Globally, infertility rates are increasing, with 2.5\% of all births being assisted by in vitro fertilisation (IVF) in 2022. Male infertility is the cause for approximately half of these cases. The quality of sperm DNA has substantial impact on the success of IVF. The assessment of sperm DNA is traditionally done through chemical assays which render sperm cells ineligible for IVF. Many compounding factors lead to the population crisis, with fertility rates dropping globally in recent history. As such assisted reproductive technologies (ART) have been the focus of recent research efforts. Simultaneously, artificial intelligence has grown ubiquitous and is permeating more aspects of modern life. With the advent of state-of-the-art machine learning and its exceptional performance in many sectors, this work builds on these successes and proposes a novel framework for the prediction of sperm cell DNA fragmentation from images of unstained sperm. Rendering a predictive model which preserves sperm integrity and allows for optimal selection of sperm for IVF.

Scientists have announced that they have grown artificial testicles in a dish, a development that they claim could help treat infertility in men. These lab-grown testicles are not yet fully functioning, sperm-producing organs, but they do share many of the same structures and genetic characteristics as natural ones. This will allow scientists to investigate fertility problems in men and possibly treat them by producing artificial sperm. Additionally, the scientist who led the work told DailyMail.com Increasingly, research has suggested that environmental pollutants in everything from food to children's toys impact male fertility, and many believe the rise of these chemicals is partly fueling America's fertility problem.

Scientists have discovered that the way sperms swim defies Newton's law of motion, which states there is an equal and opposite reaction Researchers at Kyoto University found the sperms' flagella, or tail, propels the agents forward by changing their shape to interact with the fluid. Sperms do so in a non-reciprocal way, which violates Newton's third law because they do not elicit an equal and opposite reaction from their surroundings. The flagellum's elasticity also suggests that there should be no movement at all, but instead, sperms whip their tails without releasing much energy into their surroundings. Researchers at Kyoto University found the sperms' flagella, or tail, propels the agents forward by changing their shape to interact with the fluid The team used human sperm cells and algae for the research because both have flagella that help them propel through the liquid, New Scientist reports. Men's bulging waistlines are blamed for the worrying trend and'everywhere chemicals' in the environment.

A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-assisted sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.

By imitating biological microswimmers, microrobots can be designed to accomplish targeted delivery of cargos and biomedical manipulations at microscale. However, it is still a great challenge to enable microrobots to maneuver in a complex environment. Machine learning algorithms offer a tool to boost mobility and flexibility of a synthetic microswimmer, hence could help us design truly smart microrobots. In this work, we investigate how a model of sea urchin sperm cell can self-learn chemotactic motion in a chemoattractant concentration field. We employ an artificial neural network to act as a decision-making agent and facilitate the sperm cell to discover efficient maneuver strategies through a deep reinforcement learning (DRL) algorithm. Our results show that chemotactic behaviours, very similar to the realistic ones, can be achieved by the DRL utilizing only limited environmental information. In most cases, the DRL algorithm discovers more efficient strategies than the human-devised one. Furthermore, the DRL can even utilize an external disturbance to facilitate the chemotactic motion if the extra flow information is also taken into account by the artificial neural network. Our results provide insights to the chemotactic process of sea urchin sperm cells and also prepare guidance for the intelligent maneuver of microrobots.

Researchers have designed miniature robots that are inspired by cells and steered by ultrasound that could one day navigate the human body and help deliver drugs to certain parts of it. These'rocket ships,' as described by scientists at Cornell University, have a design that is inspired by both bacteria and sperm cells. The robots, which could navigate through the human body are controlled remotely and could take advantage of some features of sperm and bacteria cells, including the fact that bacteria can swim 10 times their body length and sperm can go against the flow. 'We can make airplanes that are better than birds nowadays,' said study co-author, Mingming Wu, professor of biological and environmental engineering at Cornell, in a statement. 'But at the smallest scale, there are many situations that nature is doing much better than us.

In this paper, human semen samples from the visem dataset collected by the Simula Research Laboratory are automatically assessed with machine learning methods for their quality in respect to sperm motility. Several regression models are trained to automatically predict the percentage (0 to 100) of progressive, non-progressive, and immotile spermatozoa in a given sample. The video samples are adopted for three different feature extraction methods, in particular custom movement statistics, displacement features, and motility specific statistics have been utilised. Furthermore, four machine learning models, including linear Support Vector Regressor (SVR), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), have been trained on the extracted features for the task of automatic motility prediction. Best results for predicting motility are achieved by using the Crocker-Grier algorithm to track sperm cells in an unsupervised way and extracting individual mean squared displacement features for each detected track. These features are then aggregated into a histogram representation applying a Bag-of-Words approach. Finally, a linear SVR is trained on this feature representation. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the Mean Absolute Error (MAE) could be reduced from 8.83 to 7.31. For the sake of reproducibility, we provide the source code for our experiments on GitHub.

Physiological functions of the microtubule cytoskeleton are expected to be regulated by a variety of posttranslational tubulin modifications. For instance, tubulin glycylation is almost exclusively found in cilia and flagella, but its role in the function of these organelles remains unclear. Gadadhar et al. now demonstrate in mice that glycylation, although nonessential for the formation of cilia and flagella, coordinates the beat waveform of sperm flagella. This activity is a prerequisite for progressive sperm swimming and thus for male fertility. At the ultrastructural level, lack of glycylation perturbed the distribution of axonemal dynein conformations, which may explain the observed defects in flagellar beat. Science , this issue p. [eabd4914][1] ### INTRODUCTION Microtubules are key components of the eukaryotic cytoskeleton. Although they are involved in a wide variety of functions, microtubules are structurally highly similar across most cell types and organisms. It was suggested that a “tubulin code,” formed by combinations of tubulin posttranslational modifications, adapts individual microtubules to specific functions within living cells. However, clear-cut functional and mechanistic data verifying this concept are still scarce. Glycylation is among the least explored posttranslational modifications of tubulin and has, so far, exclusively been found on microtubules of cilia and flagella from a variety of species. Previous work has suggested that glycylation might be essential for cilia and flagella, but mechanistic insight remains lacking. ### RATIONALE Two enzymes from the tubulin-tyrosine ligase-like (TTLL) family, TTLL3 and TTLL8, are essential to initiate glycylation of tubulin in mammals. To entirely abolish glycylation at the organism level and to determine its physiological function, we generated a double-knockout mouse lacking both glycylating enzymes ( Ttll3−/−Ttll8−/− ). Inactivation of these two enzymes led to a lack of glycylation in all analyzed cilia and flagella. This allowed us to investigate the role of glycylation in the function of these organelles. ### RESULTS Despite the absence of glycylation in Ttll3−/−Ttll8−/− mice, no gross defects were observed at the organism and tissue levels. Motile ependymal cilia in brain ventricles as well as motile cilia in the respiratory tract were present and appeared normal. Sperm flagella were also assembled normally, and sperm were able to swim. However, in vitro fertility assays showed that male Ttll3−/−Ttll8−/− mice were subfertile. Computer-assisted sperm analyses revealed motility defects of Ttll3−/−Ttll8−/− sperm. Further analyses showed that lack of glycylation leads to perturbed flagellar beat patterns, causing Ttll3−/−Ttll8−/− sperm to swim predominantly along circular paths. This is highly unusual for mammalian sperm and interferes with their ability to reach the oocyte for fertilization. To determine the molecular mechanisms underlying this aberrant flagellar beat, we used cryo–electron tomography. The three-dimensional structure of the 96-nm repeat of the Ttll3−/−Ttll8−/− sperm axoneme showed no aberrations in its overall assembly. By contrast, the structure of both outer and inner dynein arms (ODAs and IDAs) was perturbed in Ttll3−/−Ttll8−/− flagella. Classification analysis showed that the incidence and distribution of pre-powerstroke and post-powerstroke conformations of ODAs and IDAs were altered in Ttll3−/−Ttll8−/− sperm. These ultrastructural findings indicate that glycylation is required to efficiently control the dynein powerstroke cycle, which is essential for the generation of a physiological flagellar beat. ### CONCLUSION Our work shows that tubulin glycylation regulates the beat of mammalian flagella by modulating axonemal dynein motor activity. Lack of glycylation leads to perturbed sperm motility and male subfertility in mice. Considering that human sperm are more susceptible than mouse sperm to deficiencies in sperm motility, our findings imply that a perturbation of tubulin glycylation could underlie some forms of male infertility in humans. ![Figure][2] Tubulin glycylation controls sperm motility. ( A ) Microtubules in sperm flagella are rich in tubulin posttranslational modifications. Mice deficient for the glycylating enzymes TTLL3 and TTLL8 lack glycylation. ( B ) Mammalian sperm swim in linear paths. In the absence of glycylation, abnormal, mostly circular swimming patterns are observed, which impede progressive swimming. ( C ) Absence of glycylation leads to perturbed distribution of axonemal dynein conformations in Ttll3−/−Ttll8−/− flagella, which impedes normal flagellar beating. Posttranslational modifications of the microtubule cytoskeleton have emerged as key regulators of cellular functions, and their perturbations have been linked to a growing number of human pathologies. Tubulin glycylation modifies microtubules specifically in cilia and flagella, but its functional and mechanistic roles remain unclear. In this study, we generated a mouse model entirely lacking tubulin glycylation. Male mice were subfertile owing to aberrant beat patterns of their sperm flagella, which impeded the straight swimming of sperm cells. Using cryo–electron tomography, we showed that lack of glycylation caused abnormal conformations of the dynein arms within sperm axonemes, providing the structural basis for the observed dysfunction. Our findings reveal the importance of microtubule glycylation for controlled flagellar beating, directional sperm swimming, and male fertility. [1]: /lookup/doi/10.1126/science.abd4914 [2]: pending:yes

We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cells acquired by quantitative phase imaging. The proposed method, called transferring of pre-trained generative adversarial network (TOP-GAN), is a hybridization between transfer learning and generative adversarial networks (GANs). Healthy cells and cancer cells of different metastatic potential have been imaged by low-coherence off-axis holography. After the acquisition, the optical path delay maps of the cells have been extracted and directly used as an input to the deep networks. In order to cope with the small number of classified images, we have used GANs to train a large number of unclassified images from another cell type (sperm cells). After this preliminary training, and after transforming the last layer of the network with new ones, we have designed an automatic classifier for the correct cell type (healthy/primary cancer/metastatic cancer) with 90-99% accuracy, although small training sets of down to several images have been used. These results are better in comparison to other classic methods that aim at coping with the same problem of a small training set. We believe that our approach makes the combination of holographic microscopy and deep learning networks more accessible to the medical field by enabling a rapid, automatic and accurate classification in stain-free imaging flow cytometry. Furthermore, our approach is expected to be applicable to many other medical image classification tasks, suffering from a small training set.

By way of a break in this polarized era, let's briefly consider the single topic that men and women of every culture and nationality have happily agreed on, from the beginning of time to this very minute -- babies are good. But until astonishingly recent times, nearly every aspect of where babies come from was utterly mysterious. The titans of the scientific revolution had no notion. Leonardo da Vinci did not know, Galileo did not know, Isaac Newton did not know. They knew, that is, that men and women have sex and as a result, sometimes, babies, but they did not know how those babies are created.