health literacy in order to improve their health self-management skills, which will not only bring about a better health outcome for the students, but will also benefit the health of the social population.

LncRNA-BC069792 suppresses tumor progression by targeting KCNQ4 in breast cancer.

BACKGROUND: Breast cancer is the most common malignant tumor that threatens women's health. Attention has been paid on the study of long- non-coding RNA (lncRNA) in breast cancer. However, the specific mechanism remains not clear. METHODS: In this study, we explored the role of lncRNA BC069792 in breast cancer. In vitro and in vivo functional experiments were carried out in cell culture and mouse models. High-throughput next-generation sequencing technology and real-time fluorescence quantitative PCR technology were used to evaluate differentially expressed genes and mRNA expression, Western blot and immunohistochemical staining were used to detect protein expression. RNA immunoprecipitation assay and dual-luciferase activity assay were used to evaluate the competing endogenous RNAs (ceRNA), and rescue and mutation experiments were used for verification. RESULTS: We found that lncRNA BC069792 was expressed at a low level in breast cancer tissues, and significantly decreased in breast cancer with high pathological grade, lymph node metastasis and high Ki-67 index groups. Moreover, BC069792 inhibited the proliferation, invasion and metastasis of breast cancer cells in vitro and in vivo. Mechanically, BC069792 acts as a molecular sponge to adsorb hsa-miR-658 and hsa-miR-4739, to up-regulate the protein expression of Potassium Voltage-Gated Channel Q4 (KCNQ4), inhibits the activities of JAK2 and p-AKT, and plays a role in inhibiting breast cancer growth. CONCLUSIONS: LncRNA BC069792 plays the role of tumor suppressor gene in breast cancer and is a new diagnostic index and therapeutic target in breast cancer.

PTEN/AKT and Wnt/ß-catenin signaling pathways regulate the proliferation of Lgr5+ cells in liver cancer.

The progression and spread of tumors are believed to be primarily caused by cancer stem cells (CSCs). Nevertheless, the task of focusing on CSCs for cancer treatment continues to be difficult. Lgr5, a G-protein-coupled receptor containing leucine-rich repeats, is highly expressed in different types of cancer and serves as a distinctive marker for cancer stem cells (CSCs). In this study, we employed the Cre-loxP system and Lgr5 tracking mice of male to selectively remove PTEN and ß-catenin in Lgr5+ cells of DEN-induced liver cancer and monitor the behavior of Lgr5+ cells. The tracking data revealed that the activation of PTEN-mediated AKT signaling in Lgr5 led to a significant rise in the quantity of Lgr5+ cells, whereas the inhibition of Wnt/ß-catenin signaling decreased the number of cells in DEN-induced liver cancer. Therefore, we have shown that the growth of Lgr5+ cells can be controlled by the PTEN/AKT and Wnt/ß-catenin pathways, offering a potential treatment option for fighting against liver cancer.

Single-cell analysis reveals distinct functional heterogeneity of CD34+ cells in anagen wound and diabetic wound.

Wound healing is a complex biological process involving multiple cell types with their critical functions. The diabetic wounds show delayed wound healing, while the anagen wounds display accelerated wound closure. However, the mechanisms underlying the effect of cellular heterogeneity on wound healing are still unclear. CD34+ cells exhibit high heterogeneity in wound skins and improve wound healing. Herein, we investigated the phenotypic and functional heterogeneity of CD34+ cells in normal, anagen, and diabetic wounds. We obtained CD34 lineage tracing mice, constructed distinct wound models, collected CD34+ cells from wound edges, and performed single-cell RNA sequencing. We identified 10 cell clusters and 6 cell types of CD34+ cells, including endothelial cells, fibroblasts, keratinocytes, neutrophils, macrophages, and T cells. 5 subclusters were defined as fibroblasts. The CD34+ fibroblasts C2 highly expressed papillary fibroblastic markers took up the largest proportion in anagen wounds and were associated with inflammation and extracellular matrix. Increased CD34+ endothelial cells, fibroblasts C4, and neutrophils as well as decreased fibroblasts C1 were discovered in diabetic wounds. We also filtered out differentially expressed genes (DEGs) of each cell cluster in anagen wounds and diabetic wounds. Functional enrichment analysis was performed on these DEGs to figure out the enriched pathways and items for each cell cluster. Pseudotime analysis of CD34+ fibroblasts was next carried out indicating fibroblast C4 mainly with low differentiation. Our results have important implications for understanding CD34+ cell type-specific roles in anagen and diabetic wounds, provide the possible mechanisms of wound healing from a new perspective, and uncover potential therapeutic approaches to treating wounds.

NMCMDA: neural multicategory MiRNA-disease association prediction.

MOTIVATION: There is growing evidence showing that the dysregulations of miRNAs cause diseases through various kinds of the underlying mechanism. Thus, predicting the multiple-category associations between microRNAs (miRNAs) and diseases plays an important role in investigating the roles of miRNAs in diseases. Moreover, in contrast with traditional biological experiments which are time-consuming and expensive, computational approaches for the prediction of multicategory miRNA-disease associations are time-saving and cost-effective that are highly desired for us. RESULTS: We present a novel data-driven end-to-end learning-based method of neural multiple-category miRNA-disease association prediction (NMCMDA) for predicting multiple-category miRNA-disease associations. The NMCMDA has two main components: (i) encoder operates directly on the miRNA-disease heterogeneous network and leverages Graph Neural Network to learn miRNA and disease latent representations, respectively. (ii) Decoder yields miRNA-disease association scores with the learned latent representations as input. Various kinds of encoders and decoders are proposed for NMCMDA. Finally, the NMCMDA with the encoder of Relational Graph Convolutional Network and the neural multirelational decoder (NMR-RGCN) achieves the best prediction performance. We compared the NMCMDA with other baselines on three experimental datasets. The experimental results show that the NMR-RGCN is significantly superior to the state-of-the-art method TDRC in terms of Top-1 precision, Top-1 Recall, and Top-1 F1. Additionally, case studies are provided for two high-risk human diseases (namely, breast cancer and lung cancer) and we also provide the prediction and validation of top-10 miRNA-disease-category associations based on all known data of HMDD v3.2, which further validate the effectiveness and feasibility of the proposed method.

Trace Elements Open a New Direction for the Diagnosis of Atherosclerosis.

Abnormal or excessive accumulation of adipose tissue leads to a condition called obesity. Long-term positive energy balance arises when energy intake surpasses energy expenditure, which increases the risk of metabolic and other chronic diseases, such as atherosclerosis. In industrialized countries, the prevalence of coronary heart disease is positively correlated with the human development index. Atherosclerotic cardiovascular disease (ACD) is among the primary causes of death on a global scale. There is evidence to support the notion that individuals from varied socioeconomic origins may experience varying mortality effects as a result of high blood pressure, high blood sugar, raised cholesterol levels, and high body mass index (BMI). However, it is believed that changes in the concentration of trace elements in the human body are the main contributors to the development of some diseases and the transition from a healthy to a diseased state. Metal trace elements, non-metal trace elements, and the sampling site will be examined to determine whether trace elements can aid in the diagnosis of atherosclerosis. This article will discuss whether trace elements, discussed under three sections of metal trace elements, non-metal trace elements, and the sampling site, can participate in the diagnosis of atherosclerosis.

Discovery and Isolation of Two Arsenotungastate Species: [As4W48O168]36- and [As2W21O77(H2O)3]22.

Two novel arsenotungstate species, [As4W48O168]36- (1a) and [As2W21O77(H2O)3]22- (2a), have been successfully isolated under a one-pot synthetic method. 1a is the second largest arsenotungstate cluster and is constructed from four {AsW12} clusters combined together. 2a can be described as lacunary sites of {As2W19} filled by {W2O8} units. Compounds 1 and 2 exhibit proton conductivity properties, and the conductivity value of 1 is 5.0 × 10-3 S cm-1 at 98% relative humidity and 75 °C. This work proves that the lattice water molecules and polyoxoanions can participate in the formation of a hydrogen bond, acting as effective pathway for intermolecular proton conduction.

Detection of Rail Defects Using NDT Methods.

The rapid development of high-speed and heavy-haul railways caused rapid rail defects and sudden failure. This requires more advanced rail inspection, i.e., real-time accurate identification and evaluation for rail defects. However, existing applications cannot meet future demand. In this paper, different types of rail defects are introduced. Afterwards, methods that have the potential to achieve rapid accurate detection and evaluation of rail defects are summarized, including ultrasonic testing, electromagnetic testing, visual testing, and some integrated methods in the field. Finally, advice on rail inspection is given, such as synchronously utilizing the ultrasonic testing, magnetic flux leakage, and visual testing for multi-part detection. Specifically, synchronously using the magnetic flux leakage and visual testing technologies can detect and evaluate surface and subsurface defects, and UT is used to detect internal defects in the rail. This will obtain full rail information, to prevent sudden failure, then ensure train ride safety.

The mitigating effects of diatom-bacteria biofilm on coastal harmful algal blooms: A lab-based study concerning species-specific competition and biofilm formation.

Harmful algal blooms (HABs) in coastal areas severely affected the health of ecosystem and human beings. The HABs control by biological methods, especially for biofilms, has been research hotspots in freshwater ecosystem. However, the biofilm-relating control of HABs in marine environment was very limited. In the present study, we found the population growth of two harmful algal species, Prorocentrum obtusidens Schiller (formerly P. donghaiense Lu) and Heterosigma akashiwo, were inhibited by a diatom-bacteria biofilm. The highest inhibitory rate was 79.6 ± 2.1% for P. obtusidens when co-cultured with biofilm suspension, and was 88.6 ± 5.8% for H. akashiwo when co-cultured with the biofilm filtrate without nutrient replenishment. When nitrate and phosphate were added, the inhibition rate for P. obtusidens was 72.3 ± 2.0%, but the population inhibition was not found in H. akashiwo. It suggested that P. obtusidens was mainly inhibited via interference competition, while the inhibition of H. akashiwo was resulted from exploitation competition. We further investigated the role of fatty acids for the interference competition in P. obtusidens, and found that fatty acids at their environmental-relevance concentrations can inhibit the photosynthetic capacity of P. obtusidens, but cannot inhibit the population growth. The community of biofilm shifted, and was finally dominated by the photoheterotrophic bacterium Dinoroseobacter shibae, and the diatom Fistulifera sp. with relative abundance of higher than 90%. Our study indicated that the diatom-bacteria biofilm was likely the candidate for the HABs control in marine environment. D. shibae and Fistulifera sp. were probably the effective species in the biofilm.

Toward Achieving Rapid Estimation of Vitamin C in Citrus Peels by NIR Spectra Coupled with a Linear Algorithm.

Citrus peels are rich in bioactive compounds such as vitamin C and extraction of vitamin C is a good strategy for citrus peel recycling. It is essential to evaluate the levels of vitamin C in citrus peels before reuse. In this study, a near-infrared (NIR)-based method was proposed to quantify the vitamin C content of citrus peels in a rapid way. The spectra of 249 citrus peels in the 912-1667 nm range were acquired, preprocessed, and then related to measured vitamin C values using the linear partial least squares (PLS) algorithm, indicating that normalization correction (NC) was more suitable for spectral preprocessing and NC-PLS model built with full NC spectra (375 wavelengths) showed a better performance in predicting vitamin C. To accelerate the predictive process, wavelength selection was conducted, and 15 optimal wavelengths were finally selected from NC spectra using the stepwise regression (SR) method, to predict vitamin C using the multiple linear regression (MLR) algorithm. The results showed that SR-NC-MLR model had the best predictive ability with correlation coefficients (rP) of 0.949 and root mean square error (RMSEP) of 14.814 mg/100 mg in prediction set, comparable to the NC-PLS model in predicting vitamin C. External validation was implemented using 40 independent citrus peels samples to validate the suitability of the SR-NC-MLR model, obtaining a good correlation (R2 = 0.9558) between predicted and measured vitamin C contents. In conclusion, it was reasonable and feasible to achieve the rapid estimation of vitamin C in citrus peels using NIR spectra coupled with MLR algorithm.