Crystallization Control of N,N′-Dioctyl Perylene Diimide by Amphiphilic Block Copolymers Containing poly(3-Hexylthiophene) and Polyethylene Glycol

The preparation of micron- to nanometer-sized functional materials with well-defined shapes and packing is a key process to their applications. There are many ways to control the crystal growth of organic semiconductors. Adding polymer additives has been proven a robust strategy to optimize semiconductor crystal structure and the corresponding optoelectronic properties. We have found that poly(3-hexylthiophene) (P3HT) can effectively regulate the crystallization behavior of N,N′-dioctyl perylene diimide (C8PDI). In this study, we combined P3HT and polyethylene glycol (PEG) to amphiphilic block copolymers and studied the crystallization modification effect of these block copolymers. It is found that the crystallization modification effect of the block copolymers is retained and gradually enhanced with P3HT content. The length of C8PDI crystals were well controlled from 2 to 0.4 μm, and the width from 210 to 35 nm. On the other hand, due to the water solubility of PEG block, crystalline PEG-b-P3HT/C8PDI micelles in water were successfully prepared, and this water phase colloid could be stable for more than 2 weeks, which provides a new way to prepare pollution-free aqueous organic semiconductor inks for printing electronic devices

Symbiotic Combination and Accumulation of Coal Measure Gas in the Daning–Jixian Block, Eastern Margin of Ordos Basin, China

Coal measure gas resources, including coalbed methane (CBM), shale gas, and tight gas are abundant in the Daning–Jixian Block. The complexity of the source–reservoir–cap relationship in the coal measure strata leads to unclear symbiotic characteristics and gas accumulation, which in turn, restrict the exploration and exploitation of the coal measure gas. In this study, the enrichment and accumulation of coal measure gas are discussed and summarized in detail. The results show that there are eight lithofacies and six reservoir combinations in the superposed strata of the coal measures in the study area. Controlled by the tidal flat-lagoon facies, the “sand-mud-coal” type mainly distributes in P1s2 and P1t, showing a good gas indication. Based on the variation of the total hydrocarbon content, key strata, and pressure coefficient of the coal measure gas reservoir, four superposed gas-bearing systems are identified in the vertical direction. According to the relationship between the gas-bearing system and gas reservoir, the enrichment of coal measure gas in the study area can be divided into three modes, including an intra-source enrichment mode, a near-source migration enrichment mode, and a far-source migration enrichment mode. The symbiotic accumulation of a coal measure gas model is further proposed, that is, an “Adjacent to co-source reservoir” type superimposed coalbed methane and shale gas reservoir model, a “Three gas symbiosis” superimposed reservoir model in the local gas-bearing system, and a “Co-source far reservoir” tight sandstone gas reservoir model. Clarifying the symbiotic relationship of coal measure gas reservoirs is beneficial to the exploration and further production of unconventional gas in the study area

Mesoproterozoic (ca. 1.3 Ga) A-Type Granites on the Northern Margin of the North China Craton: Response to Break-Up of the Columbia Supercontinent

Mesoproterozoic (ca. 1.3 Ga) magmatism in the North China Craton (NCC) was dominated by mafic intrusions (dolerite sills) with lesser amounts of granitic magmatism, but our lack of knowledge of this magmatism hinders our understanding of the evolution of the NCC during this period. This study investigated porphyritic granites from the Huade–Kangbao area on the northern margin of the NCC. Zircon dating indicates the porphyritic granites were intruded during the Mesoproterozoic between 1285.4 ± 2.6 and 1278.6 ± 6.1 Ma. The granites have high silica contents (SiO2 = 63.10–73.73 wt.%), exhibit alkali enrichment (total alkalis = 7.71–8.79 wt.%), are peraluminous, and can be classified as weakly peraluminous A2-type granites. The granites have negative Eu anomalies (δEu = 0.14–0.44), enrichments in large-ion lithophile elements (LILEs; e.g., K, Rb, Th, and U), and depletions in high-field-strength elements (HFSEs; e.g., Nb, Ta, and Ti). εHf(t) values range from –6.43 to +2.41, with tDM2 ages of 1905–2462 Ma, suggesting the magmas were derived by partial melting of ancient crustal material. The geochronological and geochemical data, and regional geological features, indicate the Mesoproterozoic porphyritic granites from the northern margin of the NCC formed in an intraplate tectonic setting during continental extension and rifting, which represents the response of the NCC to the break-up of the Columbia supercontinent

Identification of potential immune-related hub genes in Parkinson's disease based on machine learning and development and validation of a diagnostic classification model.

BackgroundParkinson's disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson's disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model.MethodsWe downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples).ResultsUltimately, we screened for four immune-related Parkinson's disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson's disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson's disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes.ConclusionIn this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets

Sensitive magnetic particle imaging of haemoglobin degradation for the detection and monitoring of intraplaque haemorrhage in atherosclerosisResearch in context

Summary: Background: Intraplaque haemorrhage (IPH) drives atherosclerosis progression and is a key imaging biomarker of unstable plaques. Non-invasive and sensitive monitoring of IPH is challenging due to the compositional complexity and dynamic nature of atherosclerotic plaques. Magnetic particle imaging (MPI) is a highly sensitive, radiation-free, and no-tissue-background tomographic technique that detects superparamagnetic nanoparticles. Thus, we aimed to investigate whether MPI can in vivo detect and monitor IPH. Methods: Thirty human carotid endarterectomy samples were collected and scanned with MPI. The tandem stenosis (TS) model was employed to establish unstable plaques with IPH in ApoE−/− mice. MPI and 7 T T1-weighted magnetic resonance imaging (MRI) were performed on TS ApoE−/− mice. Plaque specimens were analyzed histologically. Findings: Human carotid endarterectomy samples exhibited endogenous MPI signals, which histologically colocalized with IPH. In vitro experiments identified haemosiderin, a haemoglobin degradation product, as a potential source of MPI signals. Longitudinal MPI of TS ApoE−/− mice detected IPH at unstable plaques, of which MPI signal-to-noise ratio values increased from 6.43 ± 1.74 (four weeks) to 10.55 ± 2.30 (seven weeks) and reduced to 7.23 ± 1.44 (eleven weeks). In contrast, 7 T T1-weighted MRI did not detect the small-size IPH (329.91 ± 226.82 μm2) at four weeks post-TS. The time-course changes in IPH were shown to correlate with neovessel permeability providing a possible mechanism for signal changes over time. Interpretation: MPI is a highly sensitive imaging technology that allows the identification of atherosclerotic plaques with IPH and may help detect and monitor unstable plaques in patients. Funding: This work was supported in part by the Beijing Natural Science Foundation under Grant JQ22023; the National Key Research and Development Program of China under Grant 2017YFA0700401; the National Natural Science Foundation of China under Grant 62027901, 81827808, 81730050, 81870178, 81800221, 81527805, and 81671851; the CAS Youth Innovation Promotion Association under Grant Y2022055 and CAS Key Technology Talent Program; and the Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai HLHPTP201703)

Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Forgetting Factor Recursive Least Squares and Nonlinear Kalman Filter

State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms

Immunoactivation by Cutaneous Blue Light Irradiation Inhibits Remote Tumor Growth and Metastasis

An improved innate immunity will respond quickly to pathogens and initiate efficient adaptive immune responses. However, up to now, there have been limited clinical ways for effective and rapid consolidation of innate immunity. Here, we report that cutaneous irradiation with blue light of 450 nm rapidly stimulates the innate immunity through cell endogenous reactive oxygen species (ROS) regulation in a noninvasive way. The iron porphyrin-containing proteins, mitochondrial cytochrome c (Cyt-c), and cytochrome p450 (CYP450) can be mobilized by blue light, which boosts electron transport and ROS production in epidermal and dermal tissues. As a messenger of innate immune activation, the increased level of ROS activates the NF-κB signaling pathway and promotes the secretion of immunomodulatory cytokines in skin. Initiated from skin, a regulatory network composed of cytokines and immune cells is established through the circulation system for innate immune activation. The innate immunity activated by whole-body blue light irradiation inhibits tumor growth and metastasis by increasing the infiltration of antitumor neutrophils and tumor-associated macrophages. Our results elucidate the remote immune modulation mechanism of blue light and provide a clinically applicable way for innate immunity activation