Impact of heavy alcohol consumption and cigarette smoking on sperm DNA integrity

The purposes of the presents study were to investigate the impact of alcohol consumption and cigarette smoking on semen parameters and sperm DNA quality, as well as to determine whether tobacco smoking, or alcohol consumption causes more deterioration of sperm quality. Two hundred and eleven semen samples of men were included in this study. Four groups were studied: heavy smokers (N = 48), heavy drinkers (N = 52), non-smokers (n = 70), and non-drinkers (n = 41). Semen parameters were determined according to WHO guidelines, protamine deficiency assessed by chromomycin (CMA3) staining, and sperm DNA fragmentation (sDF) evaluated by TUNEL assay. Sperm parameters were significantly higher in non-smokers versus smokers and in non-drinkers versus drinkers (p < 0.005). However, protamine deficiency and sDF were significantly lower in non-smokers versus smokers and in non-drinkers versus drinkers (p < 0.0001). No significant difference in the semen analysis parameters was observed between heavy smokers and heavy drinkers (semen volume: 3.20 ± 1.43 vs. 2.81 ± 1.56 ml, semen count: 65.75 ± 31.32 vs. 53.51 ± 32.67 mill/ml, total motility: 24.27 ± 8.18 vs. 23.75 ± 1.75%, sperm vitality: 36.15 ± 18.57 vs. 34.62 ± 16.65%, functional integrity: 41.56 ± 18.57 vs. 45.96 ± 17.98% and the morphologically normal spermatozoa: 28.77 ± 11.82 vs. 27.06 ± 13.13%, respectively). However, protamine deficiency was significantly higher among drinkers than smokers (37.03 ± 9.75 vs. 33.27 ± 8.56%, p = 0.020). The sDF was also significantly higher among drinkers than smokers (22.37 ± 7.60 vs. 15.55 ± 3.33%, p < 0.0001). Thus, cigarette smoking, and heavy alcohol intake can deteriorate sperm quality. However, alcohol consumption deteriorates sperm maturity and damages DNA integrity at significantly higher rates than cigarette smoking

Intelligent Transportation Systems Using External Infrastructure: A Literature Survey

The main problems in transportation are accidents, increasingly slow traffic flow, and pollution. An intelligent transportation system (ITS) using external infrastructure can overcome these problems. For this reason, the number of such systems is increasing dramatically, and therefore requires an adequate overview. To the best of our knowledge, no current systematic review of existing ITS solutions exists. To fill this knowledge gap, our paper provides an overview of existing ITS that use external infrastructure worldwide. Accordingly, this paper addresses current questions and challenges. For this purpose, we performed a literature review of documents that describe existing ITS solutions from 2009 until today. We categorized the results according to technology levels and analyzed its hardware system setup and value-added contributions. In doing so, we made the ITS solutions comparable and highlighted past development alongside current trends. We analyzed more than 357 papers, including 52 test bed projects. In summary, current ITSs can deliver accurate information about individuals in traffic situations in real-time. However, further research into ITS should focus on more reliable perception of the traffic using modern sensors, plug-and-play mechanisms, and secure real-time distribution of the digital twins in a decentralized manner. By addressing these topics, the development of intelligent transportation systems will be able to take a step towards its comprehensive roll-out.Comment: 18 Pages, 4 Tables, 5 Figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Advanced methods in reproductive medicine: Application of optical nanoscopy, artificial intelligence-assisted quantitative phase microscopy and mitochondrial DNA copy numbers to assess human sperm cells

Declined fertility rate and population is a matter of serious concern, especially in the developed nations. Assisted Reproductive Technologies (ART), including in vitro fertilization (IVF), have provided great hope for infertility treatment and maintaining population growth and social structure. With the help of ART, more than 8 million babies have already been born so far. Despite the worldwide expansion of ART, there is a number of open questions on the IVF success rates. Male factors for infertility contribute equally as female factors, however, male infertility is primarily focused on the “semen quality”. Therefore, the search of new semen parameters for male fertility evaluation and the exploration of the optimal method of sperm selection in IVF have been included among the top 10 research priorities for male infertility and medically assisted reproduction. The development of imaging systems coupled with image processing by Artificial Intelligence (AI) could be the revolutionary step for semen quality analysis and sperm cell selection in IVF procedures. For this work, we applied optical nanoscopy technology for the analysis of human spermatozoa, i.e., label-based Structured Illumination Microscopy (SIM) and non-invasive Quantitative Phase Microscopy (QPM). The SIM results demonstrated a prominent contrast and resolution enhancement for subcellular structures of living sperm cells, especially for mitochondria-containing midpiece, where features around 100 nm length-scale were resolved. Further, non-labeled QPM combined with machine learning technique revealed the association between gradual progressive motility loss and the morphology changes of the sperm head after external exposure to various concentrations of hydrogen peroxide. Moreover, to recognize healthy and stress-affected sperm cells, we applied Deep Neural Networks (DNNs) to QPM images achieving an accuracy of 85.6% on a dataset of 10,163 interferometric images of sperm cells. Additionally, we summarized the evidence from published literature regarding the association between mitochondrial DNA copy numbers (mtDNAcn) and semen quality. To conclude, we set up the high-resolution imaging of living human sperm cells with a remarkable level of subcellular structural details provided by SIM. Next, the morphological changes of sperm heads resulting from peroxidation have been revealed by QPM, which may not be explored by microscopy currently used in IVF settings. Besides, the implementation of DNNs for QPM image processing appears to be a promising tool in the automated classification and selection of sperm cells during IVF procedures. Moreover, the results of our meta-analysis showed an association of mtDNAcn in human sperm cells and semen quality, which seems to be a relevant sperm parameter for routine clinical practice in male fertility assessment

Simplifying credit scoring rules using LVQ+PSO

One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borrower. Processing this information can be time consuming, and presents some difficulties due to the heterogeneous structure of data. We offer in this paper an alternative method that is able to classify customers' profiles from numerical and nominal attributes. The key feature of our method, called LVQ+PSO, is the finding of a reduced set of classifying rules. This is possible, due to the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method not only useful for credit officers aiming to make quick decisions about granting a credit, but also could act as borrower's self selection. Our method was applied to an actual database of a credit consumer financial institution in Ecuador. We obtain very satisfactory results. Future research lines are exposed