Meta.mp4

Visualization 1 is a collection of photos in our experiment when we rotated the NPC sample. It’s an animation that shows the dynamic process to assist the understanding of the readers. It starts with the perpendicular incidence of the FW, and rotated along in a clockwise direction with respect to the z-axis. Each frame has their corresponding rotation angle labeled. The largest rotation angle in it is 33°. The animation includes all phenomena shown in Figure 5-7 with the experimental data in Table 1 and Table 2

The effect of anticodons (AC) of the two tRNA-Met genes in bivalve mitochondrial genomes on P_UUA and P_AUA.

<p>Those with only CAU-tRNA^Met genes have reduced AUA usage than those with both CAU-tRNA^Met and UAU-tRNA-^Met genes.</p

Dichloroacetonitrile and Dichloroacetamide Can Form Independently during Chlorination and Chloramination of Drinking Waters, Model Organic Matters, and Wastewater Effluents

The increasing usage of organic nitrogen-rich wastewater- or algal-impacted waters, and chloramines for secondary disinfection, raises concerns regarding the formation of haloacetonitriles, haloacetamides and other nitrogenous disinfection byproducts (N-DBPs). Previous research obtained contradictory results regarding the relative importance of chlorination or chloramination for promoting these byproducts, but applied chlorine and chloramines at different doses and exposure periods. Additionally, mechanistic work, mostly using model precursors, suggested that haloacetonitrile and haloacetamide formation should be correlated because hydrolysis of haloacetonitriles forms haloacetamides. In this work, the formation of dichloroacetonitrile (DCAN) and dichloroacetamide (DCAcAm) were compared across a range of chlorine and chloramine exposures for drinking waters, wastewater effluents, algal extracellular polymeric substances (EPS), NOM isolates and model precursors. While chlorination favored formation of DCAN over DCAcAm, chloramination nearly always formed more DCAcAm than DCAN, suggesting the existence of haloacetamide formation pathways that are independent of the hydrolysis of haloacetonitriles. Experiments with asparagine as a model precursor also suggested DCAcAm formation without a DCAN intermediate. Application of ¹⁵N-labeled monochloramine indicated initial rapid formation of both DCAN and DCAcAm by pathways where the nitrogen originated from organic nitrogen precursors. However, slower formation occurred by pathways involving chloramine incorporation into organic precursors. While wastewater effluents and algal EPS tended to be more potent precursors for DCAN during chlorination, humic materials were more potent precursors for DCAcAm during chlorination and for both DCAN and DCAcAm during chloramination. These results suggest that, rather than considering haloacetamides as haloacetonitrile hydrolysis products, they should be treated as a separate N-DBP class associated with chloramination. While use of impaired waters may promote DCAN formation during chlorination, use of chloramines may promote haloacetamide formation for a wider array of waters

Optimization and Characterization of an Amino Acid Ionic Liquid and Polyethylene Glycol Blend Solvent for Precombustion CO₂ Capture: Experiments and Model Fitting

Amino acid ionic liquids are green solvents with low toxicities and properties suitable for precombustion CO₂ capture, such as high CO₂ absorption, good thermal stability, and negligible vapor pressure. However, their high viscosities and relatively high costs have hampered their industrial applications. In this work, we systematically studied the amino acid ionic liquid tetrabutylphosphonium glycinate ([P₄₄₄₄]­[Gly]) and its blends with the low-cost and less viscous cosolvent poly­(ethylene glycol) (PEG400). The concentration of the blend solvent was optimized with respect to CO₂ solubility, regeneration efficiency, and cyclic capacity. The solubilities of CO₂ in [P₄₄₄₄]­[Gly], PEG400, and their blends of four different concentrations were measured experimentally in the temperature range of 60–140 °C and up to a pressure of 17 bar. The results showed that the CO₂ solubility increased with increasing ionic liquid concentration in the blend and decreased with increasing temperature. The optimum CO₂ absorption was determined to occur at 30 wt % of [P₄₄₄₄]­[Gly] in the blend. The regeneration study of the 30 wt % [P₄₄₄₄]­[Gly]–70 wt % PEG400 blend for three cycles verified its reusability in the process and confirmed that the reaction between [P₄₄₄₄]­[Gly] and CO₂ can be reversed at 140 °C. The CO₂ absorption capacity of the blend absorbent was found to be up to a loading of 1.23 mol of CO₂/mol of absorbent. The parameter fitting of the experimental data using empirical correlations was evaluated, and these correlations were developed in particular to predict the blend solvent of an amino acid ionic liquid–PEG400 system based on the ionic liquid concentration and temperature

Results from the 13 CDSs from the four urochordate species, <i>Halocynthia roretzi, Ciona intestinalis, Ciona savignyi</i>, and <i>Doliolum nationalis</i>, whose mitochondrial genomes each have a UAU-tRNA^Met gene in addition to a CAU-tRNA^Met gene.

<p>Results from the 13 CDSs from the four urochordate species, <i>Halocynthia roretzi, Ciona intestinalis, Ciona savignyi</i>, and <i>Doliolum nationalis</i>, whose mitochondrial genomes each have a UAU-tRNA^Met gene in addition to a CAU-tRNA^Met gene.</p

The node players with cooperation strategy survive because of the isolation zone formed by being surrounded by link players.

Red, blue, gold, and cyan denote node players with cooperation strategy, node players with defection strategy, link players with cooperation strategy, and link players with defection strategy, respectively. The two rows of snapshots correspond to b values of 1.04 and 1.2, respectively. The images from left to right show the number of MC steps as 0, 5, 100, and 3000 respectively.</p

Additional file 1: of A case-control study of exposure to organophosphate flame retardants and risk of thyroid cancer in women

Table S1a. Spearman correlations among organophosphate flame retardants (specific gravity-corrected) (n = 200). Table S1b. Spearman correlations among organophosphate flame retardants (not specific gravity-corrected) (n = 200). (DOCX 17 kb

Image_2_Research into the biological differences and targets in lung cancer patients with diverse immunotherapy responses.tif

BackgroundImmunotherapy has gradually become an important therapy option for lung cancer patients.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were responsible for all the public data.ResultsIn our study, we firstly identified 22 characteristic genes of NSCLC immunotherapy response using the machine learning algorithm. Molecule subtyping was then conducted and two patient subtypes were identified Cluster1 and Cluster2. Results showed that Cluster1 patients had a lower TIDE score and were more sensitive to immunotherapy in both TCGA and combined GEO cohorts. Biological enrichment analysis showed that pathways of epithelial-mesenchymal transition (EMT), apical junction, KRAS signaling, myogenesis, G2M checkpoint, E2F targets, WNT/β-catenin signaling, hedgehog signaling, hypoxia were activated in Cluster2 patients. Genomic instability between Cluster1 and Cluster2 patients was not significantly different. Interestingly, we found that female patients were more adaptable to immunotherapy. Biological enrichment revealed that compared with female patients, pathways of MYC target, G2M checkpoints, mTORC1 signaling, MYC target, E2F target, KRAS signaling, oxidative phosphorylation, mitotic spindle and P53 pathway were activated. Meanwhile, monocytes might have a potential role in affecting NSCLC immunotherapy and underlying mechanism has been explored. Finally, we found that SEC14L3 and APCDD1L were the underlying targets affecting immunotherapy, as well as patients survival.ConclusionsThese results can provide direction and guidance for future research focused on NSCLC immunotherapy.</p

Image_3_Research into the biological differences and targets in lung cancer patients with diverse immunotherapy responses.tif

BackgroundImmunotherapy has gradually become an important therapy option for lung cancer patients.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were responsible for all the public data.ResultsIn our study, we firstly identified 22 characteristic genes of NSCLC immunotherapy response using the machine learning algorithm. Molecule subtyping was then conducted and two patient subtypes were identified Cluster1 and Cluster2. Results showed that Cluster1 patients had a lower TIDE score and were more sensitive to immunotherapy in both TCGA and combined GEO cohorts. Biological enrichment analysis showed that pathways of epithelial-mesenchymal transition (EMT), apical junction, KRAS signaling, myogenesis, G2M checkpoint, E2F targets, WNT/β-catenin signaling, hedgehog signaling, hypoxia were activated in Cluster2 patients. Genomic instability between Cluster1 and Cluster2 patients was not significantly different. Interestingly, we found that female patients were more adaptable to immunotherapy. Biological enrichment revealed that compared with female patients, pathways of MYC target, G2M checkpoints, mTORC1 signaling, MYC target, E2F target, KRAS signaling, oxidative phosphorylation, mitotic spindle and P53 pathway were activated. Meanwhile, monocytes might have a potential role in affecting NSCLC immunotherapy and underlying mechanism has been explored. Finally, we found that SEC14L3 and APCDD1L were the underlying targets affecting immunotherapy, as well as patients survival.ConclusionsThese results can provide direction and guidance for future research focused on NSCLC immunotherapy.</p

Image_1_Research into the biological differences and targets in lung cancer patients with diverse immunotherapy responses.tif

BackgroundImmunotherapy has gradually become an important therapy option for lung cancer patients.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were responsible for all the public data.ResultsIn our study, we firstly identified 22 characteristic genes of NSCLC immunotherapy response using the machine learning algorithm. Molecule subtyping was then conducted and two patient subtypes were identified Cluster1 and Cluster2. Results showed that Cluster1 patients had a lower TIDE score and were more sensitive to immunotherapy in both TCGA and combined GEO cohorts. Biological enrichment analysis showed that pathways of epithelial-mesenchymal transition (EMT), apical junction, KRAS signaling, myogenesis, G2M checkpoint, E2F targets, WNT/β-catenin signaling, hedgehog signaling, hypoxia were activated in Cluster2 patients. Genomic instability between Cluster1 and Cluster2 patients was not significantly different. Interestingly, we found that female patients were more adaptable to immunotherapy. Biological enrichment revealed that compared with female patients, pathways of MYC target, G2M checkpoints, mTORC1 signaling, MYC target, E2F target, KRAS signaling, oxidative phosphorylation, mitotic spindle and P53 pathway were activated. Meanwhile, monocytes might have a potential role in affecting NSCLC immunotherapy and underlying mechanism has been explored. Finally, we found that SEC14L3 and APCDD1L were the underlying targets affecting immunotherapy, as well as patients survival.ConclusionsThese results can provide direction and guidance for future research focused on NSCLC immunotherapy.</p