scholarly journals Universal Response-Adaptation Relation in Bacterial Chemotaxis

2014 ◽  
Vol 197 (2) ◽  
pp. 307-313 ◽  
Author(s):  
Anna K. Krembel ◽  
Silke Neumann ◽  
Victor Sourjik
Keyword(s):  
Short Term Memory ◽  
Bacterial Chemotaxis ◽  
Fine Tuning ◽  
Universal Relation ◽  
Short Term ◽  
Content Type ◽  
Cooperative Interactions ◽  
Memory Time ◽  
Chemotaxis Receptors ◽  
Activity Dependent

The bacterial strategy of chemotaxis relies on temporal comparisons of chemical concentrations, where the probability of maintaining the current direction of swimming is modulated by changes in stimulation experienced during the recent past. A short-term memory required for such comparisons is provided by the adaptation system, which operates through the activity-dependent methylation of chemotaxis receptors. Previous theoretical studies have suggested that efficient navigation in gradients requires a well-defined adaptation rate, because the memory time scale needs to match the duration of straight runs made by bacteria. Here we demonstrate that the chemotaxis pathway ofEscherichia colidoes indeed exhibit a universal relation between the response magnitude and adaptation time which does not depend on the type of chemical ligand. Our results suggest that this alignment of adaptation rates for different ligands is achieved through cooperative interactions among chemoreceptors rather than through fine-tuning of methylation rates for individual receptors. This observation illustrates a yet-unrecognized function of receptor clustering in bacterial chemotaxis.

scholarly journals Complexity Reduction in the Negotiation of New Lexical Conventions

2018 ◽  
Author(s):  
William Schueller ◽  
Vittorio Loreto ◽  
Pierre-Yves Oudeyer
Keyword(s):  
Ad Hoc ◽  
Short Term Memory ◽  
Fine Tuning ◽  
Global State ◽  
High Complexity ◽  
Short Term ◽  
New Words ◽  
Efficient Control ◽  
New Strategy ◽  
Multi Agent

In the process of collectively inventing new words for new con-cepts in a population, conflicts can quickly become numerous,in the form of synonymy and homonymy. Remembering all ofthem could cost too much memory, and remembering too fewmay slow down the overall process. Is there an efficient be-havior that could help balance the two? The Naming Game isa multi-agent computational model for the emergence of lan-guage, focusing on the negotiation of new lexical conventions,where a common lexicon self-organizes but going through aphase of high complexity. Previous work has been done onthe control of complexity growth in this particular model, byallowing agents to actively choose what they talk about. How-ever, those strategies were relying on ad hoc heuristics highlydependent on fine-tuning of parameters. We define here a newprincipled measure and a new strategy, based on the beliefsof each agent on the global state of the population. The mea-sure does not rely on heavy computation, and is cognitivelyplausible. The new strategy yields an efficient control of com-plexity growth, along with a faster agreement process. Also,we show that short-term memory is enough to build relevantbeliefs about the global lexicon.

Pharmacological Foundations of Cannabis Chemovars

Planta Medica ◽  
2017 ◽  
Vol 84 (04) ◽  
pp. 225-233 ◽  
Author(s):  
Mark Lewis ◽  
Ethan Russo ◽  
Kevin Smith
Keyword(s):  
Short Term Memory ◽  
Symptomatic Relief ◽  
Breeding Program ◽  
Type I ◽  
Type Ii ◽  
Short Term ◽  
Content Type ◽  
Therapeutic Benefits ◽  
Predominant Type ◽  
Chemical Profiles

AbstractAn advanced Mendelian Cannabis breeding program has been developed utilizing chemical markers to maximize the yield of phytocannabinoids and terpenoids with the aim to improve therapeutic efficacy and safety. Cannabis is often divided into several categories based on cannabinoid content. Type I, Δ 9-tetrahydrocannabinol-predominant, is the prevalent offering in both medical and recreational marketplaces. In recent years, the therapeutic benefits of cannabidiol have been better recognized, leading to the promotion of additional chemovars: Type II, Cannabis that contains both Δ 9-tetrahydrocannabinol and cannabidiol, and cannabidiol-predominant Type III Cannabis. While high-Δ 9-tetrahydrocannabinol and high-myrcene chemovars dominate markets, these may not be optimal for patients who require distinct chemical profiles to achieve symptomatic relief. Type II Cannabis chemovars that display cannabidiol- and terpenoid-rich profiles have the potential to improve both efficacy and minimize adverse events associated with Δ 9-tetrahydrocannabinol exposure. Cannabis samples were analyzed for cannabinoid and terpenoid content, and analytical results are presented via PhytoFacts, a patent-pending method of graphically displaying phytocannabinoid and terpenoid content, as well as scent, taste, and subjective therapeutic effect data. Examples from the breeding program are highlighted and include Type I, II, and III Cannabis chemovars, those highly potent in terpenoids in general, or single components, for example, limonene, pinene, terpinolene, and linalool. Additionally, it is demonstrated how Type I – III chemovars have been developed with conserved terpenoid proportions. Specific chemovars may produce enhanced analgesia, anti-inflammatory, anticonvulsant, antidepressant, and anti-anxiety effects, while simultaneously reducing sequelae of Δ 9-tetrahydrocannabinol such as panic, toxic psychosis, and short-term memory impairment.

Detecting hate speech against politicians in Arabic community on social media

2020 ◽  
Vol 16 (3) ◽  
pp. 295-313
Author(s):  
Imane Guellil ◽  
Ahsan Adeel ◽  
Faical Azouaou ◽  
Sara Chennoufi ◽  
Hanene Maafi ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Hate Speech ◽  
Short Term Memory ◽  
Arabic Language ◽  
Short Term ◽  
Speech Corpus ◽  
Term Memory ◽  
Content Type ◽  
Speech Detection

Purpose This paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors’ knowledge, not much work has been conducted in the Arabic language. Design/methodology/approach This approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW). Findings Simulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus. Originality/value The principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.

Deep learning-based container throughput forecasting: a triple bottom line approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonali Shankar ◽  
Sushil Punia ◽  
P. Vigneswara Ilavarasan
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Bottom Line ◽  
Content Type ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
Memory Network ◽  
Long Short Term Memory

PurposeContainer throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.Design/methodology/approachA novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.FindingsThe result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”Originality/valueA novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

Factors affecting relevance judgment: a report from TREC Legal track

2011 ◽  
Vol 67 (2) ◽  
pp. 264-278 ◽  
Author(s):  
Heting Chu
Keyword(s):  
Short Term Memory ◽  
Human Beings ◽  
Short Term ◽  
Content Type ◽  
Factors Affecting ◽  
Relevance Judgment ◽  
Relevance Judgments ◽  
Interactive Task ◽  
Practical Implications ◽  
Made In

PurposeThis study intends to identify factors that affect relevance judgment of retrieved information as part of the 2007 TREC Legal track interactive task.Design/methodology/approachData were gathered and analyzed from the participants of the 2007 TREC Legal track interactive task using a questionnaire which includes not only a list of 80 relevance factors identified in prior research, but also a space for expressing their thoughts on relevance judgment in the process.FindingsThis study finds that topicality remains a primary criterion, out of various options, for determining relevance, while specificity of the search request, task, or retrieved results also helps greatly in relevance judgment.Research limitations/implicationsRelevance research should focus on the topicality and specificity of what is being evaluated as well as conducted in real environments.Practical implicationsIf multiple relevance factors are presented to assessors, the total number in a list should be below ten to take account of the limited processing capacity of human beings' short‐term memory. Otherwise, the assessors might either completely ignore or inadequately consider some of the relevance factors when making judgment decisions.Originality/valueThis study presents a method for reducing the artificiality of relevance research design, an apparent limitation in many related studies. Specifically, relevance judgment was made in this research as part of the 2007 TREC Legal track interactive task rather than a study devised for the sake of it. The assessors also served as searchers so that their searching experience would facilitate their subsequent relevance judgments.

Forecasting container throughput with long short-term memory networks

2019 ◽  
Vol 120 (3) ◽  
pp. 425-441 ◽  
Author(s):  
Sonali Shankar ◽  
P. Vigneswara Ilavarasan ◽  
Sushil Punia ◽  
Surya Prakash Singh
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Error Matrix ◽  
Term Memory ◽  
Content Type ◽  
Forecasting Performance ◽  
Long Short Term Memory

Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

Long-term neurological, visual, and endocrine outcomes following transnasal resection of craniopharyngioma

2005 ◽  
Vol 102 (4) ◽  
pp. 650-657 ◽  
Author(s):  
Indro Chakrabarti ◽  
Arun P. Amar ◽  
William Couldwell ◽  
Martin H. Weiss
Keyword(s):  
Short Term Memory ◽  
Gh Deficiency ◽  
Memory Loss ◽  
Magnetic Resonance Images ◽  
Endocrine Function ◽  
Short Term ◽  
Term Memory ◽  
Content Type ◽  
Fine Print

Object. The authors report on a cohort of patients with craniopharyngioma treated principally through transnasal (TN) resection and followed up for a minimum of 5 years. More specifically, they evaluate the role of the TN approach in the management of craniopharyngioma. Methods. Between 1984 and 1994, 68 patients underwent TN resection of craniopharyngiomas at the University of Southern California. The tumor was at least partially cystic in 88% of cases. Four tumors were purely intrasellar, 53 had intra- and suprasellar components, and 11 were exclusively suprasellar. During the same period, 18 patients underwent transcranial (TC) resection of purely suprasellar craniopharyngiomas. Long-term neurological, visual, and endocrine outcomes were reviewed for all patients. In 61 (90%) of 68 patients in the TN group, total resection was achieved, according to 3-month postoperative magnetic resonance images, although four patients suffered a recurrence. Three (43%) of the seven tumors that had been partially resected were enlarged on serial imaging. Fifty-four (87%) of 62 patients with preoperative visual loss experienced improvement in one or both eyes, but two patients (3%) with exclusively suprasellar tumors experienced postoperative visual worsening in one or both eyes. New instances of postoperative endocrinopathy (that is, not present preoperatively) occurred as follows: hypogonadism (eight of 22 cases), growth hormone (GH) deficiency (four of 18 cases), hypothyroidism (11 of 49 cases), hypocortisolemia (nine of 52 cases), and diabetes insipidus (DI; four of 61 cases). One case each of hypocortisolemia and hypothyroidism resolved after surgery. Hyperphagia occurred in 27 (40%) of 68 patients. One patient had short-term memory loss. Postoperative complications included one case of cerebrospinal fluid leak. Among the 18 patients in the TC group, 11 had complete resections. In one case (9%) the tumors recurred. Three (43%) of the seven subtotally resected tumors grew during the follow-up interval. Vision improved in 11 (61%) of 18 cases and worsened in three (17%) as a result of surgery. New instances of postoperative endocrinopathy occurred as follows: hypogonadism (one of six cases), GH deficiency (four of seven cases), hypothyroidism (11 of 14 cases), hypocortisolemia (eight of 15 cases), and DI (nine of 16 cases). No instance of preoperative endocrinopathy was corrected through TC surgery. Four patients (22%) exhibited short-term memory loss and 11 (61%) had hyperphagia after surgery. When compared with those in the TC group, patients in the TN group had shorter hospital stays. Conclusions. Use of the TN approach can render good outcomes in properly selected patients with craniopharyngioma, particularly when the tumor is cystic. Even in mostly suprasellar cases, an extended TN approach can afford complete resection. Note that endocrine function often worsens after surgery and that postoperative obesity can be a significant problem.

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