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.

Effects of parasternal intercostal block on surgical site wound infection and pain in patients undergoing cardiac surgery: A meta-analysis.

This study aimed to assess the effect of parasternal intercostal block on postoperative wound infection, pain, and length of hospital stay in patients undergoing cardiac surgery. PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP, and Wanfang databases were extensively queried using a computer, and randomised controlled studies (RCTs) from the inception of each database to July 2023 were sought using keywords in English and Chinese language. Literature quality was assessed using Cochrane-recommended tools, and the included data were collated and analysed using Stata 17.0 software for meta-analysis. Ultimately, eight RCTs were included. Meta-analysis revealed that utilising parasternal intercostal block during cardiac surgery significantly reduced postoperative wound pain (standardised mean difference [SMD] = -1.01, 95% confidence intervals [CI]: -1.70 to -0.31, p = 0.005) and significantly shortened hospital stay (SMD = -0.40, 95% CI: -0.77 to -0.04, p = 0.029), though it may increase the risk of wound infection (OR = 5.03, 95% CI:0.58-44.02, p = 0.144); however, the difference was not statistically significant. The application of parasternal intercostal block during cardiac surgery can significantly reduce postoperative pain and shorten hospital stay. This approach is worth considering for clinical implementation. Decisions regarding its adoption should be made in conjunction with the relevant clinical indices and surgeon's experience.

Glycomacropeptide (GMP) rescued the oxidative and inflammatory activity of free L-AAs in human Caco-2 cells: New insights that support GMP as a valid and health-promoting product for the dietary management of phenylketonuria (PKU) patients.

Phenylketonuria represents the most prevalent inborn error of amino acid metabolism. In early diagnosed patients adequate and continued dietary treatment results in a good neurologic outcome. However, due to the natural protein and phenylalanine-restricted diet, oxidative stress represents a concern in phenylketonuric patients. Clear evidences suggest that the pathophysiology of PKU is also dependent by mitochondrial impairment and oxidative stress. In this context due to the tight connection between oxidative and inflammatory stress and noncommunicable diseases (NCDs) development, it is reasonable to hypothesize that PKU patients may present a higher risk to develop NCDs during their life. Currently available protein substitutes on the market include free amino acids (L-AAs), prolonged-release protein substitute and formula containing glycomacropeptide (GMP). Our results suggest that free L-AAs significanlty worsens the intestinal hydrogen peroxide (H2O2) and lipopolysaccharides (LPS)-induced oxidative and inflammatory status in Caco-2 cells, which are significantly restored towards physiological condition by GMP alone and when present in a 1:1 mixture with free L-AAs, providing new preclinical piece of information which can shed a shadow on the mechanism of action of these products on PKU patients and their future management.

The Influencing Factors of "Post-African Swine Fever" Pig Farm Biosecurity: Evidence from Sichuan Province, China.

Effective biosecurity measures are crucial in controlling and preventing major pig diseases, ultimately ensuring farm income and social stability. This study extracted data from 205 farmer surveys in Sichuan Province, China, to construct a biosecurity index system for pig farms. The biosecurity levels of pig farms were evaluated using a projection pursuit method to identify weak areas. The Tobit model was then utilized to determine the factors that influenced the biosecurity levels. The results indicated that the overall biosecurity levels of the pig farms were low. The study found that the average biosecurity score among farms was 0.61, with a minimum score of 0.37 and a maximum score of 0.89 (on a scale of 0 to 1). These results suggest that there are significant differences in biosecurity levels among the farms. The study also found that the scores for first-level indicators related to breeding environment management, as well as second-level indicators related to personnel management and awareness of African swine fever prevention and control, were significantly lower than scores for other indicators in the farmers' biosecurity systems. This study investigated the factors influencing biosecurity on farms and found that technical training, farm size, income share, production organization, and government inspections had a significant impact on the level of biosecurity implemented. This study emphasizes the significance of biosecurity in enhancing pig farm biosecurity and its role in improving farm resilience to major animal diseases like African swine fever. It also provides valuable insights for policymakers to make informed decisions regarding related policies.

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.

A novel double-ribonuclease toxin-antitoxin system linked to the stress response and survival of Acidovorax citrulli.

IMPORTANCE: Bacterial fruit blotch (BFB), which is caused by the seed-borne bacterium Acidovorax citrulli, is a devastating disease affecting cucurbit crops throughout the world. Although seed fermentation and treatment with disinfectants can provide effective management of BFB, they cannot completely guarantee pathogen-free seedstock, which suggests that A. citrulli is a highly stress-resistant pathogen. Toxin-antitoxin (TA) systems are common among a diverse range of bacteria and have been reported to play a role in bacterial stress response. However, there is currently much debate about the relationship between TA systems and stress response in bacteria. The current study characterized a novel TA system (Aave_1720-Aave_1719) from A. citrulli that affects both biofilm formation and survival in response to sodium hypochlorite stress. The mechanism of neutralization differed from typical TA systems as two separate mechanisms were associated with the antitoxin, which exhibited characteristics of both type II and type V TA systems. The Aave_1720-Aave_1719 system described here also constitutes the first known report of a double-ribonuclease TA system in bacteria, which expands our understanding of the range of regulatory mechanisms utilized by bacterial TA systems, providing new insight into the survival of A. citrulli in response to stress.