Recycling Organic Dyes within the Metal-Organic Framework for Photothermal Conversion.

The pursuit of a straightforward method to recycle organic dyes from effluents and repurpose them into valuable materials represents a highly sought-after yet huge challenge within the realms of chemistry, environment, and materials science. In this context, we employ a host-guest strategy that leverages the recycling of the rhodamine B molecule within the porous structure of a metal-organic framework to facilitate photothermal conversion. This achievement is realized through the electrostatic interaction, which then gives rise to remarkable selectivity and unparalleled uptake capacity for the cationic rhodamine B molecule. Capitalizing on this approach, the application of a columnar device and membrane technology for efficiently trapping rhodamine B molecules becomes feasible. On account of the aggregation effect resulting from the confined pore structure of the host matrix, the fluorescence emission of the encapsulated RhB molecules is significantly reduced, which consequently enhances the photothermal performance of the hybrid material through nonradiative transition. Moreover, the photothermal conversion achieved showcases a myriad of high-performance applications, including bacterial inhibition against Escherichia coli and seawater desalination.

High-Performance Inverted Planar Perovskite Solar Cells Enhanced by Thickness Tuning of New Dopant-Free Hole Transporting Layer.

A new hole transporting material (HTM) named DMZ is synthesized and employed as a dopant-free HTM in inverted planar perovskite solar cells (PSCs). Systematic studies demonstrate that the thickness of the hole transporting layer can effectively enhance the morphology and crystallinity of the perovskite layer, leading to low series resistance and less defects in the crystal. As a result, the champion power conversion efficiency (PCE) of 18.61% with JSC = 22.62 mA cm-2 , VOC = 1.02 V, and FF = 81.05% (an average one is 17.62%) is achieved with a thickness of ≈13 nm of DMZ (2 mg mL-1 ) under standard global AM 1.5 illumination, which is ≈1.5 times higher than that of devices based on poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonic acid) (PEDOT:PSS). More importantly, the devices based on DMZ exhibit a much better stability (90% of maximum PCE retained after more than 556 h in air (relative humidity ≈ 45%-50%) without any encapsulation) than that of devices based on PEDOT:PSS (only 36% of initial PCE retained after 77 h in same conditions). Therefore, the cost-effective and facile material named DMZ offers an appealing alternative to PEDOT:PSS or polytriarylamine for highly efficient and stable inverted planar PSCs.

Carbon Dioxide (CO2) Fixation: Linearly Bridged Zn2 Paddlewheel Nodes by CO2 in a Metal-Organic Framework.

When the reaction of zinc nitrate with 4',4‴,4‴″,4‴‴'-(ethene-1,1,2,2-tetrayl)tetrakis[(1,1'-biphenyl-3-carboxylic acid)] (H4tmpe) in dimethylformamide (DMF) under hydrothermal condition is performed in air or carbon dioxide (CO2), [Zn4(tmpe)2(H2O)2(µ2-CO2)]·8DMF·18H2O (1) crystallizes out. However, if it is in dioxygen, argon, or carbon monoxide, [Zn2(tmpe)(DMF)]·2DMF·8H2O (2) is the product. Both compounds are chemically stable coordination polymers. 1 contains zinc carboxylate paddlewheels as nodes linearly bridged by CO2 into two interpenetrating lattices, and 2 has an infinite single framework formed by a tetranuclear node. 1 is the second example containing the linear CO2-bridged paddlewheel node. Interestingly, CO2 fixation in a µ2-η2O,O bridging mode is observed in 1, which is rarely characterized structurally and has been confirmed using IR and gas chromatography analysis. The stability of 1 is further verified by density functional theory calculations, which found an energy minimum with a Zn-O═C angle of 180°. Both compounds display strong emission around 490 nm and excited-state lifetimes around 2.4 ns.

Comprehension of the Effect of a Hydroxyl Group in Ancillary Ligand on Phosphorescent Property for Heteroleptic Ir(III) Complexes: A Computational Study Using Quantitative Prediction.

A new Ir(III) complex (dfpypya)2Ir(pic-OH) (2) is theoretically designed by introduction of a simple hydroxyl group into the ancillary ligand on the basis of (dfpypya)2Ir(pic) (1) with the aim to get the high-efficiency and stable blue-emitting phosphors, where dfpypya is 3-methyl-6-(2',4'-difluoro-pyridinato)pyridazine, pic is picolinate, and pic-OH is 3-hydroxypicolinic acid. The other configuration (dfpypya)2Ir(pic-OH)' (3) is also investigated to compare with 2. The difference between 2 and 3 is whether the intramolecular hydrogen bond is formed in the (dfpypya)2Ir(pic-OH). The quantum yield is determined by three different methods including the semiquantitative and quantitative methods. To quantitatively determine the quantum yield is still not an easy task to be completed. This work would provide some useful advices to select the suitable method to reliably evaluate the quantum yield. Complex 2 has larger quantum yield and more stability as compared with 1 and 3. The formation of intramolecular hydrogen bond would become a new method to design new phosphor with the desired properties.

Isolation and identification of Duck tembusu virus strain lH and development of latex-agglutination diagnostic method for rapid detection of antibodies.

SUMMARY. An outbreak of egg-drop syndrome occurred on a Sheldrake duck farm in Longhai in Fujian Province, China, in 2012. The main clinical symptoms were sharply reduced egg production, crooked necks, and death. We isolated the virus from the sick ducks, identified it, and observed the histopathologic changes after viral infection. We detected viral RNA in the blood and feces of the infected ducks and developed a latex-agglutination diagnostic method to detect anti-Tembusu-virus antibodies. Our results show that the pathogenic virus is a Tembusu virus. The histopathologic changes included follicular cell degeneration and necrosis, follicular cavity filled with blood cells, massive necrosis in the brain, and degeneration and necrosis of the nerve and glial cells. When the transmission of the virus in the infected ducks was studied, the duck blood was positive for viral nucleic acid for up to 29 days, and the feces were positive for viral nucleic acid for up to 13 days. We successfully established a simple, rapid, and easy- to-use latex-agglutination diagnostic method for the detection of antibodies against duck Tembusu virus.

Identifying shared genetic loci and common risk genes of rheumatoid arthritis associated with three autoimmune diseases based on large-scale cross-trait genome-wide association studies.

Introduction: Substantial links between autoimmune diseases have been shown by an increasing number of studies, and one hypothesis for this comorbidity is that there is a common genetic cause. Methods: In this paper, a large-scale cross-trait Genome-wide Association Studies (GWAS) was conducted to investigate the genetic overlap among rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease and type 1 diabetes. Results and discussion: Through the local genetic correlation analysis, 2 regions with locally significant genetic associations between rheumatoid arthritis and multiple sclerosis, and 4 regions with locally significant genetic associations between rheumatoid arthritis and type 1 diabetes were discovered. By cross-trait meta-analysis, 58 independent loci associated with rheumatoid arthritis and multiple sclerosis, 86 independent loci associated with rheumatoid arthritis and inflammatory bowel disease, and 107 independent loci associated with rheumatoid arthritis and type 1 diabetes were identified with genome-wide significance. In addition, 82 common risk genes were found through genetic identification. Based on gene set enrichment analysis, it was found that shared genes are enriched in exposed dermal system, calf, musculoskeletal, subcutaneous fat, thyroid and other tissues, and are also significantly enriched in 35 biological pathways. To verify the association between diseases, Mendelian randomized analysis was performed, which shows possible causal associations between rheumatoid arthritis and multiple sclerosis, and between rheumatoid arthritis and type 1 diabetes. The common genetic structure of rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease and type 1 diabetes was explored by these studies, and it is believed that this important discovery will lead to new ideas for clinical treatment.

Development of fluorous boronic acid catalysts integrated with sulfur for enhanced amidation efficiency.

A thermally stable, fluorous sulfur-containing boronic acid catalyst has been developed and was shown to efficiently promote dehydrative condensation between carboxylic acids and amines under environmentally friendly conditions. The methodology can be applied to aliphatic, aromatic and heteroaromatic acids as well as primary and secondary amines. N-Boc protected amino acids were also successfully coupled in good yields with very little racemization. The catalyst could be reused four times with no significant loss of activity.

[Amino acids 395-416 in DNA binding domain of STAT4 is involved in IL-12-induced nuclear import of STAT4].

The purpose of the present study is to explore the mechanism of IL-12-induced nuclear import of signal transducer and activator of transcription 4 (STAT4). Assayed by analyses of homology alignment of STATs, amino acids 395-416 in DNA binding domain was found to be a potential dimer-specific nuclear localization signal (dsNLS) of STAT4. Therefore, several plasmids were constructed. Wild-type STAT4 was inserted into the SalI and BamHI sites of pEGFP-C1 for the construction of plasmid pEGFP-STAT4. The DNA fragment of STAT4 with the deletion of amino acids 395-416 was amplified by RCR and introduced into the SalI and BamHI sites of pEGFP-C1 which was named pEGFP-STAT4-Del. Classic NLS DNA sequence of SV40 T antigen was inserted into the XhoI and HindIII sites of pEGFP-C1. This plasmid was named as pEGFP-NLS and used as a positive control. Plasmid pEGFP-NLS-STAT4-Del was constructed by inserting STAT4-Del into SalI and BamHI sites of pEGFP-NLS. These plasmids were transiently transfected into Caski cells, respectively. The results showed that, after these transfected cells were stimulated by IL-12, wild type STAT4 existed in the cytoplasm at 0 min, and was predominantly localized to the nucleus at 45 min, and distributed in both cytoplasm and nucleus at 60 min, suggesting that STAT4 translocates from cytoplasm into nucleus and finally re-entries into the cytoplasm during the stimulation of IL-12. However, deletion mutant of STAT4 was arrested in cytoplasm during the IL-12 stimulation. Leptomycin B, which specifically blocks protein export from nucleus into cytoplasm, was used to further demonstrate whether STAT4-Del is transferred into nucleus even with stimulation of IL-12. After the transfected cells were pre-treated by leptomycin B, the wild type STAT4 was mainly localized in nucleus after the IL-12 stimulation, suggesting that STAT4 was translocated from cytoplasm into nucleus by the stimulation of IL-12. On the other hand, the deletion mutant of STAT4 distributed in cytoplasm throughout, implying that the mutant STAT4 lacking of amino acids 395-416 cannot move into nucleus. Furthermore, the insertion of classic NLS into EGFP-STAT4-Del restored nuclear import of STAT4-Del. These results suggest the amino acids 395-416 is a dsNLS mediating IL-12-stimulated nuclear import of activated STAT4.

Simultaneous Optimization of Donor/Acceptor Pairs and Device Specifications for Nonfullerene Organic Solar Cells Using a QSPR Model with Morphological Descriptors.

Optimally efficient organic solar cells require not only a careful choice of new donor (D) and/or acceptor (A) molecules but also the fine-tuning of experimental fabrication conditions for organic solar cells (OSCs). Herein, a new framework for simultaneously optimizing D/A molecule pairs and device specifications of OSCs is proposed, through a quantitative structure-property relationship (QSPR) model built by machine learning. Combining the device bulk properties with structural and electronic properties, the built QSPR model achieved unprecedentedly high accuracy and consistency. Additionally, a large chemical space of 1 942 785 D/A pairs is explored to find potential synergistic ones. Favorable device bulk properties such as the root-mean-square of surfaces roughness for D/A blends and the D/A weight ratio are further screened by grid search methods. Overall, this study indicates that the simultaneous optimization of D/A molecule pairs and device specifications by theoretical calculations can accelerate the improvement of OSC efficiencies.

Unraveling fundamental active units in carbon nitride for photocatalytic oxidation reactions.

Covalently bonded carbon nitride (CN) has stimulated extensive attention as a metal-free semiconductor. However, because of the complexity of polymeric structures, the acquisition of critical roles of each molecular constituent in CN for photocatalysis remains elusive. Herein, we clarify the fundamental active units of CN in photocatalysis by synthesizing CN with more detailed molecular structures. Enabled by microwave synthesis, the as-prepared CN consists of distinguishable melem (M1) and its incomplete condensed form (M2). We disclose rather than the traditional opinion of being involved in the whole photocatalytic processes, M1 and M2 make primary contributions in light absorption and charge separation, respectively. Meanwhile, oxygen molecules are unusually observed to be activated by participating in the photoexcited processes via electronic coupling mainly to M2. As a result, such CN has a higher activity, which was up to 8 times that of traditional bulk CN for photocatalytic oxidation of tetracycline in water.

Antioxidant Proteins' Identification Based on Support Vector Machine.

BACKGROUND: Evidence have increasingly indicated that for human disease, cell metabolism are deeply associated with proteins. Structural mutations and dysregulations of these proteins contribute to the development of the complex disease. Free radicals are unstable molecules that seek for electrons from the surrounding atoms for stability. Once a free radical binds to an atom in the body, a chain reaction occurs, which causes damage to cells and DNA. An antioxidant protein is a substance that protects cells from free radical damage. Accurate identification of antioxidant proteins is important for understanding their role in delaying aging and preventing and treating related diseases. Therefore, computational methods to identify antioxidant proteins have become an effective prior-pinpointing approach to experimental verification. METHODS: In this study, support vector machines was used to identify antioxidant proteins, using amino acid compositions and 9-gap dipeptide compositions as feature extraction, and feature reduction by Principal Component Analysis. RESULTS: The prediction accuracy Acc of this experiment reached 98.38%, the recall rate Sn of the positive sample was found to be 99.27%, the recall rate Sp of the negative sample reached 97.54%, and the MCC value was 0.9678. To evaluate our proposed method, the predictive performance of 20 antioxidant proteins from the National Center for Biotechnology Information(NCBI) was studied. As a result, 20 antioxidant proteins were correctly predicted by our method. Experimental results demonstrate that the performance of our method is better than the state-of-the-art methods for identification of antioxidant proteins. CONCLUSION: We collected experimental protein data from Uniport, including 253 antioxidant proteins and 1552 non-antioxidant proteins. The optimal feature extraction used in this paper is composed of amino acid composition and 9-gap dipeptide. The protein is identified by support vector machine, and the model evaluation index is obtained based on 5-fold cross-validation. Compared with the existing classification model, it is further explained that the SVM recognition model constructed in this paper is helpful for the recognition of antioxidized proteins.

Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.

The cancerlectin plays an important role in the initiation, survival, growth, metastasis, and spread of cancer. Therefore, to study the function of cancerlectin is greatly significant because it can help to identify tumor markers and tumor prevention, treatment, and prognosis. However, plenty of studies have generated a large amount of protein data. Traditional prediction methods have been unable to meet the needs of analysis. Developing powerful computational models based on these data to discriminate cancerlectins and non-cancerlectins on a large scale has been treated as one of the most important topics. In this study, we developed a feature extraction method to identify cancerlectins based on fusion of g-gap dipeptides. The analysis of variance was used to select the optimal feature set and a support vector machine was used to classify the data. The rigorous nested 10-fold cross-validation results, demonstrated that our method obtained the prediction accuracy of 83.91% and sensitivity of 83.15%. At the same time, in order to evaluate the performance of the classification model constructed in this work, we constructed a new data set. The prediction accuracy of the new data set reaches 83.3%. Experimental results show that the performance of our method is better than the state-of-the-art methods.

Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations.

BACKGROUND: Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification. RESULTS: In this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases. Particularly, this method applies a Laplacian technique to normalize the lncRNA similarity matrix and the disease similarity matrix before the construction of the lncRNA similarity network and disease similarity network. The two networks are then connected via existing lncRNA-disease associations. After that, bi-random walks are applied on the heterogeneous network to predict the potential associations between the lncRNAs and the diseases. Experimental results demonstrate that the performance of our method is highly comparable to or better than the state-of-the-art methods for predicting lncRNA-disease associations. Our analyses on three cancer data sets (breast cancer, lung cancer, and liver cancer) also indicate the usefulness of our method in practical applications. CONCLUSIONS: Our proposed method, including the construction of the lncRNA similarity network and disease similarity network and the bi-random walks algorithm on the heterogeneous network, could be used for prediction of potential associations between the lncRNAs and the diseases.