Photon Upconversion Hydrogels for 3D Optogenetics

The ability to optically induce biological responses in 3D has been dwarfed by the physical limitations of visible light penetration to trigger photochemical processes. However, many biological systems are relatively transparent to low-energy light, which does not provide sufficient energy to induce photochemistry in 3D. To overcome this challenge, hydrogels that are capable of converting red or near-IR (NIR) light into blue light within the cell-laden 3D scaffolds are developed. The upconverted light can then excite optically active proteins in cells to trigger a photochemical response. The hydrogels operate by triplet–triplet annihilation upconversion. As proof-of-principle, it is found that the hydrogels trigger an optogenetic response by red/NIR irradiation of HeLa cells that have been engineered to express the blue-light sensitive protein Cry2olig. While it is remarkable to photoinduce the clustering of Cry2olig with blanket NIR irradiation in 3D, it is also demonstrated how the hydrogels trigger clustering within a single cell with great specificity and spatiotemporal control. In principle, these hydrogels may allow for photochemical control of cell function within 3D scaffolds, which can lead to a wealth of fundamental studies and biochemical applications.     

A Comparative Study of Continuous and Periodic Inspection Policies in Deteriorating Production Systems

Continuous or periodic inspection policies are often used to monitor the maintenance of a production process. In this paper, a comparative study of continuous and periodic inspection policies in deteriorating production systems is provided. The overall cost function is comprised of: set-up, inspection, inventory holding, defective and restoration costs. Different models, constant, linear and exponential, of restoration costs are considered. Tradeoffs between the continuous and the periodic inspection policies are analyzed, and ranges of values of system or cost parameters for which the continuous inspection policy is preferred are established.     

COVID-19, Vaccination,and Female Fertility in the Czech Republic

The fast-track process to approve vaccines against COVID-19 has raised questions about their safety, especially in relation to fertility. Over the last 2 years, studies have appeared monitoring female fertility, especially from assisted reproduction centers or in animal experiments. However, studies monitoring healthy populations are still limited. The aim of our study was to monitor the relevant parameters of female fertility (sex and other steroids, LH, FSH, SHBG, Antimüllerian hormone and antral follicle count) before and then 2–4 months after the third dose of vaccination against COVID-19 in a group of 25 healthy fertile woman. In addition, anti-SARS-CoV-2 and anti-SARS-CoV-2S antibodies were determined. We did not observe significant changes in the measured parameters before and after the third dose of vaccination. By comparing levels of the analytes with antibodies indicating a prior COVID-19 infection, we found that women who had experienced the disease had statistically lower levels of estrone, estradiol, SHBG and 5α-dihydroprogesterone, and conversely, higher levels of androgen active dehydroepiandrosterone and dihydrotestosterone. Our results confirm that vaccination does not affect female fertility, and that what fertile women should be worried about is not vaccination, but rather COVID-19 infection itself.     

The kernel recursive least-squares algorithm

We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm performs linear regression in a high-dimensional feature space induced by a Mercer kernel and can therefore be used to recursively construct minimum mean-squared-error solutions to nonlinear least-squares problems that are frequently encountered in signal processing applications. In order to regularize solutions and keep the complexity of the algorithm bounded, we use a sequential sparsification process that admits into the kernel representation a new input sample only if its feature space image cannot be sufficiently well approximated by combining the images of previously admitted samples. This sparsification procedure allows the algorithm to operate online, often in real time. We analyze the behavior of the algorithm, compare its scaling properties to those of support vector machines, and demonstrate its utility in solving two signal processing problems-time-series prediction and channel equalization.