Article
Immunology
Rong-Rong Cao, Xing-Hao Yu, Meng-Fei Xiong, Xue-Ting Li, Fei-Yan Deng, Shu-Feng Lei
Summary: This study provides the first comprehensive evaluation of the causal effects of immune traits on the risk of osteoporosis, highlighting the complex and important role of immune-derived factors in the pathogenesis of osteoporosis.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Mathematical & Computational Biology
Shirin Dora, Sander M. Bohte, Cyriel M. A. Pennartz
Summary: The paragraph discusses the computational paradigm of predictive coding and introduces a scalable, deep neural network architecture trained using a gated Hebbian learning rule. The models developed can reconstruct original images and exhibit properties such as orientation selectivity and object selectivity. Additionally, the models demonstrate increased image selectivity and sparseness from lower to higher areas, providing insight into inconsistent experimental results on sparseness across the cortical hierarchy.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2021)
Article
Biotechnology & Applied Microbiology
Danielle Santos-Lima, Cristina de Castro Spadari, Vinicius de Morais Barroso, Juliana C. S. Carvalho, Larissa Costa de Almeida, Felipe Santiago Chambergo Alcalde, Marcelo Jose Pena Ferreira, Miriam Sannomiya, Kelly Ishida
Summary: This study isolated and identified a lipopeptide-producing bacterium from a bacterial strain, which showed significant antifungal effects and potential for pharmaceutical development. The lipopeptides exhibited excellent performance in inhibiting fungal growth, disrupting fungal biofilms, and inducing morphological changes, making them promising antifungal agents.
APPLIED MICROBIOLOGY AND BIOTECHNOLOGY
(2023)
Article
Engineering, Civil
Jingqiu Guo, Senlin Cheng, Yangzexi Liu
Summary: This paper examines the impacts of Connected and Autonomous Vehicles (CAVs) on mixed regular-automated traffic flow, introducing a cooperative lane-changing strategy to improve traffic efficiency. Results suggest that CAVs considerably enhance traffic flow, mean speed, and traffic capacity, while the presence of on/off-ramps significantly affects lane-changing processes.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Business
Kristin Ystmark Bjerkan, Marianne Ryghaug
Summary: Ports, as crucial nodes in the intersection of energy and transport systems, have great potential for transition, although their heterogeneity suggests that transition pathways may vary. Through studying two frontrunner ports in Norway, it is found that social processes play a significant role in shaping transition pathways in ports, with deep learning, resource capacity, wider network actions, and specificity of expectations influencing the divergence of transition pathways. Contrary to previous findings, broad and diversified networks in ports can also present challenges to the directionality of transition work, highlighting the importance of aligning expectations with sustainability goals in specific value chains for promoting transition work.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2021)
Article
Biotechnology & Applied Microbiology
Subhajit Konar, Scott M. Bolam, Brendan Coleman, Nicola Dalbeth, Sue R. McGlashan, Sophia Leung, Jillian Cornish, Dorit Naot, David S. Musson
Summary: Tendinopathy is characterized by pathological changes in tendon matrix composition, architecture, and stiffness, with inflammation also playing an important role. This study found that substrate stiffness affects tendon-derived cells and macrophages. Tendon-derived cells showed minor responses to substrate stiffness, while macrophages exhibited a more inflammatory phenotype on non-physiological stiffness substrates. These subtle variations in matrix stiffness may contribute to the onset and progression of tendinopathy.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Engineering, Civil
Yong Luo, Jianying Zheng, Xiang Wang, Yanyun Tao, Xingxing Jiang
Summary: This study proposes a deep learning model based on graph convolutional network (GCN) and bidirectional long short-term memory network (Bi-LSTM) with a non-parallel structure (D-BLGCN) to improve the prediction accuracy of urban rail transit (URT) ridership. The URT stations are decoupled according to intersecting subway lines, and different patterns of ridership are diverged into tributaries. A non-parallel structure is designed to capture the intrinsic spatio-temporal correlations of ridership. The experimental results demonstrate that the proposed model achieves better prediction performance compared with baselines.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Orthopedics
Li-Juan Jie, Melanie Kleynen, Kenneth Meijer, Anna Beurskens, Susy Braun
Summary: This study compared the effects of implicit and explicit motor learning on walking speed in people after stroke in the chronic phase of recovery, finding no significant differences between the two approaches. Physical therapists can use tailored implicit and explicit motor learning strategies to improve walking speed in this patient population.
Article
Oncology
Karen L. Reader, Simon John-McHaffie, Sylvia Zellhuber-McMillan, Tim Jowett, David G. Mottershead, Heather E. Cunliffe, Elspeth J. Gold
Summary: Current tests for prostate cancer cannot accurately distinguish between aggressive and latent cancer. This study investigated the expression of TGFB family members in prostate tumor tissue and the effects of activins on prostate cell growth. The results showed that activin B expression increased in higher Gleason grades, and its overexpression inhibited growth of PNT1A cells but promoted growth and migration of PC3 cells. On the other hand, activin C expression decreased in higher Gleason grades and its overexpression had the opposite effects on cell growth. The findings suggest that activin B and activin C could potentially serve as prognostic markers for aggressive prostate cancer.
Article
Economics
Tanmoy Das, Priyodorshi Banerjee
Summary: Complex financial decisions often need to be made, and increased complexity can lead to social learning and reliance on observed decisions of peers. This study examines the relationship between decision complexity and peer effects on financial decisions through a field experiment. The experiment involves subjects making the same portfolio allocation decision twice, with the second decision influenced by unexpectedly observing a peer's choice. The study finds that increased complexity heightens revision activity, contributing to the understanding of peer effects in financial decision-making.
ECONOMIC MODELLING
(2023)
Article
Environmental Sciences
Shuo Yu, Nicola Falco, Nivedita Patel, Yuxin Wu, Haruko Wainwright
Summary: In this paper, a software package was developed to analyze the trends and responses of carbon use efficiency (CUE) and corn yield to climate factors in the contiguous United States. The algorithm allows automatic retrieval of remote sensing data and agricultural production data at the county level. The results show that growing degree days (GDD) has the highest predictive power for CUE and yield, while extreme degree days (EDD) is the least important variable. Additionally, CUE decreases with higher GDD in the north and shows more mixed interactions in the south.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Cell Biology
Qianwen Hu, Tingting Xu, Min Zhang, Heng Zhang, Yongbo Liu, Hua-bing Li, Chiqi Chen, Junke Zheng, Zhen Zhang, Fubin Li, Nan Shen, Wenqian Zhang, Ari Melnick, Chuanxin Huang
Summary: The study reveals that Bach2 protein and transcripts in activated B cells have differential roles in controlling their cell-fate outcomes and the fate of their descendant effector cells.
Article
Computer Science, Hardware & Architecture
Tong Chen, Ji-Qiang Liu, He Li, Shuo-Ru Wang, Wen-Jia Niu, En-Dong Tong, Liang Chang, Qi Alfred Chen, Gang Li
Summary: This paper conducts the first robustness assessment of A3C based on parallel computing, proposing static and dynamic methods to measure robustness. Experimental results demonstrate that the proposed robustness assessment can effectively gauge the robustness of A3C with an accuracy of 83.3%.
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
(2021)
Article
Mathematics, Applied
Dallas Albritton, Zachary Bradshaw
Summary: The goal of this paper is to provide a simple proof that sufficiently sparse Navier-Stokes solutions do not develop singularities and to analyze the prior estimates on the sparseness of the vorticity and higher velocity derivatives.
Article
Cell Biology
Muireann Ni Bhaoighill, Juan M. Falcon-Perez, Felix Royo, Andrew R. Tee, Jason P. Webber, Elaine A. Dunlop
Summary: This study investigates the role of extracellular vesicles (EVs) in modulating the tumor microenvironment and their impact on the development of TSC tumors. It shows that EVs secreted from TSC2-deficient cells contain a specific protein cargo that promotes cell viability, proliferation, and growth factor secretion in the tumor microenvironment. The study also demonstrates that rapamycin can alter the cargo of EVs and reduce their ability to promote cell proliferation.
JOURNAL OF EXTRACELLULAR VESICLES
(2023)
Article
Neurosciences
Alexandra Smolyanskaya, Ralf M. Haefner, Stephen G. Lomber, Richard T. Born
Article
Neurosciences
Ralf M. Haefner, Pietro Berkes, Jozsef Fiser
Article
Biology
Liu D. Liu, Ralf M. Haefner, Christopher C. Pack
Article
Neurosciences
Adrian G. Bondy, Ralf M. Haefner, Bruce G. Cumming
NATURE NEUROSCIENCE
(2018)
Article
Neurosciences
Katsuhisa Kawaguchi, Stephane Clery, Paria Pourriahi, Lenka Seillier, Staff M. Haefner, Hendrikje Nienborg
JOURNAL OF NEUROSCIENCE
(2018)
Article
Neurosciences
Ralf M. Haefner, Sebastian Gerwinn, Jakob H. Macke, Matthias Bethge
NATURE NEUROSCIENCE
(2013)
Article
Multidisciplinary Sciences
Madeline S. Cappelloni, Sabyasachi Shivkumar, Ralf M. Haefner, Ross K. Maddox
Article
Biology
Daniel Chicharro, Stefano Panzeri, Ralf M. Haefner
Summary: This study investigates the perceptual decision-making process by exploring the stimulus dependencies of activity-choice covariations. The authors provide theoretical conditions for understanding how sensory neural responses are linked to behavioral choices, and develop new tools to assess the stimulus-driven signals of each neuron accurately. The analysis on macaque MT neurons during a motion discrimination task offers preliminary empirical evidence for studying the stimulus dependencies of choice-related signals, encouraging further research in wider data sets.
Article
Biochemical Research Methods
Richard Lange, Ankani J. Chattoraj, Jeffrey Beck, Jacob J. Yates, Ralf Haefner
Summary: This study reveals that the confirmation bias in perceptual decision-making is driven by the interaction of hierarchical inference models, which can accurately predict the strength of biases. The new model explains the presence of various bias effects and verifies its effectiveness.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Biochemical Research Methods
Richard D. Lange, Ralf M. Haefner
Summary: This study derives predictions for the responses of sensory neurons under the assumption of hierarchical Bayesian inference in the brain. The predictions are in contrast to some conclusions drawn in the classic framework and provide a strong test of the Bayesian Brain hypothesis. The results explain the task-dependence of correlated variability in prior studies and offer insights into the cause and function of these correlations.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Biology
Jean-Paul Noel, Sabyasachi Shivkumar, Kalpana Dokka, Ralf M. Haefner, Dora E. Angelaki
Summary: Autism spectrum disorder (ASD) is a disorder characterized by social, communicative, and sensory anomalies. Computational psychiatry aims to understand the underlying computations that give rise to the heterogeneous phenotypes observed in ASD. This study suggests that individuals with ASD have different internal models for attributing world causes to sensory signals compared to neurotypical individuals, and that there may be an explicit compensatory mechanism in ASD to counterbalance their bias towards integration.
Article
Neurosciences
Richard D. Lange, Camille Gomez-Laberge, Vladimir K. Berezovskii, Anton Pletenev, Ariana Sherdil, Till Hartmann, Ralf M. Haefner, Richard T. Born
Summary: A central goal of systems neuroscience is to understand how populations of sensory neurons encode and relay information to the rest of the brain. Previous empirical work suggests that both choice probability and noise correlations are affected by task training, with decision-related information fed back to sensory areas and aligned to neural sensitivity on a task-by-task basis. However, the data from recording activity in primary visual cortex (V1) neurons of monkeys trained to switch between tasks provide weak support for the hypothesis that trial-by-trial task-switching induces changes to noise correlations and choice probabilities in V1.
JOURNAL OF NEUROPHYSIOLOGY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Sabyasachi Shivkumar, Richard D. Lange, Ankani Chattoraj, Ralf M. Haefner
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018)
(2018)
Review
Neurosciences
Richard D. Lange, Ralf M. Haefner
CURRENT OPINION IN NEUROBIOLOGY
(2017)