The Effects of SARS-CoV-2 Infection on Female Fertility: A Review of the Literature

As the coronavirus pandemic is far from ending, more questions regarding the female reproductive system, particularly fertility issues, arise. The purpose of this paper is to bring light upon the possible link between COVID-19 and women’s reproductive health. This review emphasizes the effect of SARS-CoV-2 on the hormones, endometrium and menstrual cycle, ovarian reserve, follicular fluid, oocytes, and embryos. The results showed that endometrial samples did not express SARS-CoV-2 RNA. Regarding the menstrual cycle, there is a large range of alterations, but they were all reversible within the following months. The ovarian reserve was not significantly affected in patients recovering from both mild and severe infection in most cases, except one, where the levels of AMH were significantly lower and basal follicle-stimulating hormone (FSH) levels were increased. All COVID-19 recovered patients had positive levels of SARS-CoV-2 IgG in the follicular fluid. The amount of retrieved and mature oocytes and the fertilization rate were unharmed in three studies, except for one study, where the quantity of retrieved and mature oocytes was reduced in patients with higher levels of SARS-CoV-2 antibodies. The numbers of blastocysts, top-quality embryos, and euploid embryos were affected in most of the studies reviewed.

A Large Range Compliant Nano-Manipulator Supporting Electron Beam Lithography

Electron beam lithography (EBL) is an important lithographic process of scanning a focused electron beam (e-beam) to direct write a custom pattern with nanometric accuracy. Due to the very limited field of the focused election beam, a motion stage is needed to move the sample to the e-beam field for processing large patterns. In order to eliminate the stitching error induced by the existing “step and scan” process, we in this paper propose a large range compliant nano-manipulator so that the manipulator and the election beam can be moved in a simultaneous manner. We also present an optimization design for the geometric parameters of the compliant manipulator under the vacuum environment. Experimental results demonstrate 1 mm × 1 mm travel range with high linearity, ~ 0.5% cross-axis error and 5 nm resolution. Moreover, the high natural frequency (~ 56 Hz) of the manipulator facilitates it to achieve high-precision motion of EBL.

Design of a large-range rotary microgripper with freeform geometries using a genetic algorithm

This paper describes a novel electrostatically actuated microgripper with freeform geometries designed by a genetic algorithm. This new semiautomated design methodology is capable of designing near-optimal MEMS devices that are robust to fabrication tolerances. The use of freeform geometries designed by a genetic algorithm significantly improves the performance of the microgripper. An experiment shows that the designed microgripper has a large displacement (91.5 μm) with a low actuation voltage (47.5 V), which agrees well with the theory. The microgripper has a large actuation displacement and can handle micro-objects with a size from 10 to 100 μm. A grasping experiment on human hair with a diameter of 77 μm was performed to prove the functionality of the gripper. The result confirmed the superior performance of the new design methodology enabling freeform geometries. This design method can also be extended to the design of many other MEMS devices.

Obesity-Related Adipose Tissue Remodeling in the Light of Extracellular Mitochondria Transfer

Adipose tissue dysfunction is strongly associated with obesity and its metabolic complications such as type 2 diabetes and cardiovascular diseases. It is well established that lipid-overloaded adipose tissue produces a large range of secreted molecules that contribute a pro-inflammatory microenvironment which subsequently disseminates towards multi-organ metabolic homeostasis disruption. Besides physiopathological contribution of adipose-derived molecules, a new paradigm is emerging following the discovery that adipocytes have a propensity to extrude damaged mitochondria in the extracellular space, to be conveyed through the blood and taken up by cell acceptors, in a process called intercellular mitochondria transfer. This review summarizes the discovery of mitochondria transfer, its relation to cell quality control systems and recent data that demonstrate its relevant implication in the context of obesity-related adipose tissue dysfunction.

Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals

We propose reconstructive reservoir computing (RRC) for anomaly detection working for time-series signals. This paper investigates its fundamental properties with experiments employing echo state networks (ESNs). The RRC model is a reconstructor to replicate a normal input time-series signal with no delay or a certain delay (delay ≥ 0). In its anomaly detection process, we evaluate instantaneous reconstruction error defined as the difference between input and output signals at each time. Experiments with a sound dataset from industrial machines demonstrate that the error is low for normal signals while it becomes higher for abnormal ones, showing successful anomaly detection. It is notable that the RRC models’ behavior is very different from that of conventional anomaly detection models, that is, those based on forecasting (delay < 0). The error of the proposed reconstructor is explicitly lower than that of a forecaster, resulting in superior distinction between normal and abnormal states. We show that the RRC model is effective over a large range of reservoir parameters. We also illustrate the distribution of the output weights optimized through a training to discuss their roles in the reconstruction. Then, we investigate the influence of the neuronal leaking rate and the delay time shift amount on the transient response and the reconstruction error, showing high effectiveness of the reconstructor in anomaly detection. The proposed RRC will play a significant role for anomaly detection in the present and future sensor network society

Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals

We propose reconstructive reservoir computing (RRC) for anomaly detection working for time-series signals. This paper investigates its fundamental properties with experiments employing echo state networks (ESNs). The RRC model is a reconstructor to replicate a normal input time-series signal with no delay or a certain delay (delay ≥ 0). In its anomaly detection process, we evaluate instantaneous reconstruction error defined as the difference between input and output signals at each time. Experiments with a sound dataset from industrial machines demonstrate that the error is low for normal signals while it becomes higher for abnormal ones, showing successful anomaly detection. It is notable that the RRC models’ behavior is very different from that of conventional anomaly detection models, that is, those based on forecasting (delay < 0). The error of the proposed reconstructor is explicitly lower than that of a forecaster, resulting in superior distinction between normal and abnormal states. We show that the RRC model is effective over a large range of reservoir parameters. We also illustrate the distribution of the output weights optimized through a training to discuss their roles in the reconstruction. Then, we investigate the influence of the neuronal leaking rate and the delay time shift amount on the transient response and the reconstruction error, showing high effectiveness of the reconstructor in anomaly detection. The proposed RRC will play a significant role for anomaly detection in the present and future sensor network society

Toward Multiplexed Optogenetic Circuits

Owing to its ubiquity and easy availability in nature, light has been widely employed to control complex cellular behaviors. Light-sensitive proteins are the foundation to such diverse and multilevel adaptive regulations in a large range of organisms. Due to their remarkable properties and potential applications in engineered systems, exploration and engineering of natural light-sensitive proteins have significantly contributed to expand optogenetic toolboxes with tailor-made performances in synthetic genetic circuits. Progressively, more complex systems have been designed in which multiple photoreceptors, each sensing its dedicated wavelength, are combined to simultaneously coordinate cellular responses in a single cell. In this review, we highlight recent works and challenges on multiplexed optogenetic circuits in natural and engineered systems for a dynamic regulation breakthrough in biotechnological applications.

Methods of Thermal Treatment of Radioactive Waste

Throughout the world, and especially in the European Union, numerous technologies for the thermal treatment of radioactive waste are available or being developed. These technologies can be applied to a large range of different radioactive waste streams, including non-standard types of waste that present specific waste management challenges. Thermal treatment can result in a significant reduction in volume and hazard, which are beneficial for safe storage and disposal. Thermal treatment also removes organic material that can form complexing agents and increase the mobility of radionuclides in the landfill. In the paper, basic thermal techniques are presented, and some examples of the installations are shown. Common knowledge of these methods may result in an increased public acceptance of nuclear energy in a country just introducing it, as Poland is.

Extension of Dancer’s Legs: Increasing Angles Show Motion

Usain Bolt’s Lightning Bolt pose, one arm highly extended to one side, suggests action. Likewise, static pictures of animals, legs extended, show animation. We tested a new cue for motion perception—extension—and in particular extension of dancer’s legs. An experiment with pictures of a dancer finds larger angles between the legs suggest greater movement, especially with in-air poses and in lateral views. Leg positions graded from simply standing to very difficult front and side splits. Liking ratings (a small range) were more related to Difficulty ratings (a large range) than Movement ratings (a moderate range).