Article
Plant Sciences
Alexander G. Volkov, Leon Chua
Summary: Plants have sensory, short-term, and long-term memory and can exhibit electrical responses that may contain memory resistors. The discovery of volatile memristors in plants through electrical stimulation opens up new avenues for modeling and understanding electrical phenomena in the plant kingdom.
FUNCTIONAL PLANT BIOLOGY
(2021)
Article
Immunology
Zhen Zhang, Wentao Zhang, Kuo Yang, Shujing Zhang
Summary: The paper proposes a hybrid model combining an attention mechanism and a bidirectional long short-term memory network to enhance accurate prediction of the remaining useful life of lithium-ion batteries. By using multiple temporal features strongly correlated with battery health as input and incorporating attention mechanism in the network, the model achieves good prediction results and outperforms other machine learning models in prediction accuracy.
FRONTIERS IN IMMUNOLOGY
(2021)
Article
Engineering, Multidisciplinary
Zhen Zhang, Wentao Zhang, Kuo Yang, Shujing Zhang
Summary: A hybrid model based on an attention mechanism and a bidirectional long short-term memory network is proposed for accurate prediction of the remaining useful life of lithium-ion batteries. Experimental results show that the proposed framework outperforms other machine learning models in prediction accuracy and reducing uncertainty for multi-step prediction.
Article
Nanoscience & Nanotechnology
Liwei Cheng, Qiaonan Zhu, Jiandong Liang, Mengyao Tang, Yan Yang, Sicong Wang, Puguang Ji, Gongkai Wang, Wenxing Chen, Xiuhui Zhang, Hua Wang
Summary: The research demonstrates the use of organic phenazine molecules with flexible electron-rich ion channels as cathode materials for aqueous zinc-ion batteries, enabling fast-charging and long-term stability. These materials exhibit excellent capacity and cycling performance, outperforming previously reported aqueous ZIBs.
ACS APPLIED MATERIALS & INTERFACES
(2021)
Article
Cell Biology
Xinxin Yin, Yu Wang, Jiejue Li, Zengcai V. Guo
Summary: Despite symmetric brain structures, the left and right hemispheres in mammals do not contribute equally to certain cognitive functions. This study focuses on the anterior lateral motor cortex (ALM) in mice and investigates the asymmetrical interaction between the left and right ALM during a tactile-based decision-making task. The results show that neural activity and encoding capability are similar across hemispheres, but only one hemisphere dominates in behavior. Inhibition of the dominant ALM disrupts encoding capability in the non-dominant ALM. Variable behavioral deficits can be predicted by the influence on contralateral activity across sessions, mice, and tasks.
Article
Electrochemistry
Tadele Mamo, Fu-Kwun Wang
Summary: Monitoring battery cycle life can predict remaining life, and a hybrid neural network model with optimized parameters shows better prediction performance on battery datasets compared to public models.
Review
Biochemistry & Molecular Biology
Michael Levin
Summary: Recent advancements in analyzing developmental bioelectric circuits and channelopathies have shed light on the cooperation of cellular collectives towards achieving organ-level structural order. These progressions open up possibilities for utilizing bioelectric signaling in interventions related to developmental disorders, regenerative medicine, cancer reprogramming, and synthetic bioengineering.
Article
Computer Science, Artificial Intelligence
Shih-Hsien Tseng, Khoa-Dang Tran
Summary: In this work, a novel deep learning module called ALSTMP is proposed for RUL estimation, which utilizes attention mechanisms and the time-window length method to collect key features effectively. The proposed model outperforms traditional LSTM and its extension, as well as recent deep learning approaches, with fewer parameters.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Environmental Sciences
Haixiao Li, Le Liu
Summary: This study found that microplastics have certain effects on soil nitrogen and phosphorus cycling. The presence of microplastics significantly decreased the content of available phosphate in the soil, while the addition of PP microplastics increased the content of available ammonium. Microplastics had limited effects on soil microorganisms.
Article
Engineering, Civil
Yingfei Wang, Yingping Huang, Min Xiao, Shuangshuang Zhou, Biao Xiong, Zhuan Jin
Summary: This study proposes a spatio-temporal attention LSTM (STA-LSTM) model to improve the accuracy of flood prediction by utilizing spatial and temporal attention mechanisms, and provides interpretation of the model through attention visualization. The experimental results show that the MAE of the STA-LSTM model is 0.74835, RMSE is 0.90197, MAPE is 3.29924, and R2 is 0.94138. This study enhances the accuracy of water level prediction, making it easier for decision-makers to plan evacuations in advance.
JOURNAL OF HYDROLOGY
(2023)
Article
Chemistry, Multidisciplinary
Xinwei Ma, Yurui Yin, Yuchuan Jin, Mingjia He, Minqing Zhu
Summary: Bike-sharing is an important mode of transportation that improves urban mobility, but achieving balanced utilization of shared bikes is challenging. This study proposes a deep learning model that predicts short-run bike-sharing demand using multi-source data sets. The model outperforms several baseline models and can help users choose routes and operators implement redistribution strategies.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Wujing Xian, Matthew R. Hennefarth, Michelle W. Lee, Tran Do, Ernest Y. Lee, Anastassia N. Alexandrova, Gerard C. L. Wong
Summary: AMPs preferentially permeate prokaryotic membranes via electrostatic binding and membrane remodeling. High salt suppresses this action, but histidines in marine AMPs can interact with salt ions to neutralize phosphate charge and facilitate pore formation.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Computer Science, Information Systems
Wang Xing, Wu Qi-liang, Tan Gui-rong, Qian Dai-li, Zhou Ke
Summary: This study proposes a method using deep learning technology to forecast wind speed. By utilizing time series data from the target station and adjacent stations, the method predicts the future wind speed at the target station. The results show excellent performance in wind speed forecast, providing technical support for operational applications like gale disaster early-warning and wind power prediction.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Thermodynamics
Lu Peng, Lin Wang, De Xia, Qinglu Gao
Summary: Energy consumption forecasting is crucial for balancing energy demand and production. The study applied a long short-term memory-based model to achieve better prediction accuracy. The proposed model demonstrated superior performance with lower average absolute percentage errors in real-life cases, making it suitable for high-precision energy consumption forecasting.
Article
Computer Science, Artificial Intelligence
Peng Wu, Xiaotong Li, Chen Ling, Shengchun Ding, Si Shen
Summary: The study proposes a sentiment classification method for large scale microblog text using the attention mechanism and bidirectional long short-term memory network. Through experimental comparison with baseline methods, the efficacy of the proposed method is demonstrated. The main novelty lies in the incorporation of the attention mechanism in a deep learning network for analyzing large scale social media data.
APPLIED SOFT COMPUTING
(2021)
Article
Neurosciences
Fabian C. Roth, Katinka M. Beyer, Martin Both, Andreas Draguhn, Alexei V. Egorov
Article
Physics, Multidisciplinary
Alessandro Michelangeli, Paul Pfeiffer
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2016)
Article
Biochemistry & Molecular Biology
Peter Mueller, Andreas Draguhn, Alexei V. Egorov
JOURNAL OF NEUROCHEMISTRY
(2018)
Article
Neurosciences
Elodie De Bruyckere, Ruth Simon, Sigrun Nestel, Bernd Heimrich, Dennis Kaetzel, Alexei V. Egorov, Pentao Liu, Nancy A. Jenkins, Neal G. Copeland, Herbert Schwegler, Andreas Draguhn, Stefan Britsch
FRONTIERS IN MOLECULAR NEUROSCIENCE
(2018)
Article
Biochemistry & Molecular Biology
Viktoria Fischer, Martin Both, Andreas Draguhn, Alexei V. Egorov
JOURNAL OF NEUROCHEMISTRY
(2014)
Article
Neurosciences
Christian Thome, Fabian C. Roth, Joshua Obermayer, Antonio Yanez, Andreas Draguhn, Alexei V. Egorov
JOURNAL OF PHYSIOLOGY-LONDON
(2018)
Article
Neurosciences
Christian Thome, Tony Kelly, Antonio Yanez, Christian Schultz, Maren Engelhardt, Sidney B. Cambridge, Martin Both, Andreas Draguhn, Heinz Beck, Alexei V. Egorov
Article
Neurosciences
Maria S. Lemak, Oksana Voloshanenko, Andreas Draguhn, Alexei V. Egorov
FRONTIERS IN CELLULAR NEUROSCIENCE
(2014)
Article
Multidisciplinary Sciences
P. Pfeiffer, I. L. Egusquiza, M. Di Ventra, M. Sanz, E. Solano
SCIENTIFIC REPORTS
(2016)
Article
Neurosciences
Alexei V. Egorov, Dagmar Schumacher, Rebekka Medert, Lutz Birnbaumer, Marc Freichel, Andreas Draguhn
Article
Neurosciences
Andrei Rozov, Mart Rannap, Franziska Lorenz, Azat Nasretdinov, Andreas Draguhn, Alexei Egorov
JOURNAL OF NEUROSCIENCE
(2020)
Article
Multidisciplinary Sciences
Yangfan Peng, Federico J. Barreda Tomas, Paul Pfeiffer, Moritz Drangmeister, Susanne Schreiber, Imre Vida, Joerg R. P. Geiger
Summary: This study reveals that inhibition by fast-spiking interneurons in the rat superficial presubiculum is organized in the form of a dominant super-reciprocal microcircuit motif. The unique connectivity arises from the asymmetric, polarized morphology of fast-spiking interneuron axons, improving head direction tuning of pyramidal cells. The structured inhibition based on asymmetrical axons is proposed as an overarching spatial connectivity principle for tailored computation across brain regions.
Article
Biology
Paul Pfeiffer, Federico Jose Barreda Tomas, Jiameng Wu, Jan-Hendrik Schleimer, Imre Vida, Susanne Schreiber
Summary: This study introduces a new technique called capacitance clamp, which allows for simulation of altered capacitance in biological neurons. Through mathematical modeling and experimental validation, the feasibility of this technique is demonstrated, providing a new avenue for probing the mechanisms of neuronal signaling.