Enantioselective Synthesis of Nitrogen-Nitrogen Biaryl Atropisomers via Copper-Catalyzed Friedel-Crafts Alkylation Reaction.

Nitrogen-nitrogen bonds containing motifs are ubiquitous in natural products and bioactive compounds. However, the atropisomerism arising from a restricted rotation around an N-N bond is largely overlooked. Here, we describe a method to access the first enantioselective synthesis of N-N biaryl atropisomers via a Cu-bisoxazoline-catalyzed Friedel-Crafts alkylation reaction. A wide range of axially chiral N-N bisazaheterocycle compounds were efficiently prepared in high yields with excellent enantioselectivities via desymmetrization and kinetic resolution. Heating experiments showed that the axially chiral bisazaheterocycle products have high rotational barriers.

Deep-Learning Approach to Automatic Identification of Facial Anomalies in Endocrine Disorders.

BACKGROUND: Deep learning has the potential to assist the medical diagnostic process. We aimed to identify facial anomalies associated with endocrinal disorders using a deep-learning approach to facilitate the process of diagnosis and follow-up. METHODS: We collected facial images of patients with hypercortisolism and acromegaly, and we augmented these images with additional negative samples from public databases. A model with a pretrained deep-learning network was constructed to automatically identify these hypersecretion statuses based on characteristic facial changes. We compared its performance to that of endocrine experts and further investigated key factors upon which the best performing model focused. FINDINGS: The model achieved areas under the receiver operating characteristic curve of 0.9647 (Cushing's syndrome) and 0.9556 (acromegaly), accuracies of 0.9593 (Cushing's syndrome) and 0.9479 (acromegaly), and recalls of 0.7593 (Cushing's syndrome) and 0.8089 (acromegaly). It performed better than any level of our endocrine experts. Furthermore, the regions of interest on the part of the machine were primarily the same as those upon which the humans focused. INTERPRETATION: Our findings suggest that the deep-learning model learned the facial characters based merely on labeled data without learning prerequisite medical knowledge, and its performance was comparable with professional medical practitioners. The model has the potential to assist in the diagnosis and follow-up of these hypersecretion statuses.

MicroRNA-486 Alleviates Hypoxia-Induced Damage in H9c2 Cells by Targeting NDRG2 to Inactivate JNK/C-Jun and NF-κB Signaling Pathways.

BACKGROUND/AIMS: Acute myocardial infarction is a serious disease with high morbidity and mortality. microRNAs (miRNAs) have been proved to play an important role in modulating myocardial ischemia and reperfusion injury. Hence, in this study, we constructed H9c2 cell model to elucidate the roles of microRNA-486 (miR-486) in preventing hypoxia-induced damage in H9c2 cells. METHODS: H9c2 cells were cultured in hypoxic incubator with 1% O2 to simulate hypoxia and/or transfected with miR-486 mimic, scramble, anti-miR-486, si-N-myc downstream-regulated gene 2 (NDRG2) and their corresponding negative controls (NC). Effects of miR-486 and/or NDRG2 dysregulation on hypoxia-induced myocardial injury in H9c2 cells were investigated by evaluating cell viability, migration, invasion and apoptosis using Cell Counting Kit-8 (CCK-8), transwell assay, flow cytometry, respectively. The proteins expression and RNA expression were detected by western blot and quantitative real time polymerase chain reaction (qRT-PCR), respectively. RESULTS: Hypoxia treatment induced damage in H9c2 cells by decreasing cell viability, migration and invasion and increasing cell apoptosis. Moreover, hypoxia inhibited the expression of miR-486 in H9c2 cells. Overexpression of miR-486 alleviated hypoxia-induced myocardial injury in H9c2 cells, while suppression of miR-486 further aggravated hypoxia-induced injury. Furthermore, NDRG2 expression was negatively regulated by miR-486, and NDRG2 was confirmed as a target of miR-486. Knockdown of NDRG2 alleviated the effects of miR-486 suppression on hypoxia-induced myocardial injury. Besides, knockdown of NDRG2 markedly inhibited the activation of c-Jun N-terminal kinase (JNK) /c-jun and nuclear factor κB (NF-κB) signaling pathways in hypoxia-induced H9c2 cells. CONCLUSION: Our findings indicate that miR-486 may alleviate hypoxia-induced myocardial injury possibly by targeting NDRG2 to inactivate JNK/c-jun and NF-κB signaling pathways. miR-486 may be a potential target for treating ischemic myocardial injury following acute myocardial infarction.

CUL4A as a marker and potential therapeutic target in multiple myeloma.

Multiple myeloma is the most common cause of death of hematological malignancy worldwide. Cullin 4A has been proposed as oncogene in several types of human cancer, but the expression and function of cullin 4A in multiple myeloma remain unclear. Here, we demonstrate that cullin 4A plays an oncogenic role in multiple myeloma development. The expression of cullin 4A was detected by quantitative real-time polymerase chain reaction in multiple myeloma patients and multiple myeloma cell lines. In addition, silencing of cullin 4A with small interfering RNA was performed in human multiple myeloma cells, and the impact on proliferation, cell cycle, apoptosis, migration, and invasion of the multiple myeloma cells was analyzed. We found that the level of cullin 4A in serum samples was significantly upregulated in patients with multiple myeloma compared with healthy control subjects. Knockdown of cullin 4A via small interfering RNA inhibited the proliferation of the multiple myeloma cell lines by delaying cell-cycle progression and increasing apoptosis. cullin 4A downregulation inhibited multiple myeloma cell migration and invasion in vitro. Our results suggested that cullin 4A could be a promising therapy target in multiple myeloma patients.

Impacts of reservoirs on the streamflow and sediment load of the Hanjiang River, China.

The Hanjiang River is an important tributary of the Yangtze River in China. Long-term observed streamflow and sediment load data that spanned 1951-2014 was collected from the Huangzhuang hydrometric station, which is located at the middle reaches. The data was analyzed to reveal the impacts of reservoirs on streamflow and sediment load of the Hanjiang River. The coefficient of variation (C v) and concentration degree (C d) were applied to describe the intra-annual distribution characteristics. Abrupt changes in the time series of the streamflow and sediment load were detected by the heuristic segmentation algorithm. The annual streamflow significantly decreased from 1561 to 1263 m3/s after 1991, which was mainly caused by climate change. Two significant change points in the annual sediment load series occurred at 1966 and 1985, and the average values of the sub-series were 3198, 952, and 251 kg/s, respectively. Significant change points in the C v and C d series of the streamflow and sediment load occurred around 1967. The C v and C d series decreased dramatically after the change points. Abrupt changes in the time series of the streamflow and sediment load mainly occurred around 1967 when the Danjiangkou reservoir began to impound water, indicating that the Danjiangkou reservoir was an important factor that caused hydrological changes. The reservoir trapped sediment, reduced sediment concentration, mitigated the monthly streamflow and sediment load fluctuations, and reduced the intra-annual variation and concentration. Assessed by the range of variability approaches, the overall alteration degrees of the streamflow and sediment regimes were 36 and 60 %, respectively, which qualified as a moderate degree. The reservoir exerted greater influence on the sediment regime than on the streamflow regime.

Joint Camera Spectral Response Selection and Hyperspectral Image Recovery.

Hyperspectral image (HSI) recovery from a single RGB image has attracted much attention, whose performance has recently been shown to be sensitive to the camera spectral response (CSR). In this paper, we present an efficient convolutional neural network (CNN) based method, which can jointly select the optimal CSR from a candidate dataset and learn a mapping to recover HSI from a single RGB image captured with this algorithmically selected camera under multi-chip or single-chip setups. Given a specific CSR, we first present a HSI recovery network, which accounts for the underlying characteristics of the HSI, including spectral nonlinear mapping and spatial similarity. Later, we append a CSR selection layer onto the recovery network, and the optimal CSR under both multi-chip and single-chip setups can thus be automatically determined from the network weights under the nonnegative sparse constraint. Experimental results on three hyperspectral datasets and two camera spectral response datasets demonstrate that our HSI recovery network outperforms state-of-the-art methods in terms of both quantitative metrics and perceptive quality, and the selection layer always returns a CSR consistent to the best one determined by exhaustive search. Finally, we show that our method can also perform well in the real capture system, and collect a hyperspectral flower dataset to evaluate the effect from HSI recovery on classification problem.

LORM: a novel reinforcement learning framework for biped gait control.

Legged robots are better able to adapt to different terrains compared with wheeled robots. However, traditional motion controllers suffer from extremely complex dynamics properties. Reinforcement learning (RL) helps to overcome the complications of dynamics design and calculation. In addition, the high autonomy of the RL controller results in a more robust response to complex environments and terrains compared with traditional controllers. However, RL algorithms are limited by the problems of convergence and training efficiency due to the complexity of the task. Learn and outperform the reference motion (LORM), an RL based framework for gait controlling of biped robot is proposed leveraging the prior knowledge of reference motion. The proposed trained agent outperformed the reference motion and existing motion-based methods. The RL environment was finely crafted for optimal performance, including the pruning of state space and action space, reward shaping, and design of episode criterion. Several improvements were implemented to further improve the training efficiency and performance including: random state initialization (RSI), the noise of joint angles, and a novel improvement based on symmetrization of gait. To validate the proposed method, the Darwin-op robot was set as the target platform and two different tasks were designed: (I) Walking as fast as possible and (II) Tracking specific velocity. In task (I), the proposed method resulted in the walking velocity of 0.488 m/s, with a 5.8 times improvement compared with the original traditional reference controller. The directional accuracy improved by 87.3%. The velocity performance achieved 2× compared with the rated max velocity and more than 8× compared with other recent works. To our knowledge, our work achieved the best velocity performance on the platform Darwin-op. In task (II), the proposed method achieved a tracking accuracy of over 95%. Different environments are introduced including plains, slopes, uneven terrains, and walking with external force, where the robot was expected to maintain walking stability with ideal speed and little direction deviation, to validate the performance and robustness of the proposed method.

Analysis of the polarization effects in the gyrokinetic theory of magnetized plasmas.

By adopting the hybrid coordinates, in which the nonlinearity of polarization displacement is included in the configuration space variables compared to the conventional gyrocenter coordinates, the polarization effects are analyzed by using the modern gyrokinetic (GK) theory of magnetized plasmas. Based on the invariant property, the velocity transformation between the gyrocenter and hybrid coordinates is calculated, and the phase-space velocity in terms of the hybrid coordinates is obtained. The linear and nonlinear polarization distribution functions are defined, and the evolutions for the polarization distribution functions are derived. It is well known that the polarization density is important in the GK calculation of particle density. Analogously, it is shown that the polarization current should be considered in the GK calculation of current density. In the case with electrostatic fluctuations, the roles of the polarization current are illustrated in the derivations of the Hasegawa-Mima equation and the dispersion relation for geodesic acoustic mode. In the case with magnetic fluctuations, the procedure for the GK calculation of perpendicular current is clarified, the dispersion relation for compressional Alfvén wave is derived, in which the effect of polarization current is discussed.

Determination of Chemical Oxygen Demand in Water Samples Using Gas-phase Molecular Absorption Spectrometry.

Chemical oxygen demand (COD) is important for water quality assessment as it represents the level of reductive organic pollution from eutrophication in aquatic systems. For surface water quality monitoring, permanganate is usually applied as an oxidizing reagent, and the routine CODMn determination is mostly achieved by titration method. However, this titration method is tedious and time consuming, and the results suffer from environmental temperature fluctuations and complicated operation techniques. In this study, a novel CODMn determination method was developed using gas-phase molecular absorption spectrometry equipped with an online automated digestion device for the first time. The effects of digestion temperature, digestion time and sulfuric acid content were thoroughly studied. This method exhibited good linearity (0.35 to 12 mg/L), a low detection limit (0.12 mg/L), and good RSD from various water samples (0.71 - 2.37%). When used for CODMn determination in routine water quality monitoring, this automated GPMAS can considerably improve analysis speed, efficiency, accuracy and stability compared to the traditional titration method.

Effects of permanent cardiac pacemaker implantation on vascular endothelial function, blood coagulation and cardiac function in patients with bradycardia.

Changes in vascular endothelial function, blood coagulation and cardiac function indexes after the implantation of a permanent cardiac pacemaker in patients with bradycardia were investigated. A total of 53 healthy people and 117 patients with bradycardia in Jining First People's Hospital from January 2015 to August 2017 were selected. Factor VIII: coagulation (FVIII:C), von Willebr and factor (vWF), antithrombin activity (AT:A), D-dimmer (D-D), thrombomodulin (TM), tissue factor (TF), left ventricular ejection fraction (LVEF) and left ventricular end-systolic volume (LVESV) in the non-pacemaker group and the pacemaker group were significantly different from those in the control group (P<0.05), in which FVIII:C, vWF, D-D, TM, TF and LVESV were significantly higher than those in the control group, while LVEFs were significantly lower than that in the control group. After the implantation of a pacemaker, the FVIII:C, vWF, fibrinogen (FIB), D-D, TF and LVESV in patients were significantly higher than those before implantation (P<0.05), while the LVEF was significantly lower than that before implantation (P<0.05). In addition, in different pacemaker groups, there were no significant differences in blood coagulation and vascular endothelial indexes, but differences in cardiac function levels were obvious, in which LVEF in dual-chamber (DDD) pacemaker group was significantly higher than that in ventricular inhibited (VVI) pacemaker group, and LVESV in the former was significantly lower than that in the latter (P<0.05). Finally, here was no significant difference in the quality of life of patients implanted with different pacemakers (P>0.05), but the quality of life of patients in the DDD pacemaker group was better than that of patients in the VVI group. Therefore, implanting pacemakers in patients with bradycardia affects vascular endothelial function, blood coagulation, and cardiac function indexes in patients, and complications become less after the implantation of DDD pacemakers.

Fast and accurate hashing via iterative nearest neighbors expansion.

Recently, the hashing techniques have been widely applied to approximate the nearest neighbor search problem in many real applications. The basic idea of these approaches is to generate binary codes for data points which can preserve the similarity between any two of them. Given a query, instead of performing a linear scan of the entire data base, the hashing method can perform a linear scan of the points whose hamming distance to the query is not greater than rh , where rh is a constant. However, in order to find the true nearest neighbors, both the locating time and the linear scan time are proportional to O(∑i=0(rh)(c || i)) ( c is the code length), which increase exponentially as rh increases. To address this limitation, we propose a novel algorithm named iterative expanding hashing in this paper, which builds an auxiliary index based on an offline constructed nearest neighbor table to avoid large rh . This auxiliary index can be easily combined with all the traditional hashing methods. Extensive experimental results over various real large-scale datasets demonstrate the superiority of the proposed approach.

Fast and accurate matrix completion via truncated nuclear norm regularization.

Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real applications, such as image inpainting and recommender systems. Many existing approaches formulate this problem as a general low-rank matrix approximation problem. Since the rank operator is nonconvex and discontinuous, most of the recent theoretical studies use the nuclear norm as a convex relaxation. One major limitation of the existing approaches based on nuclear norm minimization is that all the singular values are simultaneously minimized, and thus the rank may not be well approximated in practice. In this paper, we propose to achieve a better approximation to the rank of matrix by truncated nuclear norm, which is given by the nuclear norm subtracted by the sum of the largest few singular values. In addition, we develop a novel matrix completion algorithm by minimizing the Truncated Nuclear Norm. We further develop three efficient iterative procedures, TNNR-ADMM, TNNR-APGL, and TNNR-ADMMAP, to solve the optimization problem. TNNR-ADMM utilizes the alternating direction method of multipliers (ADMM), while TNNR-AGPL applies the accelerated proximal gradient line search method (APGL) for the final optimization. For TNNR-ADMMAP, we make use of an adaptive penalty according to a novel update rule for ADMM to achieve a faster convergence rate. Our empirical study shows encouraging results of the proposed algorithms in comparison to the state-of-the-art matrix completion algorithms on both synthetic and real visual datasets.