High-efficient depletion and separation of histidine-rich proteins via Cu2+-chelated porous polymer microspheres.

Depletion and separation of histidine-rich proteins from complicated biosamples are crucial for various downstream applications in proteome research and clinical diagnosis. Herein, porous polymer microspheres coated with polyacrylic acid (SPSDVB-PAA) were fabricated through double emulsion interfacial polymerization technique and followed by immobilization of Cu2+ ions on the surface of SPSDVB-PAA. The as-prepared SPSDVB-PAA-Cu with uniform size and nanoscale pore structure enabled coordination interaction of Cu2+ with histidine residues in his-rich proteins, resulting in high-performance adsorption. As metal affinity adsorbent, the SPSDVB-PAA-Cu exhibited favorable selectivity for adsorbing hemoglobin (Hb) and human serum albumin (HSA) with the maximum adsorption capacities of 152.2 and 100.7 mg g-1. Furthermore, the polymer microspheres were used to isolate histidine-rich proteins from human whole blood and plasma, underscoring their effectiveness. The liquid chromatography tandem mass spectrometry (LC-MS/MS) results indicated that the content of 14 most abundant proteins in human plasma was depleted from 81.6 % to 30.7 % and low-abundance proteins were enriched from 18.4 % to 69.3 % after treatment with SPSDVB-PAA-Cu, illustrating potential application of SPSDVB-PAA-Cu in proteomic research.

Semi-Supervised 3D Shape Segmentation via Self Refining.

3D shape segmentation is a fundamental and crucial task in the field of image processing and 3D shape analysis. To segment 3D shapes using data-driven methods, a fully labeled dataset is usually required. However, obtaining such a dataset can be a daunting task, as manual face-level labeling is both time-consuming and labor-intensive. In this paper, we present a semi-supervised framework for 3D shape segmentation that uses a small, fully labeled set of 3D shapes, as well as a weakly labeled set of 3D shapes with sparse scribble labels. Our framework first employs an auxiliary network to generate initial fully labeled segmentation labels for the sparsely labeled dataset, which helps in training the primary network. During training, the self-refine module uses increasingly accurate predictions of the primary network to improve the labels generated by the auxiliary network. Our proposed method achieves better segmentation performance than previous semi-supervised methods, as demonstrated by extensive benchmark tests, while also performing comparably to supervised methods.

Identifying the kind behind SMILES-anatomical therapeutic chemical classification using structure-only representations.

Anatomical Therapeutic Chemical (ATC) classification for compounds/drugs plays an important role in drug development and basic research. However, previous methods depend on interactions extracted from STITCH dataset which may make it depend on lab experiments. We present a pilot study to explore the possibility of conducting the ATC prediction solely based on the molecular structures. The motivation is to eliminate the reliance on the costly lab experiments so that the characteristics of a drug can be pre-assessed for better decision-making and effort-saving before the actual development. To this end, we construct a new benchmark consisting of 4545 compounds which is with larger scale than the one used in previous study. A light-weight prediction model is proposed. The model is with better explainability in the sense that it is consists of a straightforward tokenization that extracts and embeds statistically and physicochemically meaningful tokens, and a deep network backed by a set of pyramid kernels to capture multi-resolution chemical structural characteristics. Its efficacy has been validated in the experiments where it outperforms the state-of-the-art methods by 15.53% in accuracy and by 69.66% in terms of efficiency. We make the benchmark dataset, source code and web server open to ease the reproduction of this study.

Low Contact Resistivity and Interfacial Behavior of p-Type NbFeSb/Mo Thermoelectric Junction.

Half-Heusler compounds are a class of promising thermoelectric (TE) materials for power generation. However, the large contact resistivity at the interface between TE legs and metal electrode of the TE device seriously hinders the full play of the material performance. Here we report an Ohmic contact for the junction of p-type Nb0.8Ti0.2FeSb and Mo electrode with a low contact resistivity of <1 µΩ cm2 due to the matching of work functions between Nb0.8Ti0.2FeSb and FeMo interlayer. The interface carrier transport is dominated by the field emission and consequently a strong tunneling electric current is obtained due to the high doping level and relatively low dielectric constant of p-type Nb0.8Ti0.2FeSb semiconductor. The interface microstructure analysis indicates that there is a FeMo alloy interlayer with a thickness of 5 µm and a mixing layer of Nb0.8Ti0.2FeSb and Nb3Ti with a thickness of 25 µm. After a long time heat treatment at 1073 K, the FeMo alloy transforms into a FeSb2 layer, while the mixing layer is occupied totally by Nb3Ti. Due to the relatively high electrical resistivity for FeSb2 phase, the increasing content of Nb3Ti and the crack at both sides of Nb3Ti interlayer, the contact resistivity rises up to 18.4 µΩ cm2 after 32 days' aging. These results demonstrate that the applicability of low contact resistivity NbFeSb/Mo junction in high performance TE devices.

Approaching the minimum lattice thermal conductivity of p-type SnTe thermoelectric materials by Sb and Mg alloying.

SnTe, as the nontoxic analogue to high-performance PbTe thermoelectric material, has captured the worldwide interest recently. Many triumphant instances focus on the strategies of band convergence, resonant doping, and nano-precipitates phonon scattering. Herein, the p-type SnTe-based materials Sn0.85-xSb0.15MgxTe (x = 0-0.10) are fabricated and a combined effect of Sb and Mg is investigated. Sb alloying tunes the hole carrier concentration of SnTe and decreases the lattice thermal conductivity. Mg alloying leads to a nearly hundredfold rise of disorder parameter due to the large mass and strain fluctuations, and as a consequence the lattice thermal conductivity decreases further down to ∼0.64 W m-1 K-1 at 773 K, close to the theoretical minimum of the lattice thermal conductivity (∼0.50 W m-1 K-1) of SnTe. In conjunction with the enhancement of the Seebeck coefficient caused by band convergence due to Mg alloying, the maximum zTmax reaches ∼1.02 and the device zTdevice of ∼0.50 at 773 K for Sn0.79Sb0.15Mg0.06Te, suggesting this SnTe-based composition has a promising potential in intermediate temperature thermoelectric applications.

Smart Ocean: A New Fast Deconvolved Beamforming Algorithm for Multibeam Sonar.

A new fast deconvolved beamforming algorithm is proposed in this paper, and it can greatly reduce the computation complexity of the original Richardson⁻Lucy (R⁻L algorithm) deconvolution algorithm by utilizing the convolution theorem and the fast Fourier transform technique. This algorithm makes it possible for real-time high-resolution beamforming in a multibeam sonar system. This paper applies the new fast deconvolved beamforming algorithm to a high-frequency multibeam sonar system to obtain a high bearing resolution and low side lobe. In the sounding mode, it restrains the tunnel effect and makes the topographic survey more accurate. In the 2D acoustic image mode, it can obtain clear images, more details, and can better distinguish two close targets. Detailed implementation methods of the fast deconvolved beamforming are given, its computational complexity is analyzed, and its performance is evaluated with simulated and real data.

A14MgBi11 (A = Ca, Sr, Eu): Magnesium Bismuth Based Zintl Phases as Potential Thermoelectric Materials.

A series of new magnesium bismuth Zintl phases, A14MgBi11 (A = Ca, Sr, Eu), have been synthesized, and their thermoelectric properties were systematically evaluated. These novel phases belong to the well-known Yb14MnSb11 family, whose structure adopts the tetragonal space group I41/acd (No. 142) with cell parameters of a = 17.0470(17)/17.854(2)/17.6660(7) Å and c = 22.665(5)/23.580(6)/23.2446(18) Å for Ca14MgBi11, Sr14MgBi11, and Eu14MgBi11, respectively. Without intentional optimization, these materials exhibit high potential as new thermoelectric candidates. Especially for Sr14MgBi11, a high zT value of 0.72 has been approached at 1073 K. The discovery of these new Zintl series is very interesting, which implies the high possibility of extending the 14-1-11 thermoelectric system to the bismuth analogues in the development of highly efficient thermoelectric materials. Density functional theory (DFT) calculations were incorporated as well to help better understand the properties of these important compounds.

Stereoselective fate kinetics of chiral neonicotinoid insecticide paichongding in aerobic soils.

Man-made chemicals such as pesticides, when released into the soil environment, are transformed into extractable residue (ER), bound residue (BR), or mineralized. These processes all play a pivotal role in the risk assessment for the use of man-made chemicals. In this study, BR, ER, and mineralization of a novel chiral pesticide, paichongding (IPP), 1-((6-chloropyridin-3-yl)methyl)-7-methyl-8-nitro-5-propoxy-1,2,3,5,6,7-hexahydro-imidazo[1,2-a]pyridine, were investigated in different soils under aerobic conditions. Significant specificity was observed for diastereoisomers of IPP in the formation of BR or mineralization in neutral and alkaline soils. In contrast, no significant difference was found between enantiomers. The overall mineralization was less than 8% of the applied radioactivity and was related to soil pH. Our findings suggest that the environmental fate of chiral pesticides may be influenced by many factors such as soil properties (e.g. pH). More comprehensive and individualized risk assessments should be carried out for individual stereoisomers of a chiral product.

Econometric analysis of the changing effects in wind strength and significant wave height on the probability of casualty in shipping.

This study uses econometric models to measure the effect of significant wave height and wind strength on the probability of casualty and tests whether these effects changed. While both effects are in particular relevant for stability and strength calculations of vessels, it is also helpful for the development of ship construction standards in general to counteract increased risk resulting from changing oceanographic conditions. The authors analyzed a unique dataset of 3.2 million observations from 20,729 individual vessels in the North Atlantic and Arctic regions gathered during the period 1979-2007. The results show that although there is a seasonal pattern in the probability of casualty especially during the winter months, the effect of wind strength and significant wave height do not follow the same seasonal pattern. Additionally, over time, significant wave height shows an increasing effect in January, March, May and October while wind strength shows a decreasing effect, especially in January, March and May. The models can be used to simulate relationships and help understand the relationships. This is of particular interest to naval architects and ship designers as well as multilateral agencies such as the International Maritime Organization (IMO) that establish global standards in ship design and construction.