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Bees are known for their ability to forage with high efficiency. One of their strategies to avoid unproductive foraging is to be at the food source at the right time of the day. Approximately one hundred years ago, researchers discovered that honeybees have a remarkable time memory, which they use for optimizing foraging. Ingeborg Beling was the first to examine this time memory experimentally. In her doctoral thesis, completed under the mentorship of Karl von Frisch in 1929, she systematically examined the capability of honeybees to remember specific times of the day at which they had been trained to appear at a feeding station. Beling was a pioneer in chronobiology, as she described the basic characteristics of the circadian clock on which the honeybee's time memory is based. Unfortunately, after a few years of extremely productive research, she ended her scientific career, probably due to family reasons or political pressure to reduce the number of women in the workforce. Here, we present a biographical sketch of Ingeborg Beling and review her research on the time memory of honeybees. Furthermore, we discuss the significance of her work, considering what is known about time memory today - nearly 100 years after she conducted her experiments.

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Circadian clocks regulate ecologically important complex behaviors in honey bees, but it is not clear whether similar capacities exist in other species of bees. One key behavior influenced by circadian clocks is time-memory, which enables foraging bees to precisely time flower visitation to periods of maximal pollen or nectar availability and reduces the costs of visiting a non-rewarding flower patch. Bumble bees live in smaller societies and typically forage over shorter distances than honey bees, and it is therefore not clear whether they can similarly associate reward with time of day. We trained individually marked bumble bee (Bombus terrestris) workers to forage for sugar syrup in a flight cage with yellow or blue feeders rewarding either during the morning or evening. After training for over two weeks, we recorded all visitations to colored feeders filled with only water. We performed two experiments, each with a different colony. We found that bees tended to show higher foraging activity during the morning and evening training sessions compared to other times during the day. During the test day, the trained bees were more likely to visit the rewarding rather than the non-rewarding colored feeders at the same time of day during the test sessions, indicating that they associated time of day and color with the sugar syrup reward. These observations lend credence to the hypothesis that bumble bees have efficient time-memory, indicating that this complex behavior is not limited to honey bees that evolved sophisticated social foraging behaviors over large distances.

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Objective:To explore whether emotional motivation affects judgment of learning and its mechanism in short-term memory stage.Methods:Through the preliminary test and using E-Prime software, a set of suitable experimental procedures was compiled, and 134 middle school students were selected as subjects.Taking the instant judgment of learning after emotional video induction as the experimental paradigm, a four-factor mixed experiment of 2(emotional motivation direction: positive approach and negative avoidance) ×2(motivation intensity: high and low) ×2(word pair relevance: high and low) ×2(short-term memory load: more and less) was conducted to observe the subjects' judgment of learning scores.SPSS 23.0 software was used for repeated measurement analysis of variance.Results:Regarding learning judgment level, there was a significant interaction effect between emotional motivation direction and intensity( F=5.177, P=0.025, η2 =0.040). Emotional motivation intensity and direction both significantly interacted with short-term memory load capacity( F=4.778, P=0.031, η2=0.037 ; F=4.302, P=0.040, η2=0.034 ). Furthermore, emotional motivation direction( F=15.256, P<0.001, η2=0.110), motivation intensity( F=7.518, P=0.007, η2=0.057), short-term memory load( F=13.384, P<0.001, η2=0.097), and word pair relevance( F=212.238, P<0.001, η2 =0.631) all showed significant main effects.Regarding learning judgment accuracy, there was a significant interaction effect between emotional motivation direction and intensity( F=5.646, P=0.019, η2 =0.044). Both emotional motivation intensity and direction significantly interacted with short-term memory load capacity( F=4.593, P=0.034, η2 =0.036; F=4.033, P=0.047, η2 =0.031). Additionally, emotional motivation direction( F=15.318, P<0.001, η2=0.110), motivation intensity( F=7.572, P=0.007, η2=0.058), short-term memory load( F=11.119, P=0.001, η2=0.082), and word pair relevance( F=135.814, P<0.001, η2 =0.523) all showed significant main effects.Regarding recall performance, word pair relevance( F=416.326, P<0.001, η2=0.771) and short-term memory load( F=9.609, P=0.002, η2=0.772) showed significant main effects. Conclusion:The direction and intensity of emotional motivation have an impact on judgement of learning, emotional motivation and short-term memory jointly affect the level and accuracy of judgment of learning, and emotional motivation affects judgement of learning through clues related to the learning process in short-term memory stage.

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BACKGROUND: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. METHODS: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. RESULTS: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. CONCLUSIONS: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.

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BACKGROUND AND OBJECTIVE: The promoter is a fragment of DNA and a specific sequence with transcriptional regulation function in DNA. Promoters are located upstream at the transcription start site, which is used to initiate downstream gene expression. So far, promoter identification is mainly achieved by biological methods, which often require more effort. It has become a more effective classification and prediction method to identify promoter types through computational methods. METHODS: In this study, we proposed a new capsule network and recurrent neural network hybrid model to identify promoters and predict their strength. Firstly, we used one-hot to encode DNA sequence. Secondly, we used three one-dimensional convolutional layers, a one-dimensional convolutional capsule layer and digit capsule layer to learn local features. Thirdly, a bidirectional long short-time memory was utilized to extract global features. Finally, we adopted the self-attention mechanism to improve the contribution of relatively important features, which further enhances the performance of the model. RESULTS: Our model attains a cross-validation accuracy of 86% and 73.46% in prokaryotic promoter recognition and their strength prediction, which showcases a better performance compared with the existing approaches in both the first layer promoter identification and the second layer promoter's strength prediction. CONCLUSIONS: our model not only combines convolutional neural network and capsule layer but also uses a self-attention mechanism to better capture hidden information features from the perspective of sequence. Thus, we hope that our model can be widely applied to other components.

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Meteorological events constantly affect human life, especially the occurrence of excessive precipitation in a short time causes important events such as floods. However, in case of insufficient precipitation for a long time, drought occurs. In recent years, significant changes in precipitation regimes have been observed and these changes cause socio-economic and ecological problems. Therefore, it is of great importance to correctly predict and analyze the precipitation data. In this study, a reliable and accurate precipitation forecasting model is proposed. For this aim, three deep neural network models, long short-time memory networks (LSTM), gated recurrent unit (GRU), and bidirectional long short time memory networks (biLSTM), were applied for one ahead forecasting of daily precipitation data and compared the performances of these models. Moreover, to increase the far ahead forecasting performance of the biLSTM model, the instantaneous frequency (IF) feature was applied as the input parameter for the first time in the literature. Therefore, a novel model ensemble of IF and biLSTM was employed for the aim of one-six ahead forecasting of daily precipitation data. The performance of the proposed IF-biLSTM model was evaluated using mean absolute error (MAE), mean square error (MSE), correlation coefficient (R), and determination coefficient (R2) performance parameter and spider charts were used to assess the model performances. According to the numerical results, the biLSTM model outperformed compared with the LSTM and GRU models. After the good score achieved with biLSTM model, IF feature applied to biLSTM and IF-biLSTM model has the best forecasting performance for daily precipitation data with R2 value 0.9983, 0.9827, 0.9092, 0.8508, 0.7827, and 0.7646, respectively, for one-six ahead forecasting of daily precipitation data. It has been observed that the IF-biLSTM model has higher forecasting performance than the biLSTM model, especially in far ahead forecasting studies, and the IF feature improves the estimation performance.

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Our memory for time is a fundamental ability that we use to judge the duration of events, put our experiences into a temporal context, and decide when to initiate actions. The medial entorhinal cortex (MEC), with its direct projections to the hippocampus, has been proposed to be the key source of temporal information for hippocampal time cells. However, the behavioral relevance of such temporal firing patterns remains unclear, as most of the paradigms used for the study of temporal processing and time cells are either spatial tasks or tasks for which MEC function is not required. In this study, we asked whether the MEC is necessary for rats to perform a time duration discrimination task (TDD), in which rats were trained to discriminate between 10-s and 20-s delay intervals. After reaching a 90% performance criterion, the rats were assigned to receive an excitotoxic MEC-lesion or sham-lesion surgery. We found that after recovering from surgery, rats with MEC lesions were impaired on the TDD task in comparison to rats with sham lesions, failing to return to criterion performance. Their impairment, however, was specific to the longer, 20-s delay trials. These results indicate that time processing is dependent on MEC neural computations only for delays that exceed 10 s, perhaps because long-term memory resources are needed to keep track of longer time intervals.

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The worldwide coronavirus (COVID-19) pandemic made dramatic and rapid progress in the year 2020 and requires urgent global effort to accelerate the development of a vaccine to stop the daily infections and deaths. Several types of vaccine have been designed to teach the immune system how to fight off certain kinds of pathogens. mRNA vaccines are the most important candidate vaccines because of their capacity for rapid development, high potency, safe administration and potential for low-cost manufacture. mRNA vaccine acts by training the body to recognize and response to the proteins produced by disease-causing organisms such as viruses or bacteria. This type of vaccine is the fastest candidate to treat COVID-19 but it currently facing several limitations. In particular, it is a challenge to design stable mRNA molecules because of the inefficient in vivo delivery of mRNA, its tendency for spontaneous degradation and low protein expression levels. This work designed and implemented a sequence deep model based on bidirectional GRU and LSTM models applied on the Stanford COVID-19 mRNA vaccine dataset to predict the mRNA sequences responsible for degradation by predicting five reactivity values for every position in the sequence. Four of these values determine the likelihood of degradation with/without magnesium at high pH (pH 10) and high temperature (50 degrees Celsius) and the fifth reactivity value is used to determine the likely secondary structure of the RNA sample. The model relies on two types of features, namely numerical and categorical features, where the categorical features are extracted from the mRNA sequences, structure and predicted loop. These features are represented and encoded by numbers, and then, the features are extracted using embedding layer learning. There are five numerical features depending on the likelihood for each pair of nucleotides in the RNA. The model gives promising results because it predicts the five reactivity values with a validation mean columnwise root mean square error (MCRMSE) of 0.125 using LSTM model with augmentation and the codon encoding method. Codon encoding outperforms Base encoding in MCRMSE validation error using the LSTM model meanwhile Base encoding outperforms codon encoding due to less over-fitting and the difference between the training and validation loss error is 0.008.

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Honey bee (Apis mellifera) foragers can remember both the location and time of day food is collected and, even in the absence of a reward, reconnoiter the food source at the appropriate time on subsequent days. This spatiotemporal memory (time-memory) is linked to the circadian clock and enables foragers to synchronize their behavior with floral nectar secretion rhythms, thus eliminating the need to rediscover productive food sources each day. Here, we asked whether the establishment of one time-memory influences the formation of another time-memory at the same time of day. In other words, can two time-place memories with the same 'time-stamp' co-exist? We simultaneously trained two groups of foragers from a single hive to two separate feeders at the same restricted time of day. After 5 days of training, one feeder was shut off. The second feeder continued being productive 4 more days. Our results showed that (1) foragers with high experience levels at the first source were significantly more likely than low-experience foragers to maintain fidelity to their original source and resist recruitment to the alternative source, (2) nearly one-third of foragers demonstrated multiple, overlapping time-memories by visiting both feeders at the correct time and (3) significantly more high-experience than low-experience foragers exhibited this multitasking behavior. The ability to maintain and act upon two different, yet contemporaneous, time-memories gives the forager bee a previously unknown level of versatility in attending to multiple food sources. These findings have major implications for understanding the formation and management of circadian spatiotemporal memories.

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In honey bees, continuous foraging is accompanied by a sustained up-regulation of the immediate early gene Egr-1 (early growth response protein-1) and candidate downstream genes involved in learning and memory. Here, we present a series of feeder training experiments indicating that Egr-1 expression is highly correlated with the time and duration of training even in the absence of the food reward. Foragers that were trained to visit a feeder over the whole day and then collected on a day without food presentation showed Egr-1 up-regulation over the whole day with a peak expression around 14:00. When exposed to a time-restricted feeder presentation, either 2 h in the morning or 2 h in the evening, Egr-1 expression in the brain was up-regulated only during the hours of training. Foragers that visited a feeder in the morning as well as in the evening showed two peaks of Egr-1 expression. Finally, when we prevented time-trained foragers from leaving the colony using artificial rain, Egr-1 expression in the brains was still slightly but significantly up-regulated around the time of feeder training. In situ hybridization studies showed that active foraging and time-training induced Egr-1 up-regulation occurred in the same brain areas, preferentially the small Kenyon cells of the mushroom bodies and the antennal and optic lobes. Based on these findings we propose that foraging induced Egr-1 expression can get regulated by the circadian clock after time-training over several days and Egr-1 is a candidate transcription factor involved in molecular processes underlying time-memory.

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Time can be an important contextual cue for cognitive performance, with implications for reward-associated learned behaviors such as (drug and food) addiction. So, we analyzed: (1) if marmoset monkeys develop a place preference that is conditioned to previous pairings with a highly-palatable food reward; (2) if the response is strongest when training and testing times match - time stamp effect; and (3) if there is an optimal time of the day (morning vs. afternoon) when this preference occurs - time-of-day effect. Subjects were first habituated to a two-compartment conditioned-place-preference (CPP) box. Then, during six training sessions held either in the morning or afternoon, a mixture of jellybeans and live mealworms was made available in a specific compartment. Marmosets were subsequently tested for preferring the food-paired context at the circadian time that either matched or was different from that of training. Compared to baseline levels, only subjects trained and tested in the afternoon made significantly longer and more frequent visits to the food-paired context and with a shorter latency to first entry. Thus, highly-palatable food rewards induced a CPP response. This behavior was exhibited only when training and testing times overlapped and during a restricted circadian timeframe (afternoon), consistent with a time-stamp and time-of-day effect, respectively. In this case, time may have been an internal circadian contextual cue. Whether due to circadian-mediated oscillations in memory and/or reward processes, such findings may be applied to addiction and other learned behaviors.

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