An enduring challenge in personalized medicine lies in selecting a suitable drug for each individual patient. Here we concentrate on predicting drug responses based on a cohort of genomic, chemical ...structure, and target information. Therefore, a recently study such as GDSC has provided an unprecedented opportunity to infer the potential relationships between cell line and drug. While existing approach rely primarily on regression, classification or multiple kernel learning to predict drug responses. Synthetic approach indicates drug target and protein-protein interaction could have the potential to improve the prediction performance of drug response. In this study, we propose a novel heterogeneous network-based method, named as HNMDRP, to accurately predict cell line-drug associations through incorporating heterogeneity relationship among cell line, drug and target. Compared to previous study, HNMDRP can make good use of above heterogeneous information to predict drug responses. The validity of our method is verified not only by plotting the ROC curve, but also by predicting novel cell line-drug sensitive associations which have dependable literature evidences. This allows us possibly to suggest potential sensitive associations among cell lines and drugs. Matlab and R codes of HNMDRP can be found at following https://github.com/USTC-HIlab/HNMDRP .
A systematic review and meta-analysis were conducted, aiming to establish a scientifically grounded understanding of trichloroethylene (TCE)-induced hepatotoxicity. Relevant studies published prior ...to February 29, 2024 were meticulously searched. The standardized mean difference (SMD) was employed to assess the correlation between the control group and the TCE exposure group, while heterogeneity was quantified using the I2 index. After a thorough screening and exclusion process, a total of 57 articles met the eligibility criteria. Important parameters pertaining to liver health, such as ALT, AST, TNF-α, IL-1β, and others, were analyzed, resulting in the identification of 40 parameters related to liver injury. The results revealed that GLB, ALP, CYP2E1, GR, IL-6 (mRNA), and TGF-β (mRNA) did not exhibit statistically significant differences (P > 0.05). However, high heterogeneity was observed in indicators other than F4/80, EGR1 (mRNA), and MDA-protein adducts (P < 0.1, I2 > 50%). Notably, TCE exposure significantly increased the activity of ALT, AST, LDH, as well as the expression levels of TNF-α, IL-1β, TNFR1, IL-6, P65, P-P65, F4/80, IFN-γ, iNOS, C3a, TNF-α (mRNA), IL-1β (mRNA), IL-6R (mRNA), GP130 (mRNA), EGR1 (mRNA), CCL2 (mRNA), CCL5 (mRNA), iNOS (mRNA), liver coefficient, MDA, anti-dsDNA, MDA protein adduct, anti-MDA protein adduct antibody, and ANA. Conversely, TCE exposure decreased the activities of CAT, GPx, SOD, as well as the expression levels of Nrf2, TP, ALB, and GSH. Additionally, this study provided a comprehensive review of the two mechanisms underlying liver injury following TCE exposure. In conclusion, our findings furnish compelling evidence that TCE exposure induces liver injury, as manifested by alterations in various physiological indicators, including cytokines and oxidative stress-related markers, among others.
In recent years, Wi-Fi-based indoor positioning has attracted increasing research attention due to its ubiquitous deployment. Although extensive research has been conducted on Wi-Fi fingerprint-based ...positioning, especially, in complex environments and long-term deployments, the automatic adaptation of radio map has not been fully studied and the problems remain open. When the positions of some Access Points (APs) change, the traditional approach regularly conducts site surveying which is time-consuming and labor-intensive. In this paper, we propose a crowdsourcing indoor positioning approach based on ensemble learning for automatic Altered APs Identification and Fingerprints Updating, namely AAIFU. We propose an algorithm to detect and identify the altered APs in crowdsourcing data. After getting the altered APs, we use the relationship between the received signal strength values of the altered APs and the unaltered APs in the crowdsourcing data to train a prediction model used to update the radio map. We also handle the device diversity on all the processes of AAIFU. Our proposed solution is light-weight which does not rely on additional infrastructure and inertial sensors with high power consumption. The comprehensive experiments have been carried out in our teaching building to evaluate the effectiveness of AAIFU. The results show that our proposed AAIFU can effectively adapt the radio map to the movement of APs and improve positioning accuracy. Correspondingly, we achieve an average positioning accuracy of <inline-formula> <tex-math notation="LaTeX">2.6m </tex-math></inline-formula> which outperforms the fingerprinting approach with the original radio map by 63.9%.
The Mediator complex plays an essential role in the regulation of eukaryotic transcription. The Saccharomyces cerevisiae core Mediator comprises 21 subunits, which are organized into Head, Middle and ...Tail modules. Previously, the Head module was assigned to a distinct dense domain at the base, and the Middle and Tail modules were identified to form a tight structure above the Head module, which apparently contradicted findings from many biochemical and functional studies. Here, we compared the structures of the core Mediator and its subcomplexes, especially the first 3D structure of the Head ＋ Middle modules, which permitted an unambiguous assignment of the three modules. Furthermore, nanogold labeling pinpointing four Mediator subunits from different modules conclusively validated the modular assignment, in which the Head and Middle modules fold back on one another and form the upper portion of the core Mediator, while the Tail module forms a distinct dense domain at the base. The new modular model of the core Mediator has reconciled the previous inconsistencies between the structurally and functionally defined Mediator modules. Collectively, these analyses completely redefine the modular organization of the core Mediator, which allow us to integrate the structural and functional information into a coherent mechanism for the Mediator＇s modularity and regulation in transcription initiation.
Abstract
The practical application of lithium-metal batteries is hindered by insufficient lithium Coulombic efficiency and uncontrolled dendrite growth, bringing a challenge concerning how to create ...robust solid electrolyte interphases (SEIs) that can regulate Li
+
transport and protect reactive lithium-metal. Here, we present the rational construction of a multi-component jigsaw-like artificial SEI by the integration of fluorine-containing silane and polyether-containing silane. The fluorine-donating group prevents the parasitic reaction and yields a dense structure for homogeneous lithium deposition. Additionally, the lithophilicity of ethylene glycol backbone facilitates the rapid transport of Li
+
and the cross-linked network increases mechanical robustness of the SEI. With this artificial SEI, we show highly reversible lithium plating and stripping cycling for more than 500 hours. Moreover, we also demonstrate stable operation of high-voltage LiNi
0.8
Co
0.1
Mn
0.1
O
2
cathode in both coin and pouch cells under high loading, limiting excess lithium, and lean electrolyte conditions, holding great prospects for the practical application of high-voltage lithium-metal batteries.
Prediction of cancer patient's response to therapeutic agent is important for personalized treatment. Because experimental verification of reactions between large cohort of patients and drugs is ...time-intensive, expensive and impractical, preclinical prediction model based on large-scale pharmacogenomic of cancer cell line is highly expected. However, most of the existing computational studies are primarily based on genomic profiles of cancer cell lines while ignoring relationships among genes and failing to capture functional similarity of cell lines.
In this study, we present a novel approach named NRL2DRP, which integrates protein-protein interactions and captures similarity of cell lines' functional contexts, to predict drug responses. Through integrating genomic aberrations and drug responses information with protein-protein interactions, we construct a large response-related network, where the neighborhood structure of cell line provides a functional context to its therapeutic responses. Representation vectors of cell lines are extracted through network representation learning method, which could preserve vertices' neighborhood similarity and serve as features to build predictor for drug responses. The predictive performance of NRL2DRP is verified by cross-validation on GDSC dataset and methods comparison, where NRL2DRP achieves AUC > 79% for half drugs and outperforms previous methods. The validity of NRL2DRP is also supported by its effectiveness on uncovering accurate novel relationships between cell lines and drugs. Lots of newly predicted drug responses are confirmed by reported experimental evidences.
The code and documentation are available on https://github.com/USTC-HIlab/NRL2DRP.
Supplementary data are available at Bioinformatics online.
•A fail-safe topology optimization is presented for multiscale structures.•The damage in both the macroscale and microscale is considered.•Simplified damage models for truss-like microstructures are ...utilized.•The use of Heaviside projection speeds up convergence and obtains better results.•Multiscale fail-safe structures are more damage-resistant and self-supporting.
This paper presents a novel fail-safe topology optimization method for multiscale structures. The partial damage of both macroscopic and microscopic scales is considered for structural design. To ensure precision, the effective elasticity tensor obtained by the homogenization method is fitted as a high-order polynomial function. Meanwhile, the simplified models of partially damaged truss-like microstructure are employed to reduce the computational cost and the difficulty of fitting. Moreover, Heaviside projection is applied to speed up the convergence and yield a relatively clear configuration. Three numerical examples are tested to demonstrate that the optimized multiscale structures successfully obtain comprehensive performances than optimized solid structures when appropriate microstructure configurations are chosen. Besides, multiscale structures are more self-supporting than solid structures and thus more suitable for additive manufacturing due to the large number of gray elements diffused.
Chitin has been subjected to regiospecific oxidation at C-6 with NaOCl in the presence of 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) and NaBr at room temperature in aqueous solution to yield fully ...soluble 6-carboxychitin. Several physical and chemical pretreatments of the original chitin changed its crystal structure from α to β. After this pretreatment of the chitin the oxidation was easier to effect and the yield was greatly increased from 36% to 97% and the molecular weight was about 4
×
10
4 which was ca. 8 times that from the unpretreated chitin. Infrared spectroscopy (IR), X-ray diffraction,
13C NMR and solid-state NMR measurements, and thermal analysis techniques were used to characterize their molecular structures. The moisture absorption and retention abilities of these types of compounds were compared with those of sodium hyaluronan and carboxymethyl chitosan (CMCS) and were found to be superior. They therefore have the potential to substitute for hyaluronan for use in cosmetics and clinical medicine fields.
Aqueous Zn‐metal batteries are the most promising system for large‐scale energy storage due to their high capacity, high safety, and low cost. The Zn‐metal anode, however, suffers from continuous ...parasitic reactions, random dendrite growth, and sluggish kinetics in aqueous electrolytes. Herein, a high donor number solvent, tetramethylurea (TMU), is introduced as electrolyte additive to enable highly reversible Zn‐metal anode, where the TMU can 1) preferentially adsorb on the Zn surface to inhibit Zn corrosion and suppress parasitic reaction, 2) solvate with Zn2+ and exclude the H2O from Zn2+ solvation sheath to weaken water activity significantly, and 3) contribute to form an inorganic‐organic bilayer solid electrolyte interphase to enable homogeneous and rapid Zn2+ transport kinetic for dendrite‐free Zn deposition. Benefiting from these three merits, the resulting aqueous electrolyte demonstrates a highly reversible Zn plating/stripping cycling in a Zn||Ti asymmetric cell for over 1200 cycles and in a Zn||Zn symmetric cell for over 4000 h. As a proof‐of‐concept, the aqueous Zn‐metal full cells assembled with various state‐of‐the‐art cathodes also deliver excellent cycling performance even with a 10 µm thin Zn anode, favoring the practical application.
A high donor solvent, tetramethylurea, is introduced as an electrolyte additive to enable highly reversible Zn‐metal anode with superior cyclability under harsh conditions. Rationally, the tetramethylurea can adsorb on the Zn surface to suppress parasitic reaction, solvate with Zn2+ to weaken water activity, and contribute to form an inorganic‐organic bilayer solid electrolyte interphase to enable homogeneous Zn deposition and rapid Zn2+ transport kinetic.
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