Within the mated reproductive tracts of females of many taxa, seminal fluid proteins (SFPs) coagulate into a structure known as the mating plug (MP). MPs have diverse roles, including preventing female remating, altering female receptivity postmating, and being necessary for mated females to successfully store sperm. The Drosophila melanogaster MP, which is maintained in the mated female for several hours postmating, is comprised of a posterior MP (PMP) that forms quickly after mating begins and an anterior MP (AMP) that forms later. The PMP is composed of seminal proteins from the ejaculatory bulb (EB) of the male reproductive tract. To examine the role of the PMP protein PEBme in D. melanogaster reproduction, we identified an EB GAL4 driver and used it to target PEBme for RNA interference (RNAi) knockdown. PEBme knockdown in males compromised PMP coagulation in their mates and resulted in a significant reduction in female fertility, adversely affecting postmating uterine conformation, sperm storage, mating refractoriness, egg laying, and progeny generation. These defects resulted from the inability of females to retain the ejaculate in their reproductive tracts after mating. The uncoagulated MP impaired uncoupling by the knockdown male, and when he ultimately uncoupled, the ejaculate was often pulled out of the female. Thus, PEBme and MP coagulation are required for optimal fertility in D. melanogaster. Given the importance of the PMP for fertility, we identified additional MP proteins by mass spectrometry and found fertility functions for two of them. Our results highlight the importance of the MP and the proteins that comprise it in reproduction and suggest that in Drosophila the PMP is required to retain the ejaculate within the female reproductive tract, ensuring the storage of sperm by mated females.KEYWORDS mating plug; sperm storage; PEBme; Drosophila reproduction I N numerous species comprising diverse taxa, a solidified structure forms inside the female reproductive tract during (or shortly after) mating that is referred to as the mating plug (MP; also called the copulatory plug; we will refer to these structures collectively as MPs). MPs are largely a coagulation of male seminal fluid components. In species that produce a MP, its role in reproduction varies. In some species, MP formation is thought to guard against sperm competition. For example, in primates, MPs are seen most often in species whose females mate multiply (Dixson and Anderson 2002). Primate MPs have been suggested to prevent remating (Dorus et al. 2004), thus acting as a form of passive mate guarding (Dunham and Rudolf 2009). In the mouse, perturbing (Murer et al. 2001) or preventing (Dean 2013) MP formation reduces male fertility; in the absence of MP formation, sperm migration to the sites of fertilization is impaired (Dean 2013) receptivity in the short term (Polak et al. 1998;Bretman et al. 2010). In bumblebees, MPs physically switch off receptivity Sauter et al. 2001) and have functions related to sperm competition (Duvo...
Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.
Objective: Autonomic Dysreflexia (AD) is a potentially life-threatening syndrome which occurs in individuals with higher level spinal cord injuries (SCI). AD is caused by triggers which can lead to rapid escalation of pathophysiological responses and if the trigger is not removed, AD can be fatal. There is currently no objective, non-invasive and accurate monitoring system available to automatically detect the onset of AD symptoms in real time in a non-clinical setting. Technology or Method: We developed a user-independent method of symptomatic AD detection in real time with a wearable physiological telemetry system (PTS) and a machine learning model using data from eleven participants with SCI. Results: The PTS could detect onset of AD symptoms with an average accuracy of 94.10% and a false negative rate of 4.89%. Conclusions: The PTS can detect the onset of the symptoms AD with high sensitivity and specificity to assist people with SCIs in preventing the occurrence of AD. It would enable persons with high level SCIs to be more independent and pursue vocational activities while granting continuous medical oversight. Clinical Impact: The PTS could serve as a supplementary tool to current solutions to detect the onset of AD and prepare individuals who are newly injured to be better prepared for AD episodes. Moreover, it could be translated into a system to encourage individuals to practice better healthcare management to prevent future occurrences.
The primary interface of contact between a robotic or prosthetic hand and the external world is through the artificial skin. To make sense of that contact, tactile sensors are needed. These sensors are normally embedded in soft synthetic materials for protecting the subsurface sensor from damage or for better hand-to-object contact. It is important to understand how the mechanical signals transmit from the artificial skin to the embedded tactile sensors. In this paper, we made use of a finite element model of an artificial fingertip with viscoelastic and hyperelastic behaviors to investigate the subsurface pressure profiles when flat, curved, and Braille surfaces were indented on the surface of the model. Furthermore, we investigated the effects of 1, 3, and 5 mm thickness of the skin on the subsurface pressure profiles. The simulation results were experimentally validated using a 25.4 µm thin pressure detecting film that was able to follow the contours of a non-planar surface, which is analogous to an artificial bone. Results show that the thickness of the artificial skin has an effect on the peak pressure, on the span of the pressure distribution, and on the overall shape of the pressure profile that was encoded on a curved subsurface structure. Furthermore, the flat, curved, and Braille surfaces can be discriminated from one another with the 1 and 3 mm artificial skin layers, but not with the 5 mm thick skin.
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