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Purpose: To assess the predictive value of three scoring systems based on diffusion weighted imaging in basilar artery occlusion patients after endovascular treatment. Methods: We analyzed clinical and radiological data of patients with basilar artery occlusion from January 2010 to June 2019, with modified Rankin Scale of 0–2 and 3–6 defined as favorable outcome and unfavorable outcome at three months. Diffusion weighted imaging posterior circulation ASPECTS Score (DWI pc-ASPECT Score), Renard diffusion weighted imaging Score, and diffusion weighted imaging Brainstem Score were used to evaluate the early ischemic changes. Results: There were a total of 88 basilar artery occlusion patients enrolled in the study after endovascular treatment, with 33 of them getting a favorable outcome. According to the analysis, the time from onset to puncture within 12 h (odds ratio: 4.34; 95% confidence interval: 1.55–12.16; P = 0.01), presence of collateral flow via PCoA (odds ratio: 0.31; 95%CI: 0.12–0.79; P = 0.01) or between PICA and SCA (odds ratio: 0.18; 95%CI: 0.07–0.47; P = 0.00), equal or less than 15 points on baseline NIHSS (area under the curve 0.79, 95% CI 0.69–0.89; sensitivity = 69.1%, specificity = 81.8%; P = 0.00), and equal or less than 1.5 points on diffusion weighted imaging Renard score (area under the curve 0.63, 95% CI 0.51–0.75; sensitivity = 83.6%, specificity = 39.4%; P = 0.046) were independently associated with favorable outcome. Conclusions: Renard diffusion weighted imaging score may be an independent predictor of functional outcome in basilar artery occlusion patients after endovascular treatment. [ABSTRACT FROM AUTHOR]

Recently, inorganic perovskite CsPbI2Br has gained much attention for photovoltaic applications owing to its excellent thermal stability. However, low device performance and high open‐voltage loss, which are the result of its intrinsic trap states, are hindering its progress. Herein, planar CsPbI2Br solar cells with enhanced performance and stability were demonstrated by incorporating rubidium (Rb) cations. The Rb‐doped CsPbI2Br film exhibited excellent crystallinity, pinhole‐free surface morphology, and enhanced optical absorbance. By using low‐cost carbon electrodes to replace the organic hole‐transportation layer and metal electrode, an excellent efficiency of 12 % was achieved with a stabilized efficiency of over 11 % owing to the suppressed trap states and recombination in the CsPbI2Br film. Additionally, the annealing temperature for the Rb‐doped CsPbI2Br film could be as low as 150 °C with a comparable high efficiency over 11 %, which is one of the best efficiencies reported for hole‐transporting‐layer‐free all‐inorganic perovskite solar cells. These results could provide new opportunities for high‐performance and stable inorganic CsPbI2Br solar cells by employing A‐site cation substitution. Defect passivation: All‐inorganic carbon‐based perovskite solar cells (PSCs), with Rb‐doped CsPbI2Br as the absorber layer, exhibit improved efficiency and enhanced stability compared with PSCs with pristine CsPbI2Br owing to the effectiveness of Rb incorporation in passivating defects states. [ABSTRACT FROM AUTHOR]

Computer vision obtains object and environment information by simulating human visual senses and borrowing human sensory activity. As one of the main tasks of computer vision, image classification can be used not only for face recognition, traffic scene recognition, image retrieval, and automatic photo categorization but also as a theoretical basis for target detection and image segmentation. In this paper, we use the existing CNN architecture network-ConvNeXt. By adapting and modifying the residual connectivity and convolutional structure of the network, we achieve a balance between classification accuracy and inference speed. These modifications are able to reduce both computation and memory consumption while keeping accuracy largely unchanged, thus better facilitating network lightweighting. [ABSTRACT FROM AUTHOR]

Table of contents A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker Steuber O8 Analytic solution of cable energy function for cortical axons and dendrites Huiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo Yu O9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal network Jimin Kim, Will Leahy, Eli Shlizerman O10 Is the model any good? Objective criteria for computational neuroscience model selection Justas Birgiolas, Richard C. Gerkin, Sharon M. Crook O11 Cooperation and competition of gamma oscillation mechanisms Atthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan Gielen O12 A discrete structure of the brain waves Yuri Dabaghian, Justin DeVito, Luca Perotti O13 Direction-specific silencing of the Drosophila gaze stabilization system Anmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby Maimon O14 What does the fruit fly think about values? A model of olfactory associative learning Chang Zhao, Yves Widmer, Simon Sprecher,Walter Senn O15 Effects of ionic diffusion on power spectra of local field potentials (LFP) Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen, Gaute T. Einevoll O16 Large-scale cortical models towards understanding relationship between brain structure abnormalities and cognitive deficits Yasunori Yamada O17 Spatial coarse-graining the brain: origin of minicolumns Moira L. Steyn-Ross, D. Alistair Steyn-Ross O18 Modeling large-scale cortical networks with laminar structure Jorge F. Mejias, John D. Murray, Henry Kennedy, Xiao-Jing Wang O19 Information filtering by partial synchronous spikes in a neural population Alexandra Kruscha, Jan Grewe, Jan Benda, Benjamin Lindner O20 Decoding context-dependent olfactory valence in Drosophila Laurent Badel, Kazumi Ohta, Yoshiko Tsuchimoto, Hokto Kazama P1 Neural network as a scale-free network: the role of a hub B. Kahng P2 Hemodynamic responses to emotions and decisions using near-infrared spectroscopy optical imaging Nicoladie D. Tam P3 Phase space analysis of hemodynamic responses to intentional movement directions using functional near-infrared spectroscopy (fNIRS) optical imaging technique Nicoladie D.Tam, Luca Pollonini, George Zouridakis P4 Modeling jamming avoidance of weakly electric fish Jaehyun Soh, DaeEun Kim P5 Synergy and redundancy of retinal ganglion cells in prediction Minsu Yoo, S. E. Palmer P6 A neural field model with a third dimension representing cortical depth Viviana Culmone, Ingo Bojak P7 Network analysis of a probabilistic connectivity model of the Xenopus tadpole spinal cord Andrea Ferrario, Robert Merrison-Hort, Roman Borisyuk P8 The recognition dynamics in the brain Chang Sub Kim P9 Multivariate spike train analysis using a positive definite kernel Taro Tezuka P10 Synchronization of burst periods may govern slow brain dynamics during general anesthesia Pangyu Joo P11 The ionic basis of heterogeneity affects stochastic synchrony Young-Ah Rho, Shawn D. Burton, G. Bard Ermentrout, Jaeseung Jeong, Nathaniel N. Urban P12 Circular statistics of noise in spike trains with a periodic component Petr Marsalek P14 Representations of directions in EEG-BCI using Gaussian readouts Hoon-Hee Kim, Seok-hyun Moon, Do-won Lee, Sung-beom Lee, Ji-yong Lee, Jaeseung Jeong P15 Action selection and reinforcement learning in basal ganglia during reaching movements Yaroslav I. Molkov, Khaldoun Hamade, Wondimu Teka, William H. Barnett, Taegyo Kim, Sergey Markin, Ilya A. Rybak P17 Axon guidance: modeling axonal growth in T-Junction assay Csaba Forro, Harald Dermutz, László Demkó, János Vörös P19 Transient cell assembly networks encode persistent spatial memories Yuri Dabaghian, Andrey Babichev P20 Theory of population coupling and applications to describe high order correlations in large populations of interacting neurons Haiping Huang P21 Design of biologically-realistic simulations for motor control Sergio Verduzco-Flores P22 Towards understanding the functional impact of the behavioural variability of neurons Filipa Dos Santos, Peter Andras P23 Different oscillatory dynamics underlying gamma entrainment deficits in schizophrenia Christoph Metzner, Achim Schweikard, Bartosz Zurowski P24 Memory recall and spike frequency adaptation James P. Roach, Leonard M. Sander, Michal R. Zochowski P25 Stability of neural networks and memory consolidation preferentially occur near criticality Quinton M. Skilling, Nicolette Ognjanovski, Sara J. Aton, Michal Zochowski P26 Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems Sheng-Jun Wang, Guang Ouyang, Jing Guang, Mingsha Zhang, K. Y. Michael Wong, Changsong Zhou P27 Neurofield: a C++ library for fast simulation of 2D neural field models Peter A. Robinson, Paula Sanz-Leon, Peter M. Drysdale, Felix Fung, Romesh G. Abeysuriya, Chris J. Rennie, Xuelong Zhao P28 Action-based grounding: Beyond encoding/decoding in neural code Yoonsuck Choe, Huei-Fang Yang P29 Neural computation in a dynamical system with multiple time scales Yuanyuan Mi, Xiaohan Lin, Si Wu P30 Maximum entropy models for 3D layouts of orientation selectivity Joscha Liedtke, Manuel Schottdorf, Fred Wolf P31 A behavioral assay for probing computations underlying curiosity in rodents Yoriko Yamamura, Jeffery R. Wickens P32 Using statistical sampling to balance error function contributions to optimization of conductance-based models Timothy Rumbell, Julia Ramsey, Amy Reyes, Danel Draguljić, Patrick R. Hof, Jennifer Luebke, Christina M. Weaver P33 Exploration and implementation of a self-growing and self-organizing neuron network building algorithm Hu He, Xu Yang, Hailin Ma, Zhiheng Xu, Yuzhe Wang P34 Disrupted resting state brain network in obese subjects: a data-driven graph theory analysis Kwangyeol Baek, Laurel S. Morris, Prantik Kundu, Valerie Voon P35 Dynamics of cooperative excitatory and inhibitory plasticity Everton J. Agnes, Tim P. Vogels P36 Frequency-dependent oscillatory signal gating in feed-forward networks of integrate-and-fire neurons William F. Podlaski, Tim P. Vogels P37 Phenomenological neural model for adaptation of neurons in area IT Martin Giese, Pradeep Kuravi, Rufin Vogels P38 ICGenealogy: towards a common topology of neuronal ion channel function and genealogy in model and experiment Alexander Seeholzer, William Podlaski, Rajnish Ranjan, Tim Vogels P39 Temporal input discrimination from the interaction between dynamic synapses and neural subthreshold oscillations Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona P40 Different roles for transient and sustained activity during active visual processing Bart Gips, Eric Lowet, Mark J. Roberts, Peter de Weerd, Ole Jensen, Jan van der Eerden P41 Scale-free functional networks of 2D Ising model are highly robust against structural defects: neuroscience implications Abdorreza Goodarzinick, Mohammad D. Niry, Alireza Valizadeh P42 High frequency neuron can facilitate propagation of signal in neural networks Aref Pariz, Shervin S. Parsi, Alireza Valizadeh P43 Investigating the effect of Alzheimer’s disease related amyloidopathy on gamma oscillations in the CA1 region of the hippocampus Julia M. Warburton, Lucia Marucci, Francesco Tamagnini, Jon Brown, Krasimira Tsaneva-Atanasova P44 Long-tailed distributions of inhibitory and excitatory weights in a balanced network with eSTDP and iSTDP Florence I. Kleberg, Jochen Triesch P45 Simulation of EMG recording from hand muscle due to TMS of motor cortex Bahar Moezzi, Nicolangelo Iannella, Natalie Schaworonkow, Lukas Plogmacher, Mitchell R. Goldsworthy, Brenton Hordacre, Mark D. McDonnell, Michael C. Ridding, Jochen Triesch P46 Structure and dynamics of axon network formed in primary cell culture Martin Zapotocky, Daniel Smit, Coralie Fouquet, Alain Trembleau P47 Efficient signal processing and sampling in random networks that generate variability Sakyasingha Dasgupta, Isao Nishikawa, Kazuyuki Aihara, Taro Toyoizumi P48 Modeling the effect of riluzole on bursting in respiratory neural networks Daniel T. Robb, Nick Mellen, Natalia Toporikova P49 Mapping relaxation training using effective connectivity analysis Rongxiang Tang, Yi-Yuan Tang P50 Modeling neuron oscillation of implicit sequence learning Guangsheng Liang, Seth A. Kiser, James H. Howard, Jr., Yi-Yuan Tang P51 The role of cerebellar short-term synaptic plasticity in the pathology and medication of downbeat nystagmus Julia Goncharenko, Neil Davey, Maria Schilstra, Volker Steuber P52 Nonlinear response of noisy neurons Sergej O. Voronenko, Benjamin Lindner P53 Behavioral embedding suggests multiple chaotic dimensions underlie C. elegans locomotion Tosif Ahamed, Greg Stephens P54 Fast and scalable spike sorting for large and dense multi-electrodes recordings Pierre Yger, Baptiste Lefebvre, Giulia Lia Beatrice Spampinato, Elric Esposito, Marcel Stimberg et Olivier Marre P55 Sufficient sampling rates for fast hand motion tracking Hansol Choi, Min-Ho Song P56 Linear readout of object manifolds SueYeon Chung, Dan D. Lee, Haim Sompolinsky P57 Differentiating models of intrinsic bursting and rhythm generation of the respiratory pre-Bötzinger complex using phase response curves Ryan S. Phillips, Jeffrey Smith P58 The effect of inhibitory cell network interactions during theta rhythms on extracellular field potentials in CA1 hippocampus Alexandra Pierri Chatzikalymniou, Katie Ferguson, Frances K. Skinner P59 Expansion recoding through sparse sampling in the cerebellar input layer speeds learning N. Alex Cayco Gajic, Claudia Clopath, R. Angus Silver P60 A set of curated cortical models at multiple scales on Open Source Brain Padraig Gleeson, Boris Marin, Sadra Sadeh, Adrian Quintana, Matteo Cantarelli, Salvador Dura-Bernal, William W. Lytton, Andrew Davison, R. Angus Silver P61 A synaptic story of dynamical information encoding in neural adaptation Luozheng Li, Wenhao Zhang, Yuanyuan Mi, Dahui Wang, Si Wu P62 Physical modeling of rule-observant rodent behavior Youngjo Song, Sol Park, Ilhwan Choi, Jaeseung Jeong, Hee-sup Shin P64 Predictive coding in area V4 and prefrontal cortex explains dynamic discrimination of partially occluded shapes Hannah Choi, Anitha Pasupathy, Eric Shea-Brown P65 Stability of FORCE learning on spiking and rate-based networks Dongsung Huh, Terrence J. Sejnowski P66 Stabilising STDP in striatal neurons for reliable fast state recognition in noisy environments Simon M. Vogt, Arvind Kumar, Robert Schmidt P67 Electrodiffusion in one- and two-compartment neuron models for characterizing cellular effects of electrical stimulation Stephen Van Wert, Steven J. Schiff P68 STDP improves speech recognition capabilities in spiking recurrent circuits parameterized via differential evolution Markov Chain Monte Carlo Richard Veale, Matthias Scheutz P69 Bidirectional transformation between dominant cortical neural activities and phase difference distributions Sang Wan Lee P70 Maturation of sensory networks through homeostatic structural plasticity Júlia Gallinaro, Stefan Rotter P71 Corticothalamic dynamics: structure, number of solutions and stability of steady-state solutions in the space of synaptic couplings Paula Sanz-Leon, Peter A. Robinson P72 Optogenetic versus electrical stimulation of the parkinsonian basal ganglia. Computational study Leonid L. Rubchinsky, Chung Ching Cheung, Shivakeshavan Ratnadurai-Giridharan P73 Exact spike-timing distribution reveals higher-order interactions of neurons Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S. Nader Rasuli P74 Neural mechanism of visual perceptual learning using a multi-layered neural network Xiaochen Zhao, Malte J. Rasch P75 Inferring collective spiking dynamics from mostly unobserved systems Jens Wilting, Viola Priesemann P76 How to infer distributions in the brain from subsampled observations Anna Levina, Viola Priesemann P77 Influences of embedding and estimation strategies on the inferred memory of single spiking neurons Lucas Rudelt, Joseph T. Lizier, Viola Priesemann P78 A nearest-neighbours based estimator for transfer entropy between spike trains Joseph T. Lizier, Richard E. Spinney, Mikail Rubinov, Michael Wibral, Viola Priesemann P79 Active learning of psychometric functions with multinomial logistic models Ji Hyun Bak, Jonathan Pillow P81 Inferring low-dimensional network dynamics with variational latent Gaussian process Yuan Zaho, Il Memming Park P82 Computational investigation of energy landscapes in the resting state subcortical brain network Jiyoung Kang, Hae-Jeong Park P83 Local repulsive interaction between retinal ganglion cells can generate a consistent spatial periodicity of orientation map Jaeson Jang, Se-Bum Paik P84 Phase duration of bistable perception reveals intrinsic time scale of perceptual decision under noisy condition Woochul Choi, Se-Bum Paik P85 Feedforward convergence between retina and primary visual cortex can determine the structure of orientation map Changju Lee, Jaeson Jang, Se-Bum Paik P86 Computational method classifying neural network activity patterns for imaging data Min Song, Hyeonsu Lee, Se-Bum Paik P87 Symmetry of spike-timing-dependent-plasticity kernels regulates volatility of memory Youngjin Park, Woochul Choi, Se-Bum Paik P88 Effects of time-periodic coupling strength on the first-spike latency dynamics of a scale-free network of stochastic Hodgkin-Huxley neurons Ergin Yilmaz, Veli Baysal, Mahmut Ozer P89 Spectral properties of spiking responses in V1 and V4 change within the trial and are highly relevant for behavioral performance Veronika Koren, Klaus Obermayer P90 Methods for building accurate models of individual neurons Daniel Saska, Thomas Nowotny P91 A full size mathematical model of the early olfactory system of honeybees Ho Ka Chan, Alan Diamond, Thomas Nowotny P92 Stimulation-induced tuning of ongoing oscillations in spiking neural networks Christoph S. Herrmann, Micah M. Murray, Silvio Ionta, Axel Hutt, Jérémie Lefebvre P93 Decision-specific sequences of neural activity in balanced random networks driven by structured sensory input Philipp Weidel, Renato Duarte, Abigail Morrison P94 Modulation of tuning induced by abrupt reduction of SST cell activity Jung H. Lee, Ramakrishnan Iyer, Stefan Mihalas P95 The functional role of VIP cell activation during locomotion Jung H. Lee, Ramakrishnan Iyer, Christof Koch, Stefan Mihalas P96 Stochastic inference with spiking neural networks Mihai A. Petrovici, Luziwei Leng, Oliver Breitwieser, David Stöckel, Ilja Bytschok, Roman Martel, Johannes Bill, Johannes Schemmel, Karlheinz Meier P97 Modeling orientation-selective electrical stimulation with retinal prostheses Timothy B. Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin P98 Ion channel noise can explain firing correlation in auditory nerves Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell P99 Limits of temporal encoding of thalamocortical inputs in a neocortical microcircuit Max Nolte, Michael W. Reimann, Eilif Muller, Henry Markram P100 On the representation of arm reaching movements: a computational model Antonio Parziale, Rosa Senatore, Angelo Marcelli P101 A computational model for investigating the role of cerebellum in acquisition and retention of motor behavior Rosa Senatore, Antonio Parziale, Angelo Marcelli P102 The emergence of semantic categories from a large-scale brain network of semantic knowledge K. Skiker, M. Maouene P103 Multiscale modeling of M1 multitarget pharmacotherapy for dystonia Samuel A. Neymotin, Salvador Dura-Bernal, Alexandra Seidenstein, Peter Lakatos, Terence D. Sanger, William W. Lytton P104 Effect of network size on computational capacity Salvador Dura-Bernal, Rosemary J. Menzies, Campbell McLauchlan, Sacha J. van Albada, David J. Kedziora, Samuel Neymotin, William W. Lytton, Cliff C. Kerr P105 NetPyNE: a Python package for NEURON to facilitate development and parallel simulation of biological neuronal networks Salvador Dura-Bernal, Benjamin A. Suter, Samuel A. Neymotin, Cliff C. Kerr, Adrian Quintana, Padraig Gleeson, Gordon M. G. Shepherd, William W. Lytton P107 Inter-areal and inter-regional inhomogeneity in co-axial anisotropy of Cortical Point Spread in human visual areas Juhyoung Ryu, Sang-Hun Lee P108 Two bayesian quanta of uncertainty explain the temporal dynamics of cortical activity in the non-sensory areas during bistable perception Joonwon Lee, Sang-Hun Lee P109 Optimal and suboptimal integration of sensory and value information in perceptual decision making Hyang Jung Lee, Sang-Hun Lee P110 A Bayesian algorithm for phoneme Perception and its neural implementation Daeseob Lim, Sang-Hun Lee P111 Complexity of EEG signals is reduced during unconsciousness induced by ketamine and propofol Jisung Wang, Heonsoo Lee P112 Self-organized criticality of neural avalanche in a neural model on complex networks Nam Jung, Le Anh Quang, Seung Eun Maeng, Tae Ho Lee, Jae Woo Lee P113 Dynamic alterations in connection topology of the hippocampal network during ictal-like epileptiform activity in an in vitro rat model Chang-hyun Park, Sora Ahn, Jangsup Moon, Yun Seo Choi, Juhee Kim, Sang Beom Jun, Seungjun Lee, Hyang Woon Lee P114 Computational model to replicate seizure suppression effect by electrical stimulation Sora Ahn, Sumin Jo, Eunji Jun, Suin Yu, Hyang Woon Lee, Sang Beom Jun, Seungjun Lee P115 Identifying excitatory and inhibitory synapses in neuronal networks from spike trains using sorted local transfer entropy Felix Goetze, Pik-Yin Lai P116 Neural network model for obstacle avoidance based on neuromorphic computational model of boundary vector cell and head direction cell Seonghyun Kim, Jeehyun Kwag P117 Dynamic gating of spike pattern propagation by Hebbian and anti-Hebbian spike timing-dependent plasticity in excitatory feedforward network model Hyun Jae Jang, Jeehyun Kwag P118 Inferring characteristics of input correlations of cells exhibiting up-down state transitions in the rat striatum Marko Filipović, Ramon Reig, Ad Aertsen, Gilad Silberberg, Arvind Kumar P119 Graph properties of the functional connected brain under the influence of Alzheimer’s disease Claudia Bachmann, Simone Buttler, Heidi Jacobs, Kim Dillen, Gereon R. Fink, Juraj Kukolja, Abigail Morrison P120 Learning sparse representations in the olfactory bulb Daniel Kepple, Hamza Giaffar, Dima Rinberg, Steven Shea, Alex Koulakov P121 Functional classification of homologous basal-ganglia networks Jyotika Bahuguna,Tom Tetzlaff, Abigail Morrison, Arvind Kumar, Jeanette Hellgren Kotaleski P122 Short term memory based on multistability Tim Kunze, Andre Peterson, Thomas Knösche P123 A physiologically plausible, computationally efficient model and simulation software for mammalian motor units Minjung Kim, Hojeong Kim P125 Decoding laser-induced somatosensory information from EEG Ji Sung Park, Ji Won Yeon, Sung-Phil Kim P126 Phase synchronization of alpha activity for EEG-based personal authentication Jae-Hwan Kang, Chungho Lee, Sung-Phil Kim P129 Investigating phase-lags in sEEG data using spatially distributed time delays in a large-scale brain network model Andreas Spiegler, Spase Petkoski, Matias J. Palva, Viktor K. Jirsa P130 Epileptic seizures in the unfolding of a codimension-3 singularity Maria L. Saggio, Silvan F. Siep, Andreas Spiegler, William C. Stacey, Christophe Bernard, Viktor K. Jirsa P131 Incremental dimensional exploratory reasoning under multi-dimensional environment Oh-hyeon Choung, Yong Jeong P132 A low-cost model of eye movements and memory in personal visual cognition Yong-il Lee, Jaeseung Jeong P133 Complex network analysis of structural connectome of autism spectrum disorder patients Su Hyun Kim, Mir Jeong, Jaeseung Jeong P134 Cognitive motives and the neural correlates underlying human social information transmission, gossip Jeungmin Lee, Jaehyung Kwon, Jerald D. Kralik, Jaeseung Jeong P135 EEG hyperscanning detects neural oscillation for the social interaction during the economic decision-making Jaehwan Jahng, Dong-Uk Hwang, Jaeseung Jeong P136 Detecting purchase decision based on hyperfrontality of the EEG Jae-Hyung Kwon, Sang-Min Park, Jaeseung Jeong P137 Vulnerability-based critical neurons, synapses, and pathways in the Caenorhabditis elegans connectome Seongkyun Kim, Hyoungkyu Kim, Jerald D. Kralik, Jaeseung Jeong P138 Motif analysis reveals functionally asymmetrical neurons in C. elegans Pyeong Soo Kim, Seongkyun Kim, Hyoungkyu Kim, Jaeseung Jeong P139 Computational approach to preference-based serial decision dynamics: do temporal discounting and working memory affect it? Sangsup Yoon, Jaehyung Kwon, Sewoong Lim, Jaeseung Jeong P141 Social stress induced neural network reconfiguration affects decision making and learning in zebrafish Choongseok Park, Thomas Miller, Katie Clements, Sungwoo Ahn, Eoon Hye Ji, Fadi A. Issa P142 Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data JeongHun Baek, Shigeyuki Oba, Junichiro Yoshimoto, Kenji Doya, Shin Ishii P145 Divergent-convergent synaptic connectivities accelerate coding in multilayered sensory systems Thiago S. Mosqueiro, Martin F. Strube-Bloss, Brian Smith, Ramon Huerta P146 Swinging networks Michal Hadrava, Jaroslav Hlinka P147 Inferring dynamically relevant motifs from oscillatory stimuli: challenges, pitfalls, and solutions Hannah Bos, Moritz Helias P148 Spatiotemporal mapping of brain network dynamics during cognitive tasks using magnetoencephalography and deep learning Charles M. Welzig, Zachary J. Harper P149 Multiscale complexity analysis for the segmentation of MRI images Won Sup Kim, In-Seob Shin, Hyeon-Man Baek, Seung Kee Han P150 A neuro-computational model of emotional attention René Richter, Julien Vitay, Frederick Beuth, Fred H. Hamker P151 Multi-site delayed feedback stimulation in parkinsonian networks Kelly Toppin, Yixin Guo P152 Bistability in Hodgkin–Huxley-type equations Tatiana Kameneva, Hamish Meffin, Anthony N. Burkitt, David B. Grayden P153 Phase changes in postsynaptic spiking due to synaptic connectivity and short term plasticity: mathematical analysis of frequency dependency Mark D. McDonnell, Bruce P. Graham P154 Quantifying resilience patterns in brain networks: the importance of directionality Penelope J. Kale, Leonardo L. Gollo P155 Dynamics of rate-model networks with separate excitatory and inhibitory populations Merav Stern, L. F. Abbott P156 A model for multi-stable dynamics in action recognition modulated by integration of silhouette and shading cues Leonid A. Fedorov, Martin A. Giese P157 Spiking model for the interaction between action recognition and action execution Mohammad Hovaidi Ardestani, Martin Giese P158 Surprise-modulated belief update: how to learn within changing environments? Mohammad Javad Faraji, Kerstin Preuschoff, Wulfram Gerstner P159 A fast, stochastic and adaptive model of auditory nerve responses to cochlear implant stimulation Margriet J. van Gendt, Jeroen J. Briaire, Randy K. Kalkman, Johan H. M. Frijns P160 Quantitative comparison of graph theoretical measures of simulated and empirical functional brain networks Won Hee Lee, Sophia Frangou P161 Determining discriminative properties of fMRI signals in schizophrenia using highly comparative time-series analysis Ben D. Fulcher, Patricia H. P. Tran, Alex Fornito P162 Emergence of narrowband LFP oscillations from completely asynchronous activity during seizures and high-frequency oscillations Stephen V. Gliske, William C. Stacey, Eugene Lim, Katherine A. Holman, Christian G. Fink P163 Neuronal diversity in structure and function: cross-validation of anatomical and physiological classification of retinal ganglion cells in the mouse Jinseop S. Kim, Shang Mu, Kevin L. Briggman, H. Sebastian Seung, the EyeWirers P164 Analysis and modelling of transient firing rate changes in area MT in response to rapid stimulus feature changes Detlef Wegener, Lisa Bohnenkamp, Udo A. Ernst P165 Step-wise model fitting accounting for high-resolution spatial measurements: construction of a layer V pyramidal cell model with reduced morphology Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M. Dale, Ole A. Andreassen, Gaute T. Einevoll P166 Contributions of schizophrenia-associated genes to neuron firing and cardiac pacemaking: a polygenic modeling approach Tuomo Mäki-Marttunen, Glenn T. Lines, Andy Edwards, Aslak Tveito, Anders M. Dale, Gaute T. Einevoll, Ole A. Andreassen P167 Local field potentials in a 4 × 4 mm2 multi-layered network model Espen Hagen, Johanna Senk, Sacha J. van Albada, Markus Diesmann P168 A spiking network model explains multi-scale properties of cortical dynamics Maximilian Schmidt, Rembrandt Bakker, Kelly Shen, Gleb Bezgin, Claus-Christian Hilgetag, Markus Diesmann, Sacha Jennifer van Albada P169 Using joint weight-delay spike-timing dependent plasticity to find polychronous neuronal groups Haoqi Sun, Olga Sourina, Guang-Bin Huang, Felix Klanner, Cornelia Denk P170 Tensor decomposition reveals RSNs in simulated resting state fMRI Katharina Glomb, Adrián Ponce-Alvarez, Matthieu Gilson, Petra Ritter, Gustavo Deco P171 Getting in the groove: testing a new model-based method for comparing task-evoked vs resting-state activity in fMRI data on music listening Matthieu Gilson, Maria AG Witek, Eric F. Clarke, Mads Hansen, Mikkel Wallentin, Gustavo Deco, Morten L. Kringelbach, Peter Vuust P172 STochastic engine for pathway simulation (STEPS) on massively parallel processors Guido Klingbeil, Erik De Schutter P173 Toolkit support for complex parallel spatial stochastic reaction–diffusion simulation in STEPS Weiliang Chen, Erik De Schutter P174 Modeling the generation and propagation of Purkinje cell dendritic spikes caused by parallel fiber synaptic input Yunliang Zang, Erik De Schutter P175 Dendritic morphology determines how dendrites are organized into functional subunits Sungho Hong, Akira Takashima, Erik De Schutter P176 A model of Ca2+/calmodulin-dependent protein kinase II activity in long term depression at Purkinje cells Criseida Zamora, Andrew R. Gallimore, Erik De Schutter P177 Reward-modulated learning of population-encoded vectors for insect-like navigation in embodied agents Dennis Goldschmidt, Poramate Manoonpong, Sakyasingha Dasgupta P178 Data-driven neural models part II: connectivity patterns of human seizures Philippa J. Karoly, Dean R. Freestone, Daniel Soundry, Levin Kuhlmann, Liam Paninski, Mark Cook P179 Data-driven neural models part I: state and parameter estimation Dean R. Freestone, Philippa J. Karoly, Daniel Soundry, Levin Kuhlmann, Mark Cook P180 Spectral and spatial information processing in human auditory streaming Jaejin Lee, Yonatan I. Fishman, Yale E. Cohen P181 A tuning curve for the global effects of local perturbations in neural activity: Mapping the systems-level susceptibility of the brain Leonardo L. Gollo, James A. Roberts, Luca Cocchi P182 Diverse homeostatic responses to visual deprivation mediated by neural ensembles Yann Sweeney, Claudia Clopath P183 Opto-EEG: a novel method for investigating functional connectome in mouse brain based on optogenetics and high density electroencephalography Soohyun Lee, Woo-Sung Jung, Jee Hyun Choi P184 Biphasic responses of frontal gamma network to repetitive sleep deprivation during REM sleep Bowon Kim, Youngsoo Kim, Eunjin Hwang, Jee Hyun Choi P185 Brain-state correlate and cortical connectivity for frontal gamma oscillations in top-down fashion assessed by auditory steady-state response Younginha Jung, Eunjin Hwang, Yoon-Kyu Song, Jee Hyun Choi P186 Neural field model of localized orientation selective activation in V1 James Rankin, Frédéric Chavane P187 An oscillatory network model of Head direction and Grid cells using locomotor inputs Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy P188 A computational model of hippocampus inspired by the functional architecture of basal ganglia Karthik Soman, Vignesh Muralidharan, V. Srinivasa Chakravarthy P189 A computational architecture to model the microanatomy of the striatum and its functional properties Sabyasachi Shivkumar, Vignesh Muralidharan, V. Srinivasa Chakravarthy P190 A scalable cortico-basal ganglia model to understand the neural dynamics of targeted reaching Vignesh Muralidharan, Alekhya Mandali, B. Pragathi Priyadharsini, Hima Mehta, V. Srinivasa Chakravarthy P191 Emergence of radial orientation selectivity from synaptic plasticity Catherine E. Davey, David B. Grayden, Anthony N. Burkitt P192 How do hidden units shape effective connections between neurons? Braden A. W. Brinkman, Tyler Kekona, Fred Rieke, Eric Shea-Brown, Michael Buice P193 Characterization of neural firing in the presence of astrocyte-synapse signaling Maurizio De Pittà, Hugues Berry, Nicolas Brunel P194 Metastability of spatiotemporal patterns in a large-scale network model of brain dynamics James A. Roberts, Leonardo L. Gollo, Michael Breakspear P195 Comparison of three methods to quantify detection and discrimination capacity estimated from neural population recordings Gary Marsat, Jordan Drew, Phillip D. Chapman, Kevin C. Daly, Samual P. Bradley P196 Quantifying the constraints for independent evoked and spontaneous NMDA receptor mediated synaptic transmission at individual synapses Sat Byul Seo, Jianzhong Su, Ege T. Kavalali, Justin Blackwell P199 Gamma oscillation via adaptive exponential integrate-and-fire neurons LieJune Shiau, Laure Buhry, Kanishka Basnayake P200 Visual face representations during memory retrieval compared to perception Sue-Hyun Lee, Brandon A. Levy, Chris I. Baker P201 Top-down modulation of sequential activity within packets modeled using avalanche dynamics Timothée Leleu, Kazuyuki Aihara Q28 An auto-encoder network realizes sparse features under the influence of desynchronized vascular dynamics Ryan T. Philips, Karishma Chhabria, V. Srinivasa Chakravarthy

In this article the affiliation details for Guoqian Chen were incorrectly given as 'Laboratory of Systems Ecology and Sustainability Science, College of EngineeS-Chem model code is open-souring, Peking University, Beijing, China' but should have been 'Laboratory of Systems Ecology and Sustainability Science, College of Engineering, Peking University, Beijing, China'. These authors contributed equally: Rong Ma, Ke Li. [Extracted from the article]

Although the emerged hybrid lead (Pb) halied perovskite solar cells (PSCs) have rapidly held the center stage of photovoltaic field in recent years, the instability of organic cation and toxicity of Pb still remain big obstructions for their further commercial application. Antimony (Sb)-based Cs 3 Sb 2 Cl x I 9−x perovskite-like compound has been considered as a promising candidate to simultaneously resolve the toxicity and thermal instability issues of hybrid Pb-based PSC. However, the power conversion efficiency (PCE) of Cs 3 Sb 2 Cl x I 9−x perovskite-like solar cell (PLSC) is still very low due to the poorer film qualify and higher defects density compared with Pb-based perovskites. What is worse, the open voltage of currently state-of-the-art Sb-based PLSC is below 0.73 V which is far away from the requirement for high efficient solar cells. Herein, we manipulate the crystal structure and improve the film quality of Cs 3 Sb 2 Cl x I 9−x by partially exchanging the A-site inorganic cation with rubidium (Rb) or potassium (K). A less-pinhole perovskite-like film with suppressed 0D phase and reduced defects can be achieved by precisely controlling the doping concentration. Low-cost planar Sb-based inorganic PLSCs were assembled with carbon electrode for the first time and the interface energy level was properly aligned by using Nb 2 O 5 electron transport layer (ETL) instead of conventional TiO 2 ETL. A PCE of 2.46% can be achieved with a respectable open voltage (V oc) of 0.88 V, which is the highest PCE reported for Sb-based inorganic PLSCs to the best of our knowledge. This work will boost the further development of wide-bandgap Pb-free inorganic perovskite-like compounds for realizing broad applications in outdoor/indoor photovoltaic devices, tandem solar cells and photodetectors. [Display omitted] • Improved film quality and suppressed zero-dimension orientation by cation substitution. • Low-cost and efficient carbon based perovskite-like solar cell. • Interface engineering to reach a unprecedented open voltage of 0.88 V. • A record PCE of 2.46% for inorganic Sb-based perovskite-like solar cells. [ABSTRACT FROM AUTHOR]

China's sustained air quality improvement is hindered by unregulated ammonia (NH3) emissions from inefficient nitrogen management in smallholder farming. Although the Chinese government is promoting a policy shift to large-scale farming, the benefits of this, when integrated with nitrogen management, remain unclear. Here we fill this gap using an integrated assessment, by combining geostatistical analysis, high-resolution emission inventories, farm surveys and air quality modeling. Smallholder-dominated farming allows only 13%–31% NH3 reduction, leading to limited PM2.5 decreases nationally due to non-linear PM2.5 chemistry. Conversely, large-scale farming would double nitrogen management adoption rates, increasing NH3 reduction potential to 48%–58% and decreasing PM2.5 by 9.4–14.0 μg·m−3 in polluted regions. The estimated PM2.5 reduction is conservative due to localized NH3-rich conditions under large-scale livestock farming. This strategy could prevent over 300 000 premature deaths and achieve a net benefit of US $68.4–86.8 billion annually, unlocking immense benefits for air quality and agricultural sustainability. [ABSTRACT FROM AUTHOR]

Soybeans are an essential source of food, protein, and oil worldwide, and the nodules on their root systems play a critical role in nitrogen fixation and plant growth. In this study, we tackled the challenge of limited high-resolution image quantities and the constraints on model learning by innovatively employing image segmentation technology for an in-depth analysis of soybean nodule phenomics. Through a meticulously designed segmentation algorithm, we broke down large-resolution images into numerous smaller ones, effectively improving the model's learning efficiency and significantly increasing the available data volume, thus laying a solid foundation for subsequent analysis. In terms of model selection and optimization, after several rounds of comparison and testing, YOLOX was identified as the optimal model, achieving an accuracy of 91.38% on the test set with an R2 of up to 86%, fully demonstrating its efficiency and reliability in nodule counting tasks. Subsequently, we utilized YOLOV5 for instance segmentation, achieving a precision of 93.8% in quickly and accurately extracting key phenotypic indicators such as the area, circumference, length, and width of the nodules, and calculated the statistical properties of these indicators. This provided a wealth of quantitative data for the morphological study of soybean nodules. The research not only enhanced the efficiency and accuracy of obtaining nodule phenotypic data and reduced costs but also provided important scientific evidence for the selection and breeding of soybean materials, highlighting its potential application value in agricultural research and practical production. [ABSTRACT FROM AUTHOR]

The mechanism of open voltage (V oc) deficit in all-inorganic CsPbBr 3 planar solar cell has been systematically investigated by depositing the TiO 2 electron transport layer with magnetron sputtering (MS) and spin-coating (SC) deposition, respectively. It was found that SC-TiO 2 film reveals higher optical band gap (3.67 eV) than MS-TiO 2 film (3.62 eV). However, CsPbBr 3 planar solar cell based on MS-TiO 2 exhibits less V oc deficit and higher photoelectric conversion efficiency (5.48%). The mechanism behind performance enhancement of MS-TiO 2 -based device is suitable Fermi level of MS-TiO 2 and high electronic transport capacity with low charge recombination at CsPbBr 3 /MS-TiO 2 interface. Moreover, the unencapsulated all-inorganic planar devices show a superior stability when stored in air at room temperature for two months. This work provides a novel approach to improve the performance of all-inorganic perovskite solar cell. • All-inorganic planar CsPbBr 3 solar cell with MS-TiO 2 shows less V oc deficit than that with SC-TiO 2. • But SC-TiO 2 film exhibits larger optical band gap than MS-TiO 2. • MS-TiO2 indicates a higher fermi level, which shows its high electronic transport capacity. • The conversion efficiency of MS-TiO 2 -based CsPbBr 3 solar cell (5.48%) is higher in comparison to the device with SC-TiO 2 (4.81%). [ABSTRACT FROM AUTHOR]

Autonomous parking is an active field of automatic driving in both industry and academia. Parking slot detection (PSD) based on a panoramic image can effectively improve the perception of a parking space and the surrounding environment, which enhances the convenience and safety of parking. The challenge of PSD implementation is identifying the parking slot in real-time based on images obtained from the around view monitoring (AVM) system, while maintaining high recognition accuracy. This paper proposes a real-time parking slot detection (RPSD) network based on semantic segmentation, which implements real-time parking slot detection on the panoramic surround view (PSV) dataset and avoids the constraint conditions of parking slots. The structural advantages of the proposed network achieve real-time semantic segmentation while effectively improving the detection accuracy of the PSV dataset. The cascade structure reduces the operating parameters of the whole network, ensuring real-time performance, and the fusion of coarse and detailed features extracted from the upper and lower layers improves segmentation accuracy. The experimental results show that the final mIoU of this work is 67.97% and the speed is up to 32.69 fps, which achieves state-of-the-art performance with the PSV dataset. [ABSTRACT FROM AUTHOR]

All-inorganic perovskite CsPbIBr 2 , has drawn much attention for photovoltaic (PV) application due to its excellent intrinsic stability. However, low device performance and high fabrication temperature still hamper its further progress in flexible application. Herein, Zn substitution has been used to improve the nucleation and growth process for low temperature processed a-phase CsPbIBr 2 film. Zn incorporated CsPbIBr 2 film exhibits good crystallinity, compact surface morphology and depressed defect state. Low temperature (100 °C and 160 °C) processed carbon based CsPbIBr 2 solar cells with improved PV performance have been prepared by using Zn incorporation and room deposited electron transport layer (ETL). A champion efficiency over 9% can be achieved through Zn substitution, which is one of the best values reported for the low temperature processed CsPbIBr 2 solar cell without using hole transport layer (HTL). Efficiency over 5% can also be achieved for larger area (1 cm2) rigid and flexible CsPbIBr 2 solar cells. These results would provide a new route for preparing high-performance and low temperature processed inorganic perovsktie solar cell. Image 1 • Growth dynamics for low temperature processed CsPbIBr 2 film was studied. • Zn dopant was used for accelerating growth process and passivating defects. • Low temperature deposited TiO 2 and carbon electrode were used for flexible application. • A champion efficiency over 9% with excellent air-stable ability has been achieved. [ABSTRACT FROM AUTHOR]

Genetic diversity is a result of evolution, enabling multiple ways for one particular physiological activity. Here, we introduce this strategy into bioengineering. We design two hydroxytyrosol biosynthetic pathways using tyrosine as substrate. We show that the synthetic capacity is significantly improved when two pathways work simultaneously comparing to each individual pathway. Next, we engineer flavin-dependent monooxygenase HpaBC for tyrosol hydroxylase, tyramine hydroxylase, and promiscuous hydroxylase active on both tyrosol and tyramine using directed divergent evolution strategy. Then, the mutant HpaBCs are employed to catalyze two missing steps in the hydroxytyrosol biosynthetic pathways designed above. Our results demonstrate that the promiscuous tyrosol/tyramine hydroxylase can minimize the cell metabolic burden induced by protein overexpression and allow the biosynthetic carbon flow to be divided between two pathways. Thus, the efficiency of the hydroxytyrosol biosynthesis is significantly improved by rearranging the metabolic flux among multiple pathways. Metabolic engineering usually focuses on manipulating enzyme(s) within a single pathway. Here, the authors show that a promiscuous enzymatic activity-based multiple-pathway design can minimize cell metabolic burden and allow carbon flow rearrangement, leading to efficient hydroxytyrosol biosynthesis. [ABSTRACT FROM AUTHOR]

The perceptual scope of deep convolutional neural networks is inherently confined to a local scale due to the inherent limitations of convolution operations. This confinement subsequently hampers a comprehensive understanding of intricate scenes. In response, an innovative approach called Bilateral Non-Local Decoupled Convergence Network (BNDCNet) is proposed to facilitate contextual interaction of global information. The proposed module decouples the information from the input feature map and uses a bilateral non-local architecture for system processing. This strategy facilitates inter-pixel interaction and aggregates global information. An important aspect of our approach involves the computation of adaptive convolutional channel weights for the feature map. This innovation greatly improves the efficacy and performance of the model. The proposed method achieved high performance on the competitive scene-parsing datasets CamVid, Cityscapes, and KITTI, and thus demonstrated effectiveness and generality. Code has been released: https://github.com/Mantee0810/BNDC. • The addition of this module improves segmentation performance. • Decoupling the channel message of the feature map for information interaction. • Extracting information and reconstructing feature maps by tensor operations. [ABSTRACT FROM AUTHOR]

Semantic segmentation in indoor environments is a crucial task for artificial intelligence-driven visual robotics, enabling pixel-level classification results to facilitate robot path planning. Inspired by the success of multimodal models, we propose an end-to-end multimodal semantic segmentation model for image segmentation tasks in indoor scenes, which we call OIPNet. We design the OIP module to enhance the network's ability to extract global information and enable information interaction in different directions. We have validated on NYUv2 and Sun RGB-D datasets, and the experiments show the generality and effectiveness of the proposed model. Our code is available at https://github.com/Mantee0810/OIP. [ABSTRACT FROM AUTHOR]

The investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition. In 3D model construction, conventional image preprocessing methods exhibit low efficiency and inherent inefficiencies, which increases the difficulty of model construction. In order to ensure the accuracy of the 3D model, while reducing the difficulty of image preprocessing and improving the speed of 3D reconstruction, deep learning semantic segmentation technology was used in the present study to preprocess original images of soybean plants. Additionally, control experiments involving soybean plants of different varieties and different growth periods were conducted. Models based on manual image preprocessing and models based on image segmentation were established. Point cloud matching, distance calculation and model matching degree calculation were carried out. In this study, the DeepLabv3+, Unet, PSPnet and HRnet networks were used to conduct semantic segmentation of the original images of soybean plants in the vegetative stage (V), and Unet network exhibited the optimal test effect. The values of mIoU, mPA, mPrecision and mRecall reached 0.9919, 0.9953, 0.9965 and 0.9953. At the same time, by comparing the distance results and matching accuracy results between the models and the reference models, a conclusion could be drawn that semantic segmentation can effectively improve the challenges of image preprocessing and long reconstruction time, greatly improve the robustness of noise input and ensure the accuracy of the model. Semantic segmentation plays a crucial role as a fundamental component in enabling efficient and automated image preprocessing for 3D reconstruction of soybean plants during the vegetative stage. In the future, semantic segmentation will provide a solution for the pre-processing of 3D reconstruction for other crops. [ABSTRACT FROM AUTHOR]

Background: Patients who undergo gastrointestinal endoscopy often require propofol-based sedation combined with analgesics. At present, the efficacy and safety of esketamine as an adjunct to propofol for sedation during endoscopic procedures in patients remains controversial. Moreover, there is no universal agreement regarding the appropriate dose of esketamine supplementation. This study aimed to assess the efficacy and safety of esketamine as an adjunct to propofol for sedation during endoscopic procedures in patients. Methods: Seven electronic databases and three clinical trial registry platforms were searched and the deadline was February 2023. Randomized controlled trials (RCTs) evaluating the efficacy of esketamine for sedation were included by two reviewers. Data from the eligible studies were combined to calculate the pooled risk ratio or standardized mean difference. Results: Eighteen studies with 1962 esketamine participants were included in the analysis. As an adjunct to propofol, the administration of esketamine reduced the recovery time compared to normal saline (NS). However, there was no significant difference between the opioids group and ketamine group. For propofol dosage, the administration of esketamine required a lower propofol dosage compared to the NS group and opioids group].For complications, the esketamine group had fewer complications compared to the NS group and opioid group in patients, but there were no significant differences between the esketamine group and ketamine group. Notably, the coadministration of esketamine was associated with a higher risk of visual disturbance compared to the NS group. In addition, we used subgroup analysis to investigate whether 0.2–0.5 mg/kg esketamine was effective and tolerable for patients. Conclusion: Esketamine as an adjunct to propofol, is an appropriate effective alternative for sedation in participants undergoing gastrointestinal endoscopy. However, considering the possibility of its psychotomimetic effects, esketamine should be used with caution. [ABSTRACT FROM AUTHOR]

Few studies have examined how demand-side management measures, alone or in conjunction with built environment interventions, affect car owners' automobile commuting choices in developing cities. Additionally, most studies overlook the difference between inner-cities and suburbs. Applying extreme gradient boosting decision trees and shapley method to the 2020 Wuhan travel survey data, this study addresses these gaps. Transportation management measures and the built environment individually exert a significant impact on car commuting, while jointly exhibiting synergistic effects on car commuting. Meanwhile, most of these effects are nonlinear and exhibit different properties in the inner-city and suburbs. In the inner-city, proximity to central development and population densification can reduce automobile commuting. Parking fees and transit allowances enhance these benefits. For suburbs, job densification and mixed development are more effective, but have limited impact on the inner city. This study demonstrated that integrating built environment interventions with management measures enhances policy effectiveness. [ABSTRACT FROM AUTHOR]

Background: Whether vitamin D supplementation during pregnancy is beneficial to bone health and offspring growth remains controversial. Moreover, there is no universal agreement regarding the appropriate dose and the time of commencement of vitamin D supplementation during pregnancy. Objective: We aimed to systematically review the effects of vitamin D supplementation during pregnancy on bone development and offspring growth. Methods: A literature search for randomized controlled trials (RCTs) was performed in 7 electronic databases to identify relevant studies about the effects of vitamin D supplementation during pregnancy on bone development and offspring growth from inception to May 22, 2022. A Cochrane Risk Assessment Tool was used for quality assessment. Vitamin D supplementation was compared with placebo or standard supplements. The effects are presented as the mean differences (MDs) with 95% CIs. The outcomes include bone mineral content (BMC), bone mineral density (BMD), bone area (BA), femur length (FL) and humeral length (HL); measurement indicators of growth, including length, weight and head circumference; and secondary outcome measures, including biochemical indicators of bone health, such as the serum 25(OH)D concentration. Additionally, subgroup analyses were carried out to evaluate the impact of different doses and different initiation times of supplementation with vitamin D. Results: Twenty-three studies with 5390 participants met our inclusion criteria. Vitamin D supplementation during pregnancy was associated with increased humeral length (HL) (MD 0.13, 95% CI 0.06, 0.21, I2 = 0, P = 0.0007) during the fetal period (third trimester). Vitamin D supplementation during pregnancy was associated with a significantly increased length at birth (MD 0.14, 95% CI 0.04, 0.24, I2 = 24%, P = 0.005) and was associated with a higher cord blood 25(OH)D concentration (MD 48.74, 95% CI 8.47, 89.01, I2 = 100%, P = 0.02). Additionally, subgroup analysis revealed that birth length was significantly higher in the vitamin D intervention groups of ≤1000 IU/day and ≥4001 IU/day compared with the control group. Prenatal (third trimester) vitamin D supplementation was associated with a significant increase in birth length, while prenatal (second trimester) vitamin D supplementation was associated with a significant increase in birth weight. Conclusion: Vitamin D supplementation during pregnancy may be associated with increased humeral length (HL) in the uterus, increased body length at birth and higher cord blood 25(OH)D concentration. Evidence of its effect on long-term growth in children is lacking. Additional rigorous high-quality, long-term and larger randomized trials are required to more fully investigate the effects of vitamin D supplementation during pregnancy. [ABSTRACT FROM AUTHOR]

In order to achieve good identification performance, the existing identification method of crop diseases based on deep learning needs considerable annotated images to train. However, collection of crop leaves disease images in field is time-consuming and laborious, so it is urgent to propose a timely and effective identification model for limited labeled crop disease images. This paper proposed a Few-shot Learning method based on Siamese Network to identify crop leaves diseases, which used randomly generated image sample pairs as input. In experiments, since crops are divided into monocotyledons and dicotyledons, this paper used Few-shot Learning method to train monocotyledon plant diseases model named SiamNet2 and dicotyledon plant diseases model named SiamNet1, which were used to identify monocotyledon and dicotyledon plant diseases. The results of 5-way 5-shot dicotyledon plant disease identification and 10-way 10-shot monocotyledon plant disease identification showed that the identification accuracy of SiamNet2 was 8.7% and 12.4% higher than that of SiamNet1, respectively. [ABSTRACT FROM AUTHOR]