Riverbed Substrate Requirements for Natural Reproduction of Gymnocypris przewalskii

Gymnocypris przewalskii (i.e., Qinghai Lake naked carp) is a migratory fish species that lives in highland brackish water. It is important to understand the abiotic environment required by this fish to reproduce naturally so that its habitat can be protected and the wild population can be conserved. Here, artificial simulation and spawning ground substrate transformation experiments were conducted to examine the riverbed substrate requirements for G. przewalskii to naturally reproduce. Using various techniques (in vitro markers, videography, and Ethovision XT behavior tracking), this study systematically investigated the riverbed substrate preferences of G. przewalskii as well as the characteristics and effectiveness of natural reproduction induced by pebble riverbed substrate. The findings can be summarized as follows: (1) the habitat preferences of G. przewalskii differed significantly between various riverbed substrate, with pebble substrate being preferred during natural reproduction, and sand substrate being preferred pre- and post-spawning, and (2) the natural reproduction of G. przewalskii was heavily reliant on pebble riverbed substrate. Specifically, pebble substrate significantly improved spawn quantity and fertilization rate. These findings provide scientific evidence for the improvement and restoration of G. przewalskii spawning grounds, and insights regarding the artificial bionic reproduction of G. przewalskii.

DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning

Behavioral measurement and evaluation are broadly used to understand brain functions in neuroscience, especially for investigations of movement disorders, social deficits, and mental diseases. Numerous commercial software and open-source programs have been developed for tracking the movement of laboratory animals, allowing animal behavior to be analyzed digitally. In vivo optical imaging and electrophysiological recording in freely behaving animals are now widely used to understand neural functions in circuits. However, it is always a challenge to accurately track the movement of an animal under certain complex conditions due to uneven environment illumination, variations in animal models, and interference from recording devices and experimenters. To overcome these challenges, we have developed a strategy to track the movement of an animal by combining a deep learning technique, the You Only Look Once (YOLO) algorithm, with a background subtraction algorithm, a method we label DeepBhvTracking. In our method, we first train the detector using manually labeled images and a pretrained deep-learning neural network combined with YOLO, then generate bounding boxes of the targets using the trained detector, and finally track the center of the targets by calculating their centroid in the bounding box using background subtraction. Using DeepBhvTracking, the movement of animals can be tracked accurately in complex environments and can be used in different behavior paradigms and for different animal models. Therefore, DeepBhvTracking can be broadly used in studies of neuroscience, medicine, and machine learning algorithms.

Preferences for mobile-supported e-cigarette cessation interventions among young adults: A qualitative descriptive study (Preprint)

BACKGROUND Despite the steady rise in e-cigarette uptake among young adults, increasingly more young people want to quit. Given the popularity of smartphones among young adults, mobile-based e-cigarette cessation interventions hold significant promise. Smartphone apps are particularly promising due to their varied and complex capabilities to engage and end-user. However, evidence around young adults’ preferences and expectations from an e-cigarette cessation smartphone app remains unexplored. OBJECTIVE The purpose of this study was to take an initial step towards understanding young adults' preferences and perceptions on app-based e-cigarette cessation interventions. METHODS Using a qualitative descriptive approach, we interviewed 12 young adults who used e-cigarettes and wanted to quit. We inductively derived themes using the framework analysis approach and NVivo 12 qualitative data analysis software. RESULTS All participants agreed that a smartphone app for supporting cessation was desirable. In addition, we found 4 key themes related to their preferences for an app: (1) flexible personalization (2) e-cigarette behavior tracking, (3) safely managed social support, and (4) positive-framed notifications. CONCLUSIONS The findings provide direction for the development and testing of app-based e-cigarette cessation interventions for young adults.

Developing an Integrated Virtual Assistant (IVA): Feasibility of a Behavior Tracking and Reminder Prototype Solution to Assist Persons with Dementia and Their Care Partners

The number of people living with Alzheimer’s and related dementias is increasing worldwide. Much of the care that persons with dementia receive is from informal, family care partners, and solutions that help support the dyad are needed. The purpose of this study was to design and test the feasibility of an application that can track behavior and provide reminders for persons with cognitive impairment or dementia and their care partners. Participants (N=20) included ten family care partners and ten persons with mild cognitive impairment. Participants engaged in a presentation of the application user interface (UI) and accompanying care reports that aggregate data collected by the software. Reactions to the UI and care reports were generally positive, with most participants expressing they would find the application useful. Qualitative themes were identified based on improving the application and care reports’ usability. Overall, results support utilizing this technology to facilitate aging in place and reduce care partner strain.

C. elegans enteric motor neurons fire synchronized action potentials underlying the defecation motor program

The C. elegans nervous system was thought to be strictly analog, constituted solely by graded neurons. We recently discovered neuronal action potentials in the sensory neuron AWA; however, the extent to which the C. elegans nervous system relies on analog or digital neural signaling and coding is unclear. Here we report that the enteric motor neurons AVL and DVB fire all-or-none calcium-mediated action potentials that play essential roles in the rhythmic defecation behavior in C. elegans. Both AVL and DVB synchronously fire giant action potentials to faithfully execute all-or-none expulsion following the intestinal pacemaker. AVL fires unusual compound action potentials with each positive calcium-mediated spike followed by a potassium-mediated negative spike. The depolarizing calcium spikes in AVL are mediated by a CaV2 calcium channel UNC-2, while the negative potassium spikes are mediated by a repolarization-activated potassium channel EXP-2. Whole-body behavior tracking and simultaneous neural imaging in free-moving animals suggest that action potentials initiated in AVL in the head propagate along its axon to the tail and activate DVB through the INX-1 gap junction. Synchronized action potential spikes between AVL and DVB, as well as the negative spike and long-lasting afterhyperpolarization in AVL, play an important function in executing expulsion behavior. This work provides the first evidence that in addition to sensory coding, C. elegans motor neurons also use digital coding scheme to perform specific functions including long-distance communication and temporal synchronization, suggesting further, unforeseen electrophysiological diversity remains to be discovered in the C. elegans nervous system.

Trends in human activity recognition using smartphones

Recognizing human activities and monitoring population behavior are fundamental needs of our society. Population security, crowd surveillance, healthcare support and living assistance, and lifestyle and behavior tracking are some of the main applications that require the recognition of human activities. Over the past few decades, researchers have investigated techniques that can automatically recognize human activities. This line of research is commonly known as Human Activity Recognition (HAR). HAR involves many tasks: from signals acquisition to activity classification. The tasks involved are not simple and often require dedicated hardware, sophisticated engineering, and computational and statistical techniques for data preprocessing and analysis. Over the years, different techniques have been tested and different solutions have been proposed to achieve a classification process that provides reliable results. This survey presents the most recent solutions proposed for each task in the human activity classification process, that is, acquisition, preprocessing, data segmentation, feature extraction, and classification. Solutions are analyzed by emphasizing their strengths and weaknesses. For completeness, the survey also presents the metrics commonly used to evaluate the goodness of a classifier and the datasets of inertial signals from smartphones that are mostly used in the evaluation phase.

Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.