Algorithmic Temperature 1 Self-Assembly

We investigate the power of the Wang tile self-assembly model at temperature 1, a threshold value that permits attachment between any two tiles that share even a single bond. When restricted to deterministic assembly in the plane, no temperature 1 assembly system has been shown to build a shape with a tile complexity smaller than the diameter of the shape. Our work shows a sharp contrast in achievable tile complexity at temperature 1 if either growth into the third dimension or a small probability of error are permitted. Motivated by applications in nanotechnology and molecular computing, and the plausibility of implementing 3 dimensional self-assembly systems, our techniques may provide the needed power of temperature 2 systems, while at the same time avoiding the experimental challenges faced by those systems

Temperature 1 Self-Assembly: Deterministic Assembly in 3D and Probabilistic Assembly in 2D

We investigate the power of the Wang tile self-assembly model at temperature 1, a threshold value that permits attachment between any two tiles that share even a single bond. When restricted to deterministic assembly in the plane, no temperature 1 assembly system has been shown to build a shape with a tile complexity smaller than the diameter of the shape. In contrast, we show that temperature 1 self-assembly in 3 dimensions, even when growth is restricted to at most 1 step into the third dimension, is capable of simulating a large class of temperature 2 systems, in turn permitting the simulation of arbitrary Turing machines and the assembly of $n\times n$ squares in near optimal $O(\log n)$ tile complexity. Further, we consider temperature 1 probabilistic assembly in 2D, and show that with a logarithmic scale up of tile complexity and shape scale, the same general class of temperature $\tau=2$ systems can be simulated with high probability, yielding Turing machine simulation and $O(\log^2 n)$ assembly of $n\times n$ squares with high probability. Our results show a sharp contrast in achievable tile complexity at temperature 1 if either growth into the third dimension or a small probability of error are permitted. Motivated by applications in nanotechnology and molecular computing, and the plausibility of implementing 3 dimensional self-assembly systems, our techniques may provide the needed power of temperature 2 systems, while at the same time avoiding the experimental challenges faced by those systems

A nanobody-based molecular toolkit for ubiquitin–proteasome system explores the main role of survivin subcellular localization

Targeted protein degradation is a powerful tool for determining the function of specific proteins nowadays. Survivin is the smallest member of the inhibitor of the apoptosis protein (IAP) family. It exists in the cytoplasm and nucleus of cells, but the exact function of survivin in different subcellular locations retained unclear updates due to the lack of effective and simple technical means. In this study, we created a novel nanoantibody-based molecular toolkit, namely, the ubiquitin–proteasome system (Nb4A-Fc-T2A-TRIM21), that can target to degrade survivin localized in cytoplasmic and cell nuclear by ubiquitinating, and by which to verify the potential roles of survivin subcellular localization. Also, the results showed that the cytoplasmic survivin mainly plays an anti-apoptotic function by directly or indirectly inhibiting the caspase pathway, and the nuclear survivin mainly promotes cell proliferation and participates in the regulation of the cell cycle. In addition, the Nb4A-Fc-T2A-TRIM21 system can degrade the endogenous survivin protein in a large amount by the ubiquitin–proteasome pathway, and the system can provide theoretical support for ubiquitination degradation targeting other endogenous proteins

Sublinear Time Motif Discovery from Multiple Sequences

In this paper, a natural probabilistic model for motif discovery has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet, Σ. A motif G = g1g2 ... gm is a string of m characters. In each background sequence is implanted a probabilistically-generated approximate copy of G. For a probabilistically-generated approximate copy b1b2 ... bm of G, every character, bi, is probabilistically generated, such that the probability for bi ≠ gi is at most α. We develop two new randomized algorithms and one new deterministic algorithm. They make advancements in the following aspects: (1) The algorithms are much faster than those before. Our algorithms can even run in sublinear time. (2) They can handle any motif pattern. (3) The restriction for the alphabet size is a lower bound of four. This gives them potential applications in practical problems, since gene sequences have an alphabet size of four. (4) All algorithms have rigorous proofs about their performances. The methods developed in this paper have been used in the software implementation. We observed some encouraging results that show improved performance for motif detection compared with other software

Model Selection for Non-Negative Tensor Factorization with Minimum Description Length

Non-negative tensor factorization (NTF) is a widely used multi-way analysis approach that factorizes a high-order non-negative data tensor into several non-negative factor matrices. In NTF, the non-negative rank has to be predetermined to specify the model and it greatly influences the factorized matrices. However, its value is conventionally determined by specialists’ insights or trial and error. This paper proposes a novel rank selection criterion for NTF on the basis of the minimum description length (MDL) principle. Our methodology is unique in that (1) we apply the MDL principle on tensor slices to overcome a problem caused by the imbalance between the number of elements in a data tensor and that in factor matrices, and (2) we employ the normalized maximum likelihood (NML) code-length for histogram densities. We employ synthetic and real data to empirically demonstrate that our method outperforms other criteria in terms of accuracies for estimating true ranks and for completing missing values. We further show that our method can produce ranks suitable for knowledge discovery

Laboratory Evaluation and Field Application of a Gas-Soluble Plugging Agent: Development of Bottom Water Plugging Fracturing Technology

The currently reported bottom water sealing materials and fracturing technologies can hardly simultaneously achieve the high production and low water cut of gas reservoirs due to the complexity of various formation conditions. Therefore, without controlling the fracturing scale and injection volume, a kind of polylactide polymer water plugging material with a density of 1.15–2.0 g/cm3 is developed, which can be used to seal the bottom water of a gas–water differential layer by contact solidification with water and automatic degradation with natural gas. This technology can not only fully release the production capacity of the gas reservoir but also effectively control water production and realize the efficient fracturing development of the target gas reservoir. Laboratory test results show that the smart plugging agent has a bottom water plugging rate of 100%, and the low-density plugging agent has a dissolution rate of 96.7% in methane gas at 90 °C for 4 h and a dissolution rate of 97.6% in methane gas at 60 °C for 6 h, showing remarkable gas degradation performance. In addition, settlement experiments show that the presence of a proppant can increase the settlement rate of a plugging agent up to many times (up to 21 times) in both water and guanidine gum solution. According to the actual conditions of well J66-8-3, a single-well water plugging fracturing scheme was prepared by optimizing the length of fracture, plugging agent dosage, and plugging agent sinking time, and a post-evaluation method was proposed. It has guiding significance to the development of similar gas reservoirs

Molecularly Imprinted Polymers for Selective Extraction of Oblongifolin C from Garcinia yunnanensis Hu

Molecularly imprinted polymers (MIPs) were synthesized and applied for the selective extraction of oblongifolin C (OC) from fruit extracts of Garcinia yunnanensis Hu. A series of experiments and computational approaches were employed to improve the efficiency of screening for optimal MIP systems in the study. The molar ratio (1:4) was eventually chosen based on the comparison of the binding energy of the complexes between the template (OC) and the functional monomers using density functional theory (DFT) at the RI-PBE-D3-gCP/def2-TZVP level of theory. The binding characterization and the molecular recognition mechanism of MIPs were further explained using the molecular modeling method along with NMR and IR spectra data. The reusability of this approach was demonstrated in over 20 batch rebinding experiments. A mass of 140.5 mg of OC (>95% purity) was obtained from the 5 g extracts, with 2 g of MIPs with the best binding properties, through a gradient elution program from 35% to 70% methanol-water solution. At the same time, another structural analog, 46.5 mg of guttiferone K (GK) (>88% purity), was also obtained by the gradient elution procedure. Our results showed that the structural analogs could be separated from the crude extracts by the molecularly imprinted solid-phase extraction (MISPE) using a gradient elution procedure for the first time

Laboratory Evaluation and Field Application of a Gas-Soluble Plugging Agent: Development of Bottom Water Plugging Fracturing Technology

The currently reported bottom water sealing materials and fracturing technologies can hardly simultaneously achieve the high production and low water cut of gas reservoirs due to the complexity of various formation conditions. Therefore, without controlling the fracturing scale and injection volume, a kind of polylactide polymer water plugging material with a density of 1.15–2.0 g/cm3 is developed, which can be used to seal the bottom water of a gas–water differential layer by contact solidification with water and automatic degradation with natural gas. This technology can not only fully release the production capacity of the gas reservoir but also effectively control water production and realize the efficient fracturing development of the target gas reservoir. Laboratory test results show that the smart plugging agent has a bottom water plugging rate of 100%, and the low-density plugging agent has a dissolution rate of 96.7% in methane gas at 90 °C for 4 h and a dissolution rate of 97.6% in methane gas at 60 °C for 6 h, showing remarkable gas degradation performance. In addition, settlement experiments show that the presence of a proppant can increase the settlement rate of a plugging agent up to many times (up to 21 times) in both water and guanidine gum solution. According to the actual conditions of well J66-8-3, a single-well water plugging fracturing scheme was prepared by optimizing the length of fracture, plugging agent dosage, and plugging agent sinking time, and a post-evaluation method was proposed. It has guiding significance to the development of similar gas reservoirs