Carrier-free self-assembled nanoparticles based on prochloraz and fenhexamid for reducing toxicity to aquatic organism.

Nanoformulations of pesticides are an effective way to increase utilization efficiency and alleviate the adverse impacts on the environments caused by conventional pesticide formulations. However, the complex preparation process, high cost, and potential environmental risk of nanocarriers severely restricted practical applications of carrier-based pesticide nanoformulations in agriculture. Herein, carrier-free self-assembled nanoparticles (FHA-PRO NPs) based on fenhexamid (FHA) and prochloraz (PRO) were developed by a facile co-assembly strategy to improve utilization efficiency and reduce toxicity to aquatic organism of pesticides. The results showed that noncovalent interactions between negatively charged FHA and positively charged PRO led to core-shell structured nanoparticles arranged in an orderly manner dispersing in aqueous solution with a diameter of 256 nm. The prepared FHA-PRO NPs showed a typical pH-responsive release profile and exhibited excellent physicochemical properties including low surface tension and high max retention. The photostability of FHA-PRO NPs was improved 2.4 times compared with free PRO. The FHA-PRO NPs displayed superior fungicidal activity against Sclerotinia sclerotiorum and Botrytis cinerea and longer duration against Sclerotinia sclerotiorum on potted rapeseed plants. Additionally, the FHA-PRO NPs reduced the acute toxicity of PRO to zebrafish significantly. Therefore, this work provided a promising strategy to develop nanoformulations of pesticides with stimuli-responsive controlled release characteristics for precise pesticide delivery.

Selective effect of winter weather on a songbird's morphology depends on individual sex and winter condition.

Knowledge of the effect of harsh weather on the phenotypic traits of organisms is essential for understanding the environmental influence on phenotype evolution and holds implications for predicting how species respond to current climate change. For many birds, harsh weather in winter often imposes a strong selective effect on their survival, and only the individuals with certain phenotypes may survive. However, whether the selective effect on phenotype varies with winter weather conditions has been poorly investigated. Here, we explored the selective effect of winter weather on black-throated tit's (Aegithalos concinnus) morphological traits under winters with and without severe snowstorms. We found that for males, the sizes of their bills, heads and wings significantly affected their overwinter survival, but the effects varied with winter conditions. In relatively benign winters, males with smaller bill depths, smaller bill surface areas, and greater head lengths survived better; whereas, in winters with severe snowstorms, a reverse pattern was found. This phenomenon was likely driven by selection pressures from heat retention and foraging requirements, with their relative importance depending on winter conditions. Additionally, wing length was positively correlated with male survival and the relationship was stronger in harsher winters, which was probably due to longer wings' higher flight efficiency in adverse weather. By contrast, we found no correlation between morphological traits and survival in females. These results suggest a sex-specific and condition-dependent selective effect of environment on bird phenotypes, implying complicated interactions between different selection pressures and phenotype evolution.

Highly transparent Ce:Nd:YAG ceramic with good light conversion capacity for solar-pumped solid-state lasers.

Developing a high quality ceramic laser gain medium for solar directly pumped solid state lasers is essential, and yet the light conversion efficiency of the gain media for solar pumping remains a challenge. In this study, Ce and Nd ions, co-doped YAG transparent ceramics with theoretical transmittance and stable Ce3+ valent state were developed, and revealed that the absorbed visible light and light conversion efficiency in Ce,Nd:YAG ceramics were 3.98 times and 1.34 times higher than those in widely reported Cr,Nd:YAG ceramics, respectively. A concentration matching principle between Ce3+ and Nd3+ ions in YAG was established, and a higher Nd3+ ion doping concentration with a relatively low Ce3+ concentration was favorable to improve both the light conversion efficiency and emission intensity at 1064 nm of Ce,Nd:YAG ceramics. Energy transfer efficiency from Ce3+ to Nd3+ of the 0.3 at.%Ce,1.5at.%Nd:YAG ceramic reached as high as 61.71% at room temperature. Surprisingly, it was further promoted to 64.31% at a higher temperature of 473 K. More excited electrons at the upper energy level of Ce3+ ion under the high temperature accounted for this novel phenomenon. This study proposes a new design strategy of gain materials for solar directly pumped solid state lasers.

Conquer coronary artery perforation with magic hands.

Coronary artery perforation (CAP) poses a significant challenge for interventional cardiologists. Management of CAP depends on the location and severity of the perforation. The conventional method for addressing the perforation of large vessels involves the placement of a covered stent, while the perforation of distal and collateral vessels is typically managed using coils, autologous skin, subcutaneous fat, microspheres, gelatin sponge, thrombin or other substances. However, the above techniques have certain limitations and are not applicable in all scenarios. Our team has developed a range of innovative strategies for effectively managing CAP. This article provides an insightful review of the various tips and tricks for the treatment of CAP.

Video evidence that cuckoos farm their hosts by ejecting nestlings.

When host nests are scarce, avian brood parasites would benefit from behaviours that increase the availability of suitable nests. Several studies reported ejection of host nestlings from nests by brood parasites; however, whether brood parasites do so to induce the host to re-nest and thus increase opportunities for future parasitism (i.e. 'farming' behaviour) remains unclear. Here, we report observational evidence of farming behaviour by a common cuckoo Cuculus canorus female in a Daurian redstart Phoenicurus auroreus population: (1) the cuckoo destroyed a host nest by ejecting all nestlings, (2) the host then produced a new nest and (3) the cuckoo successfully parasitized the replacement nest. We suggest that farming behaviour may be more common, but often goes undetected because it requires intense nest monitoring.

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

Preparation and performance of solid thermal energy storage materials based on low-grade pyrophyllite minerals.

The new sensible thermal energy storage materials were prepared by the sintering method with low-grade pyrophyllite mineral powders as main raw materials, Suzhou clay as the sintering aid and sulfite liquors as the binder. Further, the performance of sensible thermal energy storage under different size distributions and sintering temperatures was investigated and analyzed. The results show that the optimum particle size distribution is 50:15:35, the bulk density, thermal conductivity, and specific heat capacity are the largest values, which are 1.97 g cm-3, 0.87 W m-1 K-1 and 0.63 kJ kg-1 K-1, respectively. Other properties including porosity, water absorption, flexural and compressive strength and so on are optimal under this size distribution. When the sintering temperature is 1200 °C, the material has a good thermal conductivity of 0.89 W m-1 K-1 and a high bulk density of 2.05 g cm-3. Meanwhile, the sample with the used temperature from 50 to 900 °C has the best thermal energy storage capacity of 306.29 kWh·m-3.

The complete mitochondrial genome of Aglaia odorata, insights into its genomic structure and RNA editing sites.

Aglaia odorata, native to Guangdong, Guangxi, and Hainan provinces in China, has long been utilized as an herbal remedy in ancient China. In this study, we assembled and annotated the complete mitochondrial genome (mitogenome) of A. odorata, which spans a total length of 537,321 bp. Conformation of the A. odorata recombination was verified through PCR experiments and Sanger sequencing. We identified and annotated 35 protein-coding genes (PCGs), 22 tRNA genes, and 3 rRNA genes within the mitogenome. Analysis of repeated elements revealed the presence of 192 SSRs, 29 pairs of tandem repeats, and 333 pairs of dispersed repeats in the A. odorata mitogenome. Additionally, we analyzed codon usage and mitochondrial plastid DNAs (MTPTs). Twelve MTPTs between the plastome and mitogenome of A. odorata were identified, with a combined length of 2,501 bp, accounting for 0.47% of the mitogenome. Furthermore, 359 high-confidence C to U RNA editing sites were predicted on PCGs, and four selected RNA editing sites were specially examined to verify the creation of start and/or stop codons. Extensive genomic rearrangement was observed between A. odorata and related mitogenomes. Phylogenetic analysis based on mitochondrial PCGs were conducted to elucidate the evolutionary relationships between A. odorata and other angiosperms.

Extraction and characterization of polysaccharides from blackcurrant fruits and its inhibitory effects on acetylcholinesterase.

Microwave assisted aqueous two-phase system (MA-ATPS) was used to simultaneously extract two polysaccharides from blackcurrant. Under the suitable ATPS (ethanol/(NH4)2SO4, 26.75 %/18.98 %) combining with the optimal MA conditions (liquid-to-material ratio 58.5 mL/g, time 9.5 min, temperature 60.5 °C, power 587 W) predicted by response surface methodology, the yields of the top/bottom phase polysaccharides were 13.08 ± 0.37 % and 42.65 ± 0.89 %, respectively. After purification through column chromatography, the top phase polysaccharide (PRTP) and bottom phase polysaccharide (PRBP) were obtained. FT-IR, methylation and NMR analyses confirmed that the repeating unit in the backbone of PRTP was →2, 5)-α-L-Araf-(1 â†’ 3)-α-D-Manp-(1 â†’ 6)-ß-D-Galp-(1 â†’ 6)-α-D-Glcp-(1 â†’ 4)-α-L-Rhap-(1 â†’ 4)-α-D-GalAp-(1→, while the possible unit in PRBP was →4)-α-L-Rhap-(1 â†’ 3)-α-D-Manp-(1 â†’ 6)-ß-D-Galp-(1 â†’ 6)-α-D-Glcp-(1 â†’ 2, 5)-α-L-Araf-(1 â†’ 4)-α-D-GalAp-(1→. PRBP with relatively low molecular weight exhibited better stability, rheological property, free radical scavenging and acetylcholinesterase (AChE) inhibitory activities than PRTP. PRTP and PRBP were reversible mixed-type inhibitors for AChE, and the conformation of AChE was changed after binding with the polysaccharides. Molecular docking, fluorescence and isothermal titration calorimetry assays revealed that PRTP and PRBP quenched the fluorescence through static quenching mechanism, and the van der Waals interactions and hydrogen bonding played key roles in the stability of polysaccharide-enzyme complexes. This study provided a theoretical basis for blackcurrant polysaccharides as AChE inhibitors to treat Alzheimer's disease.

Olive (Olea europaea L.) Seed as New Source of Cholesterol-Lowering Bioactive Peptides: Elucidation of Their Mechanism of Action in HepG2 Cells and Their Trans-Epithelial Transport in Differentiated Caco-2 Cells.

The production of olive oil has important economic repercussions in Mediterranean countries but also a considerable impact on the environment. This production generates enormous quantities of waste and by-products, which can be exploited as new raw materials to obtain innovative ingredients and therefore make the olive production more sustainable. In a previous study, we decided to foster olive seeds by generating two protein hydrolysates using food-grade enzymes, alcalase (AH) and papain (PH). These hydrolysates have shown, both in vitro and at the cellular level, antioxidant and antidiabetic activities, being able to inhibit the activity of the DPP-IV enzyme and modulate the secretion of GLP-1. Given the multifunctional behavior of peptides, both hydrolysates displayed dual hypocholesterolemic activity, inhibiting the activity of HMGCoAR and impairing the PPI of PCSK9/LDLR, with an IC50 equal to 0.61 mg/mL and 0.31 mg/mL for AH and PH, respectively. Furthermore, both samples restored LDLR protein levels on the membrane of human hepatic HepG2 cells, increasing the uptake of LDL from the extracellular environment. Since intestinal bioavailability is a key component of bioactive peptides, the second objective of this work is to evaluate the capacity of AH and PH peptides to be transported by differentiated human intestinal Caco-2 cells. The peptides transported by intestinal cells have been analyzed using mass spectrometry analysis, identifying a mixture of stable peptides that may represent new ingredients with multifunctional qualities for the development of nutraceuticals and functional foods to delay the onset of metabolic syndrome, promoting the principles of environmental sustainability.

High-performance plastic-derived metal-free catalysts for organic pollutants degradation via Fenton-like reaction.

The preparation of waste plastics-derived catalysts is an effective strategy for the waste reclamation. However, plastic-derived material is unsuitable for wastewater purification due to its small specific surface area (SSA) and inadequate active sites (such as N/O sites). Herein, we synthesized graphene-like nanosheets using g-C3N4 as the self-sacrificing soft template and plastic as the carbon precursor. Consequently, this strategy greatly promoted the efficiencies of the emerging organic pollutants degradation with the SSA and N content of the plastic-derived biochar increasing up to 1043.4 m2/g and 17.53 at.%, respectively. In detail, 100 % sulfadiazine (SD) removal could be achieved in 180 s via the activation of peroxymonosulfate (PMS) and the catalytic activity is far higher than previous research. Mechanism experiments corroborated that such a striking performance was attributed to the generation of SO4•-, O2•- and 1O2. Meanwhile, kinds of plastic precursors, even medical waste (i.e., masks, gauze, operating caps and degreasing cotton) were also applicable. And the practical application of the plastic-derived catalyst was further demonstrated by treating pollutants in a continuous flow mode with in situ fabricated membrane. This work provides valuable insights into waste plastics processing and water pollutants removal.

Multiobjective Multitasking Optimization With Decomposition-Based Transfer Selection.

Multiobjective multitasking optimization (MTO) needs to solve a set of multiobjective optimization problems simultaneously, and tries to speed up their solution by transferring useful search experiences across tasks. However, the quality of transfer solutions will significantly impact the transfer effect, which may even deteriorate the optimization performance with an improper selection of transfer solutions. To alleviate this issue, this article suggests a new multiobjective multitasking evolutionary algorithm (MMTEA) with decomposition-based transfer selection, called MMTEA-DTS. In this algorithm, all tasks are first decomposed into a set of subproblems, and then the transfer potential of each solution can be quantified based on the performance improvement ratio of its associated subproblem. Only high-potential solutions are selected to promote knowledge transfer. Moreover, to diversify the transfer of search experiences, a hybrid transfer evolution method is designed in this article. In this way, more diverse search experiences are transferred from high-potential solutions across different tasks to speed up their convergence. Three well-known benchmark suites suggested in the competition of evolutionary MTO and one real-world problem suite are used to verify the effectiveness of MMTEA-DTS. The experiments validate its advantages in solving most of the test problems when compared to five recently proposed MMTEAs.

Hybrid Bayesian Optimization-Based Graphical Discovery for Methylation Sites Prediction.

Protein methylation is one of the most important reversible post-translational modifications (PTMs), playing a vital role in the regulation of gene expression. Protein methylation sites serve as biomarkers in cardiovascular and pulmonary diseases, influencing various aspects of normal cell biology and pathogenesis. Nonetheless, the majority of existing computational methods for predicting protein methylation sites (PMSP) have been constructed based on protein sequences, with few methods leveraging the topological information of proteins. To address this issue, we propose an innovative framework for predicting Methylation Sites using Graphs (GraphMethySite) that employs graph convolution network in conjunction with Bayesian Optimization (BO) to automatically discover the graphical structure surrounding a candidate site and improve the predictive accuracy. In order to extract the most optimal subgraphs associated with methylation sites, we extend GraphMethySite by coupling it with a hybrid Bayesian optimization (together named GraphMethySite +) to determine and visualize the topological relevance among amino-acid residues. We evaluated our framework on two extended protein methylation datasets, and empirical results demonstrate that it outperforms existing state-of-the-art methylation prediction methods.

Ultrasound-assisted extraction of emodin from Rheum officinale Baill and its antibacterial mechanism against Streptococcus suis based on CcpA.

Emodin was extracted from Rheum officinale Baill by ultrasound-assisted extraction (UAE), and ethanol was chosen as the suitable solvent through SEM and molecular dynamic simulation. Under the optimum conditions (power 541 W, time 23 min, liquid to material ratio 13:1 mL/g, ethanol concentration 83 %) predicted by RSM, the yield of emodin was 2.18 ± 0.11 mg/g. Moreover, ultrasound power and time displayed the significant effects on the extraction process. Extracting dynamics analysis indicated that the extraction process of emodin by UAE conformed to Fick's second diffusion law. The results of antibacterial experiments suggested that emodin can damage cell membrane and inhibit the expression of cps2A, sao, mrp, epf, neu and the hemolytic activity of S. suis. Biolayer interferometry and FT-IR multi-peak fitting assays demonstrated that emodin induced a secondary conformational shift in CcpA. Molecular docking and molecular dynamics confirmed that emodin bound to CcpA through hydrogen bonding (ALA248, GLU249, GLY129 and ASN196) and π-π T-shaped interaction (TYR225 and TYR130), and the mutation of amino acid residues affected the affinity of CcpA to emodin. Therefore, emodin inhibited the sugar utilization of S. suis through binding to CcpA, and CcpA may be a potential target to inhibit the growth of S. suis.

Prokaryotic virus host prediction with graph contrastive augmentaion.

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.

Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.

DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.

PURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.

Preparation of Ge-Sb-Se glass fibers with excellent bending properties by the pulsated orifice ejection method.

We have innovatively introduced the pulsated orifice ejection method into the preparation of glass fibers, successfully preparing high-purity Ge28Sb12Se60 glass fibers. These fibers have a smooth surface, uniform elemental distribution, and excellent bending properties, with a minimal bending radius of 2 mm. In the infrared spectrum from 2.5 to 13.5 µm, the fibers achieve 65% transmission. Additionally, the fibers possess a density of 4.586 g/cm3, a diameter of 35 µm, a glass transition temperature (Tg) of 369°C, and an onset crystallization temperature (Tx) of 557°C. We have also measured the surface tension of the glass fibers, finding values from 0.288 N/m to 0.124 N/m as temperatures rose from 450°C to 500°C. The POEM holds the potential to achieve fiber cores of lengths up to hundreds of meters in theory. Our work provides a distinctive perspective for the preparation of glass fibers.

CLART: A cascaded lattice-and-radical transformer network for Chinese medical named entity recognition.

Chinese medical named entity recognition (NER) is a fundamental task in Chinese medical natural language processing, aiming to recognize Chinese medical entities within unstructured medical texts. However, it poses significant challenges mainly due to the extensive usage of medical terms in Chinese medical texts. Although previous studies have made attempts to incorporate lexical or radical knowledge in order to improve the comprehension of medical texts, these studies either focus solely on one of these aspects or utilize a basic concatenation operation to combine these features, which fails to fully utilize the potential of lexical and radical knowledge. In this paper, we propose a novel Cascaded LAttice-and-Radical Transformer (CLART) network to exploit both lexical and radical information for Chinese medical NER. Specifically, given a sentence, a medical lexicon, and a radical dictionary, we first construct a flat lattice (i.e., character-word sequence) for the sentence and radical components of each Chinese character through word matching and radical parsing, respectively. We then employ a lattice Transformer module to capture the dense interactions between characters and matched words, facilitating the enhanced utilization of lexical knowledge. Subsequently, we design a radical Transformer module to model the dense interactions between the lattice and radical features, facilitating better fusion of the lexical and radical knowledge. Finally, we feed the updated lattice-and-radical-aware character representations into a Conditional Random Fields (CRF) decoder to obtain the predicted labels. Experimental results conducted on two publicly available Chinese medical NER datasets show the effectiveness of the proposed method.