Retrieval Process Research Articles

Toward an Educative EEG-Based neuroIIR System for Adapting Contents

The pursuit of information is an integral aspect of a learner’s daily routine, often facilitated by information retrieval systems. In this study, we explore the correlation between text complexity and reading comprehension through the lens of neuroinformatics. Our objective is to ascertain whether it is feasible to infer the difficulty level of a text for a learner based on their brain activity, quantified by an electroencephalogram (EEG), during the information retrieval process. To accomplish this, we administered a section of the Woodcock Reading Mastery Test to our participants, evaluating their average reading fluency. Subsequently, the 18 participants under observation read paragraphs of varying complexity and responded to questions pertaining to the text. Utilizing the amassed data, we trained a deep learning model known as EEGNet to autonomously discern the complexity of the text being read, relying on the EEG signals. Furthermore, we conducted a comparison with previous research that employed a distinct characterization approach and employed a random forest algorithm for classification. The outcomes revealed that the present approach (EEGNet) outperformed the former method, achieving an accuracy rate of 80.83% across all subjects, whereas the random forest algorithm achieved a mere 50.28% accuracy. Our findings suggest the potential to classify the EEG signals in accordance with the level of difficulty a student encounters when comprehending the text. We propose several avenues for extending our work into real-time scenarios.

On the use of consistent bias corrections to enhance the impact of AeolusLevel‐2B Rayleigh winds on National Oceanic and Atmospheric Administration global forecast skill

AbstractThe operational Aeolus Level‐2B (L2B) horizontal line‐of‐sight (HLOS) retrieved Rayleigh winds, produced by the European Space Agency (ESA), utilize European Centre for Medium‐Range Weather Forecasts (ECMWF) short‐term forecasts of temperature, pressure, and horizontal winds in the Rayleigh–Brillouin and M1 correction procedures. These model fields or backgrounds can contain ECMWF model‐specific errors, which may propagate to the retrieved Rayleigh winds. This study examines the sensitivity of the retrieved Rayleigh winds to the changes in the model backgrounds, and the potential benefit of using the same system, in this case the National Oceanic and Atmospheric Administration's Finite‐Volume Cubed Sphere Global Forecast System (FV3GFS), for both the corrections and the data assimilation and forecast procedures. It is shown that the differences in the model backgrounds (FV3GFS minus ECMWF) can propagate through the Level‐2B horizontal line‐of‐sight Rayleigh wind retrieval process, mainly the M1 correction, resulting in differences in the retrieved Rayleigh winds with mean and standard deviation of magnitude as large as 0.2 m·s−1. The differences reach up to 0.4, 0.6, and 0.7 m·s−1 for the 95th, 99th, and 99.5th percentiles of the sample distribution with maxima of ∼1.4 m·s−1. The numbers of the large differences for the combined lower and upper 5th, 1st, and 0.5th percentile pairs are ∼6,100, 1,220, and 610 between 2.5 and 25 km height globally per day respectively. The ESA‐disseminated Rayleigh wind product (based on the ECMWF corrections) already shows a significant positive impact on the FV3GFS global forecasts. In the observing system experiments performed, compared with the ESA Rayleigh winds, the use of the FV3GFS‐corrected Rayleigh winds lead to ∼0.5% more Rayleigh winds assimilated in the lower troposphere and show enhanced positive impact on FV3GFS forecasts at the day 1–10 range but limited to the Southern Hemisphere.

Image Retrieval Using Auto Encoding Features In Deep Learning

The latest technologies and growth in availability of image storage in day to day life has made a vast storage place for the images in the database. Several devices which help in capturing the image contribute to a huge repository of images. Keeping in mind the daily input in the database, one must think of retrieving those images according to certain criteria mentioned. Several techniques such as shape of the object, Discrete Wavelet transform (DWT), texture features etc. were used in determining the type of image and classifying them. Segmentation also plays a vital role in image retrieval but the robustness is lacking in most of the cases. The process of retrieval mainly depends on the special characteristics possessed by an image rather than the whole image. Two types of image retrieval can be seen. One with a general object and the other which may be specific to some type of application. Modern deep neural networks for unsupervised feature learning like Deep Autoencoder (AE) learn embedded representations by stacking layers on top of each other. These learnt embedded-representations, however, may degrade as the AE network deepens due to vanishing gradient, resulting in decreased performance. We have introduced here the ResNet Autoencoder (RAE) and its convolutional version (C-RAE) for unsupervised feature based learning. The proposed model is tested on three distinct databases Corel1K, Cifar-10, Cifar-100 which differ in size. The presented algorithm have significantly reduced computation time and provided very high image retrieval levels of accuracy.

A Systematic Review of the Role of Gummosin in Improving Memory in the Scopolamine Impaired Memory Model.

In this study, the role of gummosin in improving memory in the scopolamine memory impairment model was systematically examined. Memory and learning are the most developed and complex functions of the nervous system. Learning is the acquisition of new information that occurs as a change in behavior, and memory is the ability to store and retrieve learned information. In other words, memory is a combination of various processes of information acquisition, consolidation, storage and retrieval. The processes of memory consolidation and storage are the result of a series of time-dependent neurobiological events that occur after the initial formation of memory. In addition, this fluctuation of processes related to memory storage can fully occur shortly after the initial learning experience. Memory is a direct result of learning ,as it stores and retrieves learned experiences and information. The results of our study show that scopolamine leads to impaired memory, learning and synaptic plasticity, which is associated with a change in the expression of various genes and a reduction in the number of hippocampal neurons. The disorders that occurred in the rats of the scopolamine group confirm the model used in this study to induce memory and learning deficits, which is consistent with previous studies confirming the model used to induce Alzheimer's disease. The results of the behavioral tests in this study showed that, consistent with previous work, scopolamine caused a significant increase in anxiety behavior that was associated with a decrease in time spent in the central area compared to the control group, while donepezil injection resulted in a decrease in anxiety behavior. The time spent in the central area was increased compared to the scopolamine group.

PACOME: Optimal multi-epoch combination of direct imaging observations for joint exoplanet detection and orbit estimation

Context. Exoplanet detections and characterizations via direct imaging require high contrast and high angular resolution. These requirements are typically pursued by combining (i) cutting-edge instrumental facilities equipped with extreme adaptive optics and coronagraphic systems, (ii) optimized differential imaging to introduce a diversity between the signals of the sought-for objects and that of the star, and (iii) dedicated (post-)processing algorithms to further eliminate the residual stellar leakages. Aims. With respect to the third technique, substantial efforts have been undertaken over this last decade on the design of more efficient post-processing algorithms. The whole data collection and retrieval processes currently allow to detect massive exoplanets at angular separations greater than a few tenths of au. The performance remains upper-bounded at shorter angular separations due to the lack of diversity induced by the processing of each epoch of observations individually. We aim to propose a new algorithm that is able to combine several observations of the same star by accounting for the Keplerian orbital motion across epochs of the sought-for exoplanets in order to constructively co-add their weak signals. Methods. The proposed algorithm, PACOME, integrates an exploration of the plausible orbits of the sought-for objects within an end-to-end statistical detection and estimation formalism. The latter is extended to a multi-epoch combination of the maximum likelihood framework of PACO, which is a post-processing algorithm of single-epoch observations. From this, we derived a reliable multi-epoch detection criterion, interpretable both in terms of probability of detection and of false alarm. In addition, PACOME is able to produce a few plausible estimates of the orbital elements of the detected sources and provide their local error bars. Results. We tested the proposed algorithm on several datasets obtained from the VLT/SPHERE instrument with IRDIS and IFS using the pupil tracking mode of the telescope. By resorting to injections of synthetic exoplanets, we show that PACOME is able to detect sources remaining undetectable by the most advanced post-processing of each individual epoch. The gain in detection sensitivity scales as high as the square root of the number of epochs. We also applied PACOME on a set of observations from the HR 8799 star hosting four known exoplanets, which can be detected by our algorithm with very high signal-to-noise ratios. Conclusions. PACOME is an algorithm for combining multi-epoch high-contrast observations of a given star. Its sensitivity and the reliability of its astrophysical outputs permits the detection of new candidate companions at a statistically grounded confidence level. In addition, its implementation is efficient, fast, and fully automatized.

Efficient information retrieval model: overcoming challenges in search engines-an overview

<span>Search engines play a vital role in information retrieval (IR) indexing and processing vast and diverse data, which now encompasses the ever-expanding wealth of multimedia content. However, search engine performance relies on the efficiency and effectiveness of their information retrieval systems (IRS). To enhance search engine performance, there is a need to develop more efficient and accurate IRS that retrieves relevant information quickly and accurately. To address this challenge, various approaches, including inverted indexing, query expansion, and relevance feedback, have been proposed for IR. Although these approaches have shown promising results, but their effectiveness and limitations require a comprehensive examination This research aims to investigate the challenges and opportunities in designing an efficient IRS for search engines and identify key areas for improvement and future research. The study involves a comprehensive literature review on information retrieval impacting academia, industry, healthcare, e-commerce, and other domains. Researchers rely on search engines to access relevant scientific papers, professionals use them to gather market intelligence, and consumers utilize them for product research and decision-making. The findings of this study will contribute to the development of more efficient and effective information retrieval systems, leading to improved search engine performance and user satisfaction.</span>

Implementation Framework National Institute of Standards and Technology (Nist) Evidence Digital In The Forensic Process Social Media

Many people carry out social media activities which are not only positive activities but also activities that have a negative impact, such as crimes in cyberspace. especially forensics mobile in assisting the resolution of crime cases through social media Instagram with media access smartphone. The biggest problem in the data retrieval process is that the perpetrator deletes or tries to eliminate digital evidence on a smartphone so this research aims to implement the framework National Institute of Standards and Technology to investigate data forensic evidence on social media Instagram accessed via smartphone Android by using tools mobile Edit forensic in data extraction on a smartphone with workflow stages collection, - reporting. This study uses a conversation scenario on the Instagram feature direct message (DM) with conditions including deleting data on the Instagram application. The method used in handling cybercrime with media evidence smartphones is the method National Institute of Standards and Technology (NIST). The results of this study are in collecting data in the form of 25 conversations (chat), 206 images, 86 videos, 5 audio, and 4 call logs on smartphones. However tools MOBILEdit Forensic in returning digital evidence are unable to recover conversations and call logs as a whole.

59 Preliminary Findings of Semantic Interference in Learning and Memory Processes in Manifest Huntington’s Disease

Objective:To explore the usefulness of the Lowenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L) [Crocco et al, 2013], a novel memory-based cognitive stress test capitalizing on semantic interference, in Huntington’s Disease (HD).Participants and Methods:12 healthy adults (HA) and 14 individuals with manifest HD were administered the LASSI-L as part of an annual research visit with the UCSD Huntington’s Disease Clinical Research Center (HDCRC.) Participants in each group were well matched with regard to age and education. Individuals with manifest HD had an average MoCA score of 26, total functional capacity score of 10, and total motor score of 21 suggesting that they were in the early stages of HD. The LASSI-L examines different types of semantic interference that occur in the learning/encoding process. There are free and cued recall trials for two lists of semantically related words with certain trials specific to different aspects of semantic interference including proactive, retroactive, and failure to recover from proactive interference. T-tests for all recall trials and number of intrusions for each trial were conducted between HA and those with HD to examine whether HD renders one more prone to semantic interference in both encoding and retrieval memory processes.Results:Individuals with HD recalled fewer words on average than HA across all recall trials except for the initial free recall of the first word list. HD individuals recalled significantly fewer (∼1.5) words during the initial (t=-2.8, p=.005, Cohen’s d=2.7) and secondary (t=-2.9, p=.003, Cohen’s d=2.6) cued recall trials from the words on the first list. Individuals with HD also recalled significantly fewer words on initial free recall (t=-2.9, p=.003, Cohen’s d=2.6) and cued recall trials of the second list, with the initial cued recall (t=-2.8, p=.005, Cohen’s d=3.1) sensitive to proactive semantic interference and the second cued recall (t=-3.3, p=.001, Cohen’s d=2.6) sensitive to failure to recover from proactive semantic interference. In addition, individuals with HD also recalled significantly fewer (∼2.2) words on delayed cued recall of the first list, a measure of retroactive semantic interference, than HA (t=-4.8, p<.001, Cohen’s d=2.4). Lastly, individuals with HD recalled fewer (∼4.1) words than HA on delayed free recall of both word lists (t=-3.5, p<.001, Cohen’s d=5.9). The groups did not differ significantly with regard to number of total intrusions per trial.Conclusions:Overall, our study supports the usefulness of the LASSI-L for neuropsychological assessment of HD in clinical and research settings. In comparison to a demographically similar group of HA, individuals with manifest HD showed significant differences in frontally mediated retrieval processes as well as semantic interference processes that affect efficient encoding of novel information.

Forward and backward recall dynamics.

Although possible to recall in both forward and backward order, recall proceeds most naturally in the order of encoding. Prior studies ask whether and how forward and backward recall differ. We reexamine this classic question by studying recall dynamics while varying the predictability and timing of forward and backward cues. Although overall accuracy did not differ by recall direction, recall dynamics highlight key distinctions. Forward recall exhibits a modest advantage for correct transitions following errors, independent of cueing predictability and list length. Without consistent directional cueing, participants initiate backward recall more accurately, but this effect reverses with predictable directional cues. Following omissions, participants commit more fill-in errors in backward recall. Our findings implicate an asymmetric, cue-dependent retrieval process underlying forward and backward recall, with relative contributions of primacy and recency depending on directional predictability. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Attribute-Aware Deep Hashing With Self-Consistency for Large-Scale Fine-Grained Image Retrieval.

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods. Moreover, qualitative results demonstrate that not only the obtained hash codes can strongly correspond to certain kinds of crucial properties of fine-grained objects, but also our self-consistency designs can effectively overcome simplicity bias in fine-grained hashing.

Annotate and retrieve in vivo images using hybrid self-organizing map

Multimodal retrieval has gained much attention lately due to its effectiveness over uni-modal retrieval. For instance, visual features often under-constrain the description of an image in content-based retrieval; however, another modality, such as collateral text, can be introduced to abridge the semantic gap and make the retrieval process more efficient. This article proposes the application of cross-modal fusion and retrieval on real in vivo gastrointestinal images and linguistic cues, as the visual features alone are insufficient for image description and to assist gastroenterologists. So, a cross-modal information retrieval approach has been proposed to retrieve related images given text and vice versa while handling the heterogeneity gap issue among the modalities. The technique comprises two stages: (1) individual modality feature learning; and (2) fusion of two trained networks. In the first stage, two self-organizing maps (SOMs) are trained separately using images and texts, which are clustered in the respective SOMs based on their similarity. In the second (fusion) stage, the trained SOMs are integrated using an associative network to enable cross-modal retrieval. The underlying learning techniques of the associative network include Hebbian learning and Oja learning (Improved Hebbian learning). The introduced framework can annotate images with keywords and illustrate keywords with images, and it can also be extended to incorporate more diverse modalities. Extensive experimentation has been performed on real gastrointestinal images obtained from a known gastroenterologist that have collateral keywords with each image. The obtained results proved the efficacy of the algorithm and its significance in aiding gastroenterologists in quick and pertinent decision making.

Management of Dynamic Archives for Education Personnel in State Madrasah Aliyah

This research aimed at knowing 1) creating dynamic archives in the administrative section, and 2) storing dynamic archives in the administrative section at State Islamic Senior High School 3 Pekanbaru. It was a qualitative research with a descriptive method. The subjects of this research were the head of administration, administrative staff as key informants, and the vice of student affairs and teachers as additional informants. The object of this research was the management of archives in the administration section at State Islamic Senior High School 3 Pekanbaru. Interview, observation, and documentation techniques were used for collecting the data. The data were analyzed by using reduction, presentation, and conclusion. The results of this research show that: 1) The creation of dynamic archives in the administration section of MAN 3 Pekanbaru was carried out well, including the process of incoming and outgoing letters. 2) The storage of dynamic archives in the administration section of MAN 3 Pekanbaru was carried out well, however, there were a few obstacles in the archive retrieval process, namely archives or letters that have been disposed of and are located outside the administration room, such as the principal's room, the deputy principal's room for student affairs and curriculum, forgetting to take them back, so there are archives that are not stored in the filing cabinet.

Design of a Data Acquisition, Correction and Retrieval of Na Doppler Lidar for Diurnal Measurement of Temperature and Wind in the Mesosphere and Lower Thermosphere Region

Based on the resonance fluorescence scattering mechanism, a narrowband sodium (Na) lidar can measure temperature and wind in the mesosphere and lower thermosphere (MLT) region. By using a narrowband spectral filter, background light noise during the day can be suppressed, allowing for continuous observations. To obtain full-diurnal-cycle temperature and wind measurement results, a complex and precise retrieval process is required, along with necessary corrections to minimize measurement errors. This paper introduces the design of a data acquisition unit for three frequencies in three directions of the Na lidar system in the Chinese Meridian Project (Phase II) and investigates the calibration and retrieval methods for obtaining diurnal temperature and horizontal wind in the MLT region, using a Na Doppler lidar with Faraday anomalous dispersion optical filter (FADOF). Furthermore, these methods are applied to observations conducted by a Na lidar in Beijing, China. The wind and temperature results over full diurnal cycles obtained from the all-solid-state Na Doppler lidar are reported for the first time and compared with temperature measurements from satellite, as well as wind observations from a meteor radar. The comparison demonstrates a reasonable agreement between the results, indicating the rationality of the lidar-retrieved results and the feasibility and effectiveness of the data correction and retrieval method.

A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes

The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.

Quantification at a distance and grammatical illusions in French

AbstractRecent research in psycholinguistics supports the hypothesis that retrieval from working memory is a key component of establishing syntactic dependencies in comprehension. This can result in so‐called grammatical illusions. These illusions have been modeled as the result of a content‐addressable retrieval process in sentence comprehension that allows grammatically inaccessible licensing elements to be reactivated, creating a spurious perception of acceptability. This article reports five studies that establish the existence of a new grammatical illusion involving quantification at a distance and the licensing of so‐called de NPs in French. Our results suggest that this grammatical illusion is interestingly constrained by syntactic properties of the licensors. Specifically, quantifiers that independently participate in quantification‐at‐a‐distance constructions were seen to create grammatical illusions to a greater extent than quantifiers that do not participate in that construction. Consistent with previous work on the nature of cues in memory retrieval, we suggest that this is the result of fairly specific abstract syntactic cues that guide retrieval of a licensing element. This article thus brings further evidence that syntax is crucially used to structure working memory over the course of a parse.

Are False Memory and Creative Thinking Mediated by Common Neural Substrates? An fMRI Meta-Analysis

ABSTRACT Episodic retrieval plays a functional-adaptive role in supporting divergent creative thinking, the ability to creatively combine different pieces of information. However, the same constructive memory process that provides this benefit can also lead to memory errors. Prior behavioral work has shown that there is a positive correlation between the false recognition of lure items in the Deese-Roediger-McDermott paradigm, and divergent creative thinking as assessed on the alternate uses task. Here, we conducted a meta-analysis of functional magnetic resonance imaging studies to test for convergence in the neural substrates associated with these cognitive linkages. Our individual meta-analyses of false recognition-related activity as well as divergent thinking-related neural activity replicated prior meta-analyses. However, there was no significant statistical overlap across the neural regions associated with false recognition and divergent creative thinking. These null findings may reflect the operation of distinct generative retrieval processes engaged during divergent thinking relative to false recognition.

Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration.

Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data and information management in healthcare and medicine. However, there is scant literature guiding their integration for non-AI professionals. This study conducts a scoping literature review to address the critical need for guidance on integrating generative AI and LLMs into healthcare and medical practices. It elucidates the distinct mechanisms underpinning these technologies, such as Reinforcement Learning from Human Feedback (RLFH), including few-shot learning and chain-of-thought reasoning, which differentiates them from traditional, rule-based AI systems. It requires an inclusive, collaborative co-design process that engages all pertinent stakeholders, including clinicians and consumers, to achieve these benefits. Although global research is examining both opportunities and challenges, including ethical and legal dimensions, LLMs offer promising advancements in healthcare by enhancing data management, information retrieval, and decision-making processes. Continued innovation in data acquisition, model fine-tuning, prompt strategy development, evaluation, and system implementation is imperative for realizing the full potential of these technologies. Organizations should proactively engage with these technologies to improve healthcare quality, safety, and efficiency, adhering to ethical and legal guidelines for responsible application.

Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification for Diabetic Retinopathy Grading

Diabetic Retinopathy (DR) is a major source of sightlessness and permanent visual damage. Manual Analysis of DR is a labor-intensive and costly task that requires skilled ophthalmologists to observe and evaluate DR utilizing digital fundus images. The images can be employed for analysis and disease screening. This laborious task can gain a great advantage in automated detection by exploiting Artificial Intelligence (AI) techniques. Content-Based Image Retrieval (CBIR) approaches are utilized to retrieve related images in massive databases and are helpful in many application regions and most healthcare systems. With this motivation, this article develops the new Manta Ray Foraging Optimizer with Deep Learning-based Fundus Image Retrieval and Classification (MRFODL-FIRC) approach for the grading of DR. The suggested MRFODL-FIRC model investigates the retinal fundus imaging effectively to retrieve the relevant images and identify class labels. To achieve this, the MRFODL-FIRC technique uses Median Filtering (MF) as a pre-processing step. The Capsule Network (CapsNet) model is used to produce feature vectors with the MRFO algorithm as a hyperparameter optimizer. For the image retrieval process, the Manhattan distance metric is used. Finally, the Variational Autoencoder (VAE) model is used for recognizing and classifying DR. The investigational assessment of the MRFODL-FIRC technique is accomplished on medical DR and the outputs highlighted the improved performance of the MRFODL-FIRC algorithm over the current approaches.

Research on the influence of organizational form of inspirational information in conceptual design: Workload and creativity

The relationship between information organization and innovation behavior is an important area of study, yet research on the behavior of designers in this regard is underexplored. This study experimentally compared the effects of two forms of inspirational information organization, single-organization (using folders only) and multi-organization (folders plus tags), on designers’ design creativity in the conceptual design stage. The main findings are: (1) The use of tags in the multi-organization form facilitates a more in-depth and involved processing of inspiration information, and significantly improves the effort score. In addition, it reduces the mental and temporal demands in the retrieval process and does not add too much workload to the subjects. (2) Information organization methods have different impacts on creativity. The results show that by using a multi-organization method, the novelty of design solutions can be improved. This study verifies the positive impact of multiple organizations as an inspirational information structuring method on design creativity, which provides a directional reference for the development activities of information organization tools for designers.

Activation of multiple neuromodulatory systems in alert rats acquiring conditioned taste aversion revealed by positron emission tomography

Conditioned taste aversion (CTA) is an essential ability for animals to consume food safely and is regulated by neuromodulatory systems including the dopamine, noradrenaline, serotonin, and acetylcholine systems. However, because few studies focused on a comprehensive understanding of whole-brain activities, how these neuromodulators contribute to the process of CTA remains an open issue. 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) can visualize activated regions within the whole brain simultaneously and noninvasively. This study aimed to understand the mechanisms of CTA, especially focusing on the retrieval process after CTA acquisition by FDG-PET imaging. CTA was established in rats who received an intraoral application of saccharin solution (IOAS) on the first day (Day 1), a LiCl i.p. injection after an IOAS on Day 2, and an IOAS on Day 3 (CTA group). The subtraction images of Day 3 of the SHAM group, which received a 0.9 % NaCl (saline) injection instead of a LiCl on Day 2, from those of Day 3 of the CTA group revealed increases in FDG signals in multiple brain regions including the substantia nigra, ventral tegmental area, locus coeruleus, dorsal raphe, and nucleus basalis magnocellularis, in addition to the hippocampus and nociception-related regions, including the parabrachial nucleus and solitary nucleus. On the other hand, the visceral pain induced by the LiCl injection increased FDG signals in the primary and secondary somatosensory and insular cortices in addition to the parabrachial nucleus and solitary nucleus. These results suggest that the retrieval process of CTA induces brain regions producing neuromodulators and pain-related brainstem.