Purification of inorganic nitrogen from the mariculture tail water by anaerobic/anoxic/oxic (A2O) process.

This study aims to address the suboptimal performance of conventional denitrifying strains in treating mariculture tail water (MTW) containing inorganic nitrogen (IN). The concentration of inorganic nitrogen in the mariculture tail water is about 5-20 mg·L-1. A biofilm treatment process was developed and evaluated using an anoxic-anoxic-aerobic biofilter composite system inoculated with the denitrifying strain Meyerozyma guilliermondii Y8. The removal effect of total nitrogen (TN), IN, and Chemical Oxygen Demand (CODMn) from MTW was investigated. The results indicate that the A2O composite biological filter has excellent pollutant removal efficiency within 25 days of operation, after the acclimation of the denitrifying microorganisms. The initial concentrations of TN, IN, and CODMn ranged between 10.24 and 12.89 mg·L-1, 7.84-10.49 mg·L-1, and 9.44-11.52 mg·L-1, respectively, and the removal rates of these indexes reached 38-68 %, 45-70 %, and 55-70 %, respectively. The experiments with different hydraulic retention times (HRT = 6 h, 8 h, 10 h) demonstrated that longer HRT was more conducive to the removal of inorganic nitrogen. Moreover, scanning electron microscopy observations revealed that the target strain successfully grew and attached to the filler in large quantities. The findings of this study provide practical guidance for the development of efficient biofilm processes for the treatment of MTW.

Investigations of the prognostic value of RUNX1 mutation in acute myeloid leukemia patients: Data from a real-world study.

RUNX1 is one of the recurrent mutated genes in newly diagnosed acute myeloid leukemia (AML). Although historically recognized as a provisional distinct entity, the AML subtype with RUNX1 mutations (AML-RUNX1mut) was eliminated from the 2022 WHO classification system. To gain more insight into the characteristics of AML-RUNX1mut, we retrospectively analyzed 1065 newly diagnosed adult AML patients from the First Affiliated Hospital of Soochow University between January 2017 and December 2021. RUNX1 mutations were identified in 112 patients (10.5%). The presence of RUNX1 mutation (RUNX1mut) conferred a lower composite complete remission (CRc) rate (40.2% vs. 58.4%, P＜0.001), but no significant difference was observed in the 5-year overall survival (OS) rate (50.2% vs. 53.9%; HR=1.293; P=0.115) and event-free survival (EFS) rate (51.5% vs. 49.4%; HR=1.487, P=0.089), even within the same risk stratification. Multivariate analysis showed that RUNX1mut was not an independent prognostic factor for OS (HR=1.352, P=0.068) or EFS (HR=1.129, P=0.513). When patients were stratified according to induction regimen, RUNX1mut was an unfavorable factor for CRc both on univariate and multivariate analysis in patients receiving conventional chemotherapy, and higher risk stratification predicted worse OS. In those who received venetoclax plus hypomethylating agents, RUNX1mut was not predictive of CRc and comparable OS and EFS were seen between intermediate-risk and adverse-risk groups. The results of this study revealed that the impact of RUNX1mut is limited. Its prognostic value depended more on treatment and co-occurrent abnormalities. VEN-HMA may abrogate the prognostic impact of RUNX1, which merits a larger prospective cohort to illustrate.

A two-stage fine-tuning method for low-resource cross-lingual summarization.

Cross-lingual summarization (CLS) is the task of condensing lengthy source language text into a concise summary in a target language. This presents a dual challenge, demanding both cross-language semantic understanding (i.e., semantic alignment) and effective information compression capabilities. Traditionally, researchers have tackled these challenges using two types of methods: pipeline methods (e.g., translate-then-summarize) and end-to-end methods. The former is intuitive but prone to error propagation, particularly for low-resource languages. The later has shown an impressive performance, due to multilingual pre-trained models (mPTMs). However, mPTMs (e.g., mBART) are primarily trained on resource-rich languages, thereby limiting their semantic alignment capabilities for low-resource languages. To address these issues, this paper integrates the intuitiveness of pipeline methods and the effectiveness of mPTMs, and then proposes a two-stage fine-tuning method for low-resource cross-lingual summarization (TFLCLS). In the first stage, by recognizing the deficiency in the semantic alignment for low-resource languages in mPTMs, a semantic alignment fine-tuning method is employed to enhance the mPTMs' understanding of such languages. In the second stage, while considering that mPTMs are not originally tailored for information compression and CLS demands the model to simultaneously align and compress, an adaptive joint fine-tuning method is introduced. This method further enhances the semantic alignment and information compression abilities of mPTMs that were trained in the first stage. To evaluate the performance of TFLCLS, a low-resource CLS dataset, named Vi2ZhLow, is constructed from scratch; moreover, two additional low-resource CLS datasets, En2ZhLow and Zh2EnLow, are synthesized from widely used large-scale CLS datasets. Experimental results show that TFCLS outperforms state-of-the-art methods by 18.88%, 12.71% and 16.91% in ROUGE-2 on the three datasets, respectively, even when limited with only 5,000 training samples.

Dynamic transcriptome landscape of developing maize ear.

Seed number and harvesting ability in maize (Zea mays L.) are primarily determined by the architecture of female inflorescence, namely the ear. Therefore, ear morphogenesis contributes to grain yield and as such is one of the key target traits during maize breeding. However, the molecular networks of this highly dynamic and complex grain-bearing inflorescence remain largely unclear. As a first step toward characterizing these networks, we performed a high-spatio-temporal-resolution investigation of transcriptomes using 130 ear samples collected from developing ears with length from 0.1 mm to 19.0 cm. Comparisons of these mRNA populations indicated that these spatio-temporal transcriptomes were clearly separated into four distinct stages stages I, II, III, and IV. A total of 23 793 genes including 1513 transcription factors (TFs) were identified in the investigated developing ears. During the stage I of ear morphogenesis, 425 genes were predicted to be involved in a co-expression network established by eight hub TFs. Moreover, 9714 ear-specific genes were identified in the seven kinds of meristems. Additionally, 527 genes including 59 TFs were identified as especially expressed in ear and displayed high temporal specificity. These results provide a high-resolution atlas of gene activity during ear development and help to unravel the regulatory modules associated with the differentiation of the ear in maize.

A complete telomere-to-telomere assembly of the maize genome.

A complete telomere-to-telomere (T2T) finished genome has been the long pursuit of genomic research. Through generating deep coverage ultralong Oxford Nanopore Technology (ONT) and PacBio HiFi reads, we report here a complete genome assembly of maize with each chromosome entirely traversed in a single contig. The 2,178.6 Mb T2T Mo17 genome with a base accuracy of over 99.99% unveiled the structural features of all repetitive regions of the genome. There were several super-long simple-sequence-repeat arrays having consecutive thymine-adenine-guanine (TAG) tri-nucleotide repeats up to 235 kb. The assembly of the entire nucleolar organizer region of the 26.8 Mb array with 2,974 45S rDNA copies revealed the enormously complex patterns of rDNA duplications and transposon insertions. Additionally, complete assemblies of all ten centromeres enabled us to precisely dissect the repeat compositions of both CentC-rich and CentC-poor centromeres. The complete Mo17 genome represents a major step forward in understanding the complexity of the highly recalcitrant repetitive regions of higher plant genomes.

Rapid molecular response to dasatinib in Ph-like acute lymphoblastic leukemia patients with ABL1 rearrangements: case series and literature review.

Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is a high-risk subtype with a poor prognosis under conventional chemotherapy. Ph-like ALL has a similar gene expression profile to Philadelphia chromosome-positive (Ph+) ALL, but is highly heterogeneous in terms of genomic alterations. Approximately 10-20% of patients with Ph-like ALL harbor ABL class (e.g. ABL1, ABL2, PDGFRB, and CSF1R) rearrangements. Additional genes that form fusion genes with ABL class genes are still being researched. These aberrations result from rearrangements including chromosome translocations or deletions and may be targets of tyrosine kinase inhibitors (TKIs). However, due to the heterogeneity and rarity of each fusion gene in clinical practice, there is limited data on the efficacy of tyrosine kinase inhibitors. Here, we report three cases of Ph-like B-ALL with ABL1 rearrangements treated with the dasatinib backbone for the CNTRL::ABL1, LSM14A::ABL1, and FOXP1::ABL1 fusion genes. All three patients achieved rapid and profound remission with no significant adverse events. Our findings suggest that dasatinib is a potent TKI for the treatment of ABL1-rearranged Ph-like ALL and can be used as a first-line treatment option for such patients.

Global epidemiology and burden of tetanus from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019.

OBJECTIVES: Tetanus is a serious infectious disease. In recent decades, the epidemiology and disease characteristics of tetanus have been reported by many medical workers, but these studies usually have limited sample sizes. METHODS: We retrieved all the epidemiological data related to tetanus from the Global Burden of Disease Study 2019, and a secondary analysis was performed to report the global epidemiology and disease burden of tetanus. RESULTS: From 1990 to 2019, the incidence and death rate of tetanus decreased worldwide. In general, high sociodemographic index (SDI) countries have lower age-standard incidence rates and age-standard death rates than low SDI countries. Moreover, in low SDI regions, newborns were the highest-risk group for tetanus. In high SDI areas, half of the tetanus cases occurred in the 70+ years age group. The disease burden of tetanus was significantly higher in males than in females. CONCLUSION: The disease burden of tetanus decreased significantly worldwide from 1990 to 2019. Neonatal tetanus is serious in low SDI areas, whereas the proportion of elderly tetanus is the highest in high SDI areas. The containment of tetanus in all age groups and sex still requires effort from all sectors.

HEAT SHOCK PROTEIN 90.6 interacts with carbon and nitrogen metabolism components during seed development.

Carbon and nitrogen are the two main nutrients in maize (Zea mays L.) kernels, and kernel filling and metabolism determine seed formation and germination. However, the molecular mechanisms underlying the relationship between kernel filling and corresponding carbon and nitrogen metabolism remain largely unknown. Here, we found that HEAT SHOCK PROTEIN 90.6 (HSP90.6) is involved in both seed filling and the metabolism processes of carbon and nitrogen. A single-amino acid mutation within the HATPase_c domain of HSP90.6 led to small kernels. Transcriptome profiling showed that the expression of amino acid biosynthesis- and carbon metabolism-related genes was significantly downregulated in the hsp90.6 mutant. Further molecular evidence showed strong interactions between HSP90.6 and the 26S proteasome subunits REGULATORY PARTICLE NON-ATPASE6 (RPN6) and PROTEASOME BETA SUBUNITD2 (PBD2). The mutation of hsp90.6 significantly reduced the activity of the 26S proteasome, resulting in the accumulation of ubiquitinated proteins and defects in nitrogen recycling. Moreover, we verified that HSP90.6 is involved in carbon metabolism through interacting with the 14-3-3 protein GENERAL REGULATORY FACTOR14-4 (GF14-4). Collectively, our findings revealed that HSP90.6 is involved in seed filling and development by interacting with the components controlling carbon and nitrogen metabolism.

A Review of Fusion Methods for Omics and Imaging Data.

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.

The synergistic efficacy and safety of combined low-concentration atropine and orthokeratology for slowing the progression of myopia: A meta-analysis.

PURPOSE: To explore the efficacy and safety of combined low-concentration atropine and orthokeratology (OK) for slowing the progression of myopia. METHODS: We performed a systematic search of English and Chinese databases to collect potentially eligible randomised controlled trials (RCTs), nonrandomised controlled trials (non-RCTs) and retrospective cohort studies (REs) published between the establishment of the database and 1 January 2022. The weighted mean difference (WMD) and 95% confidence interval (CI) were calculated for each outcome. RESULTS: Fifteen studies were ultimately included in the meta-analysis, which indicated that compared with OK lenses alone, the combination of low-concentration atropine with OK lenses significantly slowed axial growth (WMD = -0.12 mm; 95% CI: -0.13 to -0.11, p < 0.001) and reduced the rate of change of the spherical equivalent refraction (WMD = 0.15 D; 95% CI: 0.06 to 0.24, p < 0.001). Additionally, the combined treatment may cause a slight increase in pupil diameter (WMD = 0.62 mm; 95% CI: 0.42 to 0.81, p < 0.001). No significant difference in the amplitude of accommodation, intraocular pressure, tear film break-up time or corneal endothelial cell density was found between the OK and combination therapy groups. CONCLUSIONS: The combination therapy of low-concentration atropine and OK lenses had a greater effect in slowing myopia progression during a 6-to-12-month treatment interval and was still effective over a 24-month period. Increased pupil diameter was the major side effect of the combination therapy, with no negative impact on the amplitude of accommodation, intraocular pressure, tear film break-up time or corneal endothelial cell density.

A Syntax-enhanced model based on category keywords for biomedical relation extraction.

Certain categories in multi-category biomedical relationship extraction have linguistic similarities to some extent. Keywords related to categories and syntax structures of samples between these categories have some notable features, which are very useful in biomedical relation extraction. The pre-trained model has been widely used and has achieved great success in biomedical relationship extraction, but it is still incapable of mining this kind of information accurately. To solve the problem, we present a syntax-enhanced model based on category keywords. First, we prune syntactic dependency trees in terms of category keywords obtained by the chi-square test. It reduces noisy information caused by current syntactic parsing tools and retains useful information related to categories. Next, to encode category-related syntactic dependency trees, a syntactic transformer is presented, which enhances the ability of the pre-trained model to capture syntax structures and to distinguish multiple categories. We evaluate our method on three biomedical datasets. Compared with state-of-the-art models, our method performs better on these datasets. We conduct further analysis to verify the effectiveness of our method.

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosis, and therapy of cancers. By integrating these heterogeneous but complementary data, many multi-modal methods are proposed to study the complex mechanisms of cancers, and most of them achieve comparable or better results from previous single-modal methods. However, these multi-modal methods are restricted to a single task (e.g., survival analysis or grade classification), and thus neglect the correlation between different tasks. In this study, we present a multi-modal fusion framework based on multi-task correlation learning (MultiCoFusion) for survival analysis and cancer grade classification, which combines the power of multiple modalities and multiple tasks. Specifically, a pre-trained ResNet-152 and a sparse graph convolutional network (SGCN) are used to learn the representations of histopathological images and mRNA expression data respectively. Then these representations are fused by a fully connected neural network (FCNN), which is also a multi-task shared network. Finally, the results of survival analysis and cancer grade classification output simultaneously. The framework is trained by an alternate scheme. We systematically evaluate our framework using glioma datasets from The Cancer Genome Atlas (TCGA). Results demonstrate that MultiCoFusion learns better representations than traditional feature extraction methods. With the help of multi-task alternating learning, even simple multi-modal concatenation can achieve better performance than other deep learning and traditional methods. Multi-task learning can improve the performance of multiple tasks not just one of them, and it is effective in both single-modal and multi-modal data.

Application of Metabolomics to the Discovery of Biomarkers for Ischemic Stroke in the Murine Model: a Comparison with the Clinical Results.

Ischemic stroke (IS) is a major cause of mortality and disability worldwide. However, the pathogenesis of IS remains unknown, and methods for early prediction and diagnosis of IS are lacking. Metabolomics can be applied to biomarker discovery and mechanism exploration of IS by exploring metabolic alterations. In this review, 62 IS metabolomics studies in the murine model published from January 2006 to December 2020 in the PubMed and Web of Science databases were systematically reviewed. Twenty metabolites (e.g., lysine, phenylalanine, methionine, tryptophan, leucine, lactate, serine, N-acetyl-aspartic acid, and glutathione) were reported consistently in more than two-third murine studies. The disturbance of metabolic pathways, such as arginine biosynthesis; alanine, aspartate and glutamate metabolism; aminoacyl-tRNA biosynthesis; and citrate cycle, may be implicated in the development of IS by influencing the biological processes such as energy failure, oxidative stress, apoptosis, and glutamate toxicity. The transient middle cerebral artery occlusion model and permanent middle cerebral artery occlusion model exhibit both common and distinct metabolic patterns. Furthermore, five metabolites (proline, serine, LysoPC (16:0), uric acid, glutamate) in the blood sample and 7 metabolic pathways (e.g., alanine, aspartate, and glutamate metabolism) are shared in animal and clinical studies. The potential biomarkers and related pathways of IS in the murine model may facilitate the biomarker discovery for early diagnosis of IS and the development of novel therapeutic targets.

A Hierarchical Graph Convolution Network for Representation Learning of Gene Expression Data.

The curse of dimensionality, which is caused by high-dimensionality and low-sample-size, is a major challenge in gene expression data analysis. However, the real situation is even worse: labelling data is laborious and time-consuming, so only a small part of the limited samples will be labelled. Having such few labelled samples further increases the difficulty of training deep learning models. Interpretability is an important requirement in biomedicine. Many existing deep learning methods are trying to provide interpretability, but rarely apply to gene expression data. Recent semi-supervised graph convolution network methods try to address these problems by smoothing the label information over a graph. However, to the best of our knowledge, these methods only utilize graphs in either the feature space or sample space, which restrict their performance. We propose a transductive semi-supervised representation learning method called a hierarchical graph convolution network (HiGCN) to aggregate the information of gene expression data in both feature and sample spaces. HiGCN first utilizes external knowledge to construct a feature graph and a similarity kernel to construct a sample graph. Then, two spatial-based GCNs are used to aggregate information on these graphs. To validate the model's performance, synthetic and real datasets are provided to lend empirical support. Compared with two recent models and three traditional models, HiGCN learns better representations of gene expression data, and these representations improve the performance of downstream tasks, especially when the model is trained on a few labelled samples. Important features can be extracted from our model to provide reliable interpretability.

A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction.

BACKGROUND: With the rapid development of sequencing technologies, collecting diverse types of cancer omics data become more cost-effective. Many computational methods attempted to represent and fuse multiple omics into a comprehensive view of cancer. However, different types of omics are related and heterogeneous. Most of the existing methods do not consider the difference between omics, so the biological knowledge of individual omics may not be fully excavated. And for a given task (e.g. predicting overall survival), these methods prefer to use sample similarity or domain knowledge to learn a more reasonable representation of omics, but it's not enough. METHODS: For the purpose of learning more useful representation for individual omics and fusing them to improve the prediction ability, we proposed an autoencoder-based method named MOSAE (Multi-omics Supervised Autoencoder). In our method, a specific autoencoder were designed for each omics according to their size of dimension to generate omics-specific representations. Then, a supervised autoencoder was constructed based on specific autoencoder by using labels to enforce each specific autoencoder to learn both omics-specific and task-specific representations. Finally, representations of different omics that generate from supervised autoencoders were fused in a traditional but powerful way, and the fused representation was used for subsequent predictive tasks. RESULTS: We applied our method over TCGA Pan-Cancer dataset to predict four different clinical outcome endpoints (OS, PFI, DFI, and DSS). Compared with traditional and state-of-the-art methods, MOSAE achieved better predictive performance. We also tested the effects of each improvement, which all have a positive effect on predictive performance. CONCLUSIONS: Predicting clinical outcome endpoints are very important for precision medicine and personalized medicine. And multi-omics fusion is an effective way to solve this problem. MOSAE is a powerful multi-omics fusion method, which can generate both omics-specific and task-specific representation for given endpoint predictive tasks and improve the predictive performance.