The A-kinase anchoring protein Yotiao decrease the ER calcium content by inhibiting the store operated calcium entry.

The meticulous regulation of ER calcium (Ca2+) homeostasis is indispensable for the proper functioning of numerous cellular processes. Disrupted ER Ca2+ balance is implicated in diverse diseases, underscoring the need for a systematic exploration of its regulatory factors in cells. Our recent genomic-scale screen identified a scaffolding protein A-kinase anchoring protein 9 (AKAP9) as a regulator of ER Ca2+ levels, but the underlying molecular mechanisms remain elusive. Here, we reveal that Yotiao, the smallest splicing variant of AKAP9 decreased ER Ca2+ content in animal cells. Additional testing using a combination of Yotiao truncations, knock-out cells and pharmacological tools revealed that, Yotiao does not require most of its interactors, including type 1 inositol 1,4,5-trisphosphate receptors (IP3R1), protein kinase A (PKA), protein phosphatase 1 (PP1), adenylyl cyclase type 2 (AC2) and so on, to reduce ER Ca2+ levels. However, adenylyl cyclase type 9 (AC9), which is known to increases its cAMP generation upon interaction with Yotiao for the modulation of potassium channels, plays an essential role for Yotiao's ER-Ca2+-lowering effect. Mechanistically, Yotiao may work through AC9 to act on Orai1-C terminus and suppress store operated Ca2+ entry, resulting in reduced ER Ca2+ levels. These findings not only enhance our comprehension of the interplay between Yotiao and AC9 but also contribute to a more intricate understanding of the finely tuned mechanisms governing ER Ca2+ homeostasis.

Novel C-type lectin mediated non-specific cytotoxic cells killing activity through NCCRP-1 in nile tilapia (Oreochromis niloticus).

Non-specific cytotoxic cells (NCCs) are vital immune cells involved in teleost's non-specific immunity. As a receptor molecule on the NCCs' surface, the non-specific cytotoxic cell receptor protein 1 (NCCRP-1) is known to play a crucial role in mediating their activity. Nevertheless, there have been limited studies on the signal molecule that transmits signals via NCCRP-1. In this study, a yeast two-hybrid (Y2H) library of tilapia liver and head kidney was constructed and subsequently screened with the bait vector NCCRP-1 of Oreochromis niloticus (On-NCCRP-1) to obtain a C-type lectin (On-CTL) with an interacting protein sequence. Consequently, the full-length sequence of On-CTL was cloned and analyzed. The expression analysis revealed that On-CTL is highly expressed in the liver and is widely distributed in other tissues. Furthermore, On-CTL expression was significantly up-regulated in the brain, intestine, and head kidney following a challenge with Streptococcus agalactiae. A point-to-point Y2H method was also used to confirm the binding between On-NCCRP-1 and On-CTL. The recombinant On-CTL (rOn-CTL) protein was purified. In vitro experiments demonstrated that rOn-CTL can up-regulate the expression of killer effector molecules in NCCs via its interaction with On-NCCRP-1. Moreover, activation of NCCs by rOn-CTL resulted in a remarkable enhancement in their ability to eliminate fathead minnow cells, indicating that rOn-CTL effectively modulates the killing activity of NCCs through the NCC receptor molecule On-NCCRP-1. These findings significantly contribute to our comprehension of the regulatory mechanisms governing NCC activity, paving the way for future research in this field.

Impact of indigenous Oenococcus oeni and Lactiplantibacillus plantarum species co-culture on Cabernet Sauvignon wine malolactic fermentation: Kinetic parameters, color and aroma.

Malolactic fermentation (MLF) is a crucial process to enhance wine quality, and the utilization of indigenous microorganisms has the potential to enhance wine characteristics distinct to a region. Here, the MLF performance of five indigenous Oenococcus oeni strains and six synthetic microbial communities (SynComs), were comparatively evaluated in Cabernet Sauvignon wine. In terms of malate metabolism rate and wine aroma diversity, the strain of O. oeni Oe114-46 demonstrated comparable MLF performance to the commercial strain of O. oeni Oe450 PreAc. Furthermore, the corresponding SynComs (Oe144-46/LpXJ25) exhibited improved fermentation properties, leading to increased viable cell counts of both species, more rapid and thorough MLF, and increased concentrations of important aroma compounds, such as linalool, 4-terpinenol, α-terpineol, diethyl succinate, and ethyl lactate. These findings highlight the remarkable MLF performance of indigenous O. oeni and O. oeni-L. plantarum microbial communities, emphasizing their immense potential in improving MLF efficiency and wine quality.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

Improving Soluble Phenolic Profile and Antioxidant Activity of Grape Pomace Seeds through Fungal Solid-State Fermentation.

Grape pomace seeds contain abundant phenolic compounds, which are also present in both soluble and insoluble forms, similar to many other plant matrices. To further increase the extractable soluble phenolics and their antioxidant activities, grape pomace seeds were fermented with different fungi. Results showed that solid-state fermentation (SSF) with Aspergillus niger, Monascus anka, and Eurotium cristatum at 28 °C and 65% humidity had a significantly positive impact on the release of soluble phenolics in grape pomace seeds. Specifically, SSF with M. anka increased the soluble phenolic contents by 6.42 times (calculated as total phenolic content) and 6.68 times (calculated as total flavonoid content), leading to an overall improvement of antioxidant activities, including DPPH (increased by 2.14 times) and ABTS (increased by 3.64 times) radical scavenging activity. Furthermore, substantial changes were observed in the composition and content of individual phenolic compounds in the soluble fraction, with significantly heightened levels of specific phenolics such as chlorogenic acid, syringic acid, ferulic acid, epicatechin gallate, and resveratrol. Notably, during M. anka SSF, positive correlations were identified between the soluble phenolic content and hydrolase activities. In particular, there is a strong positive correlation between glycosidase and soluble phenols (r = 0.900). The findings present an effective strategy for improving the soluble phenolic profiles and bioactivities of grape pomace seeds through fungal SSF, thereby facilitating the valorization of winemaking by-products.

Organic Cocrystal Alloys: From Three Primary Colors to Continuously Tunable Emission and Applications on Optical Waveguides and Displays.

Multicolor luminescence of organic fluorescent materials is an essential part of lighting and optical communication. However, the conventional construction of a multicolor luminescence system based on integrating multiple organic fluorescent materials of a single emission band remains complicated and to be improved. Herein, organic alloys (OAs) capable of full-color emission are synthesized based on charge transfer (CT) cocrystals. By adjusting the molar ratio of electron donors, the emission color of the OAs can be conveniently and continuously regulated in a wide visible range from blue (CIE: 0.187, 0.277), to green (CIE: 0.301, 0.550), and to red (CIE: 0.561, 0.435). The OAs show analogous 1D morphology with smooth surface, allowing for full-color waveguides with low optical-loss coefficient. Impressively, full-color optical displays are easily achieved through the OAs system with continuous emission, which shows promising applications in the field of optical display and promotes the development of organic photonics.

Stat3 activation-triggered transcriptional networks govern the early stage of HBV-induced hepatic inflammation.

The chronic carrier state of the hepatitis B virus (HBV) often leads to the development of liver inflammation as carriers age. However, the exact mechanisms that trigger this hepatic inflammation remain poorly defined. We analyzed the sequential processes during the onset of liver inflammation based on time-course transcriptome and transcriptional regulatory networks in an HBV transgenic (HBV-Tg) mice model and chronic HBV-infected (CHB) patients (data from GSE83148). The key transcriptional factor (TF) responsible for hepatic inflammation occurrence was identified and then validated both in HBV-Tg mice and liver specimens from young CHB patients. By time-course analysis, an early stage of hepatic inflammation was demonstrated in 3-month-old HBV-Tg mice: a marked upregulation of genes related to inflammation (Saa1/2, S100a8/9/11, or Il1ß), innate immunity (Tlr2, Tlr7, or Tlr8), and cells chemotaxis (Ccr2, Cxcl1, Cxcl13, or Cxcl14). Within CHB samples, a unique early stage of inflammation activation was discriminated from immune tolerance and immune activation groups based on distinct gene expression patterns. Enhanced activation of TF Stat3 was strongly associated with increased inflammatory gene expression in this early stage of inflammation. Expression of phosphorylated Stat3 was higher in liver specimens from young CHB patients with relatively higher alanine aminotransferase levels. Specific inhibition of Stat3 activation significantly attenuated the degree of liver inflammation, the expression of inflammation-related genes, and the inflammatory monocytes and macrophages in 3-month-old HBV-Tg mice. Stat3 activation is essential for hepatic inflammation occurrence and is a novel indicator of early-stage immune activation in chronic HBV carriers. IMPORTANCE: Until now, it remains a mystery that chronic hepatitis B virus (HBV)-infected patients in the "immune tolerance phase" will transition to the "immune activation phase" as they age. In this study, we reveal that Stat3 activation-triggered hepatic transcriptional alterations are distinctive characteristics of the early stage of immune/inflammation activation in chronic HBV infection. For the first time, we discover a mechanism that might trigger the transition from immune tolerance to immune activation in chronic HBV carriers.

Feasibility of diffusion tensor imaging in cervical spondylotic myelopathy using MUSE sequence.

BACKGROUND CONTEXT: The most frequent type of spinal cord injury is cervical spondylotic myelopathy (CSM). Conventional structural magnetic resonance imaging (MRI) is the gold diagnosis standard for CSM. Diffusion tensor imaging (DTI) could reflect microstructural changes in the spinal cord by tracing water molecular diffusion in early stages of CSM. However, due to the complex local anatomical structure and small field of view of the spinal cord, the imaging effect of traditional DTI imaging on the spinal cord is limited. MUSE (MUltiplexed Sensitivity-Encoding) -DTI is a novel diffusion-weighted imaging (DWI) sequence that achieves higher signal intensity through multiple excitation acquisition. MUSE sequence may improve the quality of spinal cord DTI imaging. STUDY DESIGN: Prospective study. PURPOSE: This study aimed to investigate the clinical diagnosis value of a novel protocol of MUSE-DTI in patients with cervical spondylotic myelopathy (CSM). PATIENT SAMPLE: From August 2021 to March 2022, a total of 60 subjects (22-71 years) were enrolled, including 51 CSM patients (22 males, 29 females) and 9 healthy subjects (4 males and 5 females). Each subject underwent a MUSE-DTI examination and a clinical Japanese Orthopedic Association (JOA) scale. OUTCOME MEASURES: We measured values of FA (Fractional Anisotropy), MD (Mean Diffusivity), AD (Axial Diffusivity), and RD (Radial Diffusivity), and collected the clinical JOA scores of each subject before the MR examination. METHODS: A 3.0T MR scanner (Signa Architect, GE Healthcare) performed the MUSE-DTI sequence on each subject. The cervical canal stenosis of subjects was classified from grade 0 to grade Ⅲ according to the method of an MRI grading system. FA, MD, AD, and RD maps were generated by postprocessing MUSE-DTI data on the GE workstation. Regions of interest (ROIs) were manually drawn at the C2 vertebral body level and C2/3-C6/7 intervertebral disc levels by covering the whole spinal cord. The clinical severity of myelopathy of subjects was assessed by the clinical Japanese Orthopedic Association scale (JOA). RESULTS: MUSE-DTI can acquire a high-resolution diffusion image compared to traditional DTI. The FAMCL values showed a decreasing trend from grade 0 to grade Ⅲ, while the MDMCL, ADMCL, and RDMCL values showed an overall increasing trend. Significant differences in MDMCL, ADMCL, and RDMCL values were found between adjacent groups among grades Ⅰ-Ⅲ (p<.05). The ADC2 values in CSM patients (grade I-Ⅲ) were significantly lower than in healthy individuals (grade 0) (p=.019). The clinical JOA score has a significant correlation with FAMCL (p=.035), MDMCL (p<.001), ADMCL (p<.001), and RDMCL (p<.001) values. CONCLUSIONS: MUSE-DTI displayed a better image quality compared to traditional DTI. MUSE-DTI parameters displayed a grade-dependent trend. All the MUSE-DTI parameters at MCL were correlated with the clinical JOA scores. The ADC2 values can reflect the secondary damage of distal spinal cord. Therefore, MUSE-DTI could be a reliable biomarker for clinical auxiliary diagnosis of spinal cord injury severity in cervical spondylotic myelopathy.

Global trade-driven transfer of atmospheric polycyclic aromatic hydrocarbon emissions and associated human inhalation exposure risk.

Polycyclic aromatic hydrocarbons (PAHs) are of significant public concern because of their toxicity and long-range transport potential. Extensive studies have been conducted to explore the source-receptor relationships of PAHs via atmospheric transport. However, the transfer of trade-driven regional and global PAHs is poorly understood. This study estimated the virtual PAHs emission transfer embodied in global trade from 2004 to 2014 and simulated the impact of international trade on global contamination and associated human inhalation exposure risk of PAHs. Results show that trade-driven PAHs flowed primarily from developed to less-developed regions, particularly in those regions with intensive heavy industries and transportation. As the result, international trade resulted in an increasing risk of lung cancer induced by exposure to PAHs (27.8% in China, 14.7% in India, and 11.3% in Southeast Asia). In contrast, we found decreasing risks of PAHs-induced lung cancer in Western Europe (63.2%) and the United States (45.9%) in 2004. Our findings indicate that final demand and emission intensity are the key driving factors contributing to rising and falling consumption-based PAHs emissions and related health risk respectively. The results could provide a useful reference for global collaboration in the reduction of PAHs pollution and related health risks.

Analysis of Dopamine Receptor D2 Gene Polymorphism and Correlation with Dyslipidemia in the Chinese Population.

Objective: The study aimed to explore the genotype and allele distributions of dopamine D2-like receptor (DRD2) gene -141C and C957T polymorphisms in the Chinese Han population with dyslipidemia, as well as their association with serum lipid levels. Methods: One hundred fifty patients with dyslipidemia and 150 healthy people were recruited as the case and the control groups, respectively. Serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol levels were detected. The target sequence of DRD2 polymorphisms was amplified by polymerase chain reaction and genotyped via Sanger sequencing. Results: In DRD2 gene C957T (rs6277), three genotypes of CC, CT, and TT were detected with the frequencies of 92.67%, 6.67%, 0.67% in dyslipidemia cases, and 83.33%, 14.67%, 2.00% in the controls, respectively. The CT genotype and T allele frequencies were significantly low in the case group relative to the control group. After adjusting to other clinical indicators, the CT genotype of C957T polymorphism (hazard ratio = 0.401, 95% confidence interval = 0.181-0.890, p < 0.05) was still related to a significantly reduced risk of dyslipidemia. The C957T CT genotype carriers had the lowest values of serum TC, TG, LDL, and the highest values of serum HDL-C. Conclusion: DRD2 gene C957T polymorphism was an independent influencing factor associated with the susceptibility to dyslipidemia, and the CT genotype was associated with decreased odds of susceptibility to dyslipidemia.

Simulation study on creep deformation of the impeller in lead-bismuth eutectic environment through fluid-solid coupling method.

Lead-based reactor is a new type of reactor using liquid lead or lead-bismuth alloy as a coolant. As the core working element of the main pump, the impeller is subjected to a huge load when conveying heavy metal liquids and is highly susceptible to damage. In this study, we used ANSYS and FLUENT software to investigate the stress, deformation, and creep deformation of the nuclear main pump impeller under a liquid lead-bismuth environment by the fluid-solid coupling method. The maximum equivalent force of the impeller was located at the junction of the blade and hub, which was prone to fatigue damage under the action of alternating load. The stress, deformation, and creep characteristics of the impeller blade were observed to generally increase with rotational speed. Particularly, the junction of the blade root and hub exhibited high susceptibility to stress concentration and fatigue damage. At a flow rate of 0.64 m/s and a speed of 690 r/min, the maximum equivalent force was 16.7 MPa, which was lower than the yield strength of 316L stainless steel. Additionally, the maximum deformation was less than 0.63 mm. Over a five-year period, the creep of the impeller ranged from a minimum of 0.228% to a maximum of 0.447%, indicating that the impeller can reliably operate in a liquid lead-bismuth environment for at least five years.

Conditional survival probability of distant-metastatic hepatocellular carcinoma: A population-based study.

BACKGROUND: The prognosis of many patients with distant metastatic hepatocellular carcinoma (HCC) improved after they survived for several months. Compared with traditional survival analysis, conditional survival (CS) which takes into account changes in survival risk could be used to describe dynamic survival probabilities. AIM: To evaluate CS of distant metastatic HCC patients. METHODS: Patients diagnosed with distant metastatic HCC between 2010 and 2015 were extracted from the Surveillance, Epidemiology and End Results database. Univariate and multivariate Cox regression analysis were used to identify risk factors for overall survival (OS), while competing risk model was used to identify risk factors for cancer-specific survival (CSS). Six-month CS was used to calculate the probability of survival for an additional 6 mo at a specific time after initial diagnosis, and standardized difference (d) was used to evaluate the survival differences between subgroups. Nomograms were constructed to predict CS. RESULTS: Positive α-fetoprotein expression, higher T stage (T3 and T4), N1 stage, non-primary site surgery, non-chemotherapy, non-radiotherapy, and lung metastasis were independent risk factors for actual OS and CSS through univariate and multivariate analysis. Actual survival rates decreased over time, while CS rates gradually increased. As for the 6-month CS, the survival difference caused by chemotherapy and radiotherapy gradually disappeared over time, and the survival difference caused by lung metastasis reversed. Moreover, the influence of age and gender on survival gradually appeared. Nomograms were fitted for patients who have lived for 2, 4 and 6 mo to predict 6-month conditional OS and CSS, respectively. The area under the curve (AUC) of nomograms for conditional OS decreased as time passed, and the AUC for conditional CSS gradually increased. CONCLUSION: CS for distant metastatic HCC patients substantially increased over time. With dynamic risk factors, nomograms constructed at a specific time could predict more accurate survival rates.

Diffusion along the perivascular space as a potential biomarker for glioma grading and isocitrate dehydrogenase 1 mutation status prediction.

Background: The diffusion tensor image analysis along the perivascular space (DTI-ALPS) may have the potential to reflect glymphatic dysfunction in patients with glioma. The study aimed to determine the correlation of DTI-ALPS with glioma grade and isocitrate dehydrogenase 1 (IDH1) genotype and to then compare the ALPS index with other diffusion metrics. Methods: In this study, 81 patients with glioma and 31 healthy controls underwent magnetic resonance imaging (MRI) examination. The ALPS-index, fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) were calculated. Comparisons were made between the left and right hemispheres and between patients and controls. IDH1 status was compared after age adjustment. The diagnostic performance of each metric was assessed via receiver operating characteristic (ROC) analysis. Results: In patients with glioma, the ALPS-index of the hemisphere ipsilateral to glioma was significantly lower than that of the hemisphere contralateral to glioma (1.417±0.177 vs. 1.478±0.165; P=0.002), and the bilateral ALPS-index values in patients were significantly decreased compared with those in healthy controls. The ALPS-index was significantly higher in lower-grade gliomas (LrGGs) than that in glioblastomas (GBMs) (1.495±0.151 vs. 1.320±0.159; P<0.001) and was significantly lower in IDH1-wild-type LrGGs than in IDH1-mutant LrGGs (1.400±0.185 vs. 1.530±0.123; P=0.036). FA, MD, and MK also showed significant differences between LrGGs and GBMs and between IDH1-mutant and IDH1-wild-type LrGGs (P<0.05). Furthermore, the combination of the ALPS-index with FA, MD, or MK, exhibited superior discrimination ability compared to each metric used alone. The ALPS-index combined with MD had the highest area under the curve (AUC) of 0.854 as compared to that of 0.614-0.807 for a single metric in glioma grading, while for IDH1 mutation prediction, this combination had the highest AUC of 0.861 as compared to that of 0.707-0.778 for a single metric. Conclusions: The reduced ALPS-index may reflect tumor-induced glymphatic system impairment, and the ALPS-index may be able to complement conventional diffusion metrics in glioma grading and IDH1 genotyping.

Peptidome analysis reveals critical roles for peptides in a rat model of intestinal ischemia/reperfusion injury.

Intestinal ischemia/reperfusion injury (IIRI) has the potential to be life threatening and is associated with significant morbidity and serious damage to distant sites in the body on account of disruption of the intestinal mucosal barrier. In the present study, we have explored this line of research by comparing and identifying peptides that originated from the intestinal segments of IIRI model rats by using liquid chromatography-mass spectrometry (LC-MS). We also analyzed the basic characteristics, cleavage patterns, and functional domains of differentially expressed peptides (DEPs) between the IIRI model rats and control (sham-operated) rats and identified bioactive peptides that are potentially associated with ischemia reperfusion injury. We also performed bioinformatics analyses in order to identify the biological roles of the DEPs based on their precursor proteins. Enrichment analysis demonstrated the role of several DEPs in impairment of the intestinal mucosal barrier caused by IIRI. Based on the results of comprehensive ingenuity pathway analysis, we identified the DEPs that were significantly correlated with IIRI. We identified a candidate precursor protein (Actg2) and seven of its peptides, and we found that Actg2-6 had a more significant difference in its expression, a longer half-life, and better lipophilicity, hydrophobicity, and stability than the other candidate Actg2 peptides examined. Furthermore, we observed that Actg2-6 might play critical roles in the protection of the intestinal mucosal barrier during IIRI. In summary, our study provides a better understanding of the peptidomics profile of IIRI, and the results indicate that Actg2-6 could be a useful target in the treatment of IIRI.

A molecular classification system for estimating radiotherapy response and anticancer immunity for individual breast cancer patients.

Objective: Radiotherapy is a cornerstone of breast cancer therapy, but radiotherapy resistance is a major clinical challenge. Herein, we show a molecular classification approach for estimating individual responses to radiotherapy. Methods: Consensus clustering was adopted to classify radiotherapy-sensitive and -resistant clusters in the TCGA-BRCA cohort based upon prognostic differentially expressed radiotherapy response-related genes (DERRGs). The stability of the classification was proven in the GSE58812 cohort via NTP method and the reliability was further verified by quantitative RT-PCR analyses of DERRGs. A Riskscore system was generated through Least absolute shrinkage and selection operator (LASSO) analysis, and verified in the GSE58812 and GSE17705. Treatment response and anticancer immunity were evaluated via multiple well-established computational approaches. Results: We classified breast cancer patients as radiotherapy-sensitive and -resistant clusters, namely C1 and C2, also verified by quantitative RT-PCR analyses of DERRGs. Two clusters presented heterogeneous clinical traits, with poorer prognosis, older age, more advanced T, and more dead status in the C2. The C1 tumors had higher activity of reactive oxygen species and response to X-ray, proving better radiotherapeutic response. Stronger anticancer immunity was found in the C1 tumors that had rich immune cell infiltration, similar expression profiling to patients who responded to anti-PD-1, and activated immunogenic cell death and ferroptosis. The Riskscore was proposed for improving patient prognosis. High Riskscore samples had lower radiotherapeutic response and stronger DNA damage repair as well as poor anticancer immunity, while low Riskscore samples were more sensitive to docetaxel, doxorubicin, and paclitaxel. Conclusion: Our findings propose a novel radiotherapy response classification system based upon molecular profiles for estimating radiosensitivity for individual breast cancer patients, and elucidate a methodological advancement for synergy of radiotherapy with ICB.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations.

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Optimization framework of sediment phosphate oxygen isotope pretreatment method based on large-scale application: A case study of Fuyang River basin.

Phosphate oxygen isotope (δ18OP) technique is an effective tool to identify the source and transformation process of phosphorus. The poor applicability of existing δ18OP pretreatment methods for sediments hindered the large-scale application of δ18OP technology. This paper presents an optimization framework for the pretreatment of sediment δ18OP samples based on large-scale applications, using the Fuyang River Basin as a case study. The typical channel landscape outflow lake, South Lake, was selected as the most favorable point for assessing the applicability and optimizing the mainstream δ18OP pretreatment method, which was achieved by clarifying the sediment environmental characteristics of South Lake. To evaluate the suitability of the Blake and McLaughlin methods in South Lake, a comparative study was carried out based on five dimensions: phosphorus recovery rate, removal efficiency of organic matter, removal efficiency of extraction liquid impurity ion, experimental time, and reagent consumption cost. The findings demonstrated that the Blake method outperformed the McLaughlin method across all five dimensions. Based on the environmental characteristics of the sediments of South Lake, the Blake method was optimized from two perspectives, namely the substitution of reagents and adjustment and optimization of experimental procedures. This resulted in an enhancement of phosphorus recovery and organic matter removal efficiency, while also reducing the experimental time required. The optimized method also yielded satisfactory results when applied to the entire watershed. This research paper can thus offer valuable technical support for the widespread application of sediment δ18OP technology.

HGCLMDA: Predicting mRNA-Drug Sensitivity Associations via Hypergraph Contrastive Learning.

The identification of drug sensitivity to mRNA interactions is crucial for drug development and disease treatment, but traditional experimental methods for verifying mRNA-drug sensitivity associations are labor-intensive and time-consuming. In this study, we present a hypergraph contrastive learning approach, HGCLMDA, to predict potential mRNA-drug sensitivity associations. HGCLMDA integrates a graph convolutional network-based method with a hypergraph convolutional network to mine high-order relationships between mRNA-drug association pairs. The proposed cross-view contrastive learning architecture improves the model's learning ability, and the inner product is used to obtain the mRNA-drug sensitivity association score. Our experiments on three mRNA-drug sensitivity association data sets show that HGCLMDA outperforms traditional graph convolutional network-based methods, graph augmentation-based contrastive learning methods, and state-of-the-art association prediction methods. The visualization experiment demonstrates the strong discrimination ability of the mRNA and drug embeddings learned by HGCLMDA, and experiments on sparse data sets showcase the performance and robustness of the method. In-depth analysis of hypergraph structures reveals a crucial role that hypergraphs play in enhancing the performance of models. The case study highlights the potential of HGCLMDA as a valuable tool for predicting mRNA-drug sensitivity associations. The interpretive analysis reveals that HGCLMDA effectively models the similarity between mRNA-mRNA and drug-drug interactions.

HGNNLDA: Predicting lncRNA-Drug Sensitivity Associations via a Dual Channel Hypergraph Neural Network.

Drug sensitivity is critical for enabling personalized treatment. Many studies have shown that long non-coding RNAs (lncRNAs) are closely related to drug sensitivity because lncRNAs can regulate genes related to drug sensitivity to affect drug efficacy. Exploring lncRNA-drug sensitivity associations has important implications for drug development and disease treatment. However, identifying lncRNA-drug sensitivity associations based on traditional biological approaches is small-scale and time-consuming. In this work, we develop a dual-channel hypergraph neural network-based method named HGNNLDA to infer unknown lncRNA-drug sensitivity associations. To our best knowledge, HGNNLDA is the first computational framework to predict lncRNA-drug sensitivity associations. HGNNLDA applies the hypergraph neural network to obtain high-order neighbor information on the lncRNA hypergraph and the drug hypergraph, respectively, and utilizes a joint update mechanism to generate lncRNA embeddings and drug embeddings. In traditional graphs, an edge contains only two nodes. However, hyperedges in hypergraphs can contain any number of nodes and hypergraphs can well describe the higher-order connectivity of the lncRNA-drug bipartite graphs. The comprehensive experimental results show that HGNNLDA significantly outperforms the other six state-of-the-art models. Case studies on two drugs further illustrate that HGNNLDA is an effective tool to predict lncRNA-drug sensitivity associations.

Two-stage process production of microbial lipid by co-fermentation of glucose and N-acetylglucosamine from food wastes with Cryptococcus curvatus.

Microbial lipids were produced through a two-stage process with Cryptococcus curvatus by co-fermenting rice and shrimp shells hydrolysates. In the first stage, biomass production of glucose and N-acetylglucosamine was optimized by response surface methodology with the maximum biomass yield (17.60 g/L) under optimum conditions (43.2 g/L mixed sugar concentration, pH 5.8, 200 rpm, and 28 °C). In the second stage, according to a single-factor optimization setting (43.2 g/L sugar mixture solutions, pH 5.5, and shift time of 36 h), lipid titer of 10.08 g/L with content of 55.30 % was achieved. Scaling up to a 5-L bioreactor increased lipid content to 60.07 % with 0.233 g/g yield. When Cryptococcus curvatus was cultured in the blends of rice hydrolysates and shrimp shells hydrolysate, lipid content and yield were 52.25 % and 0.204 g/g. The fatty acid compositions of lipid were similar to those of typical vegetable oils.