Wear-resistant CoCrNi multi-principal element alloy at cryogenic temperature.

Traditional high strength engineering alloys suffer from serious surface brittleness and inferior wear performance when servicing under sliding contact at cryogenic temperature. Here, we report that the recently emerging CoCrNi multi-principal element alloy defies this trend and presents dramatically enhanced wear resistance when temperature decreases from 273 to 153 K, surpassing those of cryogenic austenitic steels. The temperature-dependent structure characteristics and deformation mechanisms influencing the cryogenic wear resistance of CoCrNi are clarified through microscopic observation and atomistic simulation. It is found that sliding-induced subsurface structures show distinct scenarios at different deformation temperatures. At cryogenic condition, significant grain refinement and a deep plastic zone give rise to an extended microstructural gradient below the surface, which can accommodate massive sliding deformation, in direct contrast to the strain localization and delamination at 273 K. Meanwhile, the temperature-dependent cryogenic deformation mechanisms (stacking fault networks and phase transformation) also provide additional strengthening and toughening of the subsurface material. These features make the CoCrNi alloy particularly wear resistant at cryogenic conditions and an excellent candidate for safety-critical applications.

Treatment of Delayed Acetabular Fractures by Periacetabular Osteotomy through the Lateral-Rectus Approach.

OBJECTIVE: There has been a controversy in the surgical approach for delayed acetabular fracture. The objective of the present study is to investigate the feasibility, surgical techniques, safety, and efficacy of periacetabular osteotomy using the single lateral-rectus approach (LRA) for the surgical treatment of delayed acetabular fracture. METHODS: The retrospective study included 22 patients (16 males and six females, with an average age of 45 years) with delayed acetabular fractures from June 2012 to June 2019. For all cases, periacetabular osteotomy was performed through the single LRA. Fracture classification, mechanism of injury, associated injury, time to surgery, operation time, intraoperative blood loss, and complications were recorded and analyzed. The quality of the reduction was assessed based on Matta radiographic criteria. Potential impact factors affecting the quality of reduction were analyzed. Functional outcome was evaluated at the final follow-up according to a modified Mere D'Aubigne-Postel scoring system for each patient. RESULTS: All patients were followed up for at least 12 months. The duration of surgery was 140 min on average (110-205 min) and the mean intraoperative blood loss was 1250 ml (500-2100 ml). According to Matta radiographic criteria, the accuracy of reduction was "anatomical" in seven patients, "imperfect" in 11 patients, and "poor" in four patients, with an excellent and good rate of 81.8%. The time to surgery in poor reduction group was significantly longer than anatomical or imperfect reduction group (p < 0.05). All the acetabular fractures united after 8-12 weeks. The average modified Merle D'Aubigne-Postel score evaluated at the final follow-up was 14.6 (6-18), and the clinical outcomes were rated as excellent in six patients, good in 10 patients, fair in four patients, and poor in two patients, with an excellent and good rate of 72.7%. There were two cases of osteonecrosis of the femoral head (9%). No other complication was found for all cases. CONCLUSION: The LRA is an effective and minimally invasive approach in the treatment of delayed acetabular fractures excluding posterior wall fracture and posterior dislocation.

Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.

Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold-based geometric neural network for functional brain networks (called "Geo-Net4Net" for short) to learn the intrinsic low-dimensional feature representations of resting-state brain networks on the Riemannian manifold. This tool allows us to answer the scientific question of how the spontaneous fluctuation of FC supports behavior and cognition. We deploy a set of positive maps and rectified linear unit (ReLU) layers to uncover the intrinsic low-dimensional feature representations of functional brain networks on the Riemannian manifold taking advantage of the symmetric positive-definite (SPD) form of the correlation matrices. Due to the lack of well-defined ground truth in the resting state, existing learning-based methods are limited to unsupervised methodologies. To go beyond this boundary, we propose to self-supervise the feature representation learning of resting-state functional networks by leveraging the task-based counterparts occurring before and after the underlying resting state. With this extra heuristic, our Geo-Net4Net allows us to establish a more reasonable understanding of resting-state FCs by capturing the geometric patterns (aka. spectral/shape signature) associated with resting states on the Riemannian manifold. We have conducted extensive experiments on both simulated data and task-based functional resonance magnetic imaging (fMRI) data from the Human Connectome Project (HCP) database, where our Geo-Net4Net not only achieves more accurate change detection results than other state-of-the-art counterpart methods but also yields ubiquitous geometric patterns that manifest putative insights into brain function.

Learning Brain Dynamics of Evolving Manifold Functional MRI Data Using Geometric-Attention Neural Network.

Functional connectivities (FC) of brain network manifest remarkable geometric patterns, which is the gateway to understanding brain dynamics. In this work, we present a novel geometric-attention neural network to characterize the time-evolving brain state change from the functional neuroimages by tracking the trajectory of functional dynamics on high-dimension Riemannian manifold of symmetric positive definite (SPD) matrices. Specifically, we put the spotlight on learning the common state-specific manifold signatures that represent the underlying cognition. In this context, the driving force of our neural network is tied up with the learning of the evolution functionals on the Riemannian manifold of SPD matrix that underlies the known evolving brain states. To do so, we train a convolution neural network (CNN) on the Riemannian manifold of SPD matrices to seek for the putative low-dimension feature representations, followed by an end-to-end recurrent neural network (RNN) to yield the time-varying mapping function of SPD matrices which fits the evolutionary trajectories of the underlying states. Furthermore, we devise a geometric attention mechanism in CNN, allowing us to discover the latent geometric patterns in SPD matrices that are associated with the underlying states. Notably, our work has the potential to understand how brain function emerges behavior by investigating the geometrical patterns from functional brain networks, which is essentially a correlation matrix of neuronal activity signals. Our proposed manifold-based neural network achieves promising results in predicting brain state changes on both simulated data and task functional neuroimaging data from Human Connectome Project, which implies great applicability in neuroscience studies.

Systematic Identification of Proteins Binding with Cisplatin in Blood by Affinity Chromatography and a Four-Dimensional Proteomic Method.

Cisplatin is widely used for the treatment of various solid tumors. It is mainly administered by intravenous injection, and a substantial amount of the drug will bind to plasma proteins, a feature that is closely related to its pharmacokinetics, activity, toxicity, and side effects. However, due to the unique properties of platinum complexes and the complexity of the blood proteome, existing methods cannot systematically identify the binding proteome of cisplatin in blood. In this study, high-abundance protein separation and an ion mobility mass spectrometry-based 4D proteomic method were combined to systematically and comprehensively identify the binding proteins of cisplatin in blood. The characteristic isotope patterns of platinated peptides and a similarity algorithm were utilized to eliminate false-positive identification. Finally, 39 proteins were found to be platinated. Bioinformatics analysis showed that the identified proteins were mainly involved in the complement and coagulation cascade pathways. The binding ratio of some peptides with cisplatin was measured based on the area ratio of the free peptide using the parallel reaction monitoring method. This study provides a new method for systematically identifying binding proteins of metal drugs in blood, and the identified proteins might be helpful for understanding the toxicity of platinum anticancer drugs.

Selective C-H dithiocarbamation of arenes and antifungal activity evaluation.

This paper discloses a transition metal-free selective C-H dithiocarbamation of drug skeletons using disulfiram (DSF) in the presence of KI/K2S2O8 in DMF/H2O. Drug skeletons, including 5-aminopyrazoles, indoles, pyrroloquinoline, and Julolidine, underwent C-H dithiocarbamation smoothly to afford a variety of drug-like molecules in moderate to good yields. It was found that the in situ formed 5-aminopyrazole iodide is the key intermediate for the dithiocarbamation. Bioassay results show that some of these N-heterocyclic dithiocarbamate derivatives exhibit good antifungal activity against Colletotrichum gloeosprioides and Fusarium oxysporum, F. proliferatum, Fusarium solani, Geotrichum candidum, Penicillium digitatum, Penicillium italicum, Phyricularia grisea.

Reflection light-field microscope with a digitally tunable aperture by single-pixel imaging.

Reflected light microscope is a tool for imaging opaque specimens. However, most of the existing reflected light microscopes can only obtain the two-dimensional image of the specimen. Here we demonstrate that with the help of single-pixel imaging, we can develop a reflection light-field microscopy for volumetric imaging. Importantly, using single-pixel imaging, we can digitally adjust the size of the aperture diaphragm of the proposed reflection light-field microscope for changing the depth of field and for achieving three-dimensional differential phase-contrast imaging in an arbitrary direction, without a hardware change. Our approach may benefit various reflective specimens with wide depth information in the semiconductor industry and material science.

Iodine-promoted radical alkyl sulfuration of imidazopyridines with dialkyl azo compounds and elemental sulfur.

Dialkyl azo compounds were found to be effective alkyl radical sources for direct alkyl sulfuration with imidazopyridines using elemental sulfur under metal-free conditions. Iodine, an inexpensive and mild reagent, could promote alkyl sulfuration. A variety of quaternary cyanoalkyl radicals were successfully coupled with elemental sulfur. A subsequent C-H sulfuration of imidazopyridines afforded a diverse array of imidazopyridine derivatives bearing cyanoalkylthio groups. The cyano group could be modified and further underwent condensation with 2-aminothiazole to afford an interesting heterocyclic amide. Control experiments showed that iodine could greatly suppress the self-coupling of cyanoalkyl radicals, thus making the sulfuration proceed smoothly.