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The study was conducted to determine the influence of newly elaborated synbiotic preparations on piglets’ intestinal microbiota and its metabolism. Pralsetinib order Animals were distributed among six experimental groups, in reference to used feed supplements, namely, synbiotics (A, B, or C) or commercially available probiotics (BioPlus 2B®, Chr. Hansen A/S, Horsholm, Denmark or Cylactin® LBC, DSM Nutritional Products Ltd., Kaiseraugst, Switzerland), or its absence (control group). Until the 29th day of life, piglets were breastfed by sows, whose feed was supplemented, and fecal samples were collected at the 7th and 28th day of piglets’ life. After weaning of the piglets, the research was continued until the 165th day of the pigs’ life. The area of this work included the analysis of the piglets’ dominant fecal microbiota by the plate count method. Moreover, high-performance liquid chromatography analysis (HPLC) was applied to establish variations in the concentrations of organic acids, namely, lactic acid, short-chain fatty acids (SCFAs), and branched-chain fatty acids (BCFAs). It was observed that synbiotics have a more significant beneficial effect on the intestinal microbiota of piglets and their metabolism, and therefore their health, in comparison to commercial probiotics used individually. Moreover, synbiotic preparations prevent the negative impact of weaning on piglets’ microbial population in the gastrointestinal tract, which could reduce the occurrence of diarrhea.In this work, we suggest the new concept of sensing elements-bulk waveguides (BWGs) fabricated by the laser direct writing technique inside porous glass (PG). BWGs in nanoporous materials are promising to be applied in the photonics and sensors industries. Such light-guiding components interrogate the internal conditions of nanoporous materials and are able to detect chemical or physical reactions occurring inside nanopores especially with small molecules, which represent a separate class for sensing technologies. After the writing step, PG plates are impregnated with the indicator-rhodamine 6G-which penetrates through the nanoporous framework to the BWG cladding. The experimental investigation proved the concept by measuring the spectral characteristics of an output signal. We have demonstrated that the BWG is sensitive to ethanol molecules captured by the nanoporous framework. The sensitivity of the peak shift in the fluorescence spectrum to the refractive index of the solution is quantified as 6250 ± 150 nm/RIU.The propagation of light underwater is tied closely to the optical properties of water. In particular, the underwater channel imposes attenuation on the optical signal in the form of scattering, absorption, and turbulence. These attenuation factors can lead to severe spatial and temporal dispersion, which restricts communication to a limited range and bandwidth. In this paper, we propose a statistical model to estimate the probability density function of the temporal dispersion in underwater wireless optical communication (UWOC) based Internet of Underwater Things (IoUTs) using discrete histograms. The underwater optical channel is modeled using Monte Carlo simulations, and the effects of temporal dispersion are presented by measuring the magnitude response of the channel in terms of received power. The temporal response analysis is followed by an extensive performance evaluation in terms of bit error rate (BER). To facilitate in-depth theoretical analysis, we have measured and presented magnitude response and BER of the channel under different field-of-views (FoVs), apertures, and water types. The three main areas under study are (i) BER versus link distance behavior, (ii) temporal response of the channel, and (iii) effect of scattering on photon travel. Our study shows the two main factors that contribute to beam spreading and temporal dispersion are (i) diffusivity of the optical source and (ii) multiple scattering. Furthermore, our results suggest that temporal dispersion caused due to multiple scattering cannot be mitigated completely; however, it can be minimized by optimizing the receiver aperture.Parkinson’s disease (PD) is the second most common neurodegenerative disease, which is clinically and pathologically characterized by motor dysfunction and the loss of dopaminergic neurons in the substantia nigra, respectively. PD treatment with stem cells has long been studied by researchers; however, no adequate treatment strategy has been established. The results of studies so far have suggested that stem cell transplantation can be an effective treatment for PD. However, PD is a progressively deteriorating neurodegenerative disease that requires long-term treatment, and this has been insufficiently studied. Thus, we aimed to investigate the therapeutic potential of human adipose-derived stem cells (hASC) for repeated vein transplantation over long-term in an animal model of PD. In 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD model mice, hASCs were administered on the tail vein six times at two-week intervals. After the last injection of hASCs, motor function significantly improved. The number of dopaminergic neurons present in the nigrostriatal pathway was recovered using hASC transplantation. Moreover, the administration of hASC restored altered dopamine transporter expression and increased neurotrophic factors, such as brain-derived neurotrophic factor (BDNF) and glial cell-derived neurotrophic factor (GDNF), in the striatum. Overall, this study suggests that repeated intravenous transplantation of hASC may exert therapeutic effects on PD by restoring BDNF and GDNF expressions, protecting dopaminergic neurons, and maintaining the nigrostriatal pathway.This publication revises the deteriorated performance of field calibrated low-cost sensor systems after spatial and temporal relocation, which is often reported for air quality monitoring devices that use machine learning models as part of their software to compensate for cross-sensitivities or interferences with environmental parameters. The cause of this relocation problem and its relationship to the chosen algorithm is elucidated using published experimental data in combination with techniques from data science. Thus, the origin is traced back to insufficient sampling of data that is used for calibration followed by the incorporation of bias into models. Biases often stem from non-representative data and are a common problem in machine learning, and more generally in artificial intelligence, and as such a rising concern. Finally, bias is believed to be partly reducible in this specific application by using balanced data sets generated in well-controlled laboratory experiments, although not trivial due to the need for infrastructure and professional competence.