Highly stretchable strain sensors based on conducting polymer hydrogel are rapidly emerging as a promising candidate toward diverse wearable skins and sensing devices for soft machines. However, due ...to the intrinsic limitations of low stretchability and large hysteresis, existing strain sensors cannot fully exploit their potential when used in wearable or robotic systems. Here, a conducting polymer hydrogel strain sensor exhibiting both ultimate strain (300%) and negligible hysteresis (<1.5%) is presented. This is achieved through a unique microphase semiseparated network design by compositing poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) nanofibers with poly(vinyl alcohol) (PVA) and facile fabrication by combining 3D printing and successive freeze‐thawing. The overall superior performances of the strain sensor including stretchability, linearity, cyclic stability, and robustness against mechanical twisting and pressing are systematically characterized. The integration and application of such strain sensor with electronic skins are further demonstrated to measure various physiological signals, identify hand gestures, enable a soft gripper for objection recognition, and remote control of an industrial robot. This work may offer both promising conducting polymer hydrogels with enhanced sensing functionalities and technical platforms toward stretchable electronic skins and intelligent robotic systems.
A conducting‐polymer hydrogel strain sensor is proposed with both high stretchability (300% strain) and ultralow hysteresis (<1.5%). The hydrogel‐based sensor harnesses a unique microphase semiseparated network to achieve enhanced sensing properties. The fabricated sensor can be applied as electronic skins to monitor physiological signals, enable a soft gripper for object recognition and remote control of an industrial robot.
The construction of carbon-carbon bonds is a central tenet of modern synthetic chemistry. Metal-catalyzed C-H functionalization can serve as a particularly powerful tool for achieving carbon-carbon ...bond formation. This review summarizes the early adventures and recent advancements in catalytic transformation of unactivated C(sp
3
)-H bonds into C(sp
3
)-C(sp
3
) bonds. To date, three main strategies have emerged to enable this transformation, namely, metallocarbene-triggered C(sp
3
)-H alkylation, auxiliary-directed C(sp
3
)-H alkylation, and photo-induced C(sp
3
)-H alkylation. Compared with traditional cross-coupling reactions having both coupling partners activated with functional groups or base-promoted enolate chemistry, catalytic alkylation of unactivated C(sp
3
)-H bonds for C(sp
3
)-C(sp
3
) bond formation not only offers novel disconnections in retrosynthetic analysis, but also represents the future trend in green and atom-economic chemistry.
This review summarizes recent advancements in catalytic direct transformation of unactivated C(sp
3
)-H bonds into C(sp
3
)-C(sp
3
) bonds.
Warming is known to reduce soil carbon (C) stocks by promoting microbial respiration, which is associated with the decomposition of microbial residue carbon (MRC). However, the relative contribution ...of MRC to soil organic carbon (SOC) across temperature gradients is poorly understood. Here, we investigated the contribution of MRC to SOC along two independent elevation gradients of our model system (i.e., the Tibetan Plateau and Shennongjia Mountain in China). Our results showed that local temperature increases were negatively correlated with MRC and SOC. Further analyses revealed that rising temperature reduced SOC via decreasing MRC, which helps to explain future reductions in SOC under climate warming. Our findings demonstrate that climate warming has the potential to reduce C sequestration by increasing the decomposition of MRC, exacerbating the positive feedback between rising temperature and CO2 efflux. Our study also considered the influence of multiple environmental factors such as soil pH and moisture, which were more important in controlling SOC than microbial traits such as microbial life‐style strategies and metabolic efficiency. Together, our work suggests an important mechanism underlying long‐term soil C sequestration, which has important implications for the microbial‐mediated C process in the face of global climate change.
The contribution of microbial residue carbon (MRC) to soil organic carbon (SOC) was dependent on local temperature along two independent elevation gradients. Local temperature increases could reduce SOC accumulation mainly by decreasing MRC due to the increasing MRC decomposition. Our study also considered the influence of multiple environmental factors such as soil pH and moisture, which were more important in controlling SOC than microbial traits such as microbial life‐style strategies and metabolic efficiency. Our work suggests an important mechanism underlying long‐term soil carbon sequestration, which has important implications for the microbial‐mediated carbon process under global climate change.
This paper investigates the soybean futures price prediction problem from a new perspective and proposes an effective prediction model named Two‐Stage Hybrid Long Short‐Term Memory (TSH‐LSTM) by ...using text data from social media. First, the unstructured text is transformed into structured data by sentiment analysis and text classification methods. The improved sentiment score is computed by combining the degree centrality of sentiment words based on the sentiment dictionary method, and the characteristics of price fluctuations in texts are learned through the text Recurrent Convolutional Neural Networks. Second, the significant relationship between social media features and soybean futures price is assessed through stepwise regression, and the results of such an assessment are used as a basis for the identification of significant factors as input variables of the prediction model. Finally, the TSH‐LSTM prediction model is designed, and the final prediction result is acquired through the combination of prediction results of each stage using the error reciprocal method. The empirical results indicate that the incorporation of the social media text feature helps improve forecasting performances. Specifically, the proposed TSH‐LSTM is more accurate than univariate LSTM, multivariate LSTM, and eXtreme Gradient Boosting.
Functional magnetic resonance imaging (fMRI) has allowed the noninvasive study of task-based and resting-state brain dynamics in humans by inferring neural activity from blood-oxygenation-level ...dependent (BOLD) signal changes. An accurate interpretation of the hemodynamic changes that underlie fMRI signals depends on the understanding of the quantitative relationship between changes in neural activity and changes in cerebral blood flow, oxygenation and volume. While there has been extensive study of neurovascular coupling in anesthetized animal models, anesthesia causes large disruptions of brain metabolism, neural responsiveness and cardiovascular function. Here, we review work showing that neurovascular coupling and brain circuit function in the awake animal are profoundly different from those in the anesthetized state. We argue that the time is right to study neurovascular coupling and brain circuit function in the awake animal to bridge the physiological mechanisms that underlie animal and human neuroimaging signals, and to interpret them in light of underlying neural mechanisms. Lastly, we discuss recent experimental innovations that have enabled the study of neurovascular coupling and brain-wide circuit function in un-anesthetized and behaving animal models.
•We review the effects of anesthesia on neurovascular coupling and brain circuit function.•Anesthesia decreases brain metabolism and perturbs other physiological processes.•Neural excitability and neurovascular coupling are altered by anesthesia.•Brain circuit function are changed by anesthesia relative to the awake animal.•Techniques for imaging in awake rodents are presented.
Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable ...when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores' spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.
The 1,3‐enyne moiety is commonly found in cyclohexanoid natural products produced by endophytic and plant pathogenic fungi. Asperpentyn (1) is a 1,3‐enyne‐containing cyclohexanoid terpenoid isolated ...from Aspergillus and Pestalotiopsis. The genetic basis and biochemical mechanism of 1,3‐enyne biosynthesis in 1, and other natural products containing this motif, has remained enigmatic despite their potential ecological roles. Identified here is the biosynthetic gene cluster and characterization of two crucial enzymes in the biosynthesis of 1. A P450 monooxygenase that has a dual function, to first catalyze dehydrogenation of the prenyl chain to generate a cis‐diene intermediate and then serve as an acetylenase to yield an alkyne moiety, and thus the 1,3‐enyne, was discovered. A UbiA prenyltransferase was also characterized and it is unusual in that it favors transferring a five‐carbon prenyl chain, rather than a polyprenyl chain, to a p‐hydroxybenzoic acid acceptor.
Clusters: Enynes are an important functionality in organic chemistry. Reported here is the identification of the biosynthetic gene cluster and characterization of two crucial enzymes in the biosynthesis of asperpentyn (1). Notably, P450 monooxygenase was discovered to have a dual function, that is, to first catalyze dehydrogenation of the prenyl chain to generate a cis‐diene intermediate, and then serve as an acetylenase to yield an alkyne moiety.
Reliable energy consumption forecasting can provide effective decision-making support for planning development strategies to energy enterprises and for establishing national energy policies. ...Accordingly, the present study aims to apply a hybrid intelligent approach named ADE–BPNN, the back-propagation neural network (BPNN) model supported by an adaptive differential evolution algorithm, to estimate energy consumption. Most often, energy consumption is influenced by socioeconomic factors. The proposed hybrid model incorporates gross domestic product, population, import, and export data as inputs. An improved differential evolution with adaptive mutation and crossover is utilized to find appropriate global initial connection weights and thresholds to enhance the forecasting performance of the BPNN. A comparative example and two extended examples are utilized to validate the applicability and accuracy of the proposed ADE–BPNN model. Errors of the test data sets indicate that the ADE–BPNN model can effectively predict energy consumption compared with the traditional back-propagation neural network model and other popular existing models. Moreover, mean impact value based analysis is conducted for electrical energy consumption in U.S. and total energy consumption forecasting in China to quantitatively explore the relative importance of each input variable for the improvement of effective energy consumption prediction.
•Enhanced back-propagation neural network (ADE-BPNN) for energy consumption forecasting.•ADE-BPNN outperforms the current best models for two comparative cases.•Mean impact value approach explores socio-economic factors' relative importance.•ADE-BPNN's adjusted goodness-of-fit is 99.2% for China's energy consumption forecasting.
Accurate oil market forecasting plays an important role in the theory and application of oil supply chain management for profit maximization and risk minimization. However, the coronavirus disease ...2019 (COVID-19) has compelled governments worldwide to impose restrictions, consequently forcing the closure of most social and economic activities. The latter leads to the volatility of the oil markets and poses a huge challenge to oil market forecasting. Fortunately, the social media information can finely reflect oil market factors and exogenous factors, such as conflicts and political instability. Accordingly, this study collected vast online oil news and used convolutional neural network to extract relevant information automatically. Oil markets are divided into four categories: oil price, oil production, oil consumption, and oil inventory. A total of 16,794; 9,139; 8,314; and 8,548 news headlines were collected in four respective cases. Experimental results indicate that social media information contributes to the forecasting of oil price, oil production and oil consumption. The mean absolute percentage errors are respectively 0.0717, 0.0144 and 0.0168 for the oil price, production, and consumption prediction during the COVID-19 pandemic. Marketers must consider the impact of social media information on the oil or similar markets, especially during the COVID-19 outbreak.
•A novel news-based oil markets forecasting methodology was proposed.•Deep learning can extract textual features from online news automatically.•It is the first time to examine predictive relationship between news and oil markets.•News can facilitate predict oil price and consumption during the COVID-19 pandemic.
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