Ionic Hydrogen Bond-Assisted Catalytic Construction of Nitrogen Stereogenic Center via Formal Desymmetrization of Remote Diols.

The control of noncarbon stereogenic centers is of profound importance owing to their enormous interest in bioactive compounds and chiral catalyst or ligand design for enantioselective synthesis. Despite various elegant approaches have been achieved for construction of S-, P-, Si- and B-stereocenters over the past decades, the catalyst-controlled strategies to govern the formation of N-stereogenic compounds have garnered less attention. Here, we disclose the first organocatalytic approach for efficient access to a wide range of nitrogen-stereogenic compounds through a desymmetrization approach. Intriguingly, the pro-chiral remote diols, which are previously not well addressed with enantiocontrol, are well differentiated by potent chiral carbene-bound acyl azolium intermediates. Preliminary studies shed insights on the critical importance of the ionic hydrogen bond (IHB) formed between the dimer aggregate of diols to afford the chiral N-oxide products that feature a tetrahedral nitrogen as the sole stereogenic element with good yields and excellent enantioselectivities. Notably, the chiral N-oxide products could offer an attractive strategy for chiral ligand design and discovery of potential antibacterial agrochemicals.

Co-regulation of Thermosensor Pathogenic Factors by C-di-GMP-Related Two-Component Systems and a cAMP Receptor-like Protein (Clp) in Stenotrophomonas maltophilia.

Stenotrophomonas maltophilia is a major threat to the food industry and human health owing to its strong protease production and biofilm formation abilities. However, information regarding regulatory factors or potential mechanisms is limited. Herein, we observed that temperature differentially regulates biofilm formation and protease production, and a cAMP receptor-like protein (Clp) negatively regulates thermosensor biofilm formation, in contrast to protease synthesis. Among four c-di-GMP-related two-component systems (TCSs), promoter fusion analysis revealed that clp transcription levels were predominantly controlled by LotS/LotR, partially controlled by both RpfC/RpfG and a novel TCS Sm0738/Sm0737, with no obvious effect caused by Sm1912/Sm1911. Biofilm formation in Δclp and ΔTCSs strains suggested that LotS/LotR controlled biofilm formation in a Clp-mediated manner, whereas both RpfC/RpfG and Sm0738/Sm0737 may occur in a distinct pathway. Furthermore, enzymatic activity analysis combined with c-di-GMP level indicated that the enzymatic activity of c-di-GMP-related metabolism proteins may not be a vital contributor to changes in c-di-GMP level, thus influencing physiological functions. Our findings elucidate that the regulatory pathway of c-di-GMP-related TCSs and Clp in controlling spoilage or the formation of potentially pathogenic factors in Stenotrophomonas expand the understanding of c-di-GMP metabolism and provide clues to control risk factors of S. maltophilia in food safety.

Diversely regio-oxidative degradation of konjac glucomannan by lytic polysaccharide monooxygenase AA10 and generating antibacterial hydrolysate.

Konjac glucomannan (KGM) hydrolysate exhibit various biological activities and health-promoting effects. Lytic polysaccharide monooxygenases (LPMOs) play an important role on enzymatic degradation of recalcitrant polysaccharides to obtain fermentable sugars. It is generally accepted that LPMOs exhibits high substrate specificity and oxidation regioselectivity. Here, a bacteria-derived SmAA10A, with chitin-active with strict C1 oxidation, was used to catalyse KGM degradation. Through ethanol precipitation, two hydrolysed KGM components (4 kDa (KGM-1) and 5 kDa (KGM-2)) were obtained that exhibited antibacterial activity against Staphylococcus aureus. In natural KGM, KGM-1, and KGM-2, the molar ratios of mannose to glucose were 1:2.19, 1:3.05, and 1:2.87, respectively, indicating that SmAA10A preferentially degrades mannose in KGM. Fourier-transform infrared spectroscopy and scanning electron microscopy imaging revealed the breakage of glycosylic bonds during enzymatic catalysis. The regioselectivity of SmAA10A for KGM degradation was determined based on the fragmentation behaviour of the KGM-1 and KGM-2 oligosaccharides and their NaBD4-reduced forms. SmAA10A exhibited diverse oxidation degradation of KGM and generated single C1-, single C4-, and C1/C4-double oxidised oligosaccharide forms. This study provides an alternative method for obtaining KGM degradation components with antibacterial functions and expands the substrate specificity and oxidation regioselectivity of bacterial LPMOs.

Carbene organic catalytic planar enantioselective macrolactonization.

Macrolactones exhibit distinct conformational and configurational properties and are widely found in natural products, medicines, and agrochemicals. Up to now, the major effort for macrolactonization is directed toward identifying suitable carboxylic acid/alcohol coupling reagents to address the challenges associated with macrocyclization, wherein the stereochemistry of products is usually controlled by the substrate's inherent chirality. It remains largely unexplored in using catalysts to govern both macrolactone formation and stereochemical control. Here, we disclose a non-enzymatic organocatalytic approach to construct macrolactones bearing chiral planes from achiral substrates. Our strategy utilizes N-heterocyclic carbene (NHC) as a potent acylation catalyst that simultaneously mediates the macrocyclization and controls planar chirality during the catalytic process. Macrolactones varying in ring sizes from sixteen to twenty members are obtained with good-to-excellent yields and enantiomeric ratios. Our study shall open new avenues in accessing macrolactones with various stereogenic elements and ring structures by using readily available small-molecule catalysts.

4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism.

DNA methylation is one of the most extensive epigenetic modifications. DNA 4mC modification plays a key role in regulating chromatin structure and gene expression. In this study, we proposed a generic 4mC computational predictor, namely, 4mCPred-MTL using multi-task learning coupled with Transformer to predict 4mC sites in multiple species. In this predictor, we utilize a multi-task learning framework, in which each task is to train species-specific data based on Transformer. Extensive experimental results show that our multi-task predictive model can significantly improve the performance of the model based on single task and outperform existing methods on benchmarking comparison. Moreover, we found that our model can sufficiently capture better characteristics of 4mC sites as compared to existing commonly used feature descriptors, demonstrating the strong feature learning ability of our model. Therefore, based on the above results, it can be expected that our 4mCPred-MTL can be a useful tool for research communities of interest.

3D Upper Body Reconstruction with Sparse Soft Sensors.

Three-dimensional (3D) reconstruction of human body has wide applications, for example, for customized design of clothes and digital avatar production. Existing vision-based systems for 3D body reconstruction require users to wear minimal or extreme-tight clothes in front of cameras, and thus suffer from privacy problems. In this work, we explore a novel solution based on a sparse number of soft sensors on a standard garment, and use it for capturing 3D upper body shape. We utilize the maximal stretching range by modeling the nonlinear performance profile for individual sensors. The body shape can be dynamically reconstructed by analyzing the relationship between mesh deformation and sensor reading, with a learning-based approach. The wearability and flexibility of our prototype allow its use in indoor/outdoor environments and for long-term breath monitoring. Our prototype has been extensively evaluated by multiple users with different body sizes and the same user for multiple days. The results show that our garment prototype is comfortable to wear, and achieves the state-of-the-art reconstruction performance with the advantages in privacy projection and application scenarios.

Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications.

DNA N4-methylcytosine modification (4mC) plays an essential role in a variety of biological processes. Therefore, accurate identification the 4mC distribution in genome-scale is important for systematically understanding its biological functions. In this study, we present Deep4mcPred, a multi-layer deep learning based predictive model to identify DNA N4-methylcytosine modifications. In this predictor, we for the first time integrate residual network and recurrent neural network to build a multi-layer deep learning predictive system. As compared to existing predictors using traditional machine learning, our proposed method has two advantages. First, our deep learning framework does not need to specify the features when training the predictive model. It can automatically learn the high-level features and capture the characteristic specificity of 4mC sites, benefiting to distinguish true 4mC sites from non-4mC sites. On the other hand, our deep learning method outperforms the traditional machine learning predictors in performance by benchmarking comparison, demonstrating that the proposed Deep4mcPred is more effective in the DNA 4mC site prediction. Moreover, via experimental comparison, we found that attention mechanism introduced into the deep learning framework is useful to capture the critical features. Additionally, we develop a webserver implementing the proposed method for the academic use of research community, which is now available at http://server.malab.cn/Deep4mcPred.

Controlling the selectivity of an intramolecular Friedel-Crafts alkylation with alkenes using selenium under mild conditions.

An efficiently divergent intramolecular Friedel-Crafts alkylation by unactivated alkenes with seleniranium ion-controlled Markovnikov/anti-Markovnikov specificities under mild conditions has been investigated. 2-Benzoxepin, isochroman, and isochromene can be produced in one-pot procedures from the same substrate in high yields and with high regio- and stereospecificity. The products are challenging to access via 7-endo-trig carbocyclizations and by 7-endo-trig carbocyclization/rearrangement/6-exo-trig oxycyclization and 6-exo-trig carbocyclization/deselenenylation reaction sequences, respectively. Mechanistic experiments indicated that in addition to the stereospecific anti-addition processes of the cyclization reactions, the formation of a stable carbocation after ring opening of the seleniranium ion leads to an NPSP-mediated 7-endo-trig carbocyclization; the steric hindrance of the seleniranium intermediate controls the regioselectivity when using TPSCA at 60 °C, which promotes 6-exo-trig carbocyclization. Two distinct catalytic cycles were proposed, and the structures of transition states and products were identified by ab initio calculations and X-ray analyses.

A Neuro-Musculo-Skeletal Model for Insects With Data-driven Optimization.

Simulating the locomotion of insects is beneficial to many areas such as experimental biology, computer animation and robotics. This work proposes a neuro-musculo-skeletal model, which integrates the biological inspirations from real insects and reproduces the gait pattern on virtual insects. The neural system is a network of spiking neurons, whose spiking patterns are controlled by the input currents. The spiking pattern provides a uniform representation of sensory information, high-level commands and control strategy. The muscle models are designed following the characteristic Hill-type muscle with customized force-length and force-velocity relationships. The model parameters, including both the neural and muscular components, are optimized via an approach of evolutionary optimization, with the data captured from real insects. The results show that the simulated gait pattern, including joint trajectories, matches the experimental data collected from real ants walking in the free mode. The simulated character is capable of moving at different directions and traversing uneven terrains.

A novel machine learning method for cytokine-receptor interaction prediction.

Most essential functions are associated with various protein-protein interactions, particularly the cytokine-receptor interaction. Knowledge of the heterogeneous network of cytokine- receptor interactions provides insights into various human physiological functions. However, only a few studies are focused on the computational prediction of these interactions. In this study, we propose a novel machine-learning-based method for predicting cytokine-receptor interactions. A protein sequence is first transformed by incorporating the sequence evolutional information and then formulated with the following three aspects: (1) the k-skip-n-gram model, (2) physicochemical properties, and (3) local pseudo position-specific score matrix (local PsePSSM). The random forest classifier is subsequently employed to predict potential cytokine-receptor interactions. Experimental results on a dataset of Homo sapiens show that the proposed method exhibits improved performance, with 3.4% higher overall prediction accuracy, than existing methods.

Enhanced Protein Fold Prediction Method Through a Novel Feature Extraction Technique.

Information of protein 3-dimensional (3D) structures plays an essential role in molecular biology, cell biology, biomedicine, and drug design. Protein fold prediction is considered as an immediate step for deciphering the protein 3D structures. Therefore, protein fold prediction is one of fundamental problems in structural bioinformatics. Recently, numerous taxonomic methods have been developed for protein fold prediction. Unfortunately, the overall prediction accuracies achieved by existing taxonomic methods are not satisfactory although much progress has been made. To address this problem, we propose a novel taxonomic method, called PFPA, which is featured by combining a novel feature set through an ensemble classifier. Particularly, the sequential evolution information from the profiles of PSI-BLAST and the local and global secondary structure information from the profiles of PSI-PRED are combined to construct a comprehensive feature set. Experimental results demonstrate that PFPA outperforms the state-of-the-art predictors. To be specific, when tested on the independent testing set of a benchmark dataset, PFPA achieves an overall accuracy of 73.6%, which is the leading accuracy ever reported. Moreover, PFPA performs well without significant performance degradation on three updated large-scale datasets, indicating the robustness and generalization of PFPA. Currently, a webserver that implements PFPA is freely available on http://121.192.180.204:8080/PFPA/Index.html.

Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements.

Data gathering is a key operator for applications in wireless sensor networks; yet it is also a challenging problem in mobile sensor networks when considering that all nodes are mobile and the communications among them are opportunistic. This paper proposes an efficient data gathering scheme called ADG that adopts speedy mobile elements as the mobile data collector and takes advantage of the movement patterns of the network. ADG first extracts the network meta-data at initial epochs, and calculates a set of proxy nodes based on the meta-data. Data gathering is then mapped into the Proxy node Time Slot Allocation (PTSA) problem that schedules the time slots and orders, according to which the data collector could gather the maximal amount of data within a limited period. Finally, the collector follows the schedule and picks up the sensed data from the proxy nodes through one hop of message transmissions. ADG learns the period when nodes are relatively stationary, so that the collector is able to pick up the data from them during the limited data gathering period. Moreover, proxy nodes and data gathering points could also be timely updated so that the collector could adapt to the change of node movements. Extensive experimental results show that the proposed scheme outperforms other data gathering schemes on the cost of message transmissions and the data gathering rate, especially under the constraint of limited data gathering period.

Wavelet-based watermarking and compression for ECG signals with verification evaluation.

In the current open society and with the growth of human rights, people are more and more concerned about the privacy of their information and other important data. This study makes use of electrocardiography (ECG) data in order to protect individual information. An ECG signal can not only be used to analyze disease, but also to provide crucial biometric information for identification and authentication. In this study, we propose a new idea of integrating electrocardiogram watermarking and compression approach, which has never been researched before. ECG watermarking can ensure the confidentiality and reliability of a user's data while reducing the amount of data. In the evaluation, we apply the embedding capacity, bit error rate (BER), signal-to-noise ratio (SNR), compression ratio (CR), and compressed-signal to noise ratio (CNR) methods to assess the proposed algorithm. After comprehensive evaluation the final results show that our algorithm is robust and feasible.

Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set.

MicroRNA (miRNA) plays an important role as a regulator in biological processes. Identification of (pre-) miRNAs helps in understanding regulatory processes. Machine learning methods have been designed for pre-miRNA identification. However, most of them cannot provide reliable predictive performances on independent testing data sets. We assumed this is because the training sets, especially the negative training sets, are not sufficiently representative. To generate a representative negative set, we proposed a novel negative sample selection technique, and successfully collected negative samples with improved quality. Two recent classifiers rebuilt with the proposed negative set achieved an improvement of ~6 percent in their predictive performance, which confirmed this assumption. Based on the proposed negative set, we constructed a training set, and developed an online system called miRNApre specifically for human pre-miRNA identification. We showed that miRNApre achieved accuracies on updated human and non-human data sets that were 34.3 and 7.6 percent higher than those achieved by current methods. The results suggest that miRNApre is an effective tool for pre-miRNA identification. Additionally, by integrating miRNApre, we developed a miRNA mining tool, mirnaDetect, which can be applied to find potential miRNAs in genome-scale data. MirnaDetect achieved a comparable mining performance on human chromosome 19 data as other existing methods.

A Sketching Interface for Sitting Pose Design in the Virtual Environment.

Character pose design is one of the most fundamental processes in computer graphics authoring. Although there are many research efforts in this field, most existing design tools consider only character body structure, rather than its interaction with the environment. This paper presents an intuitive sketching interface that allows the user to interactively place a 3D human character in a sitting position on a chair. Within our framework, the user sketches the target pose as a 2D stick figure and attaches the selected joints to the environment (e.g., the feet on the ground) with a pin tool. As reconstructing the 3D pose from a 2D stick figure is an ill-posed problem due to many possible solutions, the key idea in our paper is to reduce solution space by considering the interaction between the character and environment and adding physics constraints, such as balance and collision. Further, we formulated this reconstruction into a nonlinear optimization problem and solved it via the genetic algorithm (GA) and the quasi-Newton solver. With the GPU implementation, our system is able to generate the physically correct and visually pleasing pose at an interactive speed. The promising experimental results and user study demonstrates the efficacy of our method.