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Abstract Background Pseudomyxoma peritonei (PMP) is a rare malignant peritoneal tumor that readily recurs and metastasizes. Studies have shown that cancer stem cells (CSCs) play an important role in tumor recurrence, metastasis, and prognosis. Objective In this study, our aim was to isolate CSCs from various tissues of PMP patients and compare their proliferation, migration, and anti-inflammatory abilities. Methods We identified CSCs subsets with markers CD133+, CD166+, and CD133+/CD166+ at the gene level using single-cell mRNA sequencing (scRNA-seq). Appendiceal CSCs (AC), peritoneal CSCs (PC), and mucous CSCs (MC) were obtained using MACSQuant Tyto sorting technology and FlowSight imaging flow cytometry. The cells were cultured and markers were identified. Finally, the functional phenotypes of the three cell types were compared. Results CSCs content was highest in the appendiceal tumor tissue and lowest in the mucous tissue. The cell viability rate of the sorted CSCs was above 98%, and the positive rate of CD133+ and CD166+ was 70–80%, and CD133+/CD166+ was about 30%. Among the three types of CSCs, MC had the highest proliferation ability, and TNF-α has the greatest inhibitory effect on AC migration. Conclusion AC in patients was more inert and anti-inflammatory, whereas abdominal cavity MC and PC were more active. This study revealed the biological characteristics of CSCs in different tumor tissues of patients with PMP, providing a reference for future targeted CSCs therapy.

Abstract Vitiligo is a skin disorder that is associated with a decreased risk of skin cancer, but it can lead to increased susceptibility to sunburn, psychological distress, and disruptions in daily life, consists of two primary subtypes: segmental and nonsegmental vitiligo, each with distinct underlying mechanisms. However, the reliable identification of diagnostic markers and the ability to differentiate between these subtypes have remained elusive challenges. This study aims to pioneer predictive algorithms for vitiligo diagnosis, harnessing the capabilities of AI (Artificial Intelligence) to amalgamate multisource data and uncover essential features for distinguishing vitiligo subtypes.An ensemble algorithm was thoughtfully developed for vitiligo diagnosis, utilizing a spectrum of machine learning techniques to evaluate the likelihood of vitiligo, whether segmental or nonsegmental. Diverse machine learning methodologies were applied to distinguish between healthy individuals and vitiligo patients, as well as to differentiate segmental from nonsegmental vitiligo. The ensemble algorithm achieved a remarkable AUC (Area Under the Curve) of 0.99 and an accuracy of 0.98 for diagnosing vitiligo. Furthermore, in predicting the development of segmental or nonsegmental vitiligo, the model exhibited an AUC of 0.79 and an accuracy of 0.73. Key parameters for vitiligo identification encompassed factors such as age, FBC (full blood count)-neutrophils, FBC-lymphocytes, LKF(liver and kidney function)-direct bilirubin, LKF-total bilirubin, and LKF-total protein levels. In contrast, vital indicators for monitoring the progression of segmental and nonsegmental vitiligo included FBC-B lymphocyte count, FBC-NK (Natural Killer) cell count, and LKF-alkaline phosphatase levels. This retrospective study underscores the potential of AI-driven analysis in identifying significant risk factors for vitiligo and predicting its subtypes at an early stage. These findings offer great promise for the development of effective diagnostic tools and the implementation of personalized treatment approaches in managing this challenging skin disorder.

Abstract Calcaneal body fractures are often associated with varying degrees of shortening deformities. Restoring calcaneal length is crucial for the functional prognosis of the foot. Through finite element analysis, this study compared the biomechanical effects of multiple fixation schemes for calcaneal fractures. We delineated and assembled the finite element model of the Sanders type II calcaneal fracture and four internal fixation simulations (namely distraction screw, lag screw, frame locking plate, and T-shaped locking plate). Different axial forces (350, 700, and 1400 N) were then applied to simulate various postures. We then compared the inner and outer shortening distances (D1 and D2, respectively), equivalent von Mises stress, and maximum von Mises stress of the calcaneus. In the individual model, with an increase in the pressure, D1, D2, and the maximum von Mises stress gradually increased. At 1400 N, D1 and D2 for the internal fixation schemes were as follows: distraction screw (0.03 mm, 0.1 mm)

Abstract Cadmium is a toxic heavy metal found in acid mine drainage. It hinders plant and animal growth and accumulates in human organs. In this study, through shake flask experiments, an iron-rich, sulphate-rich environment was simulated, and Acidithiobacillus ferrooxidans was used to mediate the formation of secondary high-iron minerals to explore the effect of calcium ions on the removal of Cd2+ from that environment. Four treatment systems were used: “Blank”, “Ca2+-30 mg/L”, “Fe/K = 3,Ca2+-30 mg/L”, and “Fe/K = 3”. The results showed that Cd2+ with an initial concentration of 20 mg/L was effectively removed in each treatment system. The removal efficiencies of Cd2+ in each treatment were 23.46%, 18.42%, 52.88%, and 45.76% respectively. The quantity and type of minerals determined the removal efficiency of Cd2+. The Fe/K = 3 treatment system can significantly increase the amount of mineral formation and improve the removal efficiency of Cd2+. In the Ca2+-30 mg/L, Fe/K = 3 treatment system, the biological oxidation ability was the strongest, and the removal effect of Cd2+ was the best under the combined action of K+ and Ca2+. Co-precipitation was the main way to remove Cd2+ during the formation of biogenic secondary iron minerals, and the removal amount was 5.64 to 14.83 times that of adsorption. Biogenetic secondary iron minerals showed high values in repairing heavy metal pollution. This study provides a theoretical basis for treating heavy metals in acid mine drainage.

Abstract With the increase in high-rise buildings in cities, public flue exhaust systems have a direct impact on residential air quality and the living environment. Although existing studies have analyzed the problems in public flue exhaust systems through computational fluid dynamics (CFD) numerical simulations and experimental tests, these studies often lack an in-depth exploration of the specific impacts of individual components in the system. To solve this problem, this study not only thoroughly analyzes the key components in the public flue system, such as branch pipes, check valves, and tee pipes, but also develops a parametric public flue simulation system software based on C# and verifies the accuracy of the simulation through experiments. The study first determines the key parameters affecting the comprehensive resistance coefficient of the branch pipe, check valve, tee pipe, and other components through CFD simulation and experimental testing. Subsequently, a visualization program is developed using the C# language, which can quickly simulate and visualize the flow changes in the flue according to different building parameters such as the number of floors, height of floors, and size of the flue. The results confirm that the program can efficiently predict the flow distribution under different design options, providing a practical tool for the optimal design and performance evaluation of public flues, which is important for improving the air quality of the living environment.

Abstract This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the International Skin Imaging Collaboration (ISIC) dataset, spanning 2016–2020, encompassing a variety of skin lesions. Our approach integrates deep learning with a comprehensive radiomics analysis, utilizing a vast array of quantitative image features to precisely quantify skin lesion patterns. The dataset includes cases of three, four, and eight different skin lesion types. Our methodology was benchmarked against seven classification methods from the ISIC 2020 challenge and prior research using a binary decision framework. The proposed hybrid model demonstrated superior performance in distinguishing benign from malignant lesions, achieving area under the receiver operating characteristic curve (AUROC) scores of 99%, 95%, and 96%, and multiclass decoding AUROCs of 98.5%, 94.9%, and 96.4%, with sensitivities of 97.6%, 93.9%, and 96.0% and specificities of 98.4%, 96.7%, and 96.9% in the internal ISIC 2018 challenge, as well as in the external Jinan and Longhua datasets, respectively. Our findings suggest that the integration of radiomics and deep learning, utilizing dermoscopic images, effectively captures the heterogeneity and pattern expression of skin lesions.

Abstract Since conventional PID (Proportional–Integral–Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

Abstract Current assessment methods for diabetic foot ulcers (DFUs) lack objectivity and consistency, posing a significant risk to diabetes patients, including the potential for amputations, highlighting the urgent need for improved diagnostic tools and care standards in the field. To address this issue, the objective of this study was to develop and evaluate the Smart Diabetic Foot Ulcer Scoring System, ScoreDFUNet, which incorporates artificial intelligence (AI) and image analysis techniques, aiming to enhance the precision and consistency of diabetic foot ulcer assessment. ScoreDFUNet demonstrates precise categorization of DFU images into “ulcer,” “infection,” “normal,” and “gangrene” areas, achieving a noteworthy accuracy rate of 95.34% on the test set, with elevated levels of precision, recall, and F1 scores. Comparative evaluations with dermatologists affirm that our algorithm consistently surpasses the performance of junior and mid-level dermatologists, closely matching the assessments of senior dermatologists, and rigorous analyses including Bland–Altman plots and significance testing validate the robustness and reliability of our algorithm. This innovative AI system presents a valuable tool for healthcare professionals and can significantly improve the care standards in the field of diabetic foot ulcer assessment.

Abstract Background Binary outcomes are likely the most common in randomized controlled trials, but ordinal outcomes can also be of interest. For example, rather than simply collecting data on diseased versus healthy study subjects, investigators may collect information on the severity of disease, with no disease, mild, moderate, and severe disease as possible levels of the outcome. While some investigators may be interested in all levels of the ordinal variable, others may combine levels that are not of particular interest. Therefore, when research synthesizers subsequently conduct a network meta-analysis on a network of trials for which an ordinal outcome was measured, they may encounter a network in which outcome categorization varies across trials. Methods The standard method for network meta-analysis for an ordinal outcome based on a multinomial generalized linear model is not designed to accommodate the multiple outcome categorizations that might occur across trials. In this paper, we propose a network meta-analysis model for an ordinal outcome that allows for multiple categorizations. The proposed model incorporates the partial information provided by trials that combine levels through modification of the multinomial likelihoods of the affected arms, allowing for all available data to be considered in estimation of the comparative effect parameters. A Bayesian fixed effect model is used throughout, where the ordinality of the outcome is accounted for through the use of the adjacent-categories logit link. Results We illustrate the method by analyzing a real network of trials on the use of antibiotics aimed at preventing liver abscesses in beef cattle and explore properties of the estimates of the comparative effect parameters through simulation. We find that even with the categorization of the levels varying across trials, the magnitudes of the biases are relatively small and that under a large sample size, the root mean square errors become small as well. Conclusions Our proposed method to conduct a network meta-analysis for an ordinal outcome when the categorization of the outcome varies across trials, which utilizes the adjacent-categories logit link, performs well in estimation. Because the method considers all available data in a single estimation, it will be particularly useful to research synthesizers when the network of interest has only a limited number of trials for each categorization of the outcome.

Abstract Tracheal pooling for Mycoplasma hyopneumoniae (M. hyopneumoniae) DNA detection allows for decreased diagnostic cost, one of the main constraints in surveillance programs. The objectives of this study were to estimate the sensitivity of pooled-sample testing for the detection of M. hyopneumoniae in tracheal samples and to develop probability of M. hyopneumoniae detection estimates for tracheal samples pooled by 3, 5, and 10. A total of 48 M. hyopneumoniae PCR-positive field samples were pooled 3-, 5-, and 10-times using field M. hyopneumoniae DNA-negative samples and tested in triplicate. The sensitivity was estimated at 0.96 (95% credible interval [Cred. Int.]: 0.93, 0.98) for pools of 3, 0.95 (95% Cred. Int: 0.92, 0.98) for pools of 5, and 0.93 (95% Cred. Int.: 0.89, 0.96) for pools of 10. All pool sizes resulted in PCR-positive if the individual tracheal sample Ct value was

Background Norovirus is the leading cause of sporadic viral gastroenteritis cases and outbreaks. Gut microbiota plays a key role in maintaining immune homeostasis. We aimed to investigate the composition and functional effects of gut microbiota in children infected with norovirus.Methods Stool samples were collected from 31 children infected with norovirus and 25 healthy children. The gut microbiota was analyzed by 16S rRNA gene sequencing, followed by composition, correlation network, functional and phenotype prediction analyses.Results Gut microbiota in children infected with norovirus was characterized by lower species richness and diversity. Veillonella is the dominant gut microbiota specie in norovirus infection. Blautia was significantly lower in norovirus infection. There was a positive correlation between Faecalibacterium, Blautia, Subdoligranulum, Eubacterium_hallii_group, Fusicatenibacter, Agathobacter, Roseburia and Dorea. Functionally, secondary metabolites biosynthesis, transport and catabolism, selenocysteine lyase and peroxiredoxin were the most significantly higher functional compositions of gut microbiota in norovirus infection. However, sn-glycerol-1-phosphate dehydrogenase and fermentation were the most significantly lower functional compositions in norovirus infection group. Phenotype analysis showed that Contains_Mobile_Elements had the highest level of phenotypes in the gut microbiota of norovirus infection.Conclusion Norovirus infection may lead to dysregulation of the gut microbiome in children.

Viruses are ubiquitous in nature and play key roles in various ecosystems. Notably, some viruses (e.g. bacteriophage) exhibit alternative life cycles, such as chronic infections without cell lysis. However, the impact of chronic infections and their interactions with the host organisms remains largely unknown. Here, we found for the first time that polysaccharides induced the production of multiple temperate phages infecting two deep-sea Lentisphaerae strains (WC36 and zth2). Through physiological assays, genomic analysis, and transcriptomics assays, we found these bacteriophages were released via a chronic style without host cell lysis, which might reprogram host polysaccharide metabolism through the potential auxiliary metabolic genes. The findings presented here, together with recent discoveries made on the reprogramming of host energy-generating metabolisms by chronic bacteriophages, shed light on the poorly explored marine virus–host interaction and bring us closer to understanding the potential role of chronic viruses in marine ecosystems.

Abstract Infection remains a major cause of mortality in individuals diagnosed with hematologic malignancies, both in children and adults. Although the range of infections affecting these patients has undergone significant changes with the widespread use of antibiotics, it is accompanied by an increasing number of drug-resistant strains. To address these concerns, metagenomic next-generation sequencing (mNGS) has emerged as a promising diagnostic tool for detecting pathogens responsible for infectious diseases. At the same time, this approach has gained popularity as a means to identify infection etiology in hematologic disease patients. This review article focuses on the evolution of mNGS technology and its relevance in diagnosing hematologic diseases, discussing both the existing situation for microbial infections among such patients and traditional methods for detecting those infections. To examine the efficacy of this technique for hematologic disease patients, we compare the benefits and drawbacks of its application in light of relevant literature and clinical practice.

Abstract The requirement of the half-bridge LLC resonant converter with a wide input voltage range is becoming higher in photovoltaic applications because of its simple structure and low switching loss. Conventional frequency modulation (FM) requires a wide switching frequency range and a high-quality factor circuit design, leading to reduced efficiency and large component volumes at light loads. To solve the problems, a high-efficiency control strategy using adaptive pulse-width and frequency modulation (APWFM) is proposed. APWFM adjusts the gain by changing the switching frequency and duty cycle simultaneously. When the output power is below the reference value, the switching frequency decreases linearly as the output power decreases, and the duty cycle is simultaneously modulated to achieve constant output voltage, so the switching frequency variation range is smaller than FM. This results in improved light or medium load efficiency in a limited frequency range while keeping a small volume of magnetic components. Also, the proposed control strategy is realized with primary-side regulation (PSR) to eliminate the optocoupler and simplify the control circuit. Experimental results demonstrate a significant improvement in efficiency at medium and light loads compared to FM, and the average efficiency is improved by 5% based on low cost and simple operation.

ObjectiveNeurofilament light chain (Nf-L) has been used to detect neuroaxonal damage in the brain caused by physical injury or disease. The purpose of this study was to determine if serum Nf-L could be used as a biomarker for pre-symptomatic detection of scrapie in sheep.MethodsFour sheep with prion protein genotype AVQQ were intranasally inoculated with the classical scrapie strain x124. Blood was collected every 4 weeks until 44 weeks post-inoculation, at which point weekly collection commenced. Serum was analyzed using single molecule array (Quanterix SR-X) to evaluate Nf-L concentrations.ResultsScrapie was confirmed in each sheep by testing homogenized brainstem at the level of the obex with a commercially available enzyme immunoassay. Increased serum Nf-L concentrations were identified above the determined cutoff during the last tenth of the respective incubation period for each sheep. Throughout the time course study, PrPSc accumulation was not detected antemortem by immunohistochemistry in rectal tissue at any timepoint for any sheep. RT-QuIC results were inconsistently positive throughout the timepoints tested for each sheep; however, each sheep had at least one timepoint detected positive. When assessing serum Nf-L utility using receiver operator characteristic curves against different clinical parameters, such as asymptomatic and symptomatic (pruritus or neurologic signs), results showed that Nf-L was most useful at being an indicator of disease only late in disease progression when neurologic signs were present.ConclusionSerum Nf-L concentrations in the cohort of sheep increased as disease progressed; however, serum Nf-L did not increase during the presymptomatic window. The levels increased substantially throughout the final 10% of the animals' scrapie incubation period when other clinical signs were present. Serum Nf-L is not a reliable biomarker for pre-clinical detection of scrapie.

Planctomycetes bacteria are ubiquitously distributed across various biospheres and play key roles in global element cycles. However, few deep-sea Planctomycetes members have been cultivated, limiting our understanding of Planctomycetes in the deep biosphere. Here, we have successfully cultured a novel strain of Planctomycetes (strain ZRK32) from a deep-sea cold seep sediment. Our genomic, physiological, and phylogenetic analyses indicate that strain ZRK32 is a novel species, which we propose be named: Poriferisphaera heterotrophicis. We show that strain ZRK32 replicates using a budding mode of division. Based on the combined results from growth assays and transcriptomic analyses, we found that rich nutrients, or supplementation with NO3- or NH4+ promoted the growth of strain ZRK32 by facilitating energy production through the tricarboxylic acid cycle and the Embden-Meyerhof-Parnas glycolysis pathway. Moreover, supplementation with NO3- or NH4+ induced strain ZRK32 to release a bacteriophage in a chronic manner, without host cell lysis. This bacteriophage then enabled strain ZRK32, and another marine bacterium that we studied, to metabolize nitrogen through the function of auxiliary metabolic genes. Overall, these findings expand our understanding of deep-sea Planctomycetes bacteria, while highlighting their ability to metabolize nitrogen when reprogrammed by chronic viruses.

To attain native-like proficiency in second-language word usage, learners have to discover intricate semantic categories in the target language. We investigated the factors influencing the development of two aspects of second-language learners' semantic categories: the category center and category boundary of word meanings. In the experiment, second-language learners of Japanese, whose first language is Mandarin, were asked to produce the best verb for 28 videos depicting various cutting and breaking events. Descriptive analyses were conducted to compare the verb patterns used by second-language learners with those of native speakers. The second-language learners' verb use pattern suggested their struggle in delineating the semantic ranges of breaking verbs in a native-like manner. Model analyses further revealed that different factors contribute to learning two different aspects of word meanings. The learning category center of word meaning depended on the similarity between the lexical domains in the first and second languages. On the contrary, the success of learning the semantic boundaries of verbs required a large input frequency and smaller semantic coverage, and smaller category ambiguity. The results suggest that constructing a semantic domain in the second language should be evaluated from at least two different aspects of semantic representation.

Summary: Background: Nasopharyngeal carcinoma (NPC) is a malignant head and neck cancer with a high incidence in Southern China and Southeast Asia. Patients with remote metastasis and recurrent NPC have poor prognosis. Thus, a better understanding of NPC pathogenesis may identify novel therapies to address the unmet clinical needs. Methods: H3K27ac ChIP-seq and HiChIP was applied to understand the enhancer landscapes and the chromosome interactions. Whole genome sequencing was conducted to analyze the relationship between genomic variations and epigenetic dysregulation. CRISPRi and JQ1 treatment were used to evaluate the transcriptional regulation of SOX2 SEs. Colony formation assay, survival analysis and in vivo subcutaneous patient-derived xenograft assays were applied to explore the function and clinical relevance of SOX2 in NPC. Findings: We globally mapped the enhancer landscapes and generated NPC enhancer connectomes, linking NPC specific enhancers and SEs. We found five overlapped genes, including SOX2, among super-enhancer regulated genes, survival related genes and NPC essential genes. The mRNA expression of SOX2 was repressed when applying CRISPRi targeting different SOX2 SEs or JQ1 treatment. Next, we identified a genetic variation (Chr3:181422197, G > A) in SOX2 SE which is correlated with higher expression of SOX2 and poor survival. In addition, SOX2 was highly expressed in NPC and is correlated with short survival in patients with NPC. Knock-down of SOX2 suppressed tumor growth in vitro and in vivo. Interpretation: Our study demonstrated the super-enhancer landscape with chromosome interactions and identified super-enhancer driven SOX2 promotes tumorigenesis, suggesting that SOX2 is a potential therapeutic target for patients with NPC. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Abstract To investigate the effect of pentoxifylline (PTX) on Chlorine (Cl2)-induced acute lung injury (ALI) by single-cell RNA sequencing (scRNA-seq). Female BALB/c mice were exposed to Cl2 at 400 ppm for 15 min. H&E staining was used to observe the degree of lung injury. scRNA-seq was conducted to analysis of normal and Cl2-exposed mice lung tissues. Immunofluorescence was used to observe genes of interest. Thirty-two mice were randomly divided into four groups: Control, Cl2, Cl2+Fer-1, Cl2+PTX. TEM, WB and ELISA were used to detect ferroptosis-related indicators. The 5, 8, 10, 12, 16, 20 clusters were epithelial cells and 4, 15, 18, 19, 21 clusters were endothelial cells. Pseudo-time analysis revealed the differentiation trajectory of epithelial cells and key regulatory genes (Gclc, Bpifa1, Dnah5 and Dnah9) during the process of injury. Cell–cell communication analysis identified several important receptor–ligand complexes (Nrp1-Vegfa, Nrp2-Vegfa, Flt1-Vegfa and Flt4-Vegfa). Ferroptosis were found up-regulated in epithelial and endothelial cells by GSVA analysis. Highly expressed genes to which closely related ferroptosis were found by SCENIC analysis. PTX could significantly decrease the levels of MDA and abnormal high expression of solute carrier family 7 member 11 (SLC7A11, the key transporter of cystine) as well as increase the expression of GSH/GSSG and glutathione peroxidase 4 (GPX4) (p

Abstract Background Acute myeloid leukemia (AML) patients with normal karyotype (NK-AML) have significant variabilities in outcomes. The European Leukemia Net stratification system and some prognostic models have been used to evaluate risk stratification. However, these common standards still have some limitations. The biological functions and mechanisms of Small Integral Membrane Protein 3 (SMIM3) have seldomly been investigated. To this date, the prognostic value of SMIM3 in AML has not been reported. This study aimed to explore the clinical significance, biological effects and molecular mechanisms of SMIM3 in AML. Methods RT-qPCR was applied to detect the expression level of SMIM3 in bone marrow specimens from 236 newly diagnosed adult AML patients and 23 healthy volunteers. AML cell lines, Kasumi-1 and THP-1, were used for lentiviral transfection. CCK8 and colony formation assays were used to detect cell proliferation. Cell cycle and apoptosis were analyzed by flow cytometry. Western blot was performed to explore relevant signaling pathways. The biological functions of SMIM3 in vivo were validated by xenograft tumor mouse model. Survival rate was evaluated by Log-Rank test and Kaplan–Meier. Cox regression model was used to analyze multivariate analysis. The correlations between SMIM3 and drug resistance were also explored. Results Through multiple datasets and our clinical group, SMIM3 was shown to be significantly upregulated in adult AML compared to healthy subjects. SMIM3 overexpression conferred a worse prognosis and was identified as an independent prognostic factor in 95 adult NK-AML patients. Knockdown of SMIM3 inhibited cell proliferation and cell cycle progression, and induced cell apoptosis in AML cells. The reduced SMIM3 expression significantly suppressed tumor growth in the xenograft mouse model. Western blot analysis showed downregulation of p-PI3K and p-AKT in SMIM3-knockdown AML cell lines. SMIM3 may also be associated with some PI3K-AKT and first-line targeted drugs. Conclusions SMIM3 was highly expressed in adult AML, and such high-level expression of SMIM3 was associated with a poor prognosis in adult AML. Knockdown of SMIM3 inhibited the proliferation of AML through regulation of the PI3K-AKT signaling pathway. SMIM3 may serve as a potential prognostic marker and a therapeutic target for AML in the future.

Abstract Structural topology constraints in topology optimization are an important research topic. The structural topology is characterized by the topological invariance of the number of holes. The holes of a structure in 3D space can be classified as internally enclosed holes and external through-holes (or tunnels). The genus is the number of tunnels. This article proposes the quotient set design variable method (QSDV) to implement the inequality constraint on the maximum genus allowed in an optimized structure for 3D structural topology optimization. The principle of the QSDV is to classify the changing design variables according to the connectivity of the elements in a structure to obtain the quotient set and update the corresponding elements in the quotient set to meet the topological constraint. Based on the standard relaxation algorithm discrete variable topology optimization method (DVTOCRA), the effectiveness of the QSDV is illustrated in numerical examples of a 3D cantilever beam.

Abstract Background Chemoresistance serves as a huge obstacle for acute myeloid leukemia (AML) patients. To counteract the chemoresistance in AML cells, we discussed the role of maternally expressed gene 3 (MEG3) in arabinocytosine (AraC) chemoresistance in AML cells. Methods MEG3, microRNA (miR)-493-5p, methyltransferase-like 3 (METTL3) and MYC expression in AML cells was determined and then their interactions were also analyzed. Then, the viability and apoptosis of AML cells were determined through loss- and gain- function assay. The level of m6A modification in AML cells was examined. AML mouse models were also established to validate the potential roles of MEG3. Results MEG3 and miR-493-5p were downregulated in AML cells, and they were lower in resistant cells than in parental cells. MEG3 led to elevated expression of miR-493-5p which targeted METTL3. METTL3 increased expression of MYC by promoting its m6A levels. Overexpression of MEG3 and miR-493-5p or knockdown of METTL3 inhibited HL-60 and Molm13 cell proliferation and promoted their apoptosis. Overexpressed MEG3 induced heightened sensitivity of AML cells to AraC. However, the suppression of miR-493-5p reversed the effects of overexpressed MEG3 on AML cells. Conclusions Collectively, MEG3 could upregulate miR-493-5p expression and suppress the METTL3/MYC axis through MYC m6A methylation, by which MEG3 promoted the chemosensitivity of AML cells.

Abstract RNAs are rapidly degraded in samples and during collection, processing and testing. In this study, we used the same method to explore the half-lives of different RNAs and the influencing factors, and compared the degradation kinetics and characteristics of different RNAs in whole blood and experimental samples. Fresh anticoagulant blood samples were incubated at room temperature for different durations, RNAs were extracted, and genes, including internal references, were amplified by real-time quantitative PCR. A linear half-life model was established according to cycle threshold (Ct) values. The effects of experimental operations on RNA degradation before and after RNA extraction were explored. Quantitative analysis of mRNA degradation in samples and during experimental processes were explored using an orthogonal experimental design. The storage duration of blood samples at room temperature had the greatest influence on RNA degradation. The half-lives of messenger RNAs (mRNAs) was 16.4 h. The half-lives of circular RNAs (circRNAs), long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) were 24.56 ± 5.2 h, 17.46 ± 3.0 h and 16.42 ± 4.2 h, respectively. RNA degradation occurred mainly in blood samples. The half-life of mRNAs was the shortest among the four kinds of RNAs. Quantitative experiments related to mRNAs should be completed within 2 h. The half-lives of circRNAs and lncRNAs were longer than those of the former two.

Abstract Cetuximab is a widely used drug for treating head and neck squamous cell carcinomas (HNSCCs); however, it provides restricted clinical benefits, and its response duration is limited by drug resistance. Here, we conducted randomized “Phase II-like clinical trials” of 49 HNSCC PDX models and reveal multiple informative biomarkers for intrinsic resistance to cetuximab (e.g., amplification of ANKH, up-regulation of PARP3). After validating these intrinsic resistance biomarkers in another HNSCC PDX cohort (61 PDX models), we generated acquired cetuximab resistance PDX models and analyzed them to uncover resistance mechanisms. Whole exome sequencing and transcriptome sequencing revealed diverse patterns of clonal selection in acquired resistant PDXs, including the emergence of subclones with strongly activated RAS/MAPK. Extending these insights, we show that a combination of a RAC1/RAC3 dual-target inhibitor and cetuximab could overcome acquired cetuximab resistance in vitro and in vivo. Beyond revealing intrinsic resistance biomarkers, our PDX-based study shows how clonal architecture changes underlying acquired resistance can be targeted to expand the therapeutic utility of this important drug to more HNSCC patients.

Randomized clinical trials (RCTs) are designed for measuring the effectiveness of the treatments and testing a hypothesis regarding the relative effect between two or more treatments. Trial designers are often interested in maximizing power when the total sample size is fixed or minimizing the required total sample size to reach a pre-specified power. One approach to maximizing power proposed by previous researchers is to leverage prior evidence using meta-analysis (NMA) to inform the sample size determination of a new trial. For example, researchers may be interested in designing a two-arm trial comparing treatments A and B which are already in the existing trial network but do not have any direct comparison. The researchers' intention is to incorporate the result into an existing network for meta-analysis. Here we develop formulas to address these options and use simulations to validate our formula and evaluate the performance of different analysis methods in terms of power. We also implement our proposed method into the R package OssaNMA and publish an R Shiny app for the convenience of the application. The goal of the package is to enable researchers to readily adopt the proposed approach which can improve the power of an RCT and is therefore resource-saving. In the R Shiny app, We also provide the option to include the cost of each treatment which would enable researchers to compare the total treatment cost associated with each design and analysis approach. Further, we explore the effect of allocation to treatment group on study power when the a priori plan is to incorporate the new trial result into an existing network for meta-analysis.

The study takes 10 urban agglomerations in China as the research object, focusing on the Chengdu-Chongqing urban agglomeration, and applies Data Envelopment Analysis (DEA) to measure and compare their scientific input and output efficiency of universities. First, this paper analyzes the input and output of scientific research in universities in major provinces in China in detail. Second, according to the construction principles of the indicator system, using qualitative interview to construct evaluation indicators of university research efficiency. Third, using DEA method, first analyze the input and output profile of some urban agglomeration universities such as Chengdu-Chongqing economic circle, measure and compare their research input and output efficiency, then compare and analyze the research efficiency of research-type sample universities within Chengdu-Chongqing economic circle, and conduct a projection study of non-DEA effective sample universities. The main conclusions are as follows: first, the average efficiency of scientific research in universities in Chengdu-Chongqing and other urban agglomerations in 2020 has slightly increased compared with that in 2016, but the gap between urban agglomerations is prominent, and the innovation level of scientific research in higher education institutions in urban agglomerations needs to be improved. Second, there is a mismatch between the themes of research, funding and human resources in research-oriented universities in the Chengdu-Chongqing economic circle. Third, there is considerable room for improvement in research efficiency, and the influence of scale on overall efficiency is weak. We found that excessive investment in scientific research in universities is the main reason for the non-effect.

Abstract Background Network meta-analysis (NMA) is a statistical method used to combine results from several clinical trials and simultaneously compare multiple treatments using direct and indirect evidence. Statistical heterogeneity is a characteristic describing the variability in the intervention effects being evaluated in the different studies in network meta-analysis. One approach to dealing with statistical heterogeneity is to perform a random effects network meta-analysis that incorporates a between-study variance into the statistical model. A common assumption in the random effects model for network meta-analysis is the homogeneity of between-study variance across all interventions. However, there are applications of NMA where the single between-study assumption is potentially incorrect and instead the model should incorporate more than one between-study variances. Methods In this paper, we develop an approach to testing the homogeneity of between-study variance assumption based on a likelihood ratio test. A simulation study was conducted to assess the type I error and power of the proposed test. This method is then applied to a network meta-analysis of antibiotic treatments for Bovine respiratory disease (BRD). Results The type I error rate was well controlled in the Monte Carlo simulation. We found statistical evidence (p value = 0.052) against the homogeneous between-study variance assumption in the network meta-analysis BRD. The point estimate and confidence interval of relative effect sizes are strongly influenced by this assumption. Conclusions Since homogeneous between-study variance assumption is a strong assumption, it is crucial to test the validity of this assumption before conducting a network meta-analysis. Here we propose and validate a method for testing this single between-study variance assumption which is widely used for many NMA.

Abstract Larval metamorphosis in bivalves is a key event for the larva-to-juvenile transformation. Previously we have identified a thyroid hormone receptor (TR) gene that is crucial for larvae to acquire “competence” for the metamorphic transition in the mussel Mytilus courscus (Mc). The mechanisms of thyroid signaling in bivalves are still largely unknown. In the present study, we molecularly characterized the full-length of two iodothyronine deiodinase genes (McDx and McDy). Phylogenetic analysis revealed that deiodinases of molluscs (McDy, CgDx and CgDy) and vertebrates (D2 and D3) shared a node representing an immediate common ancestor, which resembled vertebrates D1 and might suggest that McDy acquired specialized function from vertebrates D1. Anti-thyroid compounds, methimazole (MMI) and propylthiouracil (PTU), were used to investigate their effects on larval metamorphosis and juvenile development in M. coruscus. Both MMI and PTU significantly reduced larval metamorphosis in response to the metamorphosis inducer epinephrine. MMI led to shell growth retardation in a concentration-dependent manner in juveniles of M. coruscus after 4 weeks of exposure, whereas PTU had no effect on juvenile growth. It is hypothesized that exposure to MMI and PTU reduced the ability of pediveliger larvae for the metamorphic transition to respond to the inducer. The effect of MMI and PTU on larval metamorphosis and development is most likely through a hormonal signal in the mussel M. coruscus, with the implications for exploring the origins and evolution of metamorphosis.

Porcine respiratory coronavirus (PRCV) pathogenicity in pigs has been characterized using traditional PRCV isolates; however, information is lacking on pathogenicity of currently circulating PRCV isolates. Recently, a contemporary US PRCV variant was isolated. The infection dynamics of that strain (PRCV-var) and a traditional PRCV strain (PRCV-trad) were compared. In brief, 4-week-old pigs were divided into three groups with five pigs each. The pigs were inoculated with PRCV-trad or PRCV-var, or left uninfected. Nasal swabs were collected daily, and all pigs were necropsied at day (D) 3. PRCV nasal shedding was significantly higher in PRCV-var pigs compared to PRCV-trad pigs. To investigate the impact of trad and var PRCVs on subsequent infection with influenza A virus (IAV), four additional groups of five pigs were used: PRCV-trad-IAV (PRCV-trad at D0, co-infected with IAV at D5), PRCV-var-IAV, and IAV positive and negative controls. Significantly higher mean PRCV antibody titers and a significantly higher area under the curve (AUC) for PRCV shedding were observed in PRCV-var compared to PRCV-trad-pigs at D10. There was no impact on IAV infection. In conclusion, a 2020 PRCV variant isolate was similar in pathogenicity but more transmissible compared to a traditional 1989 isolate. These findings raise concerns about virus evolution towards more highly pathogenic and transmissible strains and the need to monitor such viruses.

Abstract This study was aimed to evaluate the differences in the composition of gut microbiota, tryptophan metabolites and short-chain fatty acids in feces between volunteers who frequently ate chicken and who frequently ate pork. Twenty male chicken-eaters and 20 male pork-eaters of 18 and 30 years old were recruited to collect feces samples for analyses of gut microbiota composition, short-chain fatty acids and tryptophan metabolites. Chicken-eaters had more diverse gut microbiota and higher abundance of Prevotella 9, Dialister, Faecalibacterium, Megamonas, and Prevotella 2. However, pork-eaters had higher relative abundance of Bacteroides, Faecalibacterium, Roseburia, Dialister, and Ruminococcus 2. In addition, chicken-eaters had high contents of skatole and indole in feces than pork-eaters, as well as higher contents of total short chain fatty acids, in particular for acetic acid, propionic acid, and branched chain fatty acids. The Spearman’s correlation analysis revealed that the abundance of Prevotella 2 and Prevotella 9 was positively correlated with levels of fecal skatole, indole and short-chain fatty acids. Thus, intake of chicken diet may increase the risk of skatole- and indole-induced diseases by altering gut microbiota.

Abstract The mutational spectrum and prognostic factors of NRAS-mutated (NRAS mut) acute myeloid leukemia (AML) are largely unknown. We performed next-generation sequencing (NGS) in 1,149 cases of de novo AML and discovered 152 NRAS mut AML (13%). Of the 152 NRAS mut AML, 89% had at least one companion mutated gene. DNA methylation-related genes confer up to 62% incidence. TET2 had the highest mutation frequency (51%), followed by ASXL1 (17%), NPM1 (14%), CEBPA (13%), DNMT3A (13%), FLT3-ITD (11%), KIT (11%), IDH2 (9%), RUNX1 (8%), U2AF1 (7%) and SF3B1(5%). Multivariate analysis suggested that age ≥ 60 years and mutations in U2AF1 were independent factors related to failure to achieve complete remission after induction therapy. Age ≥ 60 years, non-M3 types and U2AF1 mutations were independent prognostic factors for poor overall survival. Age ≥ 60 years, non-M3 types and higher risk group were independent prognostic factors for poor event-free survival (EFS) while allogenic hematopoietic stem cell transplantation was an independent prognostic factor for good EFS. Our study provided new insights into the mutational spectrum and prognostic factors of NRAS mut AML.

Abstract Chinese indigenous pigs exhibit considerable phenotypic diversity, but their population structure and the genetic basis of agriculturally important traits need further exploration. Here, we sequenced the whole genomes of 24 individual pigs representing 22 breeds distributed throughout China. For comparison with European and commercial breeds (one pig per breed), we included seven published pig genomes with our new genomes for analyses. Our results showed that breeds grouped together based on morphological classifications are not necessarily more genetically similar to each other than to breeds from other groups. We found that genetic material from European pigs likely introgressed into five Chinese breeds. We have identified two new subpopulations of domestic pigs that encompass morphology-based criteria in China. The Southern Chinese subpopulation comprises the classical South Chinese Type and part of the Central China Type. In contrast, the Northern Chinese subpopulation comprises the North China Type, the Lower Yangtze River Basin Type, the Southwest Type, the Plateau Type, and the remainder of the Central China Type. Eight haplotypes and two recombination sites were identified within a conserved 40.09 Mb linkage-disequilibrium (LD) block on the X chromosome. Potential candidate genes (LEPR, FANCC, COL1A1, and PCCA) influencing body size were identified. Our findings provide insights into the phylogeny of Chinese indigenous pig breeds and benefit gene mining efforts to improve major economic traits.

Inadequate endometrial receptivity may be responsible for the low implantation rate of transferred embryos in in vitro fertilization (IVF) treatments. Patients with repeated implantation failure (RIF) impact the clinical pregnancy rate for IVF. We collected endometrial tissue during the implantation window of hysteroscopy biopsies from September 2016 to December 2019 and clinical data were collected simultaneously. Patients were divided into RIF and pregnant controls group according to pregnancy outcomes. A total of 82 differentially expressed endometrial proteins were identified, including 55 up-regulated proteins (>1.50-fold, P

The COVID-19 pandemic brought profound changes to all corners of society and affected people in every aspect of their lives. This survey-based study investigated how household food related matters such as food sourcing and consumption behaviors of 2,126 Chinese consumers in different age groups changed approximately two months into the COVID-19 quarantine. A new food sourcing mechanism, community-based online group grocery-ordering (CoGGO), was widely adopted by households, particularly among the youngest group studied (18-24 years of age). The same group showed a higher confidence in the food supply system during the quarantine and a greater propensity for weight gain while staying-at-home. The more mature age group (≥35 years of age) showed heightened vigilance and awareness, with fewer grocery-shopping trips, a higher tendency for purchasing extra food, and less tendency to waste food. Survey findings of the new food-sourcing mechanism, attitudes to food, and changes in behavior among different age groups provide valuable insights to guide policies and management interventions to address matters pertaining to food supply and distribution, food access and household food security, and food waste reduction.

Multidrug resistance (MDR) has been a significant threat to public health and effective treatment of bacterial infections. Current identification of MDR is primarily based upon the large proportions of isolates resistant to multiple antibiotics simultaneously, and therefore is a belated evaluation. For bacteria with MDR, we expect to see strong correlations in both the quantitative minimum inhibitory concentration (MIC) and the binary susceptibility as classified by the pre-determined breakpoints. Being able to detect correlations from these two perspectives allows us to find multidrug resistant bacteria proactively. In this paper, we provide a Bayesian framework that estimates the resistance level jointly for antibiotics belonging to different classes with a Gaussian mixture model, where the correlation in the latent MIC can be inferred from the Gaussian parameters and the correlation in binary susceptibility can be inferred from the mixing weights. By augmenting the laboratory measurement with the latent MIC variable to account for the censored data, and by adopting the latent class variable to represent the MIC components, our model was shown to be accurate and robust compared with the current assessment of correlations. Applying the model to Salmonella heidelberg samples isolated from human participants in National Antimicrobial Resistance Monitoring System (NARMS) provides us with signs of joint resistance to Amoxicillin-clavulanic acid & Cephalothin and joint resistance to Ampicillin & Cephalothin. Large correlations estimated from our model could serve as a timely tool for early detection of MDR, and hence a signal for clinical intervention.

Identifying and controlling the emergence of antimicrobial resistance (AMR) is a high priority for researchers and public health officials. One critical component of this control effort is timely detection of emerging or increasing resistance using surveillance programs. Currently, detection of temporal changes in AMR relies mainly on analysis of the proportion of resistant isolates based on the dichotomization of minimum inhibitory concentration (MIC) values. In our work, we developed a hierarchical Bayesian latent class mixture model that incorporates a linear trend for the mean log2MIC of the non-resistant population. By introducing latent variables, our model addressed the challenges associated with the AMR MIC values, compensating for the censored nature of the MIC observations as well as the mixed components indicated by the censored MIC distributions. Inclusion of linear regression with time as a covariate in the hierarchical structure allowed modelling of the linear creep of the mean log2MIC in the non-resistant population. The hierarchical Bayesian model was accurate and robust as assessed in simulation studies. The proposed approach was illustrated using Salmonella enterica I,4,[5],12:i:- treated with chloramphenicol and ceftiofur in human and veterinary samples, revealing some significant linearly increasing patterns from the applications. Implementation of our approach to the analysis of an AMR MIC dataset would provide surveillance programs with a more complete picture of the changes in AMR over years by exploring the patterns of the mean resistance level in the non-resistant population. Our model could therefore serve as a timely indicator of a need for antibiotic intervention before an outbreak of resistance, highlighting the relevance of this work for public health. Currently, however, due to extreme right censoring on the MIC data, this approach has limited utility for tracking changes in the resistant population.

The target spraying effect of spray robots mainly depends on the control performance of the spraying arm during the processes of aiming and tracking. To further improve the robustness of the endpoint control and positioning accuracy of the spray arm, an improved potential field algorithm for the motion planning and control of the spray arm is proposed based on prophase research. The algorithm introduces a velocity potential field, visual field constraints and joint position limit constraints into the traditional artificial potential field method. The velocity potential field is used to ensure that the target state of the spraying arm is at the same velocity as the target crop (relative velocity) to achieve stable target tracking. The visual field constraints and joint position limit constraints are utilized to ensure the efficiency of the visual servo control and the movement of the spray arm. The algorithm can plan a feasible trajectory for the spraying arm in Cartesian space and image space, and use the speed controller to control the spraying arm movement along the trajectory for aiming and tracking. Simulation analysis shows that the algorithm can plan better motion trajectories than the servo controller based on image moments in previous studies. In addition, the experimental results show that the algorithm can effectively improve the robustness of targeting and tracking control for the spray robot.

In clinical trials and observational studies, the effect of an intervention or exposure can be reported as an absolute or relative comparative measure such as risk difference, odds ratio or risk ratio, or at the group level with the estimated risk of disease in each group. For meta-analysis of results with covariate adjustment, the log of the odds ratio (log odds ratio), with its standard error, is a commonly used measure of effect. However, extracting the adjusted log odds ratio from the reported estimates of disease risk in each group is not straightforward. Here, we propose a method to transform the adjusted probability of the event in each group to the log of the odds ratio and obtain the appropriate (approximate) standard error, which can then be used in a meta-analysis. We also use example data to compare our method with two other methods and show that our method is superior in calculating the standard error of the log odds ratio.

The causal effect of an exposure on an outcome of interest in an observational study cannot be estimated directly if the confounding variables are not controlled. Many approaches are available for estimating the causal effect of an exposure. In this manuscript, we demonstrate the advantages associated with using inverse probability weighting (IPW) and doubly robust estimation of the odds ratio in terms of reduced bias. IPW approach can be used to adjust for confounding variables and provide unbiased estimates of the exposure's causal effect. For cluster-structured data, as is common in animal populations, inverse conditional probability weighting (ICPW) approach can provide a robust estimation of the causal effect. Doubly robust estimation can provide a robust method even when the specification of the model form is uncertain. In this paper, the usage of IPW, ICPW, and doubly robust approaches are illustrated with a subset of data with complete covariates from the Australian-based National Bovine Respiratory Disease Initiative as well as simulated data. We evaluate the causal effect of prior bovine viral diarrhea exposure on bovine respiratory disease in feedlot cattle. The results show that the IPW, ICPW and doubly robust approaches would provide a more accurate estimation of the exposure effect than the traditional outcome regression model, and doubly robust approaches are the most preferable overall.

Several factors influence the environmental performance simultaneously but ethnic fractionalization, political freedom, financial development and institutional quality have a substantial impact to explain the environmental performance across economies. This study focuses to explore that how environmental performance is affected by these economic, political and social indicators by using the annual data of 163 developed and developing countries covering the time period of 1996-2016.The data is collected from World Development Indicators, World Governance Indicators, Freedom House and Cline Centre. The stationarity of variables is analyzed through LLC, IPS and ADF Fisher Chi-square test. Before applying panel ARDL approach to find out the long run relationship among variables, order of integration is determined through Pedroni's cointegration test. The findings of study highlight that ethnic diversity; institutional quality and political freedom play a significant role to decrease CO2emissions while energy consumption, GDP growth and financial development are increasing the environmental degradation. Ethnic diversity is a source of creative and innovative approaches about problem solving of environmental degradation. Political freedom allows people to participate in decision making that posits much compliance with environmental agreements. The foreign direct investment is attracted by good quality institutions which cause to advent of more environment friendly technology along with attractions for further innovations that may helpful to reduce CO2 emissions. Contrary, GDP growth, financial development and energy consumption enhance the industrialization and urbanization leading to increase the level of CO2 emissions. It is suggested for policy makers that cohesion among different ethnic groups; improving institutional quality; providing political freedom to people and inclusive financial sector will acknowledge the less pollutant environment.

Abstract Two-dimensional (2-D) boron nitride (BN) nanomaterials have received intensive attention because of their attractive mechanical, thermal and chemical stability. Here we demonstrate, for the first time, the synthesis of ribbon-like hexagonal boron nitride nano-architectures (RLBN) through a simple self-sacrificed template method using the cheap boric acid and melamine as raw materials. After the freeze-drying and thermal decomposition process, uniform ultrathin RLBN with width of 200–500 nm and thick of a few nanometers can be obtained. The RLBN with high quality tremendously improves the mechanical and thermal properties of Polyhydroxyalkanoates (PHA) polymer. The decomposition temperature (Td) of PHA increases from 368 °C to 390 °C, while the thermal conductivity increases by 46.0% with RLBN doped. The ductility (strain at break), yield strength and tensile strength of PHA@RLBN composite are also enhanced by 52.3%, 49.4% and 6.01% respectively.

Abstract The lymph node ratio(LNR) has been described as a novel predictor of the survival of patients with oral and oropharyngeal squamous cell carcinoma(O/OPSCC). The purpose of this study was to evaluate whether LNR is better at predicting survival and the need for adjuvant treatment than traditional tumour-nodal-metastasis(TNM) staging. Eight hundred nine patients with O/OPSCC and positive lymph node disease were retrospectively enrolled in this study. LNR equal to 0.075 is the best cut-off value for stratifying 5-year disease-free survival(DFS). High LNR is closely associated with more advanced T stage, higher N stage, more severe pathological grade, the presence of diffuse infiltration and extracapsular spread(ECS). LNR is better for evaluating prognosis than the pathological N stage. Patients with high LNR coupled with high number of positive lymph nodes who received adjuvant concurrent chemo-radiotherapy(CCRT) had a better 5-year DFS than patients who received surgery alone. Multivariate analyses revealed that T stage, ECS and LNR are independent prognostic factors of 5-year DFS and disease-specific survival(DSS). Therefore, high LNR is closely correlated with adverse parameters that markedly hinder prognosis. LNR is superior to traditional TNM staging for the evaluation of prognosis,and the combination of the LNR with the number of positive lymph nodes can predict the benefits of adjuvant CCRT.

Abstract Genotype Nisqually-1 is the first model woody plant with an available well-annotated genome. Nevertheless, a simple and rapid transformation of Nisqually-1 remains to be established. Here, we developed a novel shoot regeneration method for Nisqually-1 using leaf petiole and stem segment explants. Numerous shoots formed in the incision of explants within two weeks. The optimized shoot regeneration medium (SRM) contained 0.03 mg l−1 6-benzylaminopurine, 0.02 mg l−1 indole-3-butyric acid and 0.0008 mg l−1 thidiazuron. Based on this, Agrobacterium-mediated genetic transformation of stem explants was examined using the vector pBI121 that contains the β-glucuronidase (GUS) as a reporter gene. Consequently, factors affecting transformation frequency of GUS-positive shoots were optimized as follows: Agrobacteria cell suspension with an OD600 of 0.4, 20 min infection time, 2 days of co-cultivation duration and the addition of 80 µM acetosyringone into Agrobacteria infective suspension and co-cultivation SRM. Using this optimized method, transgenic plantlets of Nisqually-1 – with an average transformation frequency of 26.7% – were obtained with 2 months. Southern blot and GUS activity staining confirmed the integration of the foreign GUS gene into Nisqually-1. This novel transformation system for Nisqually-1 was rapid, efficient, and simple to operate and will improve more genetic applications in this model tree.

A common feature of preclinical animal experiments is repeated measurement of the outcome, e.g., body weight measured in mice pups weekly for 20 weeks. Separate time point analysis or repeated measures analysis approaches can be used to analyze such data. Each approach requires assumptions about the underlying data and violations of these assumptions have implications for estimation of precision, and type I and type II error rates. Given the ethical responsibilities to maximize valid results obtained from animals used in research, our objective was to evaluate approaches to reporting repeated measures design used by investigators and to assess how assumptions about variation in the outcome over time impact type I and II error rates and precision of estimates. We assessed the reporting of repeated measures designs of 58 studies in preclinical animal experiments. We used simulation modelling to evaluate three approaches to statistical analysis of repeated measurement data. In particular, we assessed the impact of (a) repeated measure analysis assuming that the outcome had non-constant variation at all time points (heterogeneous variance) (b) repeated measure analysis assuming constant variation in the outcome (homogeneous variance), (c) separate ANOVA at individual time point in repeated measures designs. The evaluation of the three model fitting was based on comparing the p-values distributions, the type I and type II error rates and by implication, the shrinkage or inflation of standard error estimates from 1000 simulated dataset. Of 58 studies with repeated measures design, three provided a rationale for repeated measurement and 23 studies reported using a repeated-measures analysis approach. Of the 35 studies that did not use repeated-measures analysis, fourteen studies used only two time points to calculate weight change which potentially means collected data was not fully utilized. Other studies reported only select time points (n = 12) raising the issue of selective reporting. Simulation studies showed that an incorrect assumption about the variance structure resulted in modified error rates and precision estimates. The reporting of the validity of assumptions for repeated measurement data is very poor. The homogeneous variation assumption, which is often invalid for body weight measurements, should be confirmed prior to conducting the repeated-measures analysis using homogeneous covariance structure and adjusting the analysis using corrections or model specifications if this is not met.

Bosten Lake is an important region of Northwest China that has transformed from a freshwater lake to a saltwater lake since the 1970s. The water quality in the Bosten Lake basin is important for social and economic development, and nitrogen (N) and phosphorus (P) are the key indicators of water quality. The land use data, precipitation data and Digital Elevation Model (DEM) data with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used to simulate the N and P exports of the Bosten Lake basin. The spatial and temporal dynamics of nitrogen and phosphorus exports, and the response of nitrogen and phosphorus exports to land use change and precipitation change were analyzed between 2000 and 2015. The results show that the amount of N and P exports increased during 2000-2015, and the N and P exports are mainly distributed around Bosten Lake. The N and P exports are greatly affected by cultivated land, built-up areas and grassland, while they are less affected by other land use types. The high precipitation areas with small exports of N and P are mainly distributed in mountain areas, while small precipitation areas with large exports of N and P are distributed in plains where the cultivated land and built-up areas are concentrated. The InVEST model can be used in Northwest China, and the statistical downscaling of reanalysis precipitation data can be used in the InVEST model to improve the simulation accuracy in the data scarce regions of Northwest China.

The pathogenesis of canine inflammatory bowel disease (IBD) involves complex interactions between mucosal immunity and the intestinal microbiota. Glucocorticoids are commonly administered to reduce mucosal inflammation and gastrointestinal signs. The study objective was to evaluate the effects of diet and oral prednisone on the spatial distribution of mucosal bacteria in IBD dogs. Eight dogs diagnosed with IBD were treated with immunosuppressive doses of prednisone. The mucosal microbiota from endoscopic biopsies of IBD dogs and healthy controls (HC; n = 15 dogs) was evaluated by fluorescence in situ hybridization (FISH) targeting the 16S rRNA genes of total bacteria and bacterial species relevant in canine/human IBD. Apicaljunction protein (AJP) expression using immunohistochemistry investigated the effect of medical therapy on intestinal barrier integrity. All IBD dogs had a reduction in GI signs following diet and prednisone therapy compared with baseline CIBDAI scores (P < 0.05). The mucosal microbiota of HC and diseased dogs was most abundant in free and adherent mucus. Only Lactobacilli were increased (P < 0.05) in the adherent mucus of IBD dogs compared to HC. The spatial distribution of mucosal bacteria was significantly different (P < 0.05) in IBD dogs following prednisone therapy, with higher numbers of Bifidobacteria and Streptococci detected across all mucosal compartments and increased numbers of Bifidobacterium spp., Faecalibacterium spp., and Streptococcus spp. present within adherent mucus. Differences in intestinal AJPs were detected with expression of occludin increased (P < 0.05) in IBD dogs versus HC. The expressions of occludin and E-cadherin were increased but zonulin decreased (P < 0.05 for each) in IBD dogs following prednisone therapy. In conclusion, the spatial distribution of mucosal bacteria differs between IBD and HC dogs, and in response to diet and glucocorticoid administration. Medical therapy was associated with beneficial changes in microbial community structure and enhanced mucosal epithelial AJP expression.

Hemipelvic resections for primary bone tumours require reconstruction to restore weight bearing along anatomic axes. However, reconstruction of the pelvic arch remains a major surgical challenge because of the high rate of associated complications. We used the pedicle screw-rod system to reconstruct the pelvis, and the purpose of this investigation was to assess the oncology, functional outcome and complication rate following this procedure. The purpose of this study was to investigate the operative indications and technique of the pedicle screw-rod system in reconstruction of the stability of the sacroiliac joint after resection of sacroiliac joint tumours. The average MSTS (Musculoskeletal Tumour Society) score was 26.5 at either three months after surgery or at the latest follow-up. Seven patients had surgery-related complications, including wound dehiscence in one, infection in two, local necrosis in four (including infection in two), sciatic nerve palsy in one and pubic symphysis subluxation in one. There was no screw loosening or deep vein thrombosis occurring in this series. Using a pedicle screw-rod after resection of a sacroiliac joint tumour is an acceptable method of pelvic reconstruction because of its reduced risk of complications and satisfactory functional outcome, as well as its feasibility of reconstruction for type IV pelvis tumour resection without elaborate preoperative customisation. Level of evidence: Level IV, therapeutic study.

A new emerging disease called “translucent post-larvae disease” (TPD) or “glass post-larvae disease” (GPD) of Penaeus vannamei, characterized by pale or colorless hepatopancreas and digestive tract, has become an urgent threat to the shrimp farming industry. Following this clue that treatment of an antibacterial agent could alleviate the disease, systematic investigation of the potential infectious agent of TPD was conducted using bacterial identification and artificial challenge tests to fulfill Koch’s postulates. A dominant bacterial isolate, Vp-JS20200428004-2, from the moribund individuals was isolated and identified as Vibrio parahaemolyticus based on multi-locus sequence analysis. However, Vp-JS20200428004-2 differed from the V. parahaemolyticus that caused typical acute hepatopancreatic necrosis disease. Immersion challenge tests revealed that Vp-JS20200428004-2 could cause 100% mortality within 40 h at a dose of 1.83 × 106 CFU/mL, and experimental infected shrimp showed similar clinical signs of TPD. The Vp-JS20200428004-2 could be re-isolated and identified from the experimental infected individuals. Moreover, histopathological analysis of diseased samples indicated that Vp-JS20200428004-2 caused severe necrosis and sloughing of epithelial cells of the hepatopancreas and midgut in shrimp individuals both naturally and experimentally infected. Our present results indicated that Vp-JS20200428004-2 is a highly virulent infectious agent associated with the TPD and deserves further attention.

The aim of this study was to discover novel biomarkers for pulmonary tuberculosis (TB). Differentially expressed proteins in the serum of patients with TB were screened and identified by iTRAQ-two dimensional liquid chromatography tandem mass spectrometry analysis. A total of 79 abnormal proteins were discovered in patients with TB compared with healthy controls. Of these, significant differences were observed in 47 abnormally expressed proteins between patients with TB or pneumonia and chronic obstructive pulmonary disease (COPD). Patients with TB (n = 136) exhibited significantly higher levels of serum amyloid A (SAA), vitamin K-dependent protein Z (PROZ), and C4b-binding protein β chain (C4BPB) than those in healthy controls (n = 66) (P