Light-Sheet Microscopy in Neuroscience.

Light-sheet microscopy is an imaging approach that offers unique advantages for a diverse range of neuroscience applications. Unlike point-scanning techniques such as confocal and two-photon microscopy, light-sheet microscopes illuminate an entire plane of tissue, while imaging this plane onto a camera. Although early implementations of light sheet were optimized for longitudinal imaging of embryonic development in small specimens, emerging implementations are capable of capturing light-sheet images in freely moving, unconstrained specimens and even the intact in vivo mammalian brain. Meanwhile, the unique photobleaching and signal-to-noise benefits afforded by light-sheet microscopy's parallelized detection deliver the ability to perform volumetric imaging at much higher speeds than can be achieved using point scanning. This review describes the basic principles and evolution of light-sheet microscopy, followed by perspectives on emerging applications and opportunities for both imaging large, cleared, and expanded neural tissues and high-speed, functional imaging in vivo.

Neuromuscular basis of Drosophila larval rolling escape behavior.

When threatened by dangerous or harmful stimuli, animals engage in diverse forms of rapid escape behaviors. In Drosophila larvae, one type of escape response involves C-shaped bending and lateral rolling followed by rapid forward crawling. The sensory circuitry that promotes larval escape has been extensively characterized; however, the motor programs underlying rolling are unknown. Here, we characterize the neuromuscular basis of rolling escape behavior. We used high-speed, volumetric, Swept Confocally Aligned Planar Excitation (SCAPE) microscopy to image muscle activity during larval rolling. Unlike sequential peristaltic muscle contractions that progress from segment to segment during forward and backward crawling, muscle activity progresses circumferentially during bending and rolling escape behavior. We propose that progression of muscular contraction around the larva's circumference results in a transient misalignment between weight and the ground support forces, which generates a torque that induces stabilizing body rotation. Therefore, successive cycles of slight misalignment followed by reactive aligning rotation lead to continuous rolling motion. Supporting our biomechanical model, we found that disrupting the activity of muscle groups undergoing circumferential contraction progression leads to rolling defects. We use EM connectome data to identify premotor to motor connectivity patterns that could drive rolling behavior and perform neural silencing approaches to demonstrate the crucial role of a group of glutamatergic premotor neurons in rolling. Our data reveal body-wide muscle activity patterns and putative premotor circuit organization for execution of the rolling escape response.

Fabrication of Metal-Organic Framework-Based Mixed-Matrix Membranes by "Soft Spray" Technique.

Metal-organic framework (MOF)-based mixed-matrix membranes (MMMs) represent a class of composite membranes that seamlessly integrate the properties of MOF fillers and polymer matrix into a hybrid system and have been widely used in countless advanced technologies. However, there remains a need for scalable and simple manufacturing techniques that can fabricate a MOF-based MMM with uniform dispersion. Herein, a series of MMMs with well-dispersed MOFs are constructed by a soft spray technique. In brief, by uniformly spraying metal ions onto the surface of a mixed solution containing polyvinylpyrrolidone (PVP) and organic ligands, a free-standing MMM is synthesized at the miscible liquid-liquid interface, facilitated by the dual function of metal ions. Moreover, soft spray technology can also introduce multifunctional materials into the MMM to customize performance. We have successfully introduced carbon black into a MOF-based MMM by soft spray, resulting in MMMs with excellent photothermal effects. The resulted MOF-based MMM exhibits favorable catalytic performance in the condensation reaction of benzaldehyde with primary amines, and the MOF-based MMM modified with carbon black significantly boosts the endothermic CO2 conversion. The work opens a new avenue for the development of MOF-based MMMs with a promising future.

Fabrication of Cu-MOF-Derived Cu/CuxO/C Bifunctional Materials for Light and Dark Catalytic Properties.

Metal-organic frameworks (MOFs) are self-assembled constitutive precursors and efficient self-sacrificial templates with metal ions/clusters and organic linkers from which multifunctional materials with carbon nanostructures can be derived. In this study, we synthesized a novel Cu-MOF with Cu(II) as the central metal ion through two ligands, N,N'-bis(pyridin-3-yl)terephthalamide (3-bpta) and fumaric acid (H2FA), which was used as a template for derivatizing carbon-based nanostructured materials of Cu and CuxO through doping with different materials (melamine, urea, and TiO2) in a simple and efficient one-step pyrolysis. The Cu/CuxO-1 catalyst possesses both dark-catalyzed degradation activity and photocatalytic reduction activity during water purification due to the hole-transfer ability between Cu+ and Cu2+ and its inhibition of electron-hole complexation. In the absence of light, force, and cocatalyst, it can also effectively remove azo dyes in water and effectively reduce Cr(VI) under the action of visible light; therefore, Cu/CuxO-1 can be used as a new type of bifunctional material for the removal of pollutants in water, which has a broad prospect.

Solvent-Induced In(III)-MOFs with Controllable Interpenetration Degree Performing High-Efficiency Separation of CO2/N2 and CO2/CH4.

Herein, three In(III)-based metal-organic frameworks (In-MOFs) with different degrees of interpenetration (DOI), namely In-MOF-1, In-MOF-2, and In-MOF-3, constructed by In3+ and Y-shaped ligands 4,4',4″-s-triazine-2,4,6-triyltribenzoate (H3TATB), are successfully synthesized through the ionothermal/solvothermal method. Subsequently, three novel In-MOFs, including noninterpenetration polycatenation, 2-fold interpenetrated, and 4-fold interpenetrated structure, are employed as the platform for systematically investigating the separation efficiency of CO2/N2, CO2/CH4, and CO2/CH4/N2 mixture gas system. Among them, In-MOF-2 shows the highest CO2 uptake capacities at 298 K and simultaneously possesses the low adsorption enthalpy of CO2 (26.4 kJ/mol at low coverage), a feature desirable for low-energy-cost adsorbent regeneration. The CO2/N2 (v: v = 15/85) selectivity of In-MOF-2 reaches 37.6 (at 298 K and 1 bar), also revealing outstanding selective separation ability from flue gases and purifying natural gas, affording a unique robust separation material as it has moderate DOI and pore size. In-MOF-2 shows exceptional stability and feasibility to achieve reproducibility. Aperture adjustment makes In-MOF-2 a versatile platform for selectively capturing CO2 from flue gases or purifying natural gas.

Ultrafast and Scalable Fabrication of Coordination Polymer Films on Network Substrates via Thermal Current-Induced Dewetting.

Coordination polymers are among the most favored active materials by researchers due to their broad application prospects. However, most of them are usually difficult to directly process into applicable devices because of their unsatisfied processability. One process of great concern for researchers is the in situ preparation of the coordination polymer on the applicable substrate, especially for the favored network substrates with good mechanical properties and 3D porous structure, which could provide obvious convenience and facilitation in the application process. Herein, we present an ultrafast and scalable thermal current-induced dewetting strategy to obtain uniform coordination polymer film in situ on network substrates, which could enable unprecedented convenience to obtain directly usable coordination polymer composites such as practical catalytic electrodes with excellent electrocatalytic performance. The proposed thermal current-induced dewetting method provides a highly adaptable and efficient practical production approach to integrate coordination polymer materials with network substrates and also provides new inspiration for understanding and applying the dewetting process on complex 3D network substrates.

Fabrication of a Co-Mo-Based Metal-Organic Framework for Growth of Double-Walled Carbon Nanotubes.

Double-walled carbon nanotubes (DWCNTs) make up a unique class of carbon nanotubes (CNTs) that are particularly intriguing for scientific research and are promising candidates for technological applications. A more precise level of control and greater yields can be achieved via catalytic chemical vapor deposition (CCVD), which involves the breakdown of a carbonaceous gas over nanoparticles. The addition of molybdenum to the system can increase the selectivity with regard to the number of walls that exist in the obtained CNTs. As reported herein, we have designed and synthesized a novel Co-Mo-MOF, [Co(3-bpta)1.5(MoO4)]·H2O (where 3-bpta = N,N'-bis(3-pyridyl)terephthalamide), and employed the Co-Mo-MOF as a bimetallic catalyst precursor for the CCVD approach to prepare high-quality DWCNTs. The Co-Mo-MOF was employed after being calcined in N2 and H2 at 1100 °C and decomposing into CoO, CoMoO4, and MoO3. Existing CoMoO4 is unaltered after reduction in H2 at 1100 °C, while CoO and MoO3 are converted into Co0 and MoO2, and more CoMoO4 is created at the expense of Co0 and MoO2 without clearly defining agglomeration. Finally, the interaction between metallic Co particles and C2H4 is what initiates the formation of DWCNTs. In-depth discussion is provided in this paper regarding the mechanism underlying the high selectivity and activity of Co-Mo catalysts in regulating the development and structure of DWCNTs. The DWCNTs also offer excellence performance when they are used as water purification agents and as selective sorbents. This work opens a feasible way to use MOFs as a way to produce MWCNTs, thus blazing a new trail in the field of MOF-derived carbon-based materials.

TFRegNCI: Interpretable Noncovalent Interaction Correction Multimodal Based on Transformer Encoder Fusion.

The interpretability is an important issue for end-to-end learning models. Motivated by computer vision algorithms, an interpretable noncovalent interaction (NCI) correction multimodal (TFRegNCI) is proposed for NCI prediction. TFRegNCI is based on RegNet feature extraction and a transformer encoder fusion strategy. RegNet is a network design paradigm that mainly focuses on local features. Meanwhile, the Vision Transformer is also leveraged for feature extraction, because it can capture global features better than RegNet while lowering the computational cost. Using a transformer encoder as the fusion strategy rather than multilayer perceptron can enhance model performance, due to its emphasis on important features with less parameters. Therefore, the proposed TFRegNCI achieved high accurate prediction (mean absolute error of ∼0.1 kcal/mol) comparing with the coupled cluster single double (triple) (CCSD(T)) benchmark. To further improve the model efficiency, TFRegNCI applies two-dimensional (2D) inputs transformed from three-dimensional (3D) electron density cubes, which saves time (30%), while the model accuracy remains. To improve model interpretability, a visualization module, Gradient-weighted Regression Activation Mapping (Grad-RAM) has been embedded. Grad-RAM is promoted from the classification algorithm, Gradient-weighted Class Activation Mapping, to perform feature visualization for the regression task. With Grad-RAM, the visual location map for features in deep learning models can be displayed. The feature map visualizations suggest that the 2D model has the similar performance as the 3D model, because of equally effective feature extractions from electron density. Moreover, the valid feature region on the location map by the 3D model is consistent with the NCIPLOT NCI isosurface. It is confirmed that the model does extract significant features related to the NCI interaction. The interpretable analyses are carried out through molecular orbital contribution on effective features. Thereby, the proposed model is likely to be a promising tool to reveal some essential information on NCIs, with regard to the level of electronic theory.

A Novel Azo-Linked Polymer Bearing Trifluoromethyl Groups for I2 Capture.

In this work, a novel three nitro-group-bearing monomer 3,6-dinitro-9-(2-trifluoromethyl-4-nitrophenyl)-carbazole (Car-3NO2 -CF3 ) via a CN coupling reaction between 3,6-dinitro-9H-carbazole (Car-2NO2 ) and 2-chloro-5-nitrobenzotrifluoride is synthesized, and obtained single crystal and single crystal analysis data for this compound. The crystal system of Car-3NO2 -CF3 is monoclinic and it has a P 21/c space group. This new monomer (Car-3NO2 -CF3 ) is also utilized to synthesize a novel azo-linked polymer (Azo-Car-CF3 ). The trifluoromethyl group has polar CF bonds, and thus it is an effective functional group for the capture of iodine. Azo-Car-CF3 has great thermal stability with a mass loss of only 10% at 414 °C, as well as good chemical stability as is demonstrated by its low solubility in common organic solvents such as tetrahydrofuran (THF), acetone, methanol, ethanol, and N,N-dimethylformamide (DMF). The specific surface area of Azo-Car-CF3 can reach as high as 335 m2  g-1 . Azo-Car-CF3 exhibits an excellent capacity for iodine adsorption and can reach up to 1198 mg g-1 in cyclohexane solution, and its adsorption capacity for iodine vapor can get to 2100 mg g-1 . In addition, ethanol can be used to trigger the release of the captured iodine to be easily released from Azo-Car-CF3 .

Enantioselective effects of chiral prothioconazole and its metabolites: Oxidative stress in HepG2 cells and lysozyme activity.

Chiral pesticides may exhibit enantioselectivity in terms of bioconcentration, environmental fate, and reproductive toxicity. Here, chiral prothioconazole and its metabolites were selected to thoroughly investigate their enantioselective toxicity and mechanisms at the molecular and cellular levels. Multispectral techniques revealed that the interaction between chiral PTC/PTCD and lysozyme resulted in the formation of a complex, leading to a change in the conformation of lysozyme. Meanwhile, the effect of different conformations of PTC/PTCD on the conformation of lysozyme differed, and its metabolites were able to exert a greater effect on lysozyme compared to prothioconazole. Moreover, the S-configuration of PTCD interacted most strongly with lysozyme. This conclusion was further verified by DFT calculations and molecular docking as well. Furthermore, the oxidative stress indicators within HepG2 cells were also affected by chiral prothioconazole and its metabolites. Specifically, S-PTCD induced more substantial perturbation of the normal oxidative stress processes in HepG2 cells, and the magnitude of the perturbation varied significantly among different configurations (P > 0.05). Overall, chiral prothioconazole and its metabolites exhibit enantioselective effects on lysozyme conformation and oxidative stress processes in HepG2 cells. This work provides a scientific basis for a more comprehensive risk assessment of the environmental behaviors and effects caused by chiral pesticides, as well as for the screening of highly efficient and less biotoxic enantiomeric monomers.

Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0.

The limited per-pixel bandwidth of most microscopy methods requires compromises between field of view, sampling density and imaging speed. This limitation constrains studies involving complex motion or fast cellular signaling, and presents a major bottleneck for high-throughput structural imaging. Here, we combine high-speed intensified camera technology with a versatile, reconfigurable and dramatically improved Swept, Confocally Aligned Planar Excitation (SCAPE) microscope design that can achieve high-resolution volumetric imaging at over 300 volumes per second and over 1.2 GHz pixel rates. We demonstrate near-isotropic sampling in freely moving Caenorhabditis elegans, and analyze real-time blood flow and calcium dynamics in the beating zebrafish heart. The same system also permits high-throughput structural imaging of mounted, intact, cleared and expanded samples. SCAPE 2.0's significantly lower photodamage compared to point-scanning techniques is also confirmed. Our results demonstrate that SCAPE 2.0 is a powerful, yet accessible imaging platform for myriad emerging high-speed dynamic and high-throughput volumetric microscopy applications.

Electrochemically Assisted Cycloaddition of Carbon Dioxide to Styrene Oxide on Copper/Carbon Hybrid Electrodes: Active Species and Reaction Mechanism.

A novel electrochemically assisted cycloaddition process is proposed, in which highly efficient coupling of CO2 with styrene oxide (SO) can be achieved to form styrene carbonate (SC) as a high-value-added product. A series of Cu catalysts with different morphologies and chemical states were fabricated on carbon paper (CP) by using in-situ electrodeposition, and the sample with nano-dendrimer structure was found to exhibit a relatively high activity of 74.8 % SC yield with 92.7 % SO conversion under gentle reaction conditions, thus showing its potential for practical applications. The relatively high electrochemically active surface area and charge transfer ability of dendrimer-like Cu benefited the electrochemical reaction. In particular, the Cu2+ species that were formed in situ during the reaction played a vital role in enhancing the activity and selectivity of the proposed Cu/CP hybrid catalyst. Cu2+ atoms served as active sites that can not only electrochemically activate CO2 but also facilitate the ring opening of SO. Mechanistic analysis suggested that the reaction followed electrochemical and liquid-phase heterogeneous paths, which provide a new green and sustainable route for efficient utilization of CO2 resources for fine chemical electrosynthesis.

DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.19 kcal/mol to ∼0.2 kcal/mol for a NCI molecular database (>1000 molecules). The representativeness of the joint features can be visualized by t-distributed stochastic neighbor embedding (t-SNE), where they can distinguish categorized NCI systems quite well. Therefore, the fused model performs better than its component networks. In addition, the 3D CNN takes electron density as inputs that are in the same range, despite the size of molecular systems, so it can promote model applicability and transferability. To clarify the applicability of DeepNCI, an application domain (AD) has been defined with merged features using the K-nearest-neighbor method. The calculations for external test sets are shown that AD can properly monitor the reliability for a prediction. The model transferability is tested with a small database of homolysis bond dissociation energy including only dozens of samples. With NCI database pretrained parameters, the same or better performance than the reported results is achieved by transfer learning. This suggests that the DeepNCI model is transferable and it may transfer to other relative tasks, which possibly can resolve some small sampling problems. The source code of DeepNCI can be freely accessed at https://github.com/wenzelee/DeepNCI.

New Insights into the Recognition and Sensing Mechanism of a H2S Fluorescent Probe: A Theoretical Perspective.

H2S is an important signal molecule in living systems and related with many physiological processes and diseases. Rapid detection of H2S, hence, is important for studying physiological processes and early diagnosis of diseases. Deep insight into the sensing mechanism is significant and inspiring for the design and modification of high-efficiency H2S probes. The current study has theoretically investigated the recognition and fluorescence mechanism of a newly reported high-efficiency H2S probe. The recognition mechanism is determined to be the reaction between the probe and HS- anion, the rationality of which is further confirmed from the fluorescence property of the recognition product. The non-fluorescence property of the probe attributes to a photoinduced electron transfer process, and the turn-on fluorescence upon exposure to H2S exhibits an intramolecular charge transfer property according to frontier molecular orbital analysis.

Theoretical Perspective on the Sensing Mechanism of a Novel Fluorescent Probe for Nitramine Explosives: The Role of Radical Reactions.

Rapid detection of hidden nitramine explosives in public areas is a pressing concern for public safety. Deep insight into the sensing mechanism is significant and inspiring to the design of new high-efficiency nitramine probes. This study has theoretically investigated the recognition and fluorescence mechanism of a newly reported high-efficiency nitramine probe, proposing a new reaction pattern and sensing product for the probe with the photodegraded radical nitro dioxide (NO2) of nitramines. The rationality of the new detection product is confirmed by the fluorescence properties, IR analysis, and energy profiles. The recognition mechanism is found to be an H-abstraction reaction via NO2 and the turn-off fluorescence mechanism is suggested as a photoinduced electron transfer (PET) process based on the results of the frontier molecular orbital (FMO) analysis. The high selectivity of the probe toward NO2 is illustrated based on the energy analysis of the sensing products.

Intercalary frozen autografts for reconstruction of bone defects following meta-/diaphyseal tumor resection at the extremities.

BACKGROUND: For patients with malignant limb tumors, salvage surgery can be achieved using endoprosthesis or biological reconstructions like allograft or autograft. In carefully selected patients, resected bone can be recycled after sterilization using methods like autoclaving, irradiation, pasteurization or freezing with liquid nitrogen. We evaluated the clinical outcome and complications of malignant limb tumors treated with intercalary resection and frozen autograft reconstruction. METHODS: We reviewed 33 patients whose malignant bone tumors were treated by wide resection and reconstruction with recycling liquid nitrogen-treated autografts between 2006 and 2017. Limb function, bone union at the osteotomy site and complications were evaluated. Functional outcome was assessed using the Musculoskeletal Tumor Society (MSTS) scoring system. RESULTS: The cohort comprised 16 males and 17 females, with a mean age of 35.4 years (14-76 years). The most common tumor was osteosarcoma (7 cases). Tumors were located in the humerus (5), ulna (1), femur (10) and tibia (17). The mean follow-up was 49.9 months (range 12-127 months). Of the 33 patients, 16 remained disease-free, and 3 were alive with disease. The mean size of the defect after tumor resection was 11.6 cm (range 6-25 cm). Bone union was achieved in 32 patients, with a mean union time of 8.8 months (range 4-18 months). Complications included 1 graft nonunion, 2 infections (1 superficial, 1 deep infection), 1 leg length discrepancy, 2 graft fractures and 3 local recurrences. The mean MSTS score was 87.2% (range 70-100%). CONCLUSION: Liquid nitrogen-treated tumor-bearing autograft is an effective option for biological reconstruction after meta-/diaphyseal tumor resection of long bones. This method has excellent clinical outcomes and is especially recommended for patients with no severe osteolytic bone tumors.

Total White Blood Cell Count Mediated the Association Between Increased Arterial Stiffness and Risk of Type 2 Diabetes Mellitus in Chinese Adults.

OBJECTIVE: Type 2 diabetes mellitus (T2DM) is known to be related to increased arterial stiffness. However, little is known about the risk of T2DM due to accelerated arterial stiffness and the underlying mechanism involved. We aimed to examine arterial stiffness, as determined by brachial-ankle pulse wave velocity (baPWV), in relation to T2DM among a community-based population and whether the association was mediated by white blood cell (WBC) counts. Approach and results: A total of 1036 Chinese adults aged 64.3 years with complete data were qualified in the present study. The dose-response association between baPWV levels, WBC counts, and risk of T2DM were explored using generalized linear models or multivariate logistic regression models. A mediation analysis was conducted to investigate the role of WBC counts on the association between baPWV and T2DM. After multivariate adjustments, we observed a dose-responsive relationship between increased baPWV and elevated risk of T2DM: comparing extreme tertiles of baPWV, the adjusted odds ratio for T2DM risk was 2.29 (95% CI, 1.32-3.98; P for trend =0.005). In addition, significant dose-dependent relationships were found across baPWV tertiles with increasing total or differential WBC counts, which in turn, were positively related to higher risk of T2DM (all P for trend <0.05). Mediation analyses indicated that total WBC count mediated 4.5% of the association between increased baPWV and elevated T2DM risk. CONCLUSIONS: Increased arterial stiffness might increase T2DM risk among middle-aged and older Chinese adults, which was partially mediated by total WBC count.

Efficient Corrections for DFT Noncovalent Interactions Based on Ensemble Learning Models.

Machine learning has exhibited powerful capabilities in many areas. However, machine learning models are mostly database dependent, requiring a new model if the database changes. Therefore, a universal model is highly desired to accommodate the widest variety of databases. Fortunately, this universality may be achieved by ensemble learning, which can integrate multiple learners to meet the demands of diversified databases. Therefore, we propose a general procedure for learning ensemble establishment based on noncovalent interactions (NCIs) databases. Additionally, accurate NCI computation is quite demanding for first-principles methods, for which a competent machine learning model can be an efficient solution to obtain high NCI accuracy with minimal computational resources. In regard to these aspects, multiple schemes of ensemble learning models (Bagging, Boosting, and Stacking frameworks), are explored in this study. The models are based on various low levels of density functional theory (DFT) calculations for the benchmark databases S66, S22, and X40. All NCIs computed by the DFT calculations can be improved to high-level accuracy (root-mean-square error RMSE = 0.22 kcal/mol in contrast to CCSD(T)/CBS benchmark) by established ensemble learning models. Compared with single machine learning models, ensemble models show better accuracy (RMSE of the best model is further lowered by ∼25%), robustness and goodness-of-fit according to evaluation parameters suggested by the OECD. Among ensemble learning models, heterogeneous Stacking ensemble models show the most valuable application potential. The standardized procedure of constructing learning ensembles has been well utilized on several NCI data sets, and this procedure may also be applicable for other chemical databases.

Safety of the Bacillus thuringiensis-derived Cry1A.105 protein: Evidence that domain exchange preserves mode of action and safety.

The lepidopteran-active Cry1A.105 protein is a chimeric three-domain insecticidal toxin with distinct structural domains derived from the naturally occurring Cry1Ab, Cry1Ac and Cry1F proteins from the soil bacterium Bacillus thuringiensis (Bt). The X-ray crystal structure of the Cry1A.105 tryptic core at 3.0 Šresolution demonstrates its high structural similarity to the tryptic core of Cry1Ac. Bioinformatics analyses demonstrate that Cry1A.105 has no significant amino acid sequence similarity to known allergens or mammalian toxins, which is the same conclusion reached for its component domains. Like its intact donor proteins, Cry1A.105 was heat labile at temperatures ≥75 °C and degraded upon exposure to gastrointestinal proteases. No adverse effects were observed in mice when Cry1A.105 was dosed orally at 2451 mg/kg body weight. Therefore, the weight of evidence supports that Cry1A.105 is safe for human and animal consumption. These results support the conclusion that the safety of a chimeric protein for human or animal consumption can be evaluated in the context of the safety of its donor proteins.

Fabrication of nanofibrous membranes decorated with metal-organic frameworks for detection of pollutants in water.

The environmental pollution caused by antibiotics, Fe3+ and MnO4- pollutants is becoming increasingly serious. Polyacrylonitrile (PAN) and polymethyl methacrylate (PMMA) were used and decorated with metal-organic frameworks (MOFs) to fabricated three kinds of nanofibrous membranes (NFMs) with different shapes and sizes were prepared by electrospinning technology using in situ growth method and mixed spinning method. The structures and properties of the above three kinds of NFMs were characterized. Among them, PAN@Co/Mn-MOF-74 NFM prepared by in-situ growth method based on PAN was a kind of nano-fluorescent NFM sensor with uniform structure and good fluorescence performance. It showed unique specificity and excellent sensitivity in the detection of ORN, Fe3+ and MnO4-. Compared with previously reported functionalized MOFs, PAN@Co/Mn-MOF-74 NFM has a lower limit of detection (LOD). This study provides a feasible technical route for the preparation of nano-fluorescent NFMs and the targeted detection of trace metal ions and antibiotics.