The evolutionary history of consciousness

Klein & Barron argue that insects are capable of subjective experience, i.e., sentience. Whereas we mostly agree with the conclusion of their arguments, we think there is an even more important message to be learned from their work. The line of reasoning opened by Klein & Barron proves instructive for how neuroscientists can and should explore the biological phenomenon of consciousness

The evolutionary history of consciousness

Klein & Barron argue that insects are capable of subjective experience, i.e., sentience. Whereas we mostly agree with the conclusion of their arguments, we think there is an even more important message to be learned from their work. The line of reasoning opened by Klein & Barron proves instructive for how neuroscientists can and should explore the biological phenomenon of consciousness

The Roles of Dopamine and Related Compounds in Reward-Seeking Behavior Across Animal Phyla

Motile animals actively seek out and gather resources they find rewarding, and this is an extremely powerful organizer and motivator of animal behavior. Mammalian studies have revealed interconnected neurobiological systems for reward learning, reward assessment, reinforcement and reward-seeking; all involving the biogenic amine dopamine. The neurobiology of reward-seeking behavioral systems is less well understood in invertebrates, but in many diverse invertebrate groups, reward learning and responses to food rewards also involve dopamine. The obvious exceptions are the arthropods in which the chemically related biogenic amine octopamine has a greater effect on reward learning and reinforcement than dopamine. Here we review the functions of these biogenic amines in behavioral responses to rewards in different animal groups, and discuss these findings in an evolutionary context

Brain microRNAs among social and solitary bees

Evolutionary transitions to a social lifestyle in insects are associated with lineage-specific changes in gene expression, but the key nodes that drive these regulatory changes are unknown. We examined the relationship between social organization and lineage-specific microRNAs (miRNAs). Genome scans across 12 bee species showed that miRNA copy-number is mostly conserved and not associated with sociality. However, deep sequencing of small RNAs in six bee species revealed a substantial proportion (20–35%) of detected miRNAs had lineage-specific expression in the brain, 24–72% of which did not have homologues in other species. Lineage-specific miRNAs disproportionately target lineage-specific genes, and have lower expression levels than shared miRNAs. The predicted targets of lineage-specific miRNAs are not enriched for genes with caste-biased expression or genes under positive selection in social species. Together, these results suggest that novel miRNAs may coevolve with novel genes, and thus contribute to lineage-specific patterns of evolution in bees, but do not appear to have significant influence on social evolution. Our analyses also support the hypothesis that many new miRNAs are purged by selection due to deleterious effects on mRNA targets, and suggest genome structure is not as influential in regulating bee miRNA evolution as has been shown for mammalian miRNAs

Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning

BackgroundRadiotherapy (RT) is increasingly being used on dogs with spontaneous head and neck cancer (HNC), which account for a large percentage of veterinary patients treated with RT. Accurate definition of the gross tumor volume (GTV) is a vital part of RT planning, ensuring adequate dose coverage of the tumor while limiting the radiation dose to surrounding tissues. Currently the GTV is contoured manually in medical images, which is a time-consuming and challenging task.PurposeThe purpose of this study was to evaluate the applicability of deep learning-based automatic segmentation of the GTV in canine patients with HNC.Materials and methodsContrast-enhanced computed tomography (CT) images and corresponding manual GTV contours of 36 canine HNC patients and 197 human HNC patients were included. A 3D U-Net convolutional neural network (CNN) was trained to automatically segment the GTV in canine patients using two main approaches: (i) training models from scratch based solely on canine CT images, and (ii) using cross-species transfer learning where models were pretrained on CT images of human patients and then fine-tuned on CT images of canine patients. For the canine patients, automatic segmentations were assessed using the Dice similarity coefficient (Dice), the positive predictive value, the true positive rate, and surface distance metrics, calculated from a four-fold cross-validation strategy where each fold was used as a validation set and test set once in independent model runs.ResultsCNN models trained from scratch on canine data or by using transfer learning obtained mean test set Dice scores of 0.55 and 0.52, respectively, indicating acceptable auto-segmentations, similar to the mean Dice performances reported for CT-based automatic segmentation in human HNC studies. Automatic segmentation of nasal cavity tumors appeared particularly promising, resulting in mean test set Dice scores of 0.69 for both approaches.ConclusionIn conclusion, deep learning-based automatic segmentation of the GTV using CNN models based on canine data only or a cross-species transfer learning approach shows promise for future application in RT of canine HNC patients

Systematic Analysis of Experiments on Sub-Carangiform Fish Hydrodynamics

Målet med denne oppgaven er å undersøke om det er en måte å systematisk samle data fra fiskesvømmeeksperimenter slik at fremtidig datautvinning for påfølgende analyse kan foregå mye raskere. Det er gjort et forarbeid for å kartlegge denne muligheten, som konkluderte med at det faktisk burde være mulig, og indikerte at detectron2 ville være en maskinlæringsalgoritme å basere arbeidet på. Hensikten med denne oppgaven er deretter å undersøke en systematisk tilnærming til fiskebevegelsesanalyse ved bruk av maskinlæringsobjektidentifikasjon og segmentering for å trekke ut fiskekonturer fra videoeksperimenter av svømmende fisk. Dette gjøres ved hjelp av detectron2, et toppmoderne rammeverk for dyplæring utviklet av FacebookAI, for objektdeteksjon og segmentering. Segmentering er når omrisset av objekter blir funnet, noe som er avgjørende for hva denne oppgaven skal oppnå. Når disse konturene, eller maskene, er funnet, blir de behandlet slik at de kan brukes til dataekstraksjon av fiskens midtlinje. Fiskens midtlinje er ryggraden til fisken. Når maskene er produsert, håndteres de av et annet program, en midtlinjeekstraktor. Denne funksjonen trekker dem ut med en enkel algoritme som ikke er robust for andre testoppsett. En mye mer robust metode for utvinning ble forsøkt implementert, men det var ikke mulig å oppnå brukbare resultater. Til slutt ble de resulterende midtlinjene behandlet i en enkel hydrodynamisk analyse for å sjekke at de ikke bare så bra ut i plott, men at metodene beskrevet ovenfor faktisk gir resultater som kan brukes til forskning. Oppgaven har blitt ansett som en suksess, på grunn av tidsmessige bekymringer kunne en dypere hydrodynamisk analyse ikke utføres, men hovedmålene for oppgaven ble likevel oppnådd

Invertebrate models in addiction research

While drug addiction is a uniquely human problem, most research examining the biological mechanisms of the transition from substance use to addiction is conducted with vertebrate animal models. Many other fields of neuroscience have greatly benefitted from contributions from simple and manipulable invertebrate model systems. However, the potential of invertebrate research has yet to be fully capitalised on in the field of addiction neuroscience. This may be because of the complexity of addiction and the clinical imperative of addiction research. We argue that the homocentric diagnostic criteria of addiction are no more a hindrance to the use of invertebrate models than they are to vertebrate models. We highlight the strengths of the diversity of different invertebrate model systems in terms of neuroanatomy and molecular machinery, and stress that working with a range of different models will aid in understanding addiction and not be a disadvantage. Finally, we discuss the specific advantages of utilising invertebrate animals for addiction research and highlight key areas in which invertebrates are suited for making unique and meaningful contributions to this field.13 page(s