CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data.

Cumulative studies have shown that many long non-coding RNAs (lncRNAs) are crucial in a number of diseases. Predicting potential lncRNA-disease associations (LDAs) can facilitate disease prevention, diagnosis and treatment. Therefore, it is vital to develop practical computational methods for LDA prediction. In this study, we propose a novel predictor named capsule network (CapsNet)-LDA for LDA prediction. CapsNet-LDA first uses a stacked autoencoder for acquiring the informative low-dimensional representations of the lncRNA-disease pairs under multiple views, then the attention mechanism is leveraged to implement an adaptive allocation of importance weights to them, and they are subsequently processed using a CapsNet-based architecture for predicting LDAs. Different from the conventional convolutional neural networks (CNNs) that have some restrictions with the usage of scalar neurons and pooling operations. the CapsNets use vector neurons instead of scalar neurons that have better robustness for the complex combination of features and they use dynamic routing processes for updating parameters. CapsNet-LDA is superior to other five state-of-the-art models on four benchmark datasets, four perturbed datasets and an independent test set in the comparison experiments, demonstrating that CapsNet-LDA has excellent performance and robustness against perturbation, as well as good generalization ability. The ablation studies verify the effectiveness of some modules of CapsNet-LDA. Moreover, the ability of multi-view data to improve performance is proven. Case studies further indicate that CapsNet-LDA can accurately predict novel LDAs for specific diseases.

Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.

RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein-protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a classification method based on convolutional neural networks is used. The experimental results of fivefold cross-validation demonstrated that the accuracy of LPI-MFF on RPI1807 and NPInter was 97.60% and 97.67%, respectively. In addition, the accuracy rate on the independent test set RPI1168 was 84.9%, and the accuracy rate on the Mus musculus dataset was 90.91%. Accordingly, LPI-MFF demonstrated greater robustness and generalization than other prevalent RPI prediction methods.

Distinctive p-d Orbital Hybridization in CuSb Porous Nanonetworks for Enhanced Nitrite Electroreduction to Ammonia.

Electrochemical nitrite reduction reaction ( NO 2 - RR ${\mathrm{NO}}_{\mathrm{2}}^{\mathrm{ - }}{\mathrm{RR}}$ ), as a green and sustainable ammonia synthesis technology, has broad application prospects and environmental friendliness. Herein, an unconventional p-d orbital hybridization strategy is reported to realize the fabrication of defect-rich CuSb porous nanonetwork (CuSb PNs) electrocatalyst for NO 2 - RR ${\mathrm{NO}}_{\mathrm{2}}^ - {\mathrm{RR}}$ . The crystalline/amorphous heterophase structure is cleverly introduced into the porous nanonetworks, and this defect-rich structure exposes more atoms and activated boundaries. CuSb PNs exhibit a large NH3 yield ( r N H 3 ${{r}_{{\mathrm{N}}{{{\mathrm{H}}}_{\mathrm{3}}}}}$ ) of 946.1 µg h-1 m cat - 1 ${\mathrm{m}}_{{\mathrm{cat}}}^{ - {\mathrm{1}}}$ and a high faradaic efficiency (FE) of 90.7%. Experimental and theoretical studies indicate that the excellent performance of CuSb PNs results from the defect-rich porous nanonetworks structure and the p-d hybridization of Cu and Sb elements. This work describes a powerful pathway for the fabrication of p-d orbital hybrid defect-rich porous nanonetworks catalysts, and provides hope for solving the problem of nitrogen oxide pollution in the field of environment and energy.

Liquid-liquid equilibrium experiment and mechanism analysis of menthol-based deep eutectic solvents extraction for separation of fuel additive tert-butanol.

Deep eutectic solvents (DESs) were synthesized using menthol as hydrogen bond acceptor (HBA) and different carbon chain carboxylic acids as hydrogen bond donors (HBD). The liquid equilibrium (LLE) experiment was used to determine the distribution coefficient (ß) and slectivity (S) at standard atmospheric pressure and temperature. The effect of DESs on the separation efficiency was discussed by changing the proportion. Non-random two fluid (NRTL) model was used to correlate the experimental data. The molecular dynamics (MD) simulation method was used to investigate the micro mechanism of the extraction process. The results show van der Waals force plays a leading role in the interaction between solvents and tert-butyl alcohol (TBA) and week force with water. Compared with experimental and simulation results, the interaction between DESs and TBA would also be affected by the change of the number of HBD carbon chains, and DESs with decanoic acid as HBD has the best separation effect, which verifies the feasibility of separating high alcohol compounds from water by DESs and then treating them by DESs.

MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.

BACKGROUND: Many long non-coding RNAs (lncRNAs) have key roles in different human biologic processes and are closely linked to numerous human diseases, according to cumulative evidence. Predicting potential lncRNA-disease associations can help to detect disease biomarkers and perform disease analysis and prevention. Establishing effective computational methods for lncRNA-disease association prediction is critical. RESULTS: In this paper, we propose a novel model named MAGCNSE to predict underlying lncRNA-disease associations. We first obtain multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network. Then, the weights are adaptively assigned to different feature matrices of lncRNAs and diseases using the attention mechanism. Next, the final representations of lncRNAs and diseases is acquired by further extracting features from the multi-channel feature matrices of lncRNAs and diseases using convolutional neural network. Finally, we employ a stacking ensemble classifier, consisting of multiple traditional machine learning classifiers, to make the final prediction. The results of ablation studies in both representation learning methods and classification methods demonstrate the validity of each module. Furthermore, we compare the overall performance of MAGCNSE with that of six other state-of-the-art models, the results show that it outperforms the other methods. Moreover, we verify the effectiveness of using multi-view data of lncRNAs and diseases. Case studies further reveal the outstanding ability of MAGCNSE in the identification of potential lncRNA-disease associations. CONCLUSIONS: The experimental results indicate that MAGCNSE is a useful approach for predicting potential lncRNA-disease associations.

First-principles study of polymer-passivated silicon nanowire outer-shell defects.

Highly effective defect passivation schemes are very important for the improvement of Si nanowire (SiNW) performances, because large numbers of outer-shell-defect states are caused by the high surface-to-volume ratios of nanowires. In this work, a polymer that can be fabricated by a simple, vacuum-free method at low temperatures, Nafion, was studied for the SiNW outer-shell defect passivation using first-principles calculations. Based on adsorption energy calculations, it was found that the Nafion molecule could firmly adsorb on the surfaces of SiNWs along the 〈112〉 direction. The Nafion-passivated SiNW outer-shell exhibited high stability to a chemical environment. Herein, the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) were confined to the center of the SiNW due to being wrapped by the Nafion. The Nafion-passivated SiNWs exhibited an equivalent quantum confinement effect and a larger absorption coefficient compared with the H-passivated SiNWs. This work demonstrated a passivation strategy of SiNW shell defects using functional groups.

Shipborne expendable all-optical fiber ocean temperature-depth profile sensor.

Traditional electrical expendable bathythermograph (XBT) is designed to fall at a known rate based on a great deal of experiments so that the depth of the temperature profile can be inferred from the time it enters the water. Unlike the traditional electrical XBT, which derives the depth from fall-rate equations, we propose an all-optical fiber (AOF) XBT (AOF-XBT) based on cascade of two fiber Bragg gratings (FBGs). In the AOF-XBT, the depth data comes from one FBG, which responds in real time to the pressure acting on the diaphragm, and temperature data can be measured via the other FBG simultaneously. First, the pressure and temperature response characteristics of the AOF-XBT are analyzed based on a finite element method. Then, the temperature and pressure calibrations for the AOF-XBT is completed after they are packaged. Results show that the mean-temperature sensitivity of two sensors are 14.765 and 13.705 pm/°C in the range of 5°C-30°C, and the mean-pressure sensitivities are -2.75586 and -3.00472nm/MPa in the range of 0-0.6 MPa, respectively. At last, by comparing the results obtained from the AOF-XBT and the SBE 911plus CTD that tested in the sea area of Weihai, the trends of the temperature-depth profile from the two devices are consistent, which presents a new all-optical technique to provide full ocean temperature-depth profile observations.

Analysis and Model Comparison of Carbon and Nitrogen Concentrations in Sediments of the Yellow Sea and Bohai Sea by Visible-Near Infrared Spectroscopy.

Visible-near infrared spectroscopy is considered an effective method for rapidly determining total carbon (TC) and total nitrogen (TN) in terrestrial soils. However, reports on measuring them by VNIR in marine sediments are limited. This article provides an analysis and spectral model comparison of TC and TN in marine sediments using VNIR. The best TC and TN spectral models were established when using the least square support vector machine algorithm with a wavelength, which extended from 226 nm to 975 nm. The prediction results of TN have a high coefficient of determination and residual predictive deviation, providing accurate quantitative predictions. The TC spectral model comes with a disadvantage might due to its usual high concentrations of organic carbon. Characteristic wavelength extraction may lead to the loss of identification information for the characteristics of TC and TN, and full wavelength spectrum contains more information helps more to the quantification.

A Brief Review of circRNA Biogenesis, Detection, and Function.

Circular RNA (circRNA) is a non-coding molecule produced through alternative splicing of one or more exons of a gene in the presence of an RNA-induced silencing complex (RISC). Its formation depends on complementary intron sequences on both sides of the circularized sequence. CircRNA functions as a sponge for miRNA, playing the role of the transcriptional regulator or potential biomarker. It has an impact on fetal growth and on synaptic facilitation in the brain. In this review, we illustrate biogenesis mechanisms, characteristics, and functions of cirRNAs. We also summarize methods using sequence feature and RNA next-generation sequencing data for circRNA prediction. Finally, we discuss the state of the research on circRNA in diseases, which will bring new contributions to future disease treatments.

Giant optical absorption and ferroelectric polarization of BiCoO2S perovskite oxysulfide by first principles prediction.

Obtaining an ideal ferroelectric photovoltaic (FE-PV) material with a narrow bandgap and a large ferroelectric polarization value can enable us to achieve great practical FE-PV performance. By the introduction of sulfur into the tetragonal BiCoO3 perovskite with a C-type antiferromagnetic ordering, it is found that the bandgap of BiCoO2S decreases significantly (about 1.2 eV) while maintaining a large polarization value (about 1.86 C m-2) that is similar to the value of 1.793 C m-2 of BiCoO3. Most noteworthy is that the optical absorption of BiCoO2S is remarkably higher than those of BiCoO3 and other FE-PV materials. The decrease of the BiCoO2S bandgap originates from the movement of Co 3d states to a low-energy position due to the reduction of the Co ionicity when the less electronegative sulfur is introduced into BiCoO3 to substitute oxygen. The narrow bandgap and the high optical absorption of the BiCoO2S films grown on different substrates are favorable for FE-PV applications. In addition, the bandgap of BiCoO2S can be modulated by the doping amount of sulfur, which can help us fabricate multilayer FE-PV devices based on different bandgaps from different layers.

Fast and Selective Semihydrogenation of Alkynes by Palladium Nanoparticles Sandwiched in Metal-Organic Frameworks.

The semihydrogenation of alkynes into alkenes rather than alkanes is of great importance in the chemical industry. Unfortunately, state-of-the-art heterogeneous catalysts hardly achieve high turnover frequencies (TOFs) simultaneously with almost full conversion, excellent selectivity, and good stability. Here, we used metal-organic frameworks (MOFs) containing Zr metal nodes ("UiO") with tunable wettability and electron-withdrawing ability as activity accelerators for the semihydrogenation of alkynes catalyzed by sandwiched palladium nanoparticles (Pd NPs). Impressively, the porous hydrophobic UiO support not only leads to an enrichment of phenylacetylene around the Pd NPs but also renders the Pd surfaces more electron-deficient, which leads to a remarkable catalysis performance, including an exceptionally high TOF of 13835 h-1 , 100 % phenylacetylene conversion 93.1 % selectivity towards styrene, and no activity decay after successive catalytic cycles. The strategy of using molecularly tailored supports is universal for boosting the selective semihydrogenation of various terminal and internal alkynes.

Sparse ECG Denoising with Generalized Minimax Concave Penalty.

The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated. A novel sparse ECG denoising framework combining low-pass filtering and sparsity recovery is proposed. Two sparsity recovery algorithms are developed based on the traditional ℓ 1 -norm penalty and the novel generalized minimax concave (GMC) penalty, respectively. Compared with the ℓ 1 -norm penalty, the non-differentiable non-convex GMC penalty has the potential to strongly promote sparsity while maintaining the convexity of the cost function. Moreover, the GMC punishes large values less severely than ℓ 1 -norm, which is utilized to overcome the drawback of underestimating the high-amplitude components for the ℓ 1 -norm penalty. The proposed methods are evaluated on ECG signals from the MIT-BIH Arrhythmia database. The results show that underestimating problem is overcome by the proposed GMC-based method. The GMC-based method shows significant improvement with respect to the average of output signal-to-noise ratio improvement ( S N R i m p ), the average of root mean square error (RMSE) and the percent root mean square difference (PRD) over almost any given SNR compared with the classical methods, thus providing promising approaches for ECG denoising.

Cry64Ba and Cry64Ca, Two ETX/MTX2-Type Bacillus thuringiensis Insecticidal Proteins Active against Hemipteran Pests.

Genetically modified crops that express insecticidal Bacillus thuringiensis (Bt) proteins have become a primary approach for control of lepidopteran (moth) and coleopteran (beetle) pests that feed by chewing the plants. However, the sap-sucking insects (Hemiptera) are not particularly susceptible to Bt toxins. In this study, we describe two Cry toxins (Cry64Ba and Cry64Ca) from Bt strain 1012 that showed toxicity against two important hemipteran rice pests, Laodelphax striatellus and Sogatella furcifera Both of these proteins contain an ETX/MTX2 domain and share common sequence features with the ß-pore-forming toxins. Coexpression of cry64Ba and cry64Ca genes in the acrystalliferous Bt strain HD73- resulted in high insecticidal activity against both hemipteran pests. No toxicity was observed on other pests such as Ostrinia furnacalis, Plutella xylostella, or Colaphellus bowringi Also, no hemolytic activity or toxicity against cancer cells was detected. Binding assays showed specific binding of the Cry64Ba/Cry64Ca toxin complex to brush border membrane vesicles isolated from L. striatellus Cry64Ba and Cry64Ca are Bt Cry toxins highly effective against hemipteran pests and could provide a novel strategy for the environmentally friendly biological control of rice planthoppers in transgenic plants.IMPORTANCE In Asia, rice is an important staple food, whose production is threatened by rice planthoppers. To date, no effective Bacillus thuringiensis (Bt) protein has been shown to have activity against rice planthoppers. We cloned two Bt toxin genes from Bt strain 1012 that showed toxicity against small brown planthoppers (Laodelphax striatellus) and white-backed planthoppers (Sogatella furcifera). To our knowledge, the proteins encoded by the cry64Ba and cry64Ca genes are the most efficient insecticidal Bt Cry proteins with activity against hemipteran insects reported so far. Cry64Ba and Cry64Ca showed no toxicity against some lepidopteran or coleopteran pests. These two proteins should be able to be used for integrated hemipteran pest management.

Cry78Aa, a novel Bacillus thuringiensis insecticidal protein with activity against Laodelphax striatellus and Nilaparvata lugens.

Transgenic plants expressing insecticidal proteins originating from Bacillus thuringiensis (Bt) have successfully been used to control lepidopteran and coleopteran pests with chewing mouthparts. However, only a handful of Bt proteins have been identified that have bioactivity against sap sucking pests (Hemiptera), including aphids, whiteflies, plant bugs and planthoppers. A novel Bt insecticidal protein with significant toxicity against a hemipteran insect pest is described here. The gene encoding the 359 amino acid, 40.7 kDa protein was cloned from strain C9F1. After expression and purification of the toxin, its median lethal concentration (LC50) values against Laodelphax striatellus and Nilaparvata lugens were determined as 6.89 µg/mL and 15.78 µg/mL respectively. Analysis of the toxin sequence revealed the presence of both Toxin_10 and Ricin_B_Lectin domains.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Low Duty-Cycling MAC Protocol for Low Data-Rate Medical Wireless Body Area Networks.

Wireless body area networks (WBANs) are severely energy constrained, and how to improve the energy efficiency so as to prolong the network lifetime as long as possible is one of the most important goals of WBAN research. Low data-rate WBANs are promising to cut down the energy consumption and extend the network lifetime. Considering the characteristics and demands of low data-rate WBANs, a low duty-cycling medium access control (MAC) protocol is specially designed for this kind of WBAN in this paper. Longer superframes are exploited to cut down the energy consumed on the transmissions and receptions of redundant beacon frames. Insertion time slots are embedded into the inactive part of a superframe to deliver the frames and satisfy the quality of service (QoS) requirements. The number of the data subsections in an insertion time slot can be adaptively adjusted so as to accommodate low data-rate WBANs with different traffic. Simulation results show that the proposed MAC protocol performs well under the condition of low data-rate monitoring traffic.

An Energy-Efficient MAC Protocol for Medical Emergency Monitoring Body Sensor Networks.

Medical emergency monitoring body sensor networks (BSNs) monitor the occurrence of medical emergencies and are helpful for the daily care of the elderly and chronically ill people. Such BSNs are characterized by rare traffic when there is no emergency occurring, high real-time and reliable requirements of emergency data and demand for a fast wake-up mechanism for waking up all nodes when an emergency happens. A beacon-enabled MAC protocol is specially designed to meet the demands of medical emergency monitoring BSNs. The rarity of traffic is exploited to improve energy efficiency. By adopting a long superframe structure to avoid unnecessary beacons and allocating most of the superframe to be inactive periods, the duty cycle is reduced to an extremely low level to save energy. Short active time slots are interposed into the superframe and shared by all of the nodes to deliver the emergency data in a low-delay and reliable way to meet the real-time and reliable requirements. The interposition slots can also be used by the coordinator to broadcast network demands to wake-up all nodes in a low-delay and energy-efficient way. Experiments display that the proposed MAC protocol works well in BSNs with low emergency data traffic.

A MAC protocol for medical monitoring applications of wireless body area networks.

Targeting the medical monitoring applications of wireless body area networks (WBANs), a hybrid medium access control protocol using an interrupt mechanism (I-MAC) is proposed to improve the energy and time slot utilization efficiency and to meet the data delivery delay requirement at the same time. Unlike existing hybrid MAC protocols, a superframe structure with a longer length is adopted to avoid unnecessary beacons. The time slots are mostly allocated to nodes with periodic data sources. Short interruption slots are inserted into the superframe to convey the urgent data and to guarantee the real-time requirements of these data. During these interruption slots, the coordinator can break the running superframe and start a new superframe. A contention access period (CAP) is only activated when there are more data that need to be delivered. Experimental results show the effectiveness of the proposed MAC protocol in WBANs with low urgent traffic.

Self-assembled PtNi layered metallene nanobowls for pH-universal electrocatalytic hydrogen evolution.

Compared with layered materials such as graphite and transition metal disulfide compounds with highly anisotropic in-plane covalent bonds, it is inherently more challenging to obtain independent metallic two-dimensional films with atomic thickness. In this study, PtNi layered metallene nanobowls (LMBs) with multilayer atomic-scale nanosheets and bowl-like structures have been synthesized in one step using structural and electronic effects. The material has the advantage of catalyzing pH-universal hydrogen evolution reaction (HER). Compared with Pt/C, PtNi LMBs exhibited excellent HER activity and stability under all pH conditions. The overpotentials of 10 mA cm-2 at 0.5 M H2SO4, 1.0 M phosphate buffer and 1.0 M KOH were 14.8, 20.3, and 34.0 mV, respectively. Under acidic, neutral and alkaline conditions, the HER Faraday efficiencies reach 98.97%, 98.85%, and 99.04%, respectively. This study provides an example for the preparation of unique multilayer nanobowls, and also provides a basic research platform for the development of special HER materials.

Nanocavity in hollow sandwiched catalysts as substrate regulator for boosting hydrodeoxygenation of biomass-derived carbonyl compounds.

Hydrodeoxygenation of oxygen-rich molecules toward hydrocarbons is attractive yet challenging in the sustainable biomass upgrading. The typical supported metal catalysts often display unstable catalytic performances owing to the migration and aggregation of metal nanoparticles (NPs) into large sizes under harsh conditions. Here, we develop a crystal growth and post-synthetic etching method to construct hollow chromium terephthalate MIL-101 (named as HoMIL-101) with one layer of sandwiched Ru NPs as robust catalysts. Impressively, HoMIL-101@Ru@MIL-101 exhibits the excellent activity and stability for hydrodeoxygenation of biomass-derived levulinic acid to gamma-valerolactone under 50°C and 1-megapascal H2, and its activity is about six times of solid sandwich counterparts, outperforming the state-of-the-art heterogeneous catalysts. Control experiments and theoretical simulation clearly indicate that the enrichment of levulinic acid and H2 by nanocavity as substrate regulator enables self-regulating the backwash of both substrates toward Ru NPs sandwiched in MIL-101 shells for promoting reaction with respect to solid counterparts, thus leading to the substantially enhanced performance.