Multi-fluid Modeling Biomass Fast Pyrolysis in the Fluidized-Bed Reactor Including Particle Shrinkage Effects

The fast pyrolysis of biomass in a bubbling fluidized-bed reactor was simulated with the multi-fluid model combing the variable particle density and diameter model based on the mass conservation at the particle scale. Different particle shrinkage effects on the reactor performance were investigated through changing the apparent density of char species. The detailed distributions of particle density and diameter in the reactor and entrained out of the system were revealed. The results demonstrate that the reactor performance, including the particle density and diameter distribution, entrainment behavior, biochar composition and yield, and biomass conversion, is dramatically affected by the particle shrinkage effects. The average particle density increases, while the average particle diameter decreases, with the increase of the char density, which means more intense shrinkage. A weaker shrinkage effect leads to stronger entrainment behavior, larger biochar yield and mass fraction of biomass in biochar, and lower biomass conversion

Table_1_Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics.docx

RNA-seq has become a state-of-the-art technique for transcriptomic studies. Advances in both RNA-seq techniques and the corresponding analysis tools and pipelines have unprecedently shaped our understanding in almost every aspects of plant sciences. Notably, the integration of huge amount of RNA-seq with other omic data sets in the model plants and major crop species have facilitated plant regulomics, while the RNA-seq analysis has still been primarily used for differential expression analysis in many less-studied plant species. To unleash the analytical power of RNA-seq in plant species, especially less-studied species and biomass crops, we summarize recent achievements of RNA-seq analysis in the major plant species and representative tools in the four types of application: (1) transcriptome assembly, (2) construction of expression atlas, (3) network analysis, and (4) structural alteration. We emphasize the importance of expression atlas, coexpression networks and predictions of gene regulatory relationships in moving plant transcriptomes toward regulomics, an omic view of genome-wide transcription regulation. We highlight what can be achieved in plant research with RNA-seq by introducing a list of representative RNA-seq analysis tools and resources that are developed for certain minor species or suitable for the analysis without species limitation. In summary, we provide an updated digest on RNA-seq tools, resources and the diverse applications for plant research, and our perspective on the power and challenges of short-read RNA-seq analysis from a regulomic point view. A full utilization of these fruitful RNA-seq resources will promote plant omic research to a higher level, especially in those less studied species.</p

DataSheet_1_Development and validation of A CT-based radiomics nomogram for prediction of synchronous distant metastasis in clear cell renal cell carcinoma.doc

BackgroundEarly identification of synchronous distant metastasis (SDM) in patients with clear cell Renal cell carcinoma (ccRCC) can certify the reasonable diagnostic examinations.MethodsThis retrospective study recruited 463 ccRCC patients who were divided into two cohorts (training and internal validation) at a 7:3 ratio. Besides, 115 patients from other hospital were assigned external validation cohort. A radiomics signature was developed based on features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables and CT findings were combined to develop clinical factors model. Integrating radiomics signature and clinical factors model, a radiomics nomogram was developed.ResultsTen features were used to build radiomics signature, which yielded an area under the curve (AUC) 0.882 in the external validation cohort. By incorporating the clinical independent predictors, the clinical model was developed with AUC of 0.920 in the external validation cohort. Radiomics nomogram (external validation, 0.925) had better performance than clinical factors model or radiomics signature. Decision curve analysis demonstrated the superiority of the radiomics nomogram in terms of clinical usefulness.ConclusionsThe CT-based nomogram could help in predicting SDM status in patients with ccRCC, which might provide assistance for clinicians in making diagnostic examinations.</p

MOF-Derived Formation of Ni₂P–CoP Bimetallic Phosphides with Strong Interfacial Effect toward Electrocatalytic Water Splitting

Bimetallic phosphides have attracted research interest for their synergistic effect and superior electrocatalytic activities for electrocatalytic water splitting. Herein, a MOF-derived phosphorization approach was developed to produce Ni₂P–CoP bimetallic phosphides as bifunctional electrocatalysts for both hydrogen and oxygen evolution reactions (HER and OER). Ni₂P–CoP shows superior electrocatalytic activities to both pure Ni₂P and CoP toward HER and OER, revealing a strong synergistic effect. High-resolution transmission electron microscopy and energy dispersive X-ray spectroscopy elemental mapping analysis show that, in the sample Ni₂P–CoP, the Ni₂P and CoP nanoparticles with an average particle size 10–20 nm were mixed closely on the nanoscale, creating numerous Ni₂P/CoP interfaces. By comparison with the sample Ni₂P+CoP, in which seldom Ni₂P/CoP interfaces exist, we documented that the Ni₂P/CoP interface is an essential prerequisite to realize the synergistic effect and to achieve the enhanced electrocatalytic activities in Ni₂P–CoP bimetallic phosphides. This finding is meaningful for designing and developing bicomponent and even multicomponent electrocatalysts

Engineering Biosensors with Dual Programmable Dynamic Ranges

Although extensively used in all fields of chemistry, molecular recognition still suffers from a significant limitation: host–guest binding displays a fixed, hyperbolic dose–response curve, which limits its usefulness in many applications. Here we take advantage of the high programmability of DNA chemistry and propose a universal strategy to engineer biorecognition-based sensors with dual programmable dynamic ranges. Using DNA aptamers as our model recognition element and electrochemistry as our readout signal, we first designed a dual signaling “signal-on” and “signal-off” adenosine triphosphate (ATP) sensor composed of a ferrocene-labeled ATP aptamer in complex to a complementary, electrode-bound, methylene-blue labeled DNA. Using this simple “dimeric” sensor, we show that we can easily (1) tune the dynamic range of this dual-signaling sensor through base mutations on the electrode-bound DNA, (2) extend the dynamic range of this sensor by 2 orders of magnitude by using a combination of electrode-bound strands with varying affinity for the aptamers, (3) create an ultrasensitive dual signaling sensor by employing a sequestration strategy in which a nonsignaling, high affinity “depletant” DNA aptamer is added to the sensor surface, and (4) engineer a sensor that simultaneously provides extended and ultrasensitive readouts. These strategies, applicable to a wide range of biosensors and chemical systems, should broaden the application of molecular recognition in various fields of chemistry

Additional file 1 of Truncated FRMD7 proteins in congenital Nystagmus: novel frameshift mutations and proteasomal pathway implications

Supplementary Material

Regulation of DNA Self-Assembly and DNA Hybridization by Chiral Molecules with Corresponding Biosensor Applications

Chirality is one of the fundamental biochemical properties in a living system, and a lot of biological and physiological processes are greatly influenced by the chirality of molecules. Inspired by this phenomenon, we study the covalent assembly of DNA on chiral molecule modified surfaces and further discuss the hybridization of DNA on chiral surfaces with nucleic acids. Take methylene blue (MB) modified DNA as a model molecule, we show that the peak current of the L-NIBC (NIBC, <i>N</i>-isobutyryl-l­(d)-cysteine) modified gold surface (L-surface) is larger than the D-surface because of a stronger interaction between short-chain DNA and the L-surface; however, the D-surface has a higher hybridization efficiency than the L-surface. Moreover, we apply this result to actual application by choosing an electrochemical DNA (E-DNA) sensor as a potential platform. Furthermore, we further amplify the difference of hybridization efficiency using the supersandwich assay. More importantly, our findings are successfully employed to program the sensitivity and limit of detection

Nanopore-Based DNA-Probe Sequence-Evolution Method Unveiling Characteristics of Protein–DNA Binding Phenomena in a Nanoscale Confined Space

Almost all of the important functions of DNA are realized by proteins which interact with specific DNA, which actually happens in a limited space. However, most of the studies about the protein–DNA binding are in an unconfined space. Here, we propose a new method, nanopore-based DNA-probe sequence-evolution (NDPSE), which includes up to 6 different DNA-probe systems successively designed in a nanoscale confined space which unveil the more realistic characteristics of protein–DNA binding phenomena. There are several features; for example, first, the edge-hindrance and core-hindrance contribute differently for the binding events, and second, there is an equilibrium between protein–DNA binding and DNA–DNA hybridization

Predicting Spatiotemporal Distributions in a Bubbling Fluidized Bed for Biomass Fast Pyrolysis Using Convolutional Neural Networks

Bubbling fluidized-bed biomass fast pyrolysis is a crucial technology for carbon neutrality and sustainability, and computational fluid dynamics (CFD) is one of the promising approaches to investigate and optimize bubbling fluidized-bed biomass fast pyrolysis. However, traditional CFD is still computationally costly for bubbling fluidized-bed biomass fast pyrolysis, especially for spatiotemporal transport-reaction behaviors, which are critical to clarifying intrinsic characteristics and optimizing operations. To address this issue, a deep learning (DL) model centered on convolutional neural networks was developed based on CFD results to efficiently predict spatiotemporal distributions of quantities of each phase in a bubbling fluidized bed for biomass fast pyrolysis. Input of the DL model is a sequence of spatiotemporal distributions, and only an initial input is required to generate continuous outputs. The model was optimized by adjusting four typical parameters, i.e., length of input sequence, number of neurons, learning rate, and prediction step size. Accuracy of short-term prediction (10 frames) and stability of long-term prediction (1000 frames) were analyzed as well as the relationship between time-averaged distributions and prediction length. It was found that with satisfactory accuracy, several orders of magnitude increase in computation efficiency can be realized. Thus, the developed model paves the way for low-cost and high-accuracy simulations of biomass fast pyrolysis

Additional file 1 of A versatile engineered extracellular vesicle platform simultaneously targeting and eliminating senescent stromal cells and tumor cells to promote tumor regression

Supplementary Material