PED: a novel predictor-encoder-decoder model for Alzheimer drug molecular generation.

Alzheimer's disease (AD) is a gradually advancing neurodegenerative disorder characterized by a concealed onset. Acetylcholinesterase (AChE) is an efficient hydrolase that catalyzes the hydrolysis of acetylcholine (ACh), which regulates the concentration of ACh at synapses and then terminates ACh-mediated neurotransmission. There are inhibitors to inhibit the activity of AChE currently, but its side effects are inevitable. In various application fields where Al have gained prominence, neural network-based models for molecular design have recently emerged and demonstrate encouraging outcomes. However, in the conditional molecular generation task, most of the current generation models need additional optimization algorithms to generate molecules with intended properties which make molecular generation inefficient. Consequently, we introduce a cognitive-conditional molecular design model, termed PED, which leverages the variational auto-encoder. Its primary function is to adeptly produce a molecular library tailored for specific properties. From this library, we can then identify molecules that inhibit AChE activity without adverse effects. These molecules serve as lead compounds, hastening AD treatment and concurrently enhancing the AI's cognitive abilities. In this study, we aim to fine-tune a VAE model pre-trained on the ZINC database using active compounds of AChE collected from Binding DB. Different from other molecular generation models, the PED can simultaneously perform both property prediction and molecule generation, consequently, it can generate molecules with intended properties without additional optimization process. Experiments of evaluation show that proposed model performs better than other methods benchmarked on the same data sets. The results indicated that the model learns a good representation of potential chemical space, it can well generate molecules with intended properties. Extensive experiments on benchmark datasets confirmed PED's efficiency and efficacy. Furthermore, we also verified the binding ability of molecules to AChE through molecular docking. The results showed that our molecular generation system for AD shows excellent cognitive capacities, the molecules within the molecular library could bind well to AChE and inhibit its activity, thus preventing the hydrolysis of ACh.

Nonasymptotic Fractional Derivative Estimation of the Pseudo-State for a Class of Fractional-Order Partial Unknown Nonlinear Systems.

This work is devoted to the nonasymptotic and robust fractional derivative estimation of the pseudo-state for a class of fractional-order nonlinear systems with partial unknown terms in noisy environments. In particular, the estimation for the pseudo-state can be obtained by setting the fractional derivative's order to zero. For this purpose, the fractional derivative estimation of the pseudo-state is achieved by estimating both the initial values and the fractional derivatives of the output, thanks to the additive index law of fractional derivatives. The corresponding algorithms are established in terms of integrals by employing the classical and generalized modulating functions methods. Meanwhile, the unknown part is fitted via an innovative sliding window strategy. Moreover, error analysis in discrete noisy cases is discussed. Finally, two numerical examples are presented to verify the correctness of the theoretical results and the noise reduction efficiency.

En Route to High-Density Chiral Single-walled Carbon Nanotube Arrays using Solid Trojan Catalysts.

Solid catalyst is widely recognized as an effective strategy to control the chirality of single-walled carbon nanotubes (SWNTs). However, it is still not compatible with high density in horizontal arrays. "Trojan" catalysts strategy is one of the most effective methods to realize SWNTs with high density and has great potential in chirality control. Here, the co-realization of high density and chirality controlling for SWNTs in a low-temperature growth process is reported based on the developed solid "Trojan" catalyst. High temperature "Trojan" catalyst formation process provides sufficient catalyst number to acquire high density. These liquid "Trojan" catalysts are cooled to solid state by adopting low growth temperature (540 °C), which can be good template to realize the chirality controlling of SWNTs with exposing six-fold symmetry face, (111). Finally, (9, 6) and (13, 1) SWNTs enriched horizontal array with the purity of ≈90% and density of 4 tubes µm-1 is realized. The comparison between the distribution of initial catalysts and the density of as-grown tubes indicates no sacrificing on catalysts number to improve chirality selectivity. This work opens a new avenue on the catalyst's design and chirality controlling in SWNTs growth.

Common salts directed the growth of metal-free horizontal SWNT arrays.

Acquiring metal-free horizontal single-walled carbon nanotube (SWNT) arrays is of paramount importance for the development of stable nanodevices. However, the majority of SWNTs are prepared with transition metal-based catalysts, which will inevitably leave metallic residuals and deteriorate the device performance. Here, green and low-cost NaCl is developed as a metal-free catalyst. By employing a strategy of rapid nucleation at a higher temperature followed by steady growth at a lower temperature, the production of a well-defined NaCl catalyst capable of growing metal-free horizontal SWNT arrays with an average density of ∼100 tubes per 100 µm is realized. Besides, we prove that the as-grown metal-free SWNT arrays have a unique advantage in preparing stable devices for eliminating the potential risk of local mass catalyst residuals. Hence, the current study can offer a feasible solution to promote practical applications of SWNT-based next-generation nanodevices.

An innovative modulating functions method for pseudo-state estimation of fractional order systems.

In this paper, the objective is to estimate the pseudo-state of fractional order systems defined by the Caputo fractional derivative from discrete noisy output measurement. For this purpose, an innovative modulating functions method is proposed, which can provide non-asymptotic estimation within finite-time and is robust against corrupting noises. First, the proposed method is directly applied to the Brunovsky's observable canonical form of the considered system. Then, the initial value of the pseudo-state is exactly expressed by an algebraic integral formula, based on which the pseudo-state is estimated. Second, the properties and construction of the required modulating functions are studied. Furthermore, error analysis is provided in discrete noise cases, which is useful for improving the estimation accuracy. In order to show the advantages of the proposed method, two numerical examples are given, where both rational order and irrational order dynamical systems are considered. After selecting the design parameters using the provided noise error bound, the pseudo-states of considered systems are estimated. The fractional order Luenberger-like observer and the fractional order H∞-like observer are also applied. Better than the applied fractional order observers, the proposed method can guarantee the convergence speed and robustness at the same time.

Selected-Area Fabrication of a Single-Walled Carbon Nanotube Schottky Junction with Tunable Gate Rectification.

Single-walled carbon nanotube (SWNT)-based devices are expected to play an important role in the next generation of electronic integrated circuits. As an important structural unit for SWNT-based electronics, the Schottky junction has a series of functions such as rectification, photoelectric detection, switching, etc. Here, we demonstrate a well-controlled localized radical reaction method to prepare an intramolecular SWNT Schottky junction with a closed edge. This junction exhibits strong gate-dependent rectifying behavior and a high rectification ratio of 962. Furthermore, the semiconducting part on the junction side could be effectively tuned from p-type doping to n-type doping, resulting in reversible rectifying behavior. Our work paves a new avenue for the design and synthesis of an SWNT Schottky junction, which is very important to future applications for carbon-based nanoelectronic devices.

IL-23 Inhibits Trophoblast Proliferation, Migration, and EMT via Activating p38 MAPK Signaling Pathway to Promote Recurrent Spontaneous Abortion.

As a vital problem in reproductive health, recurrent spontaneous abortion (RSA) affects about 1% of women. We performed this study with an aim to explore the molecular mechanism of interleukin-23 (IL-23) and find optimal or effective methods to improve RSA. First, ELISA was applied to evaluate the expressions of IL-23 and its receptor in HTR-8/SVneo cells after IL-23 treatment. CCK-8, TUNEL, wound healing and transwell assays were employed to assess the proliferation, apoptosis, migration and invasion of HTR-8/SVneo cells, respectively. Additionally, the expressions of apoptosis-, migration-, epithelial-mesenchymal transition- (EMT-) and p38 MAPK signaling pathway-related proteins were measured by western blotting. To further investigate the relationship between IL-23 and p38 MAPK signaling pathway, HTR-8/SVneo cells were treated for 1 h with p38 MAPK inhibitor SB239063, followed by a series of cellular experiments on proliferation, apoptosis, migration and invasion, as aforementioned. The results showed that IL-23 and its receptors were greatly elevated in IL-23-treated HTR-8/SVneo cells. Additionally, IL-23 demonstrated suppressive effects on the proliferation, apoptosis, migration, invasion and EMT of IL-23-treated HTR-8/SVneo cells. More importantly, the molecular mechanism of IL-23 was revealed in this study; that is to say, IL-23 inhibited the proliferation, apoptosis, migration, invasion and EMT of IL-23-treated HTR-8/SVneo cells via activating p38 MAPK signaling pathway. In conclusion, IL-23 inhibits trophoblast proliferation, migration, and EMT via activating p38 MAPK signaling pathway, suggesting that IL-23 might be a novel target for the improvement of RSA.

Predicting Drug-Target Affinity Based on Recurrent Neural Networks and Graph Convolutional Neural Networks.

BACKGROUND: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance Method: We have improved the GraphDTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in GraphDTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then inputted into a 2 layer FC network, and a valuable binding affinity is the output. RESULT: We used the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model showed better performance when compared with the experimental results of GraphDTA Conclusion: In this paper, we altered the GraphDTA model to predict drug-target affinity. It represents the drug as a graph and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time-series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

DL-SMILES#: A Novel Encoding Scheme for Predicting Compound Protein Affinity Using Deep Learning.

INTRODUCTION: Drug repositioning aims to screen drugs and therapeutic goals from approved drugs and abandoned compounds that have been identified as safe. This trend is changing the landscape of drug development and creating a model of drug repositioning for new drug development. In the recent decade, machine learning methods have been applied to predict the binding affinity of compound proteins, while deep learning is recently becoming prominent and achieving significant performances. Among the models, the way of representing the compounds is usually simple, which is the molecular fingerprints, i.e., a single SMILES string. METHODS: In this study, we improve previous work by proposing a novel representing manner, named SMILES#, to recode the SMILES string. This approach takes into account the properties of compounds and achieves superior performance. After that, we propose a deep learning model that combines recurrent neural networks with a convolutional neural network with an attention mechanism, using unlabeled data and labeled data to jointly encode molecules and predict binding affinity. RESULTS: Experimental results show that SMILES# with compound properties can effectively improve the accuracy of the model and reduce the RMS error on most data sets. CONCLUSION: We used the method to verify the related and unrelated compounds with the same target, and the experimental results show the effectiveness of the method.

CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.

The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions' prediction. The proposed method is improved by a DEEPScreen model using 2-D structural representations of compounds as input. Furthermore, a channel and spatial attention mechanism (CS) is added in feature abstractions. Data experiments conducted on ChEMBLv23 datasets show that CSConv2d performs better than the original DEEPScreen model in predicting protein-ligand binding affinity, as well as some state-of-the-art DTIs (drug-target interactions) prediction methods including DeepConv-DTI, CPI-Prediction, CPI-Prediction+CS, DeepGS and DeepGS+CS. In practice, the docking results of protein (PDB ID: 5ceo) and ligand (Chemical ID: 50D) and a series of kinase inhibitors are operated to verify the robustness.

Normal-pulse-voltage-assisted in situ fabrication of graphene-wrapped MOF-derived CuO nanoflowers for water oxidation.

Chemical vapor deposition (CVD) and normal pulse voltage (NPV) are adopted to construct high-quality graphene-wrapped CuO nanoflowers grown in situ on copper foam (CuO NP@G/CF) as an efficient oxygen evolution reaction (OER) electrocatalyst. The CuO NF@G/CF electrode exhibits a small overpotential of 320 mV to drive a current density of 10 mA cm-2 with a low Tafel slope of 63.1 mV dec-1. This enhancement in OER performance stems from the synergistic effect between highly conductive graphene and hierarchically porous CuO nanoflowers with a number of high-density active sites and open spaces.

SE-OnionNet: A Convolution Neural Network for Protein-Ligand Binding Affinity Prediction.

Deep learning methods, which can predict the binding affinity of a drug-target protein interaction, reduce the time and cost of drug discovery. In this study, we propose a novel deep convolutional neural network called SE-OnionNet, with two squeeze-and-excitation (SE) modules, to computationally predict the binding affinity of a protein-ligand complex. The OnionNet is used to extract a feature map from the three-dimensional structure of a protein-drug molecular complex. The SE module is added to the second and third convolutional layers to improve the non-linear expression of the network to improve model performance. Three different optimizers, stochastic gradient descent (SGD), Adam, and Adagrad, were also used to improve the performance of the model. A majority of protein-molecule complexes were used for training, and the comparative assessment of scoring functions (CASF-2016) was used as the benchmark. Experimental results show that our model performs better than OnionNet, Pafnucy, and AutoDock Vina. Finally, we chose the macrophage migration inhibitor factor (PDB ID: 6cbg) to test the stability and robustness of the model. We found that the prediction results were not affected by the docking position, and thus, our model is of acceptable robustness.

Methylation-Induced Reversible Metallic-Semiconducting Transition of Single-Walled Carbon Nanotube Arrays for High-Performance Field-Effect Transistors.

Acquirement of aligned semiconducting single-walled carbon nanotube (s-SWNT) arrays is one of the most promising directions to break Moore's Law, thus developing the next-generation electronic devices. Despite that widespread approaches have been developed, it is still a great challenge to facilely prepare s-SWNT arrays with tunable electronic properties. Herein, a different perspective is proposed to produce s-SWNT arrays by implementing reversible methylation reactions on the as-grown aligned SWNT arrays. In this way, the metallic single-walled carbon nanotubes (m-SWNTs) are selectively and reversibly methylated to acquire semiconducting properties, to afford tunable electronic properties of the as-obtained SWNT arrays in a highly controllable and simple manner. Electrical measurements suggest a high fraction of s-SWNTs is attained (>97.5%) after methylation, facilitating its exceptional performance as a field-effect transistor (FET) with an on-off ratio of up to 17543. This method may provide a new way for the preparation of s-SWNT arrays with tunable electronic properties and impressive prospects toward the fabrication of high-performance FETs.

Observer design for a class of nonlinear piecewise systems. Application to an epidemic model with treatment.

Susceptible Exposed Infectious and Recovered epidemic model endowed with a treatment function (SEIR-T model) is a well-known model used to reproduce the behavior of an epidemic, where the susceptible population and the exposed population need to be estimated to predict and control the propagation of a contagious disease. This paper focuses on the nonlinear observer design for a class of nonlinear piecewise systems including SEIR-T models. For this purpose, two changes of coordinates are provided to transform the considered systems into an extended nonlinear observer normal form, on which a high gain observer can be applied. Then, the proposed method is applied to a SEIR-T model. Finally, simulation results are given to show its efficiency.

Receptivity assessment of an ultrasonographic homogeneous endometrium in the late follicular phase of infertile women with natural cycles.

OBJECTIVE: The purpose of this study was to assess the receptivity of the homogeneous endometrium in the late follicular phase in infertile women with natural cycles. STUDY DESIGN: Twenty-eight infertile women with ultrasonographically homogeneous (group 1) or trilaminar (group 2) endometria in the late follicular phase underwent endometrial biopsies. Some molecular markers and development of pinopodes were evaluated. RESULTS: In the late follicular phase, the mean level of vascular endothelial growth factor was significantly lower in group 1 than in group 2 (0.96 ± 0.37 marks vs 1.39 ± 0.46 marks; P = .010). In the mid luteal phase, a decreased leukemia inhibitory factor and integrin alpha v beta 3 levels were found in group 1 (1.58 ± 0.99 marks vs 2.59 ± 0.61 marks; 1.85 ± 0.72 marks vs 2.60 ± 0.73 marks; 1.92 ± 0.91 marks vs 2.83 ± 0.57 marks; P = .003; P = .011; P = .004). The rate of fully developed pinopodes in the mid luteal phase was significantly decreased in group 1 (P = .018). CONCLUSION: An ultrasonographically homogeneous endometrium in the late follicular phase was associated with poor receptivity in infertile women with natural cycles.

[Metabolic footprint in conditioned culture medium of placental explants: a comparison between early-onset and late-onset severe preeclampsia].

OBJECTIVE: To explore the differences of metabolic footprint in the conditioned culture medium of placental explants between early-onset and late-onset severe preeclampsia. METHODS: In 13 cases of early-onset severe preeclampsia and 14 cases of late-onset severe preeclampsia, the placentas were sampled at the surface of the maternal placenta. High performance liquid chromatography-mass spectrometry (HPLC-MS) was used to determine the differences in the metabolites in the conditioned culture medium of the placental villous explants cultured in 6% atmospheric O(2) for 96 h. Standard samples were used to establish the tryptophan and kynurenine chromatography library by HPLC-MS to analyze the concentration of tryptophan and kynurenine in the conditioned culture medium. RESULTS: Thirty-six metabolites showed statistically significant differences between early-onset and late-onset severe preeclampsia (P<0.05). The concentration of kynurenine was significantly higher in early-onset severe preeclampsia than in late-onset severe preeclampsia (P<0.05). CONCLUSION: Early-onset and late-onset severe preeclampsia may have different pathogeneses. By detecting the concentration of metabolites, metabolomic strategies provide a new means for predicting the onset time of severe preeclampsia.

Ursodeoxycholic acid in the treatment of intraheptic cholestasis of pregnancy.

In order to observe the effect of ursodeoxycholic acid (UDCA) in the treatment of intrahepatic cholestasis of pregnancy (ICP), 68 patients with ICP were equally divided into treatment group and control group at random. The patients in treatment group were administered with UDCA 300 mg three times every day and those in control group received a combination of 10% glucose, Vitamin C and Inosine. Itching scores, serum ALT and total bile acids (TBA) were measured before, during and after treatment. The results showed that as compared with those before treatment, itching scores, serum ALT and TBA were significantly reduced after treatment (P < 0.05). The occurrences of premature labor, fetal asphyxia and meconium staining in amniotic fluid were significantly lower in treatment group than in control group (P < 0.05). It was suggested that UDCA was an effective drug in the treatment of ICP.