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1. Sexual selection and the ascent of women: Mate choice research since Darwin

   https://doi.org/10.1126/science.abi6308  

   Rosenthal, Gil G. ; Ryan, Michael J. ( January 2022 , Science)

   BACKGROUND Charles Darwin’s  Descent of Man, and Selection in Relation to Sex  tackled the two main controversies arising from the Origin of Species:  the evolution of humans from animal ancestors and the evolution of sexual ornaments. Most of the book focuses on the latter, Darwin’s theory of sexual selection. Research since supports his conjecture that songs, perfumes, and intricate dances evolve because they help secure mating partners. Evidence is overwhelming for a primary role of both male and female mate choice in sexual selection—not only through premating courtship but also through intimate interactions during and long after mating. But what makes one prospective mate more enticing than another? Darwin, shaped by misogyny and sexual prudery, invoked a “taste for the beautiful” without speculating on the origin of the “taste.” How to explain when the “final marriage ceremony” is between two rams? What of oral sex in bats, cloacal rubbing in bonobos, or the sexual spectrum in humans, all observable in Darwin’s time? By explaining desire through the lens of those male traits that caught his eyes and those of his gender and culture, Darwin elided these data in his theory of sexual evolution. Work since Darwin has focused on howmore »traits and preferences coevolve. Preferences can evolve even if attractive signals only predict offspring attractiveness, but most attention has gone to the intuitive but tenuous premise that mating with gorgeous partners yields vigorous offspring. By focusing on those aspects of mating preferences that coevolve with male traits, many of Darwin’s influential followers have followed the same narrow path. The sexual selection debate in the 1980s was framed as “good genes versus runaway”: Do preferences coevolve with traits because traits predict genetic benefits, or simply because they are beautiful? To the broader world this is still the conversation. ADVANCES Even as they evolve toward ever-more-beautiful signals and healthier offspring, mate-choice mechanisms and courter traits are locked in an arms race of coercion and resistance, persuasion and skepticism. Traits favored by sexual selection often do so at the expense of chooser fitness, creating sexual conflict. Choosers then evolve preferences in response to the costs imposed by courters. Often, though, the current traits of courters tell us little about how preferences arise. Sensory systems are often tuned to nonsexual cues like food, favoring mating signals resembling those cues. And preferences can emerge simply from selection on choosing conspecifics. Sexual selection can therefore arise from chooser biases that have nothing to do with ornaments. Choice may occur before mating, as Darwin emphasized, but individuals mate multiple times and bias fertilization and offspring care toward favored partners. Mate choice can thus occur in myriad ways after mating, through behavioral, morphological, and physiological mechanisms. Like other biological traits, mating preferences vary among individuals and species along multiple dimensions. Some of this is likely adaptive, as different individuals will have different optimal mates. Indeed, mate choice may be more about choosing compatible partners than picking the “best” mate in the absolute sense. Compatibility-based choice can drive or reinforce genetic divergence and lead to speciation. The mechanisms underlying the “taste for the beautiful” determine whether mate choice accelerates or inhibits reproductive isolation. If preferences are learned from parents, or covary with ecological differences like the sensory environment, then choice can promote genetic divergence. If everyone shares preferences for attractive ornaments, then choice promotes gene flow between lineages. OUTLOOK Two major trends continue to shift the emphasis away from male “beauty” and toward how and why individuals make sexual choices. The first integrates neuroscience, genomics, and physiology. We need not limit ourselves to the feathers and dances that dazzled Darwin, which gives us a vastly richer picture of mate choice. The second is that despite persistent structural inequities in academia, a broader range of people study a broader range of questions. This new focus confirms Darwin’s insight that mate choice makes a primary contribution to sexual selection, but suggests that sexual selection is often tangential to mate choice. This conclusion challenges a persistent belief with sinister roots, whereby mate choice is all about male ornaments. Under this view, females evolve to prefer handsome males who provide healthy offspring, or alternatively, to express flighty whims for arbitrary traits. But mate-choice mechanisms also evolve for a host of other reasons Understanding mate choice mechanisms is key to understanding how sexual decisions underlie speciation and adaptation to environmental change. New theory and technology allow us to explicitly connect decision-making mechanisms with their evolutionary consequences. A century and a half after Darwin, we can shift our focus to females and males as choosers, rather than the gaudy by-products of mate choice. Mate choice mechanisms across domains of life. Sensory periphery for stimulus detection (yellow), brain for perceptual integration and evaluation (orange), and reproductive structures for postmating choice among pollen or sperm (teal). ILLUSTRATION: KELLIE HOLOSKI/ SCIENCE« less
2. Specialized androgen synthesis in skeletal muscles that actuate elaborate social displays

   https://doi.org/10.1242/jeb.243730  

   Schuppe, Eric R. ; Tobiansky, Daniel ; Goller, Franz ; Fuxjager, Matthew J. ( June 2022 , Journal of Experimental Biology)

   ABSTRACT Androgens mediate the expression of many reproductive behaviors, including the elaborate displays used to navigate courtship and territorial interactions. In some vertebrates, males can produce androgen-dependent sexual behavior even when levels of testosterone are low in the bloodstream. One idea is that select tissues make their own androgens from scratch to support behavioral performance. We first studied this phenomenon in the skeletal muscles that actuate elaborate sociosexual displays in downy woodpeckers and two songbirds. We show that the woodpecker display muscle maintains elevated testosterone when the testes are regressed in the non-breeding season. Both the display muscles of woodpeckers, as well as the display muscles in the avian vocal organ (syrinx) of songbirds, express all transporters and enzymes necessary to convert cholesterol into bioactive androgens locally. In a final analysis, we broadened our study by looking for these same transporters and enzymes in mammalian muscles that operate at different speeds. Using RNA-seq data, we found that the capacity for de novo synthesis is only present in ‘superfast’ extraocular muscle. Together, our results suggest that skeletal muscle specialized to generate extraordinary twitch times and/or extremely rapid contractile speeds may depend on androgenic hormones produced locally within the muscle itself. Ourmore »study therefore uncovers an important dimension of androgenic regulation of behavior.« less
3. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEGmore »channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less
4. Evolutionary and Biomechanical Basis of Drumming Behavior in Woodpeckers

   https://doi.org/10.3389/fevo.2021.649146  

   Schuppe, Eric R. ; Rutter, Amy R. ; Roberts, Thomas J. ; Fuxjager, Matthew J. ( July 2021 , Frontiers in Ecology and Evolution)

   Understanding how and why behavioral traits diversify during the course of evolution is a longstanding goal of organismal biologists. Historically, this topic is examined from an ecological perspective, where behavioral evolution is thought to occur in response to selection pressures that arise through different social and environmental factors. Yet organismal physiology and biomechanics also play a role in this process by defining the types of behavioral traits that are more or less likely to arise. Our paper explores the interplay between ecological, physiological, and mechanical factors that shape the evolution of an elaborate display in woodpeckers called the drum. Individuals produce this behavior by rapidly hammering their bill on trees in their habitat, and it serves as an aggressive signal during territorial encounters. We describe how different components of the display—namely, speed (bill strikes/beats sec –1 ), length (total number of beats), and rhythm—differentially evolve likely in response to sexual selection by male-male competition, whereas other components of the display appear more evolutionarily static, possibly due to morphological or physiological constraints. We synthesize research related to principles of avian muscle physiology and ecology to guide inferences about the biomechanical basis of woodpecker drumming. Our aim is to introduce the woodpeckermore »as an ideal study system to study the physiological basis of behavioral evolution and how it relates to selection born through different ecological factors.« less
5. Prolonged exposure to stressors suppresses exploratory behavior in zebrafish larvae

   https://doi.org/10.1242/jeb.224964  

   Haney, William A. ; Moussaoui, Bushra ; Strother, James A. ( November 2020 , Journal of Experimental Biology)

   Environmental stressors induce rapid physiological and behavioral shifts in vertebrate animals. However, the neurobiological mechanisms responsible for stress-induced changes in behavior are complex and not well understood. Similar to mammalian vertebrates, zebrafish adults display a preference for dark environments that is associated with predator avoidance, enhanced by stressors, and broadly used in assays for anxiety-like behavior. Although the larvae of zebrafish are a prominent model organism for understanding neural circuits, fewer studies have examined the effects of stressors on their behavior. This study examines the effects of noxious chemical and electric shock stressors on locomotion and light preference in zebrafish larvae. We found that both stressors elicited similar changes in behavior. Acute exposure induced increased swimming activity, while prolonged exposure depressed activity. Neither stressor produced a consistent shift in light/dark preference, but prolonged exposure to these stressors resulted in a pronounced decrease in exploration of different visual environments. We also examined the effects of exposure to a noxious chemical cue using whole-brain calcium imaging, and identified neural correlates in the area postrema, an area of the hindbrain containing noradrenergic and dopaminergic neurons. Pharmaceutical blockade experiments showed that ɑ-adrenergic receptors contribute to the behavioral response to an acute stressor but aremore »not necessary for the response to a prolonged stressor. These results indicate that zebrafish larvae have complex behavioral responses to stressors comparable to those of adult animals, and also suggest that these responses are mediated by similar neural pathways.« less