Surface loops in a single SH2 domain are capable of encoding the spectrum of specificity of the SH2 family

Src homology 2 (SH2) domains play an essential role in cellular signal transduction by binding to proteins phos-phorylated on Tyr residue. Although Tyr phosphorylation (pY) is a prerequisite for binding for essentially all SH2 domains characterized to date, different SH2 domains prefer specific sequence motifs C-terminal to the pY residue. Because all SH2 domains adopt the same structural fold, it is not well understood how different SH2 domains have acquired the ability to recognize distinct sequence motifs. We have shown previously that the EF and BG loops that connect the secondary structure elements on an SH2 domain dictate its specificity. In this study, we investigated if these surface loops could be engineered to encode diverse specificities. By characterizing a group of SH2 variants selected by different pY peptides from phage-displayed libraries, we show that the EF and BG loops of the Fyn SH2 domain can encode a wide spectrum of specificities, including all three major specificity classes (p 2, p 3 and p 4) of the SH2 domain family. Furthermore, we found that the specificity of a given variant correlates with the sequence feature of the bait peptide used for its isolation, suggesting that an SH2 domain may acquire specificity by co-evolving with its ligand. Intriguingly, we found that the SH2 variants can employ a variety of different mechanisms to confer the same specificity, suggesting the EF and BG loops are highly flexible and adaptable. Our work provides a plausible mechanism for the SH2 domain to acquire the wide spectrum of specificity observed in nature through loop variation with minimal disturbance to the SH2 fold. It is likely that similar mechanisms may have been employed by other modular interaction domains to generate diversity in specificity

Single-Layered Organic Photovoltaics With Double Cascading Charge Transport Pathways: 18% Efficiencies

The chemical structure of donors and acceptors limit the power conversion efficiencies achievable with active layers of binary donor-acceptor mixtures. Here, using quaternary blends, double cascading energy level alignment in bulk heterojunction organic photovoltaic active layers are realized, enabling efficient carrier splitting and transport. Numerous avenues to optimize light absorption, carrier transport, and charge-transfer state energy levels are opened by the chemical constitution of the components. Record-breaking PCEs of 18.07% are achieved where, by electronic structure and morphology optimization, simultaneous improvements of the open-circuit voltage, short-circuit current and fill factor occur. The donor and acceptor chemical structures afford control over electronic structure and charge-transfer state energy levels, enabling manipulation of hole-transfer rates, carrier transport, and non-radiative recombination losses

SNORA38B promotes proliferation, migration, invasion and epithelial-mesenchymal transition of gallbladder cancer cells <em>via</em> activating TGF-β/Smad2/3 signaling

Evidence has shown that small nucleolar RNAs (snoRNAs) participate in the tumorigenesis in multiple cancers, including gallbladder cancer (GBC). Our results showed that SNORA38B level was increased in GBC tissues compared to adjacent normal tissues. Thus, this research aimed to explore the role and molecular mechanisms of SNORA38B in GBC. SNORA38B level between normal and GBC tissues was evaluated by RT-qPCR. Cell proliferation, apoptosis, migration, and invasion were tested by EdU assay, TUNEL staining and transwell assay, respectively on human intrahepatic biliary epithelial cells (HIBEpiCs) and the GBC cell lines, NOZ and GBC-SD. Expression of proteins in GBC cells was evaluated by immunofluorescence and Western blot assays. We found that, relative to normal tissues, SNORA38B level was notably elevated in GBC tissues. SNORA38B overexpression obviously enhanced GBC cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT), but weakened cell apoptosis. Conversely, SNORA38B downregulation strongly suppressed the proliferation and EMT of GBC cells and induced cell apoptosis and ferroptosis, whereas these phenomena were obviously reversed by TGF-β. Meanwhile, SNORA38B downregulation notably reduced the levels of phosphorylated-Smad2 and phosphorylated-Smad3 in GBC cells, whereas these levels were elevated by TGF-β. Collectively, downregulation of SNORA38B could inhibit GBC cell proliferation and EMT and induce ferroptosis via inactivating TGF-β1/Smad2/3 signaling. These findings showed that SNORA38B may be potential target for GBC treatment

Revealing the crystalline packing structure of Y6 in the active layer of organic solar cells: the critical role of solvent additives

The bulk heterojunction (BHJ) morphology of photovoltaic materials is crucial to the fundamental optoelectronic properties of organic solar cells (OSCs). However, in the photoactive layer, the intrinsic crystalline packing structure of Y6, currently the hallmark molecule among Y-series non-fullerene acceptors (NFAs), has not been unambiguously determined. Here, employing grazing-incidence wide-angle X-ray scattering (GIWAXS), we managed to uncover the intrinsic crystalline packing structure of Y6 in the BHJ active layer of OSCs, which is found to be different from its single-crystal structure reported previously. Moreover, we find that solvent additive 1-chloronaphthalene (CN) can induce highly ordered packing of Y6 in BHJ thin films. With the help of atomistic molecular dynamics simulations, it is revealed that π-π interactions generally exist between naphthalene derivatives and IC terminals of Y6 analogues, which would essentially improve their long-range ordering. Our work reveals the intrinsic crystalline packing structure of Y6 in the BHJ active layer as well as its crystallization mechanism in thin films, thus providing direct correlations between this crystalline packing and the device characteristics and photophysical properties.Knut och Alice Wallenbergs StiftelseImmediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Novel Association Strategy with Copy Number Variation for Identifying New Risk Loci of Human Diseases

Copy number variations (CNV) are important causal genetic variations for human disease; however, the lack of a statistical model has impeded the systematic testing of CNVs associated with disease in large-scale cohort.Here, we developed a novel integrated strategy to test CNV-association in genome-wide case-control studies. We converted the single-nucleotide polymorphism (SNP) signal to copy number states using a well-trained hidden Markov model. We mapped the susceptible CNV-loci through SNP site-specific testing to cope with the physiological complexity of CNVs. We also ensured the credibility of the associated CNVs through further window-based CNV-pattern clustering. Genome-wide data with seven diseases were used to test our strategy and, in total, we identified 36 new susceptible loci that are associated with CNVs for the seven diseases: 5 with bipolar disorder, 4 with coronary artery disease, 1 with Crohn's disease, 7 with hypertension, 9 with rheumatoid arthritis, 7 with type 1 diabetes and 3 with type 2 diabetes. Fifteen of these identified loci were validated through genotype-association and physiological function from previous studies, which provide further confidence for our results. Notably, the genes associated with bipolar disorder converged in the phosphoinositide/calcium signaling, a well-known affected pathway in bipolar disorder, which further supports that CNVs have impact on bipolar disorder.Our results demonstrated the effectiveness and robustness of our CNV-association analysis and provided an alternative avenue for discovering new associated loci of human diseases

Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases

Genome-wide interaction-based association (GWIBA) analysis has the potential to identify novel susceptibility loci. These interaction effects could be missed with the prevailing approaches in genome-wide association studies (GWAS). However, no convincing loci have been discovered exclusively from GWIBA methods, and the intensive computation involved is a major barrier for application. Here, we developed a fast, multi-thread/parallel program named “pair-wise interaction-based association mapping” (PIAM) for exhaustive two-locus searches. With this program, we performed a complete GWIBA analysis on seven diseases with stringent control for false positives, and we validated the results for three of these diseases. We identified one pair-wise interaction between a previously identified locus, C1orf106, and one new locus, TEC, that was specific for Crohn's disease, with a Bonferroni corrected P<0.05 (P = 0.039). This interaction was replicated with a pair of proxy linked loci (P = 0.013) on an independent dataset. Five other interactions had corrected P<0.5. We identified the allelic effect of a locus close to SLC7A13 for coronary artery disease. This was replicated with a linked locus on an independent dataset (P = 1.09×10−7). Through a local validation analysis that evaluated association signals, rather than locus-based associations, we found that several other regions showed association/interaction signals with nominal P<0.05. In conclusion, this study demonstrated that the GWIBA approach was successful for identifying novel loci, and the results provide new insights into the genetic architecture of common diseases. In addition, our PIAM program was capable of handling very large GWAS datasets that are likely to be produced in the future

Estimating Aboveground Carbon Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data

Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored the potential of combining LiDAR and hyperspectral data to estimate tree-level carbon stock. The objective of this study is to explore the potential of integrating unmanned aerial vehicle (UAV) LiDAR with hyperspectral data for tree-level aboveground carbon stock estimation. To achieve this goal, we first delineated individual trees by a CHM-based watershed segmentation algorithm. We then extracted structural and spectral features from UAV LiDAR and hyperspectral data respectively. Then, Pearson correlation analysis was conducted to assess the correlation between LiDAR features, hyperspectral features, and tree-level carbon stock, based on which, features were selected for model development. Finally, we developed tree-level carbon stock estimation models based on the Schumacher&ndash;Hall formula and stepwise multiple regression. Results showed that both LiDAR and hyperspectral features were strongly correlated to tree-level carbon stock. Both tree height (H, r = 0.75) and Green index (GI, r = 0.83) had the highest correlation coefficients with tree-level carbon stock in LiDAR and hyperspectral features, respectively. The best model using LiDAR features alone includes the metrics of H, the 10th height percentile of points (PH10), and mean height of points (Hmean), and can explain 74% of the variations in tree-level carbon stock. Similarly, the best model using hyperspectral data includes GI and modified normalized differential vegetation index (mNDVI), and has similar explanatory power (r2 = 0.75). The model that integrates predictors, namely, GI and the 95th height percentile of points (PH95) from hyperspectral and LiDAR data, substantially improves the explanatory power (r2 = 0.89). These results indicated that while either LiDAR data or hyperspectral data alone can estimate tree-level carbon stock with reasonable accuracy, combining LiDAR and hyperspectral features can substantially improve the explanatory power of the model. Such results suggested that tree-level carbon stock estimation can greatly benefit from the complementary nature of LiDAR-detected structural characteristics and hyperspectral-captured spectral information of vegetation

Design and application of ion potentiometer

In order to achieve quick identification of mine water bursting source and flood forecasting, an ion potentiometer used for water quality analysis was designed. The ion potentiometer uses ion selective electrode to transfer corresponding ion in water sample to voltage, and the voltage is amplified by signal conversion circuit and converted to digital value by 24 bit high-precision AD converter. Finally the ion potentiometer uses TFT LCD screen to display ion species, ion concentration and other related information, and it has high measurement accuracy of ion content and is easy to use