Background: The development of covid-19 vaccinations represents a notable scientific achievement. Nevertheless, concerns have been raised regarding their possible detrimental impact on male fertility Objective: To investigate the effect of covid-19 BNT162b2 (Pfizer) vaccine on semen parameters among semen donors (SD). Methods: Thirty-seven SD from three sperm banks that provided 216 samples were included in that retrospective longitudinal multicenter cohort study. BNT162b2 vaccination included two doses, and vaccination completion was scheduled 7 days after the second dose. The study included four phases: T0 -pre-vaccination baseline control, which encompassed 1-2 initial samples per SD; T1, T2 and T3 -short, intermediate, and long terms evaluations, respectively. Each included 1-3 semen samples per donor provided 15-45, 75-125 and over 145 days after vaccination completion, respectively. The primary endpoints were semen parameters. Three statistical analyses were conducted: (1) generalized estimated equation model; (2) first sample and (3) samples' mean of each donor per period were compared to T0. Results: Repetitive measurements revealed −15.4% sperm concentration decrease on T2 (CI −25.5%-3.9%, p = 0.01) leading to total motile count 22.1% reduction (CI −35% -−6.6%, p = 0.007) compared to T0. Similarly, analysis of first semen sample only and samples' mean per donor resulted in concentration and total motile count (TMC) reductions on T2 compared to T0 -median decline of 12 million/ml and 31.2 million motile spermatozoa, respectively (p = 0.02 and 0.002 respectively) on first sample evaluation and median decline of 9.5 × 10 6 and 27.3 million motile spermatozoa (p = 0.004 and 0.003, respectively) on samples' mean examination. T3 evaluation demonstrated overall recovery without. Semen volume and sperm motility were not impaired.Discussion: This longitudinal study focused on SD demonstrates selective temporary sperm concentration and TMC deterioration 3 months after vaccination followed by later recovery verified by diverse statistical analyses.
A new computer method for ECG signal filtering is discussed. This method is based on a concept which is called "The Prior Knowledge Principle" (PKP). The PKP assumes that the measured signal contains the ECG signal itself and environmental noises. An artificial signal with amplitude and time resembling the real ECG one has been constructed. The cases of white and network noises are analyzed, with the level of noise changed from 0 to 100%. The error of the methods has been calculated and it has been found that it does not rise above 20%.
INTRODUCTIO NThe uroblem of ECG filtering is widely discussed rl -51.
The discovery of adversarial examples revealed one of the most basic vulnerabilities of deep neural networks. Among the variety of techniques introduced to tackle this inherent weakness, adversarial training was shown to be the most common and efficient strategy to achieve robustness. It is usually done by balancing the robust and natural losses. In this work, we aim to achieve better trade-off between robust and natural performances by enforcing a domain-invariant feature representation. We present a new adversarial training method, called Domain Invariant Adversarial Learning (DIAL) that learns a feature representation which is both robust and domain invariant. DIAL uses a variant of Domain Adversarial Neural Network (DANN) on the natural domain and its corresponding adversarial domain. In a case where the source domain consists of natural examples and the target domain is the adversarially perturbed examples, our method learns a feature representation constrained not to discriminate between the natural and adversarial examples, and can therefore achieve better representation. We demonstrate our advantage by improving both robustness and natural accuracy compared to current state-of-theart adversarial training methods.
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