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Additional file 2. Supplementary Methods.

Additional file 1. Supplementary Figures 1-19.

Additional file 2: Fig. S1. Identification of optimal k of the Cancer Genome Atlas. Fig. S2. Correlation of the immune cells with TP53 and CTNNB1. *p

Additional file 3: Figure S1. The distribution of 17 included screens which focused on investigating the regulators of immune cell-mediated killing among different screening types, library types, organisms, and algorithms. Figure S2. Results of Functional similarity analysis. Figure S3. Distribution of up- (red) and down- (blue) regulated genes (between tumor tissues and adjacent tissues) of sensitizers and resistors across different TCGA cancer types. Figure S4. The ratio between the inactivation event numbers of sensitizers and resistors. Figure S5. Functional characterization of sensitizers and resistors in each cancer type. Figure S6. Experimental validation of the function of MON2. Figure S7. The overall characterization of MON2. Figure S8. Relationship between MON2 and microsatellite instability (MSI). Figure S9. Comparison of the performance for predicting response to immunotherapy between CTIS and 14 published signatures in pretreatment datasets. Figure S10. Characterization of CTIS in TCGA Pan-Cancer cohort. Figure S11. Summary of ICB-treated CRISPR screens.

Additional file 3: Figure S1. The distribution of 17 included screens which focused on investigating the regulators of immune cell-mediated killing among different screening types, library types, organisms, and algorithms. Figure S2. Results of Functional similarity analysis. Figure S3. Distribution of up- (red) and down- (blue) regulated genes (between tumor tissues and adjacent tissues) of sensitizers and resistors across different TCGA cancer types. Figure S4. The ratio between the inactivation event numbers of sensitizers and resistors. Figure S5. Functional characterization of sensitizers and resistors in each cancer type. Figure S6. Experimental validation of the function of MON2. Figure S7. The overall characterization of MON2. Figure S8. Relationship between MON2 and microsatellite instability (MSI). Figure S9. Comparison of the performance for predicting response to immunotherapy between CTIS and 14 published signatures in pretreatment datasets. Figure S10. Characterization of CTIS in TCGA Pan-Cancer cohort. Figure S11. Summary of ICB-treated CRISPR screens.

Additional file 1. Supplemental Materials and Methods.

The traditional recommendation systems mainly use offline user data to train offline models, and then recommend items for online users, thus suffering from the unreliable estimation of user preferences based on sparse and noisy historical data. Conversational Recommendation System (CRS) uses the interactive form of the dialogue systems to solve the intrinsic problems of traditional recommendation systems. However, due to the lack of contextual information modeling, the existing CRS models are unable to deal with the exploitation and exploration (E&E) problem well, resulting in the heavy burden on users. To address the aforementioned issue, this work proposes a contextual information enhancement model tailored for CRS, called Knowledge Graph-enhanced Sampling (KGenSam). KGenSam integrates the dynamic graph of user interaction data with the external knowledge into one heterogeneous Knowledge Graph (KG) as the contextual information environment. Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem. Experimental results on two real-world datasets demonstrate the superiority of KGenSam with significant improvements over state-of-the-art methods.

The high sensitivity to changes in oxygen tension makes kidney vulnerable to hypoxia. Both acute kidney injury and chronic kidney disease are almost always accompanied by hypoxia. Tubular epithelial cells (TECs), the dominant intrinsic cells in kidney tissue, are believed to be not only a victim in the pathological process of various kidney diseases, but also a major contributor to kidney damage. Hypoxia inducible factor- 1 (HIF-1) is the main regulator of adaptive response of cells to hypoxia. Under various clinical and experimental kidney disease conditions, HIF-1 plays a pivotal role in modulating multiple cellular processes in TECs, including apoptosis, autophagy, inflammation, metabolic pattern alteration, and cell cycle arrest. A comprehensive understanding of the mechanisms by which HIF-1 regulates these cellular processes in TECs may help identify potential therapeutic targets to improve the outcome of acute kidney injury and delay the progression of chronic kidney disease.

Recently, climate change associated with the increasing level of atmospheric CO2 provoked growing interest in the recycling of captured CO2 to produce methanol and DME using renewable sources of hydrogen. For CO2 hydrogenation, numerous catalysts have been investigated. Among them, bimetallic catalysts are promising due to their unique properties. In this work, the performance of Ni-Ga based catalysts for CO2 hydrogenation to methanol and DME was investigated at elevated pressures. A passivation technique using N2O was developed for the air-sensitive Ni-Ga catalysts to facilitate transfer to the reactor. The effect of Pd promotion on the reducibility and performance of a Ni-Ga catalyst was also studied. For the single-stage DME synthesis, Ni-Ga and Pd-Ga based catalysts were investigated as the methanol synthesis component with the zeolites ferrierite (FER) and ZSM-5 as the acid component. Ni-Ga(5:3 and 1:1)/SiO2 catalysts were found to have higher selectivity to methanol but lower activity compared to a commercial Cu/ZnO/Al2O3 catalyst at elevated pressures. Loss in activity and selectivity is attributed in part to the decomposition of the Ni-Ga intermetallic phase. The Ni-Ga catalyst was reversibly poisoned significantly in the presence of reaction products (especially CO). Addition of Pd to the Ni-Ga(5:3)/SiO2 showed an enhanced methanol synthesis activity, possibly due to hydrogen spillover. Ferrierite and ZSM-5 worked well as the methanol dehydration component in single-stage DME synthesis with Ni-Ga or Pd-Ga catalysts under CO2/H2. FER based mixtures demonstrated good stability and selectivity due to the unidirectional channel structure that suppresses hydrocarbon formation, while the performance of ZSM-5 was greatly dependent on the water concentration. The effect of proximity between the two catalyst components in physical mixtures was found to be more pronounced in the Ni-Ga based mixtures due to metal transfer, which appeared to be facilitated by the relatively high re-activation temperature. Open Access

The distribution of the Na−K−2Cl co-transporter (NKCC1) in the cochlear K+ cycling pathway in cochlea and cochlear histological changes in the NKCC1 knockout mice were investigated. By using immunohistochemistry and toluidine blue staining, the localization of NKCC1 in cochlea of the C57BL/6J mice and the cochlear histological changes in the NKCC1 knockout mice were observed. It was found that the NKCC1 was expressed mainly in the stria marginal cells and the fibrocytes in the inferior portion of the spiral ligament in the adult C57BL/6J mice. Subpopulation of the fibrocytes in the suprastrial region and the limbus was also moderately immunoreactive. While in the cochlea of the NKCC1 knockout mice, Reissner’s membrane was collapsed and scala media disappeared, accompanied with the loss of inner hair cells, outer hair cells and the support cells. The tunnel of Corti was often absent. All the findings suggested the localization of NKCC1 in the cochlea was closely correlated with cochlear K+ cycling. Loss of NKCC1 led to the destruction of the cochlear structures, and subsequently influenced the physiological function of cochlea.