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Sequential memory

Hang Li, Xiu-Jun Gong, Hua Yu, Chang Zhou
Machine learning based predictions of protein⁻protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods makes the prediction task more tedious and trivial. The emerging deep learning technology enabling automatic feature engineering is gaining great success in various fields. However, the over-fitting and generalization of its models are not yet well investigated in most scenarios...
August 1, 2018: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
Juan J Ortells, Jan W De Fockert, Nazaret Romera, Sergio Fernández
The present research examined whether imposing a high (or low) working memory (WM) load in different types of non-verbal WM tasks could affect the implementation of expectancy-based strategic processes in a sequential verbal Stroop task. Participants had to identify a colored (green vs. red) target patch that was preceded by a prime word (GREEN or RED), which was either incongruent or congruent with the target color on 80% and 20% of the trials, respectively. Previous findings have shown that participants can strategically use this information to predict the upcoming target color, and avoid the standard Stroop interference effect...
2018: Frontiers in Psychology
Sean Froudist-Walsh, Philip G F Browning, Paula L Croxson, Kathy L Murphy, Jul Lea Shamy, Tess L Veuthey, Charles R E Wilson, Mark G Baxter
Humans can recall a large number of memories years after the initial events. Patients with amnesia often have lesions to the hippocampus, but human lesions are imprecise, making it difficult to identify the anatomy underlying memory impairments. Rodent studies enable great precision in hippocampal manipulations, but not investigation of many interleaved memories. Thus it is not known how lesions restricted to the hippocampus affect the retrieval of multiple sequentially encoded memories. Furthermore, disagreement exists as to whether hippocampal inactivations lead to temporally graded or ungraded amnesia, which could be a consequence of differences between rodent and human studies...
July 26, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
M Alizadeh Asfestani, E Braganza, J Schwidetzky, J Santiago, S Soekadar, J Born, G B Feld
Although sleep-dependent consolidation and its neurochemical underpinnings have been strongly researched, less is known about how consolidation during sleep affects subsequent learning. Since sleep enhances memory, it can be expected to pro-actively interfere with learning after sleep, in particular of similar materials. This pro-active interference should be enhanced by substances that benefit consolidation during sleep, such as D-cycloserine. We tested this hypothesis in two groups (Sleep, Wake) of young healthy participants receiving on one occasion D-cycloserine (175 mg) and on another occasion placebo, according to a double-blind balanced crossover design...
July 2, 2018: Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology
Ayman Oweida, Mohammad Hararah, Andy V Phan, David C Binder, Shilpa Bhatia, Shelby Lennon, Sanjana Bukkapatnam, Benjamin Van Court, Nomin Uyanga, Laurel Darragh, Hyun Min Kim, David Raben, Aik-Choon Tan, Lynn Heasley, Eric T Clambey, Raphael A Nemenoff, Sana D Karam
PURPOSE: Radiation therapy (RT) can transform the immune landscape and render poorly immunogenic tumors sensitive to PD-L1 inhibition. Here we established that the response to combined RT and PD-L1 inhibition is transient and investigated mechanisms of resistance. EXPERIMENTAL DESIGN: Mechanisms of resistance to RT and PD-L1 blockade were investigated in orthotopic murine HNSCC tumors using mass cytometry and whole genome sequencing. Mice were treated with anti-PD-L1 or anti-TIM-3 alone and in combination with and without RT...
July 24, 2018: Clinical Cancer Research: An Official Journal of the American Association for Cancer Research
Gemma L Owens, Marcus J Price, Eleanor J Cheadle, Robert E Hawkins, David E Gilham, Richard J Edmondson
Epithelial ovarian cancer (EOC) is the leading cause of gynaecological cancer-related death in Europe. Although most patients achieve an initial complete response with first-line treatment, recurrence occurs in more than 80% of cases. Thus, there is a clear unmet need for novel second-line treatments. EOC is frequently infiltrated with T lymphocytes, the presence of which has been shown to be associated with improved clinical outcomes. Adoptive T-cell therapy (ACT) using ex vivo-expanded tumour-infiltrating lymphocytes (TILs) has shown remarkable efficacy in other immunogenic tumours, and may represent a promising therapeutic strategy for EOC...
July 23, 2018: Cancer Immunology, Immunotherapy: CII
Rasmus Berggren, Jonna Nilsson, Martin Lövdén
Education is positively associated with level of cognitive function but the association between education and rate of cognitive decline remains unresolved, partly for methodological reasons. In this article, we address this issue using linear mixed models and Bayesian hypothesis testing, using data from the Betula cohort-sequential longitudinal study. Our results support the null hypothesis that education does not alter the rate of cognitive decline for visuospatial ability, semantic knowledge, and episodic memory...
2018: Frontiers in Psychology
Dinis Gökaydin, Peter Brugger, Tobias Loetscher
Small and large numbers are typically associated with the left and right side of space, respectively. We conducted an online version of the classical Spatial-Numerical Association of Response Codes (SNARC) paradigm in 604 subjects in order to analyse how previous trials and responses affect SNARC. Our results point to a strong inversion of number-space associations (left/large and right/small) when the last trial was incoherent - i.e. when a response with the left hand was made to a large number or vice-versa...
July 20, 2018: Scientific Reports
Yiqun Yao, Jiaming Xu, Jing Shi, Bo Xu
Most current textual reasoning models cannotlearn human-like reasoning process, and thus lack interpretability and logical accuracy. To help address this issue, we propose a novel reasoning model which learns to activate logic rules explicitly via deep reinforcement learning. It takes the form of Memory Networks but features a special memory that stores relational tuples, mimicking the "Image Schema" in human cognitive activities. We redefine textual reasoning as a sequential decision-making process modifying or retrieving from the memory, where logic rules serve as state-transition functions...
July 3, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Mashfiqui Rabbi, Meredith Philyaw Kotov, Rebecca Cunningham, Erin E Bonar, Inbal Nahum-Shani, Predrag Klasnja, Maureen Walton, Susan Murphy
BACKGROUND: Substance use is an alarming public health issue associated with significant morbidity and mortality. Adolescents and emerging adults are at particularly high risk because substance use typically initiates and peaks during this developmental period. Mobile health apps are a promising data collection and intervention delivery tool for substance-using youth as most teens and young adults own a mobile phone. However, engagement with data collection for most mobile health applications is low, and often, large fractions of users stop providing data after a week of use...
July 18, 2018: JMIR Research Protocols
Katharina Dorn, Sabine Weinert, Terje Falck-Ytter
Investigating infants' ability to match visual and auditory speech segments presented sequentially allows us to understand more about the type of information they encode in each domain, as well as their ability to relate the information. One previous study found that 4.5- month-old infants' preference for visual French or German speech depended on whether they had previously heard the respective language, suggesting a remarkable ability to encode and relate audio-visual speech cues and to use these to guide their looking behavior...
July 11, 2018: Infant Behavior & Development
Evie Malaia, Ronnie B Wilbur
The question of apparent discrepancies in short-term memory capacity for sign language and speech has long presented difficulties for the models of verbal working memory. While short-term memory (STM) capacity for spoken language spans up to 7 ± 2 items, the verbal working memory capacity for sign languages appears to be lower at 5 ± 2. The assumption that both auditory and visual communication (sign language) rely on the same memory buffers led to the claims of impairment of STM buffers in sign language users...
June 7, 2018: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
Kouichi Kimura, Asako Koike
The Burrows-Wheeler transform (BWT) of shortread data has unexplored potential utilities, such as for efficient and sensitive variation analysis against multiple reference genome sequences, because it does not depend on any particular reference genome sequence, unlike conventional mappingbased methods. However, since the amount of read data is generally much larger than the size of the reference sequence, computation of the BWT of reads is not easy, and this hampers development of potential applications. For the alleviation of this problem, a new method of computing the BWT of reads in parallel is proposed...
May 17, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xuedan Miao, Ying Liu, Haiquan Zhao, Chunguang Li
Anomaly detection has attracted much attention in recent years since it plays a crucial role in many domains. Various anomaly detection approaches have been proposed, among which one-class support vector machine (OCSVM) is a popular one. In practice, data used for anomaly detection can be distributively collected via wireless sensor networks. Besides, as the data usually arrive at the nodes sequentially, online detection method that can process streaming data is preferred. In this paper, we formulate a distributed online OCSVM for anomaly detection over networks and get a decentralized cost function...
March 1, 2018: IEEE Transactions on Cybernetics
Yueming Wang, Kang Lin, Yu Qi, Qi Lian, Shaozhe Feng, Gang Pan, Zhaohui Wu
OBJECTIVE: Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g. epilepsy foci diagnosis. Granger causality is an effective tool for directional connection analysis in multivariate time series. However, most existing methods based on Granger causality assume fixed time lags in information transmission, while the propagation delay between brain signals is usually changing constantly...
June 1, 2018: IEEE Transactions on Bio-medical Engineering
Tolga Ergen, Suleyman S Kozat
In this brief, we investigate online training of long short term memory (LSTM) architectures in a distributed network of nodes, where each node employs an LSTM-based structure for online regression. In particular, each node sequentially receives a variable length data sequence with its label and can only exchange information with its neighbors to train the LSTM architecture. We first provide a generic LSTM-based regression structure for each node. In order to train this structure, we put the LSTM equations in a nonlinear state-space form for each node and then introduce a highly effective and efficient distributed particle filtering (DPF)-based training algorithm...
December 7, 2017: IEEE Transactions on Neural Networks and Learning Systems
Jun Liu, Amir Shahroudy, Dong Xu, Alex Kot Chichung, Gang Wang
Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously...
November 9, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Zubaida Shebani, Friedemann Pulvermüller
Recent neuropsychological studies in neurological patients and healthy subjects suggest a close functional relationship between the brain systems for language and action. Facilitation and inhibition effects of motor system activity on language processing have been demonstrated as well as causal effects in the reverse direction, from language processes on motor excitability or performance. However, as the documented effects between motor and language systems were sometimes facilitatory and sometimes inhibitory, the "sign" of these effects still remains to be explained...
2018: Frontiers in Human Neuroscience
Jianzhong Zheng, Junchang Zhang, Zi Wang, Liubiao Zhong, Yinghui Sun, Zhiqiang Liang, Youyong Li, Lin Jiang, Xiaodong Chen, Lifeng Chi
The negative differential resistance (NDR) effect observed in conducting polymer/Au nanoparticle composite devices is not yet fully clarified due to the random and disordered incorporation of Au nanoparticles into conducting polymers. It remains a formidable challenge to achieve the sequential arrangement of various components in an optimal manner during the fabrication of Au nanoparticle/conducting polymer composite devices. Here, a novel strategy for fabricating Au nanoparticle/conducting polymer composite devices based on self-assembled Au@PPy core-shell nanoparticle arrays is demonstrated...
July 10, 2018: Advanced Materials
David K Sewell, Philip L Smith, Simon D Lilburn
In his commentary, Bundesen (2018) argued that limited encoding capacity can account for the near-equivalent set size effects on performance under conditions of simultaneous and sequential presentation reported by Sewell, Lilburn, and Smith (2014). While we agree that limited encoding capacity could, in principle, account for this equivalency, we argue that such an account rests on a number of fortuitous temporal coincidences. In particular, we note that pure encoding capacity limitations appear ill equipped to explain near-equivalent simultaneous-sequential performance across a range of stimulus exposure durations, set sizes, and with stimuli with quite different attentional demands...
July 2018: Journal of Experimental Psychology. Human Perception and Performance
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