Phased Telomere-to-Telomere Reference Genome and Pangenome Reveal an Expansion of Resistance Genes during Apple Domestication.

The cultivated apple (Malus domestica Borkh.) is a cross-pollinated perennial fruit tree of great economic importance. Previous versions of apple reference genomes were unphased, fragmented, and lacked comprehensive insights into the highly heterozygous genome, which impeded genetic studies and breeding programs in apple. In this study, we assembled a haplotype-resolved telomere-to-telomere reference genome for the diploid apple cultivar Golden Delicious. Subsequently, we constructed a pangenome based on twelve assemblies from wild and cultivated apples to investigate different types of resistance gene analogs (RGAs). Our results revealed the dynamics of the gene gain and loss events during apple domestication. Compared with cultivated species, more gene families in wild species were significantly enriched in oxidative phosphorylation, pentose metabolic process, responses to salt, and abscisic acid biosynthesis process. Interestingly, our analyses demonstrated a higher prevalence of RGAs in cultivated apples than their wild relatives, partially attributed to segmental and tandem duplication events in certain RGAs classes. Other types of structural variations, mainly deletions and insertions, have affected the presence and absence of TIR-NB-ARC-LRR (TNL), NB-ARC-LRR (NL), and CC-NB-ARC-LRR (CNL) genes. Additionally, hybridization/introgression from wild species has also contributed to the expansion of resistance genes in domesticated apples. Our haplotype-resolved T2T genome and pangenome provide important resources for genetic studies of apples, emphasizing the need to study the evolutionary mechanisms of resistance genes in apple breeding programs.

A universal inverse design methodology for microfluidic mixers.

The intelligent design of microfluidic mixers encompasses both the automation of predicting fluid performance and the structural design of mixers. This article delves into the technical trajectory of computer-aided design for micromixers, leveraging artificial intelligence algorithms. We propose an automated micromixer design methodology rooted in cost-effective artificial neural network (ANN) models paired with inverse design algorithms. Initially, we introduce two inverse design methods for micromixers: one that combines ANN with multi-objective genetic algorithms, and another that fuses ANN with particle swarm optimization algorithms. Subsequently, using two benchmark micromixers as case studies, we demonstrate the automatic derivation of micromixer structural parameters. Finally, we automatically design and optimize 50 sets of micromixer structures using the proposed algorithms. The design accuracy is further enhanced by analyzing the inverse design algorithm from a statistical standpoint.

Comparative study of clinical reasoning competence and self-directed learning competence in nurses across varied years of experience.

OBJECTIVES: To compare and analyse the differences in the clinical reasoning competence of nurses with different working years and their relationship with self-directed learning competence. METHODS: A cross-sectional survey design (online investigation) was used. A total of 376 nurses were recruited from four independent hospitals in China. Online questionnaires collected data on nurses' demographic characteristics and assessed their clinical reasoning and self-directed learning competence. Pearson correlation analysis, t-test, analysis of variance (ANOVA) and multivariate regression analysis were used. RESULTS: Clinical reasoning competence scores of nurses with working years >10 years were higher than those of other nurses. Self-directed learning competence scores of nurses with working years of <1 year and (from ≥1 year to <3 years) were lower than those of nurses with working years of 6-10 years and >10 years. Self-directed learning competence scores of nurses with working years of 3-5 years were lower than those of nurses with working years of >10 years. There was a positive correlation between clinical reasoning competence, self-directed learning competence and each dimension among nurses of different working years. There are differences in the influence of different dimensions of self-directed learning competence on clinical reasoning competence among different working years. CONCLUSION: There were differences in clinical reasoning and self-directed learning competence among nurses with different working years. Self-directed learning competence is a positive predictor of nurses' clinical reasoning competence, which applied to nurses with all working years; however, the specific effect of self-directed learning competence on clinical reasoning competence differed among nurses with different working years. IMPLICATION FOR NURSING MANAGERS: Nursing managers should pay attention to the development characteristics of clinical reasoning competence and self-directed learning competence of nurses with different working years and determine effective intervention strategies according to specific influencing factors.

Carbon dioxide emissions and environmental risks: Long term and short term.

The world is currently experiencing the environmental challenge of global warming, necessitating careful planning of carbon dioxide (CO2 ) emissions to deal with this problem. This study examines the environmental challenge posed by CO2 emissions from both a long and short-term perspective. In the long term, despite efforts made by countries, our change-point detection analysis shows that there has been no structural change in CO2 emissions since 1950. Without significant efforts, the carbon budget corresponding to the Paris Agreement's target will be exhausted by 2046. To achieve this target, a significant reduction in global CO2 emissions of 3.22% per year is necessary. In the short term, COVID-19 is thought to have relieved pressure on CO2 emissions. However, this study shows that CO2 emissions quickly returned to normal levels after a brief downturn, and we provide information on the order of CO2 emissions recovery for different sectors.

Automated design of a 3D passive microfluidic particle sorter.

Microfluidic chips that can sort mixtures of cells and other particles have important applications in research and healthcare. However, designing a sorter chip for a given application is a slow and difficult process, especially when we extend the design space from 2D into a 3D scenario. Compared to the 2D scenario, we need to explore more geometries to derive the appropriate design due to the extra dimension. To evaluate sorting performance, the simulation of the particle trajectory is needed. The 3D scenario brings particle trajectory simulation more challenges of runtime and collision handling with irregular obstacle shapes. In this paper, we propose a framework to design a 3D microfluidic particle sorter for a given application with an efficient 3D particle trajectory simulator. The efficient simulator enables us to simulate more samples to ensure the robustness of the sorting performance. Our experimental result shows that the sorter designed by our framework successfully separates the particles with the targeted size.

Targeting succinylation-mediated metabolic reprogramming as a potential approach for cancer therapy.

Metabolic reprogramming is a common hallmark of cancers and involves alterations in many metabolic pathways during tumor initiation and progression. However, the cancer-specific modulation of metabolic reprogramming requires further elucidation. Succinylation, a newly identified protein posttranslational modification (PTM), participates in many cellular processes by transferring a succinyl group to a residue of the target protein, which is related to various pathological disorders including cancers. In recent years, there has been a gradual increase in the number of studies on the regulation of tumors by protein succinylation. Notably, accumulating evidence suggests that succinylation can mediate cancer cell metabolism by altering the structure or activity of metabolism-related proteins and plays vital roles in metabolic reprogramming. Furthermore, some antitumor drugs have been linked to succinylation-mediated tumor-associated metabolism. To better elucidate lysine succinylation mediated tumor metabolic reprogramming, this review mainly summarizes recent studies on the regulation and effects of protein succinylation in tumors, focusing on the metabolic regulation of tumorigenesis and development, which will provide new directions for cancer diagnosis as well as possible therapeutic targets.

The relationship between nurses' social network degree centrality and organizational citizenship behavior: The multiple mediating effects of job satisfaction and work engagement.

Background: In nursing care organizations, nurses' social networks affect their behavior and play an important role in nursing practice. This study aimed to explore the relationships among social network degree centrality, job satisfaction, work engagement and organizational citizenship behavior (OCB) among nurses. Methods: A cross-sectional survey design (one-on-one investigation) was used. The study was conducted among 254 nurses working in 10 nursing units in two hospitals in China from November 2019-February 2020. The participants completed a paper questionnaire that measured social network degree centrality, job satisfaction, work engagement and organizational citizenship behavior. A structural equation model (SEM) was used to analyze the mediating effects of job satisfaction and work engagement. SPSS 22.0 and Amos 21.0 software were used, and the significance level was set at 5% for all analyses. Results: Social network degree centrality was positively associated with job satisfaction, work engagement and organizational citizenship behavior, and job satisfaction and work engagement had significant positive relationships with organizational citizenship behavior. In addition, social network degree centrality influenced organizational citizenship behavior through the multiple mediating effects of job satisfaction and work engagement. Conclusions: Improving job satisfaction and work engagement can boost the positive effect of social network degree centrality on OCB.

Acid-Resistant Near-Infrared II Ag2Se Quantum Dots for Gastrointestinal Imaging.

With the development of near-infrared II (NIR-II) fluorescence imaging, Ag2Se quantum dots (QDs) have become promising label candidates due to their negligible toxicity and narrow band gap. Despite their potential for gastrointestinal (GI) imaging, the application of Ag2Se QDs still presents significant challenges due to issues such as fluorescence extinction or poor stability in the complex digestive microenvironment. Herein, we have proposed a novel approach to the continuous production of Se precursors using glutathione (GSH) as the reductant under acidic conditions, realizing the continuous growth of water-dispersible Ag2Se QDs. The Ag2Se QDs emitting at 600-1100 nm have been successfully synthesized. Meanwhile, the silver-rich surface of the synthesized NIR-II Ag2Se QDs has been passivated well with the dense GSH, resulting in exceptional colloidal stability and photostability and endowing them with acid resistance. As a result, the obtained NIR-II Ag2Se QDs have exhibited remarkable stability in gastric acid, thus enabling their utilization for long-term real-time monitoring of GI peristalsis via NIR-II fluorescence imaging. Moreover, in contrast to conventional barium meal-based X-ray imaging, NIR-II fluorescence imaging with as-prepared NIR-II Ag2Se QDs can offer clearer visualization of fine intestinal structures, with a width as small as 1.07 mm. The developed strategy has offered a new opportunity for the synthesis of acid-resistant nanocrystals, and the acid-resistant, low-toxicity, and biocompatible NIR-II Ag2Se QDs synthesized in this work show a great promise for GI imaging and diagnosis of GI diseases in vivo.

The relationship between resilience and quality of life in advanced cancer survivors: multiple mediating effects of social support and spirituality.

Background: While previous studies have revealed a positive association between resilience and quality of life in advanced cancer survivors, the mechanisms of the relationship is still unclear. This study aimed to explore the relationships between resilience, social support, spirituality, and quality of life and determine the multiple mediation effects of social support and spirituality on the relationship between resilience and quality of life. Methods: With 286 advanced cancer survivors, a cross-sectional, correlational survey was adopted using convenience sampling. Resilience, social support, spirituality, and quality of life were evaluated by self-report questionnaires. The PROCESS macro for SPSS was used to test the multiple mediation model. Results: The scores for resilience, social support, spirituality and quality of life were positively correlated with one another. Resilience was found to be directly impact quality of life. Meanwhile, the relationship between resilience and quality of life was mediated by social support (effect = 0.067, 95% CI [0.019, 0.120]) and by spirituality (effect = 0.221, 95% CI [0.134, 0.332]), respectively, and by these two serially (effect = 0.036, 95% CI [0.015, 0.067]). Conclusion: Social support and spirituality played multiple mediating roles in the relationship between resilience and quality of life. Interventions aimed at increasing resilience, and then boosting social support and spirituality may be beneficial for promoting quality of life of advanced cancer survivors.

Factors related to the quality of life of family cancer caregivers.

Background: Cancer caregivers directly affect patient health outcomes. To maintain the function and health of caregivers so that patients can receive efficient care, we must pay more attention to caregivers' quality of life in the process of caring for patients. However, the factors influencing caregivers' quality of life are complex. Aim: To assess caregivers' quality of life in the process of caring for cancer patients and to explore the factors associated with it. Design: This was a descriptive correlational study. A self-report questionnaire was used to anonymously collect data from one Chinese cancer hospital. The Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being (FACIT-Sp-12), General Self-efficacy Scale (GSES), Positive and Negative Affect Schedule (PANAS), Connor-Davidson Resilience Scale 10 (CD-RISC-10), 24-item Caregiver Burden Inventory (CBI) and Caregiver Evaluation Questionnaire were used to measure caregivers' spiritual well-being, self-efficacy, affective well-being, resilience, caregiver burden and quality of life. One-way analysis of variance, the Kruskal-Wallis H test and multiple regression analysis were applied to measure the factors influencing caregivers' situations. Setting and participants: A total of 315 caregivers of cancer patients were selected by convenience sampling. All participants were invited to complete the questionnaire through a one-on-one approach. Results: The mean score for caregiver quality of life was 204.62 ± 36.61. After controlling for demographic factors, self-efficacy (ß' = 0.265, p < 0.01), resilience (ß' = 0.287, p < 0.01) and positive affect (ß' = 0.103, p < 0.01) were protective factors for caregivers' quality of life. Negative affect (ß' = -0.217, p < 0.01) and caregiver burden (ß' = -0.219, p < 0.01) were negative factors. Notably, not all of these predictors can predict all dimensions of quality of life. Conclusion: Caregivers' quality of life needs to be further improved. The results of this study may provide clues to help identify factors influencing caregivers' quality of life and implement targeted strategies to improve their quality of life.

Lipid metabolism reprogramming of CD8+ T cell and therapeutic implications in cancer.

Effector, memory and exhaustion are three phenotypes of CD8+ T cell. In tumor microenvironment (TME), metabolism dysfunction of the three should take the blame for immune escape. Against background of CD8+ T cell in normal development, multiple determinants in TME, including nutrition competition, PD-1 signals and other cancer - CD8+ T cell interactions, cause metabolism reprograming, including failure in energy metabolism and other abnormal lipid metabolism. Further, incompatibility among metabolism patterns of three phenotypes results in unresponsiveness of immune checkpoint blockade (ICB). Therefore, combination of ICB and drugs aiming at abnormal lipid metabolism provides promising direction to improve cancer therapy. This review focuses on lipid metabolism of CD8+ T cell and aims to provide innovative therapy strategy to cure cancer.

Enhanced Proliferation of Visualizable Mesenchymal Stem Cell-Platelet Hybrid Cell for Versatile Intracerebral Hemorrhage Treatment.

The intrinsic features and functions of platelets and mesenchymal stem cells (MSCs) indicate their great potential in the treatment of intracerebral hemorrhage (ICH). However, neither of them can completely overcome ICH because of the stealth process and the complex pathology of ICH. Here, we fabricate hybrid cells for versatile and highly efficient ICH therapy by fusing MSCs with platelets and loading with lysophosphatidic acid-modified PbS quantum dots (LPA-QDs). The obtained LPA-QDs@FCs (FCs = fusion cells) not only inherit the capabilities of both platelets and MSCs but also exhibit clearly enhanced proliferation activated by LPA. After systemic administration, many proliferating LPA-QDs@FCs rapidly accumulate in ICH areas for responding to the vascular damage and inflammation and then efficiently prevent both the primary and secondary injuries of ICH but with no obvious side effects. Moreover, the treatment process can be tracked by near-infrared II fluorescence imaging with highly spatiotemporal resolution, providing a promising solution for ICH therapy.

[Co-occurrence of Tetracycline Antibiotic Resistance Genes and Microbial Communities in Plateau Wetlands Under the Influence of Human Activities].

The widespread use of antibiotics has led to a large number of antibiotics entering the environment, to which microorganisms have become resistant. In recent years, with the intensification of human activities in the plateau region, the occurrence and migration of antibiotic resistance genes (ARGs) in plateau wetlands have attracted considerable attention. Here, we selected the Caohai National Wetland Park, located in the Yunnan-Guizhou Plateau, as our study area. The contents of tetracyclines, sulfonamides, quinolones, and macrolides in sediments from the upstream (the pristine habitat near the spring eye) and downstream (the sewage discharge outlet of residents) areas of the river in the park were analyzed. Among them, the detection content of tetracycline antibiotics was 103.65-2185 µg·kg-1, which was the highest antibiotic detection content. To further investigate the occurrence characteristics and influencing factors of tetracycline resistance genes, the influence of environmental factors, bacterial community structure, and pathogenic bacteria on tetracycline ARGs under the influence of human activities were revealed via correlation analysis and network analysis. The results showed that a total of 15 tetracycline resistance genes were detected in the upstream and downstream sediments. Among them, seven resistance genes including tetPA, tetD, and tetPB were detected in the upstream, and 13 resistance genes such as tetPA, tetE, tetM, and tetX were detected in the downstream. The abundance of eight new resistance genes in the downstream accounted for 43.44% of the downstream genes. The tetracycline-like antibiotics and soil physicochemical indicators (i.e., available phosphorus, total organic carbon, nitrate nitrogen, and total phosphorus) were the main environmental factors affecting the distribution of tetracycline ARGs. Additionally, the bacteria detected in the upstream and downstream sediments belonged to 64 bacterial phyla, among which Proteobacteria, Firmicutes, and Bacteroidota were the main phyla affecting the abundance of tetracycline ARGs; meanwhile, 27 pathogenic bacteria were detected in the upstream and downstream sediments. Network analysis showed that the correlation between the eight new resistance genes and pathogens in the downstream area accounted for 70% of the network connectivity, and Listeria monocytogenes, Enterococcus faecalis, and Bacteroides vulgatus were identified as potential hosts for the transmission of tetracycline ARGs. Compared to the pristine habitat, the discharge of domestic sewage introduced large amounts of antibiotics and also changed the microenvironment and microbial community structure of the river wetland. Additionally, it increased the species of ARGs in sediments, which promoted the spread and transmission of ARGs among microorganisms and even pathogens.

ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics.

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories.

Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator.

Microfluidics concentration gradient generators have been widely applied in chemical and biological fields. However, the current gradient generators still have some limitations. In this work, we presented a microfluidic concentration gradient generator with its corresponding manipulation process to generate an arbitrary concentration gradient. Machine-learning techniques and interpolation algorithms were implemented to help researchers instantly analyze the current concentration profile of the gradient generator with different inlet configurations. The proposed method has a 93.71% accuracy rate with a 300× acceleration effect compared to the conventional finite element analysis. In addition, our method shows the potential application of the design automation and computer-aided design of microfluidics by leveraging both artificial neural networks and computer science algorithms.

Forecasting oil commodity spot price in a data-rich environment.

Statistical properties that vary with time represent a challenge for time series forecasting. This paper proposes a change point-adaptive-RNN (CP-ADARNN) framework to predict crude oil prices with high-dimensional monthly variables. We first detect the structural breaks in predictors using the change point technique, and subsequently train a prediction model based on ADARNN. Using 310 economic series as exogenous factors from 1993 to 2021 to predict the monthly return on the WTI crude oil real price, CP-ADARNN outperforms competing benchmarks by 12.5% in terms of the root mean square error and achieves a correlation of 0.706 between predicted and actual returns. Furthermore, the superiority of CP-ADARNN is robust for Brent oil price as well as during the COVID-19 pandemic. The findings of this paper provide new insights for investors and researchers in the oil market.

Quantum Dots with a Compact Amphiphilic Zwitterionic Coating.

Generally speaking, it is difficult to keep nanomaterials encapsulated in amphiphilic polymers like octylamine-grafted poly(acrylic acid) (OPA) compact in coating-layer, with a small hydrodynamic size. Here, we prepared stable hydrophilic quantum dots (QDs) via encapsulation in ∼3 nm-long amphiphilic and zwitterionic (AZ) molecules. After encapsulation with AZ molecules, the coated QDs are only 2.1 nm thicker in coating, instead of 5.4 nm with OPA. Meanwhile, the hydrodynamic sizes of CdSe/CdS, ZnCdSeS, ZnCdSe/ZnS, and CdSe/ZnS QDs encapsulated in AZ molecules (AZ-QDs) are less than 15 nm, and 6-7 nm smaller than those of QDs in OPA (OPA-QDs). Notably, both extracellular and intracellular nonspecific binding of AZ-QDs is approximately 100-folds lower than that of OPA-QDs.

Responses of leaf hydraulic traits of Schoenoplectus tabernaemontani to increasing temperature and CO2 concentrations.

BACKGROUND: Against the background of a changing climate, the responses of functional traits of plateau wetland plants to increasing temperatures and CO2 concentrations need to be understood. Hydraulic traits are the key for plants to maintain their ecological functions and affect their growth and survival. However, few studies have comprehensively considered the response strategies of wetland plants' hydraulic traits to environmental changes in the context of water and matter transport, loss, and retention. According to the latest IPCC prediction results, we performed experiments under increased temperature (2 °C) and CO2 levels (850 µmol/mol) in an artificial Sealed-top Chamber (STC) to investigate the responses of the hydraulic characteristics of Schoenoplectus tabernaemontani, the dominant species in plateau wetlands in China. RESULTS: Compared with the CK group, net photosynthetic rate, transpiration rate, stomatal length, cuticle thickness, vascular bundle length, vascular bundle width, and vascular bundle area of S. tabernaemontani in the ET group were significantly reduced, whereas stomatal density and vein density increased significantly. Compared with the CK group, the hydraulic traits of S. tabernaemontani in the EC group were reduced considerably in stomatal length and cuticle thickness but increased dramatically in stomatal density, and there were no significant differences between other parameter values and the control group. Net photosynthetic rate was significantly positively correlated with stomatal length, cuticle thickness, and vascular bundle length, and stomatal conductance was significantly positively correlated with cuticle thickness. The transpiration rate was significantly positively correlated with cuticle thickness, epidermal cell area, vascular bundle length, vascular bundle width, and vascular bundle area. Regarding the hydraulic traits, there was a significant negative correlation between stomatal density and stomatal length, or cuticle thickness, and a significant positive correlation between the latter two. The epidermal cell area was significantly positively correlated with epidermal thickness, vascular bundle length, vascular bundle width, and vascular bundle area. CONCLUSIONS: Increased temperature and CO2 levels are not conducive to the photosynthetic activity of S. tabernaemontani. Photosynthetic rate, stomatal density and size, vein density, epidermal structure size, and vascular bundle size play an essential role in the adaptation of this species to changes in temperature and CO2 concentration. In the process of adaptation, hydraulic traits are not isolated from each other, and there is a functional association among traits. This study provide a scientific basis for the management and protection of plateau wetlands.

DeepLRR: An Online Webserver for Leucine-Rich-Repeat Containing Protein Characterization Based on Deep Learning.

Members of the leucine-rich repeat (LRR) superfamily play critical roles in multiple biological processes. As the LRR unit sequence is highly variable, accurately predicting the number and location of LRR units in proteins is a highly challenging task in the field of bioinformatics. Existing methods still need to be improved, especially when it comes to similarity-based methods. We introduce our DeepLRR method based on a convolutional neural network (CNN) model and LRR features to predict the number and location of LRR units in proteins. We compared DeepLRR with six existing methods using a dataset containing 572 LRR proteins and it outperformed all of them when it comes to overall F1 score. In addition, DeepLRR has integrated identifying plant disease-resistance proteins (NLR, LRR-RLK, LRR-RLP) and non-canonical domains. With DeepLRR, 223, 191 and 183 LRR-RLK genes in Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa ssp. Japonica) and tomato (Solanum lycopersicum) genomes were re-annotated, respectively. Chromosome mapping and gene cluster analysis revealed that 24.2% (54/223), 29.8% (57/191) and 16.9% (31/183) of LRR-RLK genes formed gene cluster structures in Arabidopsis, rice and tomato, respectively. Finally, we explored the evolutionary relationship and domain composition of LRR-RLK genes in each plant and distributions of known receptor and co-receptor pairs. This provides a new perspective for the identification of potential receptors and co-receptors.

Eco-green C, O co-doped porous BN adsorbent for aqueous solution with superior adsorption efficiency and selectivity.

Toxic dyes in wastewater will become a significant hazard to human health if they are not treated effectively. Therefore, it is significant to separate and remove dyes from the aqueous solution. C and O co-doped BN (BCNO) with high adsorption capacity and outstanding cycle efficiency is a simple and efficient adsorbent for the cationic dye malachite green (MG). Glucose is characterized as an eco-friendly and cheap source of C and O. Benefited by the high specific surface area (1515.6 m2/g), the maximum adsorption capacity of MG is 1511.1 mg/g. Besides, the curves of adsorption fitting correspond to the Langmuir model and the pseudo-second-order model, respectively. Moreover, after 5 cycles, the adsorption efficiency reached 78% of the first time and the adsorption capacity remained above 780 mg/g. Furthermore, in the selectivity adsorption study, the cationic dyes (MG, neutral red (NR), methylene blue (MB)) can be removed more effectively in the binary dye system of MG-methyl orange (MO), NR-MO, MB-MO, MG-Orange II (OR), MB-OR, or NR-OR. BCNO-2 has a promising application in the removal of cationic dyes from complex dye wastewaters.