Bis[1-hydroxy­ethyl­idenediphosphon­ato(1−)](1,10-phenanthroline)nickel(II) mono­hydrate

In the mononuclear title compound, [Ni(C2H6O7P2)2(C12H8N2)]·H2O, the NiII atom (site symmetry 2) is bonded to two phosphate-based O,O′-bidentate chelate ligands and one N,N′-bidentate 1,10-phenanthroline ligand, resulting in a slightly distorted cis-NiN2O4 octa­hedral geometry. In the crystal structure, pairs of complexes are linked by double hydrogen bonds, forming a one-dimensional chain-like structure. Aromatic π–π stacking inter­actions [centroid–centroid separation = 3.768 (2) Å] and further hydrogen bonds generate a two-dimensional structure. The water O atom also lies on a crystallographic twofold axis

Stochastic Study of the Effect of Ionic Strength on Noncovalent Interactions in Protein Pores

Salt plays a critical role in the physiological activities of cells. We show that ionic strength significantly affects the kinetics of noncovalent interactions in protein channels, as observed in stochastic studies of the transfer of various analytes through pores of wild-type and mutant α-hemolysin proteins. As the ionic strength increased, the association rate constant of electrostatic interactions was accelerated, whereas those of both hydrophobic and aromatic interactions were retarded. Dramatic decreases in the dissociation rate constants, and thus increases in the overall reaction formation constants, were observed for all noncovalent interactions studied. The results suggest that with the increase of salt concentration, the streaming potentials for all the protein pores decrease, whereas the preferential selectivities of the pores for either cations or anions drop. Furthermore, results also show that the salt effect on the rate of association of analytes to a pore differs significantly depending on the nature of the noncovalent interactions occurring in the protein channel. In addition to providing new insights into the nature of analyte-protein pore interactions, the salt-dependence of noncovalent interactions in protein pores observed provides a useful means to greatly enhance the sensitivity of the nanopore, which may find useful application in stochastic sensing

Influence of Pre-Stress Magnitude on Fatigue Crack Growth Behavior of Al-Alloy

From tensile overload to shot peening, there have been many attempts to extend the fatigue properties of metals. A key challenge with the cold work processes is that it is hard to avoid generation of harmful effects (e.g., the increase of surface roughness caused by shot peening). Pre-stress has a positive effect on improving the fatigue property of metals, and it is expected to strength Al-alloy without introducing adverse factors. Four pre-stresses ranged from 120 to 183 MPa were incorporated in four cracked extended-compact tension specimens by application of different load based on the measured stress–strain curve. Fatigue crack growth behavior and fractured characteristic of the pre-stressed specimens were investigated systematically and were compared with those of an as-received specimen. The results show that the pre-stress ranged from 120 to 183 MPa significantly improved the fatigue resistance of Al-alloy by comparison with that of the as-received specimen. With increasing pre-stress, the fatigue life first increases, then decrease, and the specimen with pre-stress of 158 MPa has the longest fatigue life. For the manner of pre-stress, no adverse factor was observed for increasing fatigue property, and the induced pre-stress reduced gradually till to disappear during subsequent fatigue cycling

Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir

Multimodal and Covert-Overt Convertible Structural Coloration Transformed by Mechanical Stress

Most materials and devices with structurally switchable color features responsive to external stimuli can actively and flexibly display various colors. However, realizing covert-overt transformation behavior, especially switching between transparent and colored states, is more challenging. A composite laminate of soft poly(dimethylsiloxane) (PDMS) with a rigid SiO2-nanoparticle (NP) structure pattern is developed as a multidimensional structural color platform. Owing to the similarity in the optical properties of PDMS and SiO2 NPs, this device is fully transparent in the normal state. However, as their mechanical strengths differ considerably, upon compressive loading, a buckling-type instability arises on the surface of the laminate, leading to the generation of 1D or 2D wrinkled patterns in the form of gratings. Finally, an application of the device in which quick response codes are displayed or hidden as covert-overt convertible colored patterns for optical encryption/decryption, showing their remarkable potential for anticounterfeiting applications, is demonstrated

An exonic splicing enhancer mutation in DUOX2 causes aberrant alternative splicing and severe congenital hypothyroidism in Bama pigs

Pigs share many similarities with humans in terms of anatomy, physiology and genetics, and have long been recognized as important experimental animals in biomedical research. Using an N-ethyl-N-nitrosourea (ENU) mutagenesis screen, we previously identified a large number of pig mutants, which could be further established as human disease models. However, the identification of causative mutations in large animals with great heterogeneity remains a challenging endeavor. Here, we select one pig mutant, showing congenital nude skin and thyroid deficiency in a recessive inheritance pattern. We were able to efficiently map the causative mutation using family-based genome-wide association studies combined with whole-exome sequencing and a small sample size. A loss-of-function variant (c.1226 A>G) that resulted in a highly conserved amino acid substitution (D409G) was identified in the DUOX2 gene. This mutation, located within an exonic splicing enhancer motif, caused aberrant splicing of DUOX2 transcripts and resulted in lower H2O2 production, which might cause a severe defect in thyroid hormone production. Our findings suggest that exome sequencing is an efficient way to map causative mutations and that DUOX2D409G/D409G mutant pigs could be a potential large animal model for human congenital hypothyroidism