Recognition and Selectivity Analysis Monitoring of Multicomponent Steroid Estrogen Mixtures in Complex Systems Using a Group-Targeting Environmental Sensor

The same class of steroid estrogen mixtures, coexisting in the environment of 17β-estradiol, estrone (E1), and ethinyl estradiol (EE2), have strong ability to disrupt the human endocrine system and are seriously prejudicial to the health of the organism and environmental safety. Herein, a highly sensitive and group-targeting environmental monitoring sensor was fabricated for a comprehensive analysis of multicomponent steroid estrogens (multi-SEs) in complex systems. This breakthrough was based on the highly sensitive photoelectrochemical response composite material CdSe NPs-TiO2 nanotube and highly group-specific aptamers. The optimized procedure exhibited not only high sensitivity in a wide range of concentrations from 0.1 to 50 nM, indeed, the minimum detection limit was 33 pM, but also strong resistance to interference. The affinity and consistent action pockets of this sensor enable selective detection of multi-SEs in complex systems. It subsequently was applied for the analysis of multi-SEs from three real samples in the environment including medical wastewater, river water, and tap water to provide a means to clarify the fate of multi-SEs in the process of migration and transformation. This monitoring sensor has a brilliant application prospect for the identification and monitoring of the same class of endocrine-disrupting chemical mixtures in environmental complex systems

Possible result of researching for template in the target image.

<p>Possible result of researching for template in the target image.</p

Efficient One-Pot Synthesis of Uridine Diphosphate Galactose Employing a Trienzyme System

The limited availability of high-cost nucleotide sugars is a significant constraint on the application of their downstream products (glycosides and prebiotics) in the food or pharmaceutical industry. To better solve the problem, this study presented a one-pot approach for the biosynthesis of UDP-Gal using a thermophilic multienzyme system consisting of GalK, UGPase, and PPase. Under optimal conditions, a 2 h reaction resulted in a UTP conversion rate of 87.4%. In a fed-batch reaction with Gal/ATP = 20 mM:10 mM, UDP-Gal accumulated to 33.76 mM with a space-time yield (STY) of 6.36 g/L·h–1 after the second feeding. In repetitive batch synthesis, the average yield of UDP-Gal over 8 cycles reached 10.80 g/L with a very low biocatalyst loading of 0.002 genzymes/gproduct. Interestingly, Galk (Tth0595) could synthesize Gal-1P using ADP as a donor of phosphate groups, which had never been reported before. This approach possessed the benefits of high synthesis efficiency, low cost, and superior reaction system stability, and it provided new insights into the rapid one-pot synthesis of UDP-Gal and high-value glycosidic compounds

Additional file 3 of MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data

Additional file 3:. Review histor

Additional file 2 of MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data

Additional file 2:. Supplementary table

Additional file 1 of MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data

Additional file 1:. Supplementary figure

DataSheet_1_Construction of a DDR-related signature for predicting of prognosis in metastatic colorectal carcinoma.docx

BackgroundColorectal cancer (CRC) is the third most prevalent malignancy and the one of most lethal cancer. Metastatic CRC (mCRC) is the third most common cause of cancer deaths worldwide. DNA damage response (DDR) genes are closely associated with the tumorigenesis and development of CRC. In this study, we aimed to construct a DDR-related gene signature for predicting the prognosis of mCRC patients.MethodsThe gene expression and corresponding clinical information data of CRC/mCRC patients were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A prognostic model was obtained and termed DDRScore by the multivariate Cox proportional hazards regression in the patients with mCRC. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were employed to validate the predictive ability of the prognostic model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were performed for patients between the high-DDRscore and low-DDRscore groups.ResultsWe constructed a prognostic model consisting of four DDR-related genes (EME2, MSH4, MLH3, and SPO11). Survival analysis showed that patients in the high-DDRscore group had a significantly worse OS than those in the low-DDRscore group. The area under the curve (AUC) value of the ROC curve of the predictive model is 0.763 in the training cohort GSE72970, 0.659 in the stage III/IV colorectal cancer (CRC) patients from The Cancer Genome Atlas (TCGA) data portal, and 0.639 in another validation cohort GSE39582, respectively. GSEA functional analysis revealed that the most significantly enriched pathways focused on nucleotide excision repair, base excision repair, homologous recombination, cytokine receptor interaction, chemokine signal pathway, cell adhesion molecules cams, ECM-receptor interaction, and focal adhesion.ConclusionThe DDRscore was identified as an independent prognostic and therapy response predictor, and the DDR-related genes may be potential diagnosis or prognosis biomarkers for mCRC patients.</p

Table1_Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises.DOCX

Financial crisis, rooted in a lack of system resilience and robustness, is a particular type of critical transition that may cause grievous economic and social losses and should be warned against as early as possible. Regarding the financial system as a time-varying network, researchers have identified early warning signals from the changing dynamics of network motifs. In addition, network motifs have many different morphologies that unveil high-order correlation patterns of a financial system, whose synchronous change represents the dramatic shift in the financial system’s functionality and may indicate a financial crisis; however, it is less studied. This paper proposes motif transition intensity as a novel method that quantifies the synchronous change of network motifs in detail. Applying this method to stock networks, we developed three early warning indicators. Empirically, we conducted a horse race to predict ten global crises during 1991–2020. The results show evidence that the proposed indicators are more efficient than the VIX and the other 39 network-based indicators. In a detailed analysis, the proposed indicators send sensitive and comprehensible warning signals, especially for the U.S. subprime mortgage crisis and the European sovereign debt crisis. Furthermore, the proposed method provides a new perspective to detect critical signals and may be extended to predict other crisis events in natural and social systems.</p

DataSheet2_Identification and validation of a novel HOX-related classifier signature for predicting prognosis and immune microenvironment in pediatric gliomas.xlsx

Background: Pediatric gliomas (PGs) are highly aggressive and predominantly occur in young children. In pediatric gliomas, abnormal expression of Homeobox (HOX) family genes (HFGs) has been observed and is associated with the development and progression of the disease. Studies have found that overexpression or underexpression of certain HOX genes is linked to the occurrence and prognosis of gliomas. This aberrant expression may contribute to the dysregulation of important pathological processes such as cell proliferation, differentiation, and metastasis. This study aimed to propose a novel HOX-related signature to predict patients’ prognosis and immune infiltrate characteristics in PGs.Methods: The data of PGs obtained from publicly available databases were utilized to reveal the relationship among abnormal expression of HOX family genes (HFGs), prognosis, tumor immune infiltration, clinical features, and genomic features in PGs. The HFGs were utilized to identify heterogeneous subtypes using consensus clustering. Then random forest-supervised classification algorithm and nearest shrunken centroid algorithm were performed to develop a prognostic signature in the training set. Finally, the signature was validated in an internal testing set and an external independent cohort.Results: Firstly, we identified HFGs significantly differentially expressed in PGs compared to normal tissues. The individuals with PGs were then divided into two heterogeneous subtypes (HOX-SI and HOX-SII) based on HFGs expression profiles. HOX-SII showed higher total mutation counts, lower immune infiltration, and worse prognosis than HOX-SI. Then, we constructed a HOX-related gene signature (including HOXA6, HOXC4, HOXC5, HOXC6, and HOXA-AS3) based on the cluster for subtype prediction utilizing random forest supervised classification and nearest shrunken centroid algorithm. The signature was revealed to be an independent prognostic factor for patients with PGs by multivariable Cox regression analysis.Conclusion: Our study provides a novel method for the prognosis classification of PGs. The findings also suggest that the HOX-related signature is a new biomarker for the diagnosis and prognosis of patients with PGs, allowing for more accurate survival prediction.</p