Revisiting the influence of learning in predator functional response,how it can lead to shapes different from type III

Predator/parasitoid functional response is one of the main tools used to study predation behavior, and in assessing the potential of biological control candidates. It is generally accepted that predator learning in prey searching and manipulation can produce the appearance of a type III functional response. Holling proposed that in the presence of alternative prey, at some point the predator would shift the preferred prey, leading to the appearance of a sigmoid function that characterized that functional response. This is supported by the analogy between enzyme kinetics and functional response that Holling used as the basis for developing this theory. However, after several decades, sigmoidal functional responses appear in the absence of alternative prey in most of the biological taxa studied. Here, we propose modeling the effect of learning on the functional response by using the explicit incorporation of learning curves in the parameters of the Holling functional response, the attack rate (a), and the manipulation time (h). We then study how the variation in the parameters of the learning curves causes variations in the shape of the functional response curve. We found that the functional response product of learning can be either type I, II, or III, depending on what parameters act on the organism, and how much it can learn throughout the length of the study. Therefore, the presence of other types of curves should not be automatically associated with the absence of learning. These results are important from an ecological point of view because when type III functional response is associated with learning, it is generally accepted that it can operate as a stabilizing factor in population dynamics. Our results, to the contrary, suggest that depending on how it acts, it may even be destabilizing by generating the appearance of functional responses close to type I.     

Phenotype analysis of cultivation processes via unsupervised machine learning: Demonstration for Clostridium pasteurianum

A novel approach of phenotype analysis of fermentation‐based bioprocesses based on unsupervised learning (clustering) is presented. As a prior identification of phenotypes and conditional interrelations is desired to control fermentation performance, an automated learning method to output reference phenotypes (defined as vector of biomass‐specific rates) was developed and the necessary computing process and parameters were assessed. For its demonstration, time series data of 90 Clostridium pasteurianum cultivations were used which feature a broad spectrum of solventogenic and acidogenic phenotypes, while 14 clusters of phenotypic manifestations were identified. The analysis of reference phenotypes showed distinct differences, where potential conditionalities were exemplary isolated. Further, cluster‐based balancing of carbon and ATP or the use of reference phenotypes as indicator for bioprocess monitoring were demonstrated to highlight the perks of this approach. Overall, such analysis depends strongly on the quality of the data and experimental validations will be required before conclusions. However, the automated, streamlined and abstracted approach diminishes the need of individual evaluation of all noisy dataset and showed promising results, which could be transferred to strains with comparably wide‐ranging phenotypic manifestations or as indicators for repeated bioprocesses with clearly defined target.     

Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences

BackgroundA newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe, on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspectives for treatment.MethodsWe developed an alignment-free framework that utilizes machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of human-adapted coronavirus using genomic sequences. We performed extensive experiments through six different feature transformation and machine learning algorithms combining digital signal processing to identify the lethality of possible future novel coronaviruses using existing strains.ResultsThe results tested on SARS-CoV, MERS-CoV and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our framework achieves high levels of prediction performance that is alignment-free and based on RNA sequences alone without genome annotations and specialized biological knowledge.ConclusionThe results demonstrate that, for any novel human coronavirus strains, this study can offer a reliable real-time estimation for its viral lethality.     

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients'' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients'' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.     

Olfactory conditioning of the proboscis extension in bumble bees

The foraging behaviour of bumble bees is well documented for nectar and/or pollen gathering, but little is known about the learning processes underlying such behaviour. We report olfactory conditioning in worker bumble bees Bombus terrestris L. (Hymenoptera: Apidae) obtained under laboratory conditions on restrained individuals. The protocol was adapted from the proboscis extension conditioning previously described in the honey bee Apis mellifera L. Bumble bees were found to be able to learn a pure odorant when it was presented in paired association with a sugar reward, but not when odour and reward were presented in an explicitly unpaired procedure. This suggests an associative basis for this olfactory learning. Bumble bees showed similar conditioning abilities when stimulated with two different floral odours. An effect of the sugar reward concentration on the learning performances was found.     

Are herbivore-induced plant volatiles reliable indicators of herbivore identity to foraging carnivorous arthropods?

Plants that are damaged by herbivorous arthropods provide carnivorous enemies of the herbivores with important information. They emit an induced volatile blend that is highly detectable to the carnivores from a distance. Such detectable signals that indicate herbivore presence are important for the carnivores because herbivores themselves are under strong selection not to expose themselves. In addition, carnivores would benefit from a specificity of the induced plant volatiles. Whether herbivore-induced plant volatiles are reliable indicators of herbivore identity, however, has not been resolved unambiguously. Some studies support the reliability of herbivore-induced plant volatiles, while others do not. Different approaches have been used such as chemical analysis, behavioural analysis or a combination of the two. Based on the total of chemical studies one might conclude that in most cases herbivore-induced plant volatiles are not very specific for the herbivore that damages the plant. However, arthropod chemosensors are much more sensitive than the detectors of analytical instruments. Therefore, chemical analyses are not suitable to demonstrate whether or not herbivore-induced plant volatiles are reliable indicators of herbivore identity to carnivores. Behavioural studies should provide this information. In analysing carnivore behaviour it should be realised, however, that arthropod behaviour can be highly variable. Arthropod foraging decisions are affected by external and internal factors such as (a) abiotic environmental factors, (b) presence of competitors or enemies, (c) deprivation of food or oviposition sites, (d) specific deprivation of certain nutrients or (e) learning. In this paper their effect on discrimination of carnivores between volatile blends emitted by plants infested by different herbivores is reviewed. This provides testable hypotheses of why discrimination was not found in some studies. The ability of carnivores to discriminate is likely to be more common than is clear to date, which should invoke functional studies of the conditions that influence the occurrence of this discrimination.     

In vivo recordings of spontaneous and odor‐modulated dynamics in the Limax olfactory lobe

The major central site of olfactory information processing in the terrestrial slug Limax maximus is the procerebral lobe of the cerebral ganglion, which exhibits oscillatory dynamics of its local field potential and propagates activity waves from its apex to its base, as determined by multisite optical and electrical measurements in vitro. The learning‐dependent uptake of Lucifer yellow into procerebral neurons suggests that the procerebral lobe may form learned representations of odors. To determine the role of the procerebral lobe in odor processing and odor learning, we developed procedures to implant fine wire electrodes in the lobe, which allowed recordings of local field potential in freely behaving slugs. The procerebral lobe displays oscillatory dynamics of its local field potential in vivo; however the amplitude and frequency of the local field potential are much more variable in vivo than in vitro. Odor presentation leads to increased frequency and amplitude of the local field potential signal. Several lines of evidence indicate that the variations in the local field potential signal recorded in vivo are not due to movement artifacts or activity in adjacent muscles. Multiple amine, gaseous, and peptide neuromodulators known to be present in the procerebral lobe provide pathways by which activity or coupling of bursting neurons in the procerebral lobe could be altered, resulting in the observed amplitude and frequency modulation of the local field potential. © 2001 John Wiley & Sons, Inc. J Neurobiol 46: 126–141, 2001     

Acute exposure to a 60 Hz magnetic field affects rats' water-maze performance

Rats were trained in six sessions to locate a submerged platform in a circular water-maze. They were exposed to a 1 mT, 60 Hz magnetic field for one hour in a Helmholtz coil system immediately before each training session. In addition, one hour after the last training session, they were tested in a probe trial during which the platform was removed and the time spent in the quadrant of the maze in which the platform was located during the training sessions was scored. Control animals were sham-exposed using the exposure system operating with the coils activated in an anti-parallel direction to cancel the fields. A group of “non-exposed” control animals was also included in the study. There was no significant difference between the magnetic field-exposed and control animals in learning to locate the platform. However, swim speed of the magnetic field-exposed rats was significantly slower than that of the controls. During the probe trial, magnetic field-exposed animals spent significantly less time in the quadrant that contained the platform, and their swim patterns were different from those of the controls. These results indicate that magnetic field exposure causes a deficit in spatial “reference” memory in the rat. Rats subjected to magnetic field exposure probably used a different behavioral strategy in learning the maze. Bioelectromagnetics 19: 117–122, 1998. © 1998 Wiley-Liss, Inc.     

Bilateral feedback projections to the forebrain in the premotor network for singing in zebra finches

A discrete neural circuit mediates the production of learned vocalizations in oscine songbirds. Although this circuit includes some bilateral pathways at midbrain and medullary levels, the forebrain components of the song control network are not directly connected across the midline. There have been no previous reports of bilateral projections from medullary and midbrain vocal control nuclei back to the forebrain song system, but the existence of such bilateral corollary discharge pathways was strongly suggested by the recent observation that unilateral stimulation of a forebrain song nucleus during singing leads to a rapid readjustment of premotor activity in the contralateral forebrain. In the present study, we used neuroanatomical tracers to demonstrate bilateral projections from (a) the rostral ventrolateral medulla (RVL), which may control respiratory aspects of vocalization, to nucleus uvaeformis (Uva), and (b) the dorsomedial intercollicular nucleus (DM), a midbrain vocal control region, to Uva. Both RVL and DM receive descending projections from the forebrain song nucleus robustus archistriatalis, and Uva projects directly to the forebrain song nuclei interfacialis and high vocal center. We suggest that the bilateral feedback projections from DM and RVL to Uva function to coordinate the two hemispheres during singing in adult songbirds and to convey internal feedback of premotor signals to the forebrain in young birds that are learning to sing. © 1998 John Wiley & Sons, Inc. J Neurobiol 34: 27–40, 1998