Using high-resolution microscopy data to generate realistic structures for electromagnetic FDTD simulations from complex biological models.

Finite-difference time-domain (FDTD) electromagnetic simulations are a computational method that has seen much success in the study of biological optics; however, such simulations are often hindered by the difficulty of faithfully replicating complex biological microstructures in the simulation space. Recently, we designed simulations to calculate the trajectory of electromagnetic light waves through realistically reconstructed retinal photoreceptors and found that cone photoreceptor mitochondria play a substantial role in shaping incoming light. In addition to vision research and ophthalmology, such simulations are broadly applicable to studies of the interaction of electromagnetic radiation with biological tissue. Here, we present our method for discretizing complex 3D models of cellular structures for use in FDTD simulations using MEEP, the MIT Electromagnetic Equation Propagation software, including subpixel smoothing at mesh boundaries. Such models can originate from experimental imaging or be constructed by hand. We also include sample code for use in MEEP. Implementation of this algorithm in new code requires understanding of 3D mathematics and may require several weeks of effort, whereas use of our sample code requires knowledge of MEEP and C++ and may take up to a few hours to prepare a model of interest for 3D FDTD simulation. In all cases, access to a facility supercomputer with parallel processing capabilities is recommended. This protocol offers a practical solution to a significant challenge in the field of computational electrodynamics and paves the way for future advancements in the study of light interaction with biological structures.

Increased summer temperature is associated with reduced calf mass of a circumpolar large mammal through direct thermoregulatory and indirect, food quality, pathways.

Climate change represents a growing ecological challenge. The (sub) arctic and boreal regions of the world experience the most rapid warming, presenting an excellent model system for studying how climate change affects mammals. Moose (Alces alces) are a particularly relevant model species with their circumpolar range. Population declines across the southern edge of this range are linked to rising temperatures. Using a long-term dataset (1988-1997, 2017-2019), we examine the relative strength of direct (thermoregulatory costs) and indirect (food quality) pathways linking temperature, precipitation, and the quality of two important food items (birch and fireweed) to variation in moose calf mass in northern Sweden. The direct effects of temperature consistently showed stronger relationships to moose calf mass than did the indirect effects. The proportion of growing season days where the temperature exceeded a 20 °C threshold showed stronger direct negative relationships to moose calf mass than did mean temperature values. Finally, while annual forb (fireweed) quality was more strongly influenced by temperature and precipitation than were perennial (birch) leaves, this did not translate into a stronger relationship to moose calf weight. The only indirect path with supporting evidence suggested that mean growing season temperatures were positively associated with neutral detergent fiber, which was, in turn, negatively associated with calf mass. While indirect impacts of climate change deserve further investigation, it is important to recognize the large direct impacts of temperature on cold-adapted species.

Neurexin 3 is required for the specific S-cone to S-cone bipolar cell synapse in the mammalian retina.

Specific wiring is essential for sensory systems to precisely relay information to higher brain regions. The retina, an approachable part of the brain, is an ideal model for studying neural circuits due to its well-organized structure. In the retina, S-cone photoreceptors sense and relay short-wavelength (e.g., blue) light signals for encoding color information and other environmental cues. S-cones usually account for less than 10% of cones and are precisely connected to S-cone bipolar cells (SCBCs). This connection is ancient and highly conserved across species, indicating essential functions. How this wiring specificity is formed and maintained, however, is not understood. To unveil the molecular mechanisms underlying this highly specific connection, we sequenced the transcriptomes of thirteen-lined ground squirrel (TLGS) photoreceptors. We chose TLGS for their cone-rich retina and the absence of cones that co-express multiple opsin proteins, as compared to mice. We used a targeted SMART-seq approach to obtain high-resolution transcriptomes from S- and M-cone photoreceptors and identified a cell-adhesion molecule, Nrxn3, as a potential candidate mediating the S-cone to SCBC connection. Given the limitations of genetic manipulation in TLGS, we utilized mouse models to study the function of Nrxn3 in S-cones. In 'true' S-cones (S-opsin+/M-opsin-) that lack Nrxn3 expression, the number of connections with SCBCs was drastically reduced, indicating a critical role of Nrxn3 for this synapse. While neurexins are well known for their diverse roles in regulating various synapses, this study is the first to document its crucial role in mediating or maintaining a specific synapse in the central nervous system. In addition, the differentially expressed genes identified here provide a valuable resource for further investigating cone subtype-specific functions.

Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable.

The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot-ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R-squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot-ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system.

A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving.

LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast decision-making when driving. The sensor is used in the perception system, especially object detection, to understand the driving environment. Although 2D object detection has succeeded during the deep-learning era, the lack of depth information limits understanding of the driving environment and object location. Three-dimensional sensors, such as LiDAR, give 3D information about the surrounding environment, which is essential for a 3D perception system. Despite the attention of the computer vision community to 3D object detection due to multiple applications in robotics and autonomous driving, there are challenges, such as scale change, sparsity, uneven distribution of LiDAR data, and occlusions. Different representations of LiDAR data and methods to minimize the effect of the sparsity of LiDAR data have been proposed. This survey presents the LiDAR-based 3D object detection and feature-extraction techniques for LiDAR data. The 3D coordinate systems differ in camera and LiDAR-based datasets and methods. Therefore, the commonly used 3D coordinate systems are summarized. Then, state-of-the-art LiDAR-based 3D object-detection methods are reviewed with a selected comparison among methods.

Comparison of the Capacitance of a Cyclically Fatigued Stretch Sensor to a Non-Fatigued Stretch Sensor When Performing Static and Dynamic Foot-Ankle Motions.

Motion capture is the current gold standard for assessing movement of the human body, but laboratory settings do not always mimic the natural terrains and movements encountered by humans. To overcome such limitations, a smart sock that is equipped with stretch sensors is being developed to record movement data outside of the laboratory. For the smart sock stretch sensors to provide valuable feedback, the sensors should have durability of both materials and signal. To test the durability of the stretch sensors, the sensors were exposed to high-cycle fatigue testing with simultaneous capture of the capacitance. Following randomization, either the fatigued sensor or an unfatigued sensor was placed in the plantarflexion position on the smart sock, and participants were asked to complete the following static movements: dorsiflexion, inversion, eversion, and plantarflexion. Participants were then asked to complete gait trials. The sensor was then exchanged for either an unfatigued or fatigued plantarflexion sensor, depending upon which sensor the trials began with, and each trial was repeated by the participant using the opposite sensor. Results of the tests show that for both the static and dynamic movements, the capacitive output of the fatigued sensor was consistently higher than that of the unfatigued sensor suggesting that an upwards drift of the capacitance was occurring in the fatigued sensors. More research is needed to determine whether stretch sensors should be pre-stretched prior to data collection, and to also determine whether the drift stabilizes once the cyclic softening of the materials comprising the sensor has stabilized.

Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset.

Fish species recognition is crucial to identifying the abundance of fish species in a specific area, controlling production management, and monitoring the ecosystem, especially identifying the endangered species, which makes accurate fish species recognition essential. In this work, the fish species recognition problem is formulated as an object detection model to handle multiple fish in a single image, which is challenging to classify using a simple classification network. The proposed model consists of MobileNetv3-large and VGG16 backbone networks and an SSD detection head. Moreover, a class-aware loss function is proposed to solve the class imbalance problem of our dataset. The class-aware loss takes the number of instances in each species into account and gives more weight to those species with a smaller number of instances. This loss function can be applied to any classification or object detection task with an imbalanced dataset. The experimental result on the large-scale reef fish dataset, SEAMAPD21, shows that the class-aware loss improves the model over the original loss by up to 79.7%. The experimental result on the Pascal VOC dataset also shows the model outperforms the original SSD object detection model.

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review.

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

WCNN3D: Wavelet Convolutional Neural Network-Based 3D Object Detection for Autonomous Driving.

Three-dimensional object detection is crucial for autonomous driving to understand the driving environment. Since the pooling operation causes information loss in the standard CNN, we designed a wavelet-multiresolution-analysis-based 3D object detection network without a pooling operation. Additionally, instead of using a single filter like the standard convolution, we used the lower-frequency and higher-frequency coefficients as a filter. These filters capture more relevant parts than a single filter, enlarging the receptive field. The model comprises a discrete wavelet transform (DWT) and an inverse wavelet transform (IWT) with skip connections to encourage feature reuse for contrasting and expanding layers. The IWT enriches the feature representation by fully recovering the lost details during the downsampling operation. Element-wise summation was used for the skip connections to decrease the computational burden. We trained the model for the Haar and Daubechies (Db4) wavelets. The two-level wavelet decomposition result shows that we can build a lightweight model without losing significant performance. The experimental results on KITTI's BEV and 3D evaluation benchmark show that our model outperforms the PointPillars-based model by up to 14% while reducing the number of trainable parameters.

Shared antibiotic resistance and virulence genes in Staphylococcus aureus from diverse animal hosts.

The emergence of methicillin-resistant Staphylococcus aureus (MRSA) poses an important threat in human and animal health. In this study, we ask whether resistance and virulence genes in S. aureus are homogeneously distributed or constrained by different animal hosts. We carried out whole genome sequencing of 114 S. aureus isolates from ten species of animals sampled from four New England states (USA) in 2017-2019. The majority of the isolates came from cats, cows and dogs. The maximum likelihood phylogenetic tree based on the alignment of 89,143 single nucleotide polymorphisms of 1173 core genes reveal 31 sequence types (STs). The most common STs were ST5, ST8, ST30, ST133 and ST2187. Every genome carried at least eight acquired resistance genes. Genes related to resistance found in all genomes included norA (fluoroquinolone), arlRS (fluoroquinolone), lmrS (multidrug), tet(38) (tetracycline) and mepAR (multidrug and tigecycline resistance). The most common superantigen genes were tsst-1, sea and sec. Acquired antibiotic resistance (n = 10) and superantigen (n = 9) genes of S. aureus were widely shared between S. aureus lineages and between strains from different animal hosts. These analyses provide insights for considering bacterial gene sharing when developing strategies to combat the emergence of high-risk clones in animals.

Mitochondria in cone photoreceptors act as microlenses to enhance photon delivery and confer directional sensitivity to light.

Mammalian photoreceptors aggregate numerous mitochondria, organelles chiefly for energy production, in the ellipsoid region immediately adjacent to the light-sensitive outer segment to support the high metabolic demands of phototransduction. However, these complex, lipid-rich organelles are also poised to affect light passage into the outer segment. Here, we show, via live imaging and simulations, that despite this risk of light scattering or absorption, these tightly packed mitochondria "focus" light for entry into the outer segment and that mitochondrial remodeling affects such light concentration. This "microlens"-like feature of cone mitochondria delivers light with an angular dependence akin to the Stiles-Crawford effect (SCE), providing a simple explanation for this essential visual phenomenon that improves resolution. This new insight into the optical role of mitochondria is relevant for the interpretation of clinical ophthalmological imaging, lending support for the use of SCE as an early diagnostic tool in retinal disease.

Accessory Genome Dynamics of Local and Global Staphylococcus pseudintermedius Populations.

Staphylococcus pseudintermedius is a major bacterial colonizer and opportunistic pathogen in dogs. Methicillin-resistant S. pseudintermedius (MRSP) continues to emerge as a significant challenge to maintaining canine health. We sought to determine the phylogenetic relationships of S. pseudintermedius across five states in the New England region of the United States and place them in a global context. The New England dataset consisted of 125 previously published S. pseudintermedius genomes supplemented with 45 newly sequenced isolates. The core genome phylogenetic tree revealed many deep branching lineages consisting of 142 multi-locus sequence types (STs). In silico detection of the mecA gene revealed 40 MRSP and 130 methicillin-susceptible S. pseudintermedius (MSSP) isolates. MRSP were derived from five structural types of SCCmec, the mobile genetic element that carries the mecA gene conferring methicillin resistance. Although many genomes were MSSP, they nevertheless harbored genes conferring resistance to many other antibiotic classes, including aminoglycosides, macrolides, tetracyclines and penams. We compared the New England genomes to 297 previously published genomes sampled from five other states in the United States and 13 other countries. Despite the prevalence of the clonally expanding ST71 found worldwide and in other parts of the United States, we did not detect it in New England. We next sought to interrogate the combined New England and global datasets for the presence of coincident gene pairs linked to antibiotic resistance. Analysis revealed a large co-circulating accessory gene cluster, which included mecA as well as eight other resistance genes [aac (6')-Ie-aph (2″)-Ia, aad (6), aph (3')-IIIa, sat4, ermB, cat, blaZ, and tetM]. Furthermore, MRSP isolates carried significantly more accessory genes than their MSSP counterparts. Our results provide important insights to the evolution and geographic spread of high-risk clones that can threaten the health of our canine companions.

Wearables for Biomechanical Performance Optimization and Risk Assessment in Industrial and Sports Applications.

Wearable technologies are emerging as a useful tool with many different applications. While these devices are worn on the human body and can capture numerous data types, this literature review focuses specifically on wearable use for performance enhancement and risk assessment in industrial- and sports-related biomechanical applications. Wearable devices such as exoskeletons, inertial measurement units (IMUs), force sensors, and surface electromyography (EMG) were identified as key technologies that can be used to aid health and safety professionals, ergonomists, and human factors practitioners improve user performance and monitor risk. IMU-based solutions were the most used wearable types in both sectors. Industry largely used biomechanical wearables to assess tasks and risks wholistically, which sports often considered the individual components of movement and performance. Availability, cost, and adoption remain common limitation issues across both sports and industrial applications.

Fatigue Testing of Wearable Sensing Technologies: Issues and Opportunities.

Standards for the fatigue testing of wearable sensing technologies are lacking. The majority of published fatigue tests for wearable sensors are performed on proof-of-concept stretch sensors fabricated from a variety of materials. Due to their flexibility and stretchability, polymers are often used in the fabrication of wearable sensors. Other materials, including textiles, carbon nanotubes, graphene, and conductive metals or inks, may be used in conjunction with polymers to fabricate wearable sensors. Depending on the combination of the materials used, the fatigue behaviors of wearable sensors can vary. Additionally, fatigue testing methodologies for the sensors also vary, with most tests focusing only on the low-cycle fatigue (LCF) regime, and few sensors are cycled until failure or runout are achieved. Fatigue life predictions of wearable sensors are also lacking. These issues make direct comparisons of wearable sensors difficult. To facilitate direct comparisons of wearable sensors and to move proof-of-concept sensors from "bench to bedside", fatigue testing standards should be established. Further, both high-cycle fatigue (HCF) and failure data are needed to determine the appropriateness in the use, modification, development, and validation of fatigue life prediction models and to further the understanding of how cracks initiate and propagate in wearable sensing technologies.

Divergence in parturition timing and vegetation onset in a large herbivore-differences along a latitudinal gradient.

In northern environments, the period of access to high-quality forage is limited, exerting strong selective pressure to optimize the timing of parturition. We analysed timing and variation in moose (Alces alces) parturition dates of 555 females at 18 study sites across 12° of latitude (56-68° N, 1350 km) in Sweden. We found evidence for a spatial match of parturition timing to vegetation onset, but no evidence that moose adjust parturition to vegetation onset in a given year. We found a breakpoint at 64° N. Despite adaptation across latitudes, temporal divergences occurred. Females below 64° N calved after vegetation onset and females above 64° N calved before. Here, parturition before vegetation onset might be a strategy to optimize forage utilization time with the very short growing season. Highly seasonal environments such as at higher latitudes may make it advantageous to adapt parturition towards long-term climatic patterns by matching the most favourable period. Given the direction of temporal divergence, our study suggests that climate change may have less of an impact on moose parturition at northern latitudes than southern latitudes.

Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics.

Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of "Closing the Wearable Gap" journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.

True S-cones are concentrated in the ventral mouse retina and wired for color detection in the upper visual field.

Color, an important visual cue for survival, is encoded by comparing signals from photoreceptors with different spectral sensitivities. The mouse retina expresses a short wavelength-sensitive and a middle/long wavelength-sensitive opsin (S- and M-opsin), forming opposing, overlapping gradients along the dorsal-ventral axis. Here, we analyzed the distribution of all cone types across the entire retina for two commonly used mouse strains. We found, unexpectedly, that 'true S-cones' (S-opsin only) are highly concentrated (up to 30% of cones) in ventral retina. Moreover, S-cone bipolar cells (SCBCs) are also skewed towards ventral retina, with wiring patterns matching the distribution of true S-cones. In addition, true S-cones in the ventral retina form clusters, which may augment synaptic input to SCBCs. Such a unique true S-cone and SCBC connecting pattern forms a basis for mouse color vision, likely reflecting evolutionary adaptation to enhance color coding for the upper visual field suitable for mice's habitat and behavior.

Many primates, including humans, can see color better than most other mammals. This difference is due to the variety of light-detecting proteins ­ called opsins ­ that are produced in the eye by cells known as cones. While humans have three, mice only have two different opsins, known as S and M, which detect blue/UV and green light, respectively. Mouse cones produce either S-opsins, M-opsins or both. Fewer than 10 percent of cone cells in mice produce just the S-opsin, and these cells are essential for color vision. Mice are commonly used in scientific research, and so their vision has been well studied. However, previous research has produced conflicting results. Some studies report that cone cells that contain only S-opsin are evenly spread out across the retina. Other evidence suggests that color vision in mice exists only for the upper field of their vision, in other words, that mice can only distinguish colors that appeared above them. Nadal-Nicolás et al. set out to understand how to reconcile these contrasting findings. Molecular tools were used to detect S- and M-opsin in the retina of mice and revealed large differences between the lower part, known as the ventral retina, and the upper part, known as the dorsal retina. The ventral retina detects light coming from above the animal, and about a third of cone cells in this region produced exclusively S-opsin, compared to only 1 percent of cones in the dorsal retina. These S-opsin cone cells in the ventral retina group into clusters, where they connect with a special type of nerve cells that transmit this signal. To better understand these findings, Nadal-Nicolás et al. also studied albino mice. Although albino mice have a different distribution of S-opsin protein in the retina, the cone cells producing only S-opsin are similarly clustered in the ventral retina. This suggests that the concentration of S-opsin cone cells in the ventral retina is an important feature in mouse sight. This new finding corrects the misconception that S-opsin-only cone cells are evenly spread throughout the retina and supports the previous evidence that mouse color vision is greatest in the upper part of their field of vision. Nadal-Nicolás et al. suggest this arrangement could help the mice to detect predators that may attack them from above during the daytime. Together, these new findings could help to improve the design of future studies involving vision in mice and potentially other similar species.

Establishing a cost-effective hemodialysis program in the developing world.

The percentage of the population in low-middle-income countries (LMIC) with hemodialysis availability has gradually increased over the last 8 years. Note that only 3% of the treatments of these countries are done as peritoneal dialysis, which is the more cost-effective modality. The best current estimate of hemodialysis access for Africa is 25%, Southeast Asia 35%, and South America 65% [1]. The main issues that impede hemodialysis access remain poverty, the unaffordability of treatment, the substantial cost of setting up a dialysis unit as well as the lack of options to purchase dialyzers, tubing, and unit supplies at reasonable costs. This article presents cost-saving approaches for providing hemodialysis in LMIC along with words of caution on how to determine the sustainability of the project in areas with high levels of need and limited resources.

Response of GNSS-R on Dynamic Vegetated Terrain Conditions.

Global navigation satellite system reflectometry (GNSS-R) has the potential to offer a cost-effective solution for global land observations. In this study, we aim to understand GNSS-R sensitivity to changing land geophysical parameters. For this objective, we performed simulations of a ground-based receiver using a recently developed coherent bistatic vegetation scattering model (SCoBi-Veg) to detect GNSS-R signatures under varying soil moisture (SM), vegetation water content (VWC), and surface roughness during a full corn growing season. We modeled different corn growth stages by using in situ measurement data. We analyzed the simulated reflectivity and received power values based on the aforementioned variable input parameters. This study demonstrates that specular reflections dominate the diffusely scattered contribution in case of moderate roughness, regardless of the corn field row structure or the polarization. Significant correlations between VWC and cross-polarized reflectivity values are also shown. Furthermore, the study quantifies the effects of SM and surface roughness on GNSS-R deliverables.

Closing the Wearable Gap-Part II: Sensor Orientation and Placement for Foot and Ankle Joint Kinematic Measurements.

The linearity of soft robotic sensors (SRS) was recently validated for movement angle assessment using a rigid body structure that accurately depicted critical movements of the foot-ankle complex. The purpose of this study was to continue the validation of SRS for joint angle movement capture on 10 participants (five male and five female) performing ankle movements in a non-weight bearing, high-seated, sitting position. The four basic ankle movements-plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)-were assessed individually in order to select good placement and orientation configurations (POCs) for four SRS positioned to capture each movement type. PF, INV, and EVR each had three POCs identified based on bony landmarks of the foot and ankle while the DF location was only tested for one POC. Each participant wore a specialized compression sock where the SRS could be consistently tested from all POCs for each participant. The movement data collected from each sensor was then compared against 3D motion capture data. R-squared and root-mean-squared error averages were used to assess relative and absolute measures of fit to motion capture output. Participant robustness, opposing movements, and gender were also used to identify good SRS POC placement for foot-ankle movement capture.