Fibre‐based materials have received tremendous attention due to their flexibility and wearability. Although great efforts have been devoted to achieve high‐performance fibres over the past several ...years, it is still challenging for multifunctional macroscopic fibres to satisfy versatile applications. 2D transition metal carbides/nitrides (MXenes) with intriguing physical/chemical properties have been explored in broad application, and may be able to reinforce synthetic fibres. Inspired by natural materials, for the first time, flexible smart fibres and textiles are fabricated using a 3D printing process with hybrid inks of TEMPO (2,2,6,6‐tetramethylpiperidine‐1‐oxylradi‐cal)‐mediated oxidized cellulose nanofibrils (TOCNFs) and Ti3C2 MXene. The hybrid inks display good rheological properties, which allow them to achieve accurate structures and be rapidly printed. TOCNFs/Ti3C2 in hybrid inks self‐assemble to fibres with an aligned structure in ethanol, mimicking the features of the natural structures of plant fibres. In contrast to conventional synthetic fibres with limited functions, smart TOCNFs/Ti3C2 fibres and textiles exhibit significant responsiveness to multiple external stimuli (electrical/photonic/mechanical). TOCNFs/Ti3C2 textiles with electromechanical performance can be processed into sensitive strain sensors. Such multifunctional smart fibres and textiles will be promising in diverse applications, including wearable heating textiles, human health monitoring, and human–machine interfaces.
Highly flexible and conductive smart fibres and textiles with integrated multifunctionality are fabricated by assembling cellulose nanofibrils and Ti3C2 MXene using a facile 3D printing process. The resultant smart fibres and textiles exhibit excellent responsiveness to multiple external stimuli (electrical/photonic/mechanical). The smart textile can also be processed into a sensitive strain sensor to achieve real‐time human motion recognition.
Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main ...reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at
http://admet.scbdd.com/
.
Sequence-derived structural and physiochemical features have been frequently used for analysing and predicting structural, functional, expression and interaction profiles of proteins and peptides. To ...facilitate extensive studies of proteins and peptides, we developed a freely available, open source python package called protein in python (propy) for calculating the widely used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes five feature groups composed of 13 features, including amino acid composition, dipeptide composition, tripeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors, composition, transition and distribution of various structural and physicochemical properties and two types of pseudo amino acid composition (PseAAC) descriptors. These features could be generally regarded as different Chou's PseAAC modes. In addition, it can also easily compute the previous descriptors based on user-defined properties, which are automatically available from the AAindex database.
The python package, propy, is freely available via http://code.google.com/p/protpy/downloads/list, and it runs on Linux and MS-Windows.
Supplementary data are available at Bioinformatics online.
With the advances in innovative instrumentation and various valuable applications, near-infrared (NIR) spectroscopy has become a mature analytical technique in various fields. Variable (wavelength) ...selection is a critical step in multivariate calibration of NIR spectra, which can improve the prediction performance, make the calibration reliable and provide simpler interpretation. During the last several decades, there have been a large number of variable selection methods proposed in NIR spectroscopy. In this paper, we generalize variable selection methods in a simple manner to introduce their classifications, merits and drawbacks, to provide a better understanding of their characteristics, similarities and differences. We also introduce some hybrid and modified methods, highlighting their improvements. Finally, we summarize the limitations of existing variable selection methods, providing our remarks and suggestions on the development of variable selection methods, to promote the development of NIR spectroscopy.
•Generalize variable selection methods in a simple manner to provide a better understanding of their characteristics.•Introduce their modified and hybrid methods and highlighting their improvements.•Summarize the limitations and mention seven aspects of the problem affecting the existing variable selection methods.•Provide our remarks and suggestions on the trends of the development on the variable selection methods in NIR spectra.
Background
With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in ...these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline.
Results
Herein, the python library
PyBioMed
is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions.
PyBioMed
is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of
PyBioMed
could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use
PyBioMed
as an integral part of data analysis projects. By using
PyBioMed
, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently.
Conclusion
PyBioMed
provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling.
PyBioMed
is freely available at
http://projects.scbdd.com/pybiomed.html
.
Dynamic sequential control of photoluminescence by supramolecular approaches has become a great issue in supramolecular chemistry. However, developing a systematic strategy to construct polychromatic ...photoluminescent supramolecular self‐assemblies for improving the efficiency and sensitivity of artificial light‐harvesting systems still remains a challenge. Here, a series of amphiphilicity‐controlled supramolecular self‐assemblies with polychromatic fluorescence based on lower‐rim hexyl‐modified sulfonatocalix4arene (SC4A6) and N‐alkyl‐modified p‐phenylene divinylpyridiniums (PVPn, n = 2–7) as efficient light‐harvesting platforms is reported. PVPn shows wide ranges of polychromatic fluorescence by co‐assembling with SC4A6, whose emission trends significantly depend on the modified alkyl‐chains of PVPn. The formed PVPn‐SC4A6 co‐assemblies as light‐harvesting platforms are extremely sensitive for transferring the energy to two near‐infrared emissive acceptors, Nile blue (NiB) and Rhodamine 800. After optimizing the amphiphilicity of PVPn‐SC4A6 systems, the PVPn‐SC4A6‐NiB light‐harvesting systems achieve an ultrasensitive working concentration for NiB (2 nm) and an ultrahigh antenna effect up to 91.0. Furthermore, the two different kinds of light‐harvesting nanoparticles exhibit good performance on near‐infrared imaging in the Golgi apparatus and mitochondria, respectively.
A series of amphiphilicity‐controlled polychromatic emissive supramolecular self‐assemblies is constructed as highly sensitive and efficient artificial light‐harvesting platforms. Two near‐infrared emissive light‐harvesting acceptors are employed to achieve the highest antenna effect up to 91.0 and most sensitive working concentration (2 nm). Such near‐infrared emissive light‐harvesting systems show good capability for imaging in both the Golgi apparatus and mitochondria.
Drug–target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug ...reactions, drug–drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user’s molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75–100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug–drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at
http://targetnet.scbdd.com
.
Tyrosinase (TYR) is a crucial enzyme involved in melanogenesis, and its overexpression is closely associated with melanoma. To precisely monitor intracellular TYR activity, remote control of a ...molecule imaging tool is highly meaningful but remains to be explored. In this work, we present the first photocaged tyrosinase fluorogenic probe by caging the substrate of the enzymatic probe with a photolabile group. Because of the sequential light and enzyme-activation feature, this probe exhibits photocontrollable “turn on” response toward TYR with good selectivity and high sensitivity (detection limit: 0.08 U/mL). Fluorescence imaging results validate that the caged probe possesses the capability of visualizing intracellular endogenous tyrosinase activity in a photocontrol fashion, thus offering a promising molecule imaging tool for investigating TYR-related physiological function and pathological role. Moreover, our sequential activation strategy has great potential for developing more photocontrollable enzymatic fluorogenic probes with spatiotemporal resolution.
Conjugated coordination polymers have become an emerging category of redox‐active materials. Although recent studies heavily focus on the tailoring of metal centers in the complexes to achieve stable ...electrochemical performance, the effect on different substitutions of the bridging bonds has rarely been studied. An innovative tailoring strategy is presented toward the enhancement of the capacity storage and the stability of metal–organic conjugated coordination polymers. Two nanostructured d‐π conjugated compounds, NiC6H2(NH)4n (Ni‐NH) and NiC6H2(NH)2S2n (Ni‐S), are evaluated and demonstrated to exhibit hybrid electrochemical processes. In particular, Ni‐S delivers a high reversible capacity of 1164 mAh g−1, an ultralong stability up to 1500 cycles, and a fully recharge ability in 67 s. This tailoring strategy provides a guideline to design future effective conjugated coordination‐polymer‐based electrodes.
An innovative tailoring strategy toward the enhancement of the capacity storage and stability of d‐π metal‐organic conjugated coordination polymers is proposed. The tailored compound Ni‐S shows a high reversible capacity of 1164 mAh g−1, an ultralong stability up to 1500 cycles, and a fully recharge ability of only 67 s.
When analyzing high-dimensional near-infrared (NIR) spectral datasets, variable selection is critical to improving models' predictive abilities. However, some methods have many limitations, such as a ...high risk of overfitting, time-intensiveness, or large computation demands, when dealing with a high number of variables. In this study, we propose a hybrid variable selection strategy based on the continuous shrinkage of variable space which is the core idea of variable combination population analysis (VCPA). The VCPA-based hybrid strategy continuously shrinks the variable space from big to small and optimizes it based on modified VCPA in the first step. It then employs iteratively retaining informative variables (IRIV) and a genetic algorithm (GA) to carry out further optimization in the second step. It takes full advantage of VCPA, GA, and IRIV, and makes up for their drawbacks in the face of high numbers of variables. Three NIR datasets and three variable selection methods including two widely-used methods (competitive adaptive reweighted sampling, CARS and genetic algorithm-interval partial least squares, GA–iPLS) and one hybrid method (variable importance in projection coupled with genetic algorithm, VIP–GA) were used to investigate the improvement of VCPA-based hybrid strategy. The results show that VCPA–GA and VCPA–IRIV significantly improve model's prediction performance when compared with other methods, indicating that the modified VCPA step is a very efficient way to filter the uninformative variables and VCPA-based hybrid strategy is a good and promising strategy for variable selection in NIR. The MATLAB source codes of VCPA–GA and VCPA–IRIV can be freely downloaded in the website: https://cn.mathworks.com/matlabcentral/profile/authors/5526470-yonghuan-yun.
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•A hybrid variable selection strategy in two steps was proposed.•The optimization advantages of IRIV and GA have been fully exploited with the variable space optimized by modified VCPA.•VCPA-GA and VCPA-IRIV significantly improved model's prediction performance as compared to other methods.•It is a useful and promising variable selection strategy for solving the problems of high-dimensional data.
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