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1. Fish-inspired segment models for undulatory steady swimming

   https://doi.org/10.1088/1748-3190/ac6bd6  

   Akanyeti, Otar; Di Santo, Valentina; Goerig, Elsa; Wainwright, Dylan K.; Liao, James C.; Castro-Santos, Theodore; Lauder, George V. (May 2022, Bioinspiration & Biomimetics)

   Abstract Many aquatic animals swim by undulatory body movements and understanding the diversity of these movements could unlock the potential for designing better underwater robots. Here, we analyzed the steady swimming kinematics of a diverse group of fish species to investigate whether their undulatory movements can be represented using a series of interconnected multi-segment models, and if so, to identify the key factors driving the segment configuration of the models. Our results show that the steady swimming kinematics of fishes can be described successfully using parsimonious models, 83% of which had fewer than five segments. In these models, the anterior segments were significantly longer than the posterior segments, and there was a direct link between segment configuration and swimming kinematics, body shape, and Reynolds number. The models representing eel-like fishes with elongated bodies and fishes swimming at high Reynolds numbers had more segments and less segment length variability along the body than the models representing other fishes. These fishes recruited their anterior bodies to a greater extent, initiating the undulatory wave more anteriorly. Two shape parameters, related to axial and overall body thickness, predicted segment configuration with moderate to high success rate. We found that head morphology was a good predictor of its segment length. While there was a large variation in head segments, the length of tail segments was similar across all models. Given that fishes exhibited variable caudal fin shapes, the consistency of tail segments could be a result of an evolutionary constraint tuned for high propulsive efficiency. The bio-inspired multi-segment models presented in this study highlight the key bending points along the body and can be used to decide on the placement of actuators in fish-inspired robots, to model hydrodynamic forces in theoretical and computational studies, or for predicting muscle activation patterns during swimming. 

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2. Rupture segmentation on the East Anatolian fault (Turkey) controlled by along-strike variations in long-term slip rates in a structurally complex fault system

   https://doi.org/10.1130/G52403.1  

   Wang, Binhao; Barbot, Sylvain (July 2024, Geology)

   The East Anatolian fault in Turkey exhibits along-strike rupture segmentation, typically resulting in earthquakes with moment magnitude (Mw) up to 7.5 that are confined to individual segments. However, on 6 February 2023, a catastrophic Mw 7.8 earthquake struck near Kahramanmaraş (southeastern Turkey), defying previous expectations by rupturing multiple segments spanning over 300 km and overcoming multiple geometric complexities. We explore the mechanics of successive single- and multi-segment ruptures using numerical models of the seismic cycle calibrated to historical earthquake records and geodetic observations of the 2023 doublet. Our model successfully reproduces the observed historical rupture segmentation and the rare occurrence of multi-segment earthquakes. The segmentation pattern is influenced by variations in long-term slip rate along strike across the kinematically complex fault network between the Arabian and Anatolian plates. Our physics-based seismic cycle simulations shed light on the long-term variability of earthquake size that shapes seismic hazards. 

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3. Extracting Landmark and Trait Information from Segmented Digital Specimen Images Generated by Artificial Neural Networks

   https://doi.org/10.3897/biss.6.94955  

   Bakış, Yasin; Altıntaş, Bahadır; Wang, Xiaojun; Maruf, M; Karpatne, Anuj; Bart, Henry (September 2022, Biodiversity Information Science and Standards)

   We have been successfully developing Artificial Intelligence (AI) models for automatically classifying fish species using neural networks over the last three years during the “Biology Guided Neural Network” (BGNN) project*1. We continue our efforts in another broader project, “Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning”*2. One of the main topics in the Imageomics Project is “Morphological Barcoding”. Within the Morphological Barcoding study, we are trying to build a gold standard method to identify species in different taxonomic groups based on their external morphology. This list of characters will contain, but not be limited to, landmarks, quantitative traits such as measurements of distances, areas, angles, proportions, colors, histograms, patterns, shapes, and outlines. The taxonomic groups will be limited by the data available, and we will be using fish as the topic of interest in this preliminary study. In this current study, we have focused on extracting morphological characters that are relying on anatomical features of fish, such as location of the eye, body length, and area of the head. We developed a schematic workflow to describe how we processed the data and extract the information (Fig. 1). We performed our analysis on the segmented images produced by Karpatne Team within the BGNN project (Bart et al. 2021). Segmentation analysis was performed using Artificial Neural Networks - Semantic Segmentation (Long et al. 2015); the list of segments to be detected were given as eye, head, trunk, caudal fin, pectoral fin, dorsal fin, anal fin, pelvic fin for fish. Segmented images, metadata and species lists were given as input to the workflow. During the cleaning and filtering subroutines, a subset of data was created by filtering down to the desired segmented images with corresponding metadata. In the validation step, segmented images were checked by comparing the number of specimens in the original image to the separate bounding-boxed specimen images, noting: violations in the segmentations, counts of segments, comparisons of relative positions of the segments among one another, traces of batch effect; comparisons according to their size and shape and finally based on these validation criteria each segmented image was assigned a score from 1 to 5 similar to Adobe XMP Basic namespace. The landmarks and the traits to be used in the study were extracted from the current literature, while mindful that some of the features may not be extracted successfully computationally. By using the landmark list, landmarks have been extracted by adapting the descriptions from the literature on to the segments, such as picking the left most point on the head as the tip of snout and top left point on the pelvic fin as base of the pelvic fin. These 2D vectors (coordinates), are then fine tuned by adjusting their positions to be on the outline of the fish, since most of the landmarks are located on the outline. Procrustes analysis*3 was performed to scale all of the measurements together and point clouds were generated. These vectors were stored as landmark data. Segment centroids were also treated as landmarks. Extracted landmarks were validated by comparing their relative position among each other, and then if available, compared with their manually captured position. A score was assigned based on these comparisons, similar to the segmentation validation score. Based on the trait list definitions, traits were extracted by measuring the distances between two landmarks, angles between three landmarks, areas between three or more landmarks, areas of the segments, ratios between two distances or areas and between a distance and a square rooted area and then stored as trait data. Finally these values were compared within their own species clusters for errors and whether the values were still within the bounds. Trait scores were calculated from these error calculations similar to segmentation scores aiming selecting good quality scores for further analysis such as Principal Component Analysis. Our work on extraction of features from segmented digital specimen images has shown that the accuracy of the traits such as measurements, areas, and angles depends on the accuracy of the landmarks. Accuracy of the landmarks is highly dependent on segmentation of the parts of the specimen. The landmarks that are located on the outline of the body (combination of head and trunk segments of the fish) are found to be more accurate comparing to the landmarks that represents inner features such as mouth and pectoral fin in some taxonomic groups. However, eye location is almost always accurate, since it is based on the centroid of the eye segment. In the remaining part of this study we will improve the score calculation for segments, images, landmarks and traits and calculate the accuracy of the scores by comparing the statistical results obtained by analysis of the landmark and trait data. 

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4. Chimeric dihydrofolate reductases display properties of modularity and biophysical diversity

   https://doi.org/10.1002/pro.3646  

   Rodrigues, João V.; Ogbunugafor, C. Brandon; Hartl, Daniel L.; Shakhnovich, Eugene I. (May 2019, Protein Science)

   Abstract While reverse genetics and functional genomics have long affirmed the role of individual mutations in determining protein function, there have been fewer studies addressing how large‐scale changes in protein sequences, such as in entire modular segments, influence protein function and evolution. Given how recombination can reassort protein sequences, these types of changes may play an underappreciated role in how novel protein functions evolve in nature. Such studies could aid our understanding of whether certain organismal phenotypes related to protein function—such as growth in the presence or absence of an antibiotic—are robust with respect to the identity of certain modular segments. In this study, we combine molecular genetics with biochemical and biophysical methods to gain a better understanding of protein modularity in dihydrofolate reductase (DHFR), an enzyme target of antibiotics also widely used as a model for protein evolution. We replace an integral α‐helical segment ofEscherichia coliDHFR with segments from a number of different organisms (many nonmicrobial) and examine how these chimeric enzymes affect organismal phenotypes (e.g., resistance to an antibiotic) as well as biophysical properties of the enzyme (e.g., thermostability). We find that organismal phenotypes and enzyme properties are highly sensitive to the identity of DHFR modules, and that this chimeric approach can create enzymes with diverse biophysical characteristics. 

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5. Development of the early Cambrian oryctocephalid trilobite Oryctocarella duyunensis from western Hunan, China

   https://doi.org/10.1017/jpa.2020.111  

   Dai, Tao; Hughes, Nigel C.; Zhang, Xingliang; Peng, Shanchi (July 2021, Journal of Paleontology)

   Abstract Abundant articulated specimens of the oryctocarine trilobite Oryctocarella duyunensis from the lower Cambrian (Stage 4, Series 2) Balang Formation at the Bulin section in western Hunan Province, South China, permit the description of all meraspid degrees. The maximum number of thoracic segments observed in this collection is 11. Meraspid growth was accompanied by progressive and gradual change in overall form, and this animal showed an homonymously segmented trunk with variation in the number of pygidial segments during ontogeny. Such variation permits a variety of plausible explanations, but a model of successive instars defined by the number of thoracic segments, and in suborder by the number of pygidial segments, is highly unlikely to explain the growth pattern because it would result in the loss of trunk segments between some instars. Degree-based ontogenetic staging is compatible with the variation observed. 

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