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The experiments described in this research article were designed to test the effect of rare variants into genomic prediction in dairy cattle. Common polymorphisms are able to explain only a small proportion of the underlying genetic variation of complex phenotypes. Variants representing functional mutations with large effects on complex phenotypes are expected to be rare due to natural (humans) or artificial (livestock) selection pressure. Therefore, it is important to check whether the use of rare variants could increase the accuracy of ranking of animals by providing the tool for more precise differentiation among the bulls with high additive genetic merit. The goal of our study was to verify whether including rare variants in a genomic selection model allows for a more accurate description of the additive genetic background of traits under selection in dairy cattle. We used the linear mixed model for comparison SNP estimates for Holstein-Friesian cattle of the two data sets – a set containing only single nucleotide polymorphisms defined by minor allele frequency ≥ 0.01, which is routinely used in the Polish genomic evaluation system (46,216 SNPs), and a set containing SNPs selected based only on the call rate (54,378 SNPs). Based on the SNP estimates we also calculated DGV and GEBV and compared them between both data sets. In all the analyses we used production, fertility, conformation and udder health traits. We also assessed the time required for the two most computationally demanding components of genomic selection: preparing genotype data, and estimation of SNP effects between those two data sets. The results of our study indicated that the analysis including rare variants resulted in changes in the individual ranking of the top 100 male and female candidates, but had no effect on the outcome of the quality of EBV prediction as expressed by the Interbull validation test.

The thermal pyrolysis of agriculture biomass has been studied in a fixed-bed reactor, where the pyrolysis was conducted at a steady temperature of 800 °C. This work analyses the pyrolysis products of six agricultural wastes: pistachio husks, walnut husks, sunflower hulls, buckwheat husks, corncobs and coconut shells. The conducted research compared examples of large waste biomass streams from different parts of the world as a potential source of renewable energy. Additionally, the kinetics of the reaction with the activation energy were analyzed and calculated for all raw materials in pyrolysis process. Biochars are characterised by higher combustion heat in comparison to the raw material samples. The average value of the heat of combustion increased due to pyrolysis process from 10 MJ/kg, with minimal value of 2.7 MJ/kg (corncob) and maximum of 13.0 MJ/kg for coconut, which is also characterised by the maximal absolute combustion heating value (32.3 MJ/kg). The increase in calorific values varied from 15% to 172% (with 54% reference for wood chips), which indicates that charring is an effective method for increasing the energy concentration. The obtained biochar were compared with wood chips, which are widely used solid fuel of organic origin. The studied biomass-derived fuels are characterised by lower ash contribution than wood. An analogous observation was made for the obtained biochars, whose ash contribution was lower than for the chips in terms of both unit-mass and unit-combustion-heat. The main advantage of this method is the production of solid fuel from biomass, which increases the calorific value and bulk density of biochar in comparison to raw material.

Remote measurement of historic buildings and structures using the technology of terrestrial laser scanning (TLS) is becoming a more and more popular approach for conducting inventory activities, documentation and conservation works. In this paper, TLS was harnessed for analysis of historic brickwork structures from the 19th century. During the research programme, chosen brickwork heritage buildings were scanned. Based on the collected geometric data of the point cloud, it was possible to create an exact model of the scanned objects. The obtained radiometric information of the point cloud allowed us to identify changes in the surface of walls, such as cavities, cracks and previous repairs. Moisture was also identified in some cases. The conducted tests enabled the identification of brickwork in need of urgent repair. It was possible to assess the general technical state of the tested structures. The possibilities and limitations of the TLS diagnostic methodology of analysing the quality of historic brickwork and its future possible applications were indicated.

SARS-CoV-2 infection leads to severe lung damage due to pneumonia and, in more severe cases, leads to acute respiratory distress syndrome, or ARDS. This affects the viability of bronchoalveolar cells. An important role in the pathogenesis of these complications is the hyperactivation of the renin-angiotensin-aldosterone (RAA) pathway and induction of cytokine storm that occurs in an Nlrp3 inflammasome-dependent manner. To shed more light on the susceptibility of lung tissue to SARS-CoV-2 infection, we evaluated murine bronchioalveolar stem cells (BASC), alveolar type II cells (AT2), and 3D-derived organoids expression of mRNA encoding genes involved in virus entry into cells, components of RAA, and genes that comprise elements of the Nlrp3 inflammasome pathway. We noticed that all these genes are expressed by lung alveolar stem cells and organoids-derived from these cells. Interestingly, all these cells express a high level of ACE2 that, on the one hand, serves as an entry receptor for SARS-CoV-2 and, on the other, converts angiotensin II into its physiological antagonist, angiotensin 1–7 (Ang 1–7), which has been reported to have a protective role in lung damage. To shed more light on the role of Ang 1–7 on lung tissue, we exposed lung-derived BASC and AT2 cells to this mediator of RAA and noticed that it increases the proliferation of these cells. Based on this, Ang 1–7 could be employed to alleviate the damage to lung alveolar stem/progenitor cells during SARS-CoV-2 infection.

The routine pruning and cutting of fruit trees provides a considerable amount of biowaste each year. This lignocellulosic biomass, mainly in the form of branches, trunks, rootstocks, and leaves, is a potential high-quality fuel, yet often is treated as waste. The results of a feasibility study on biochar production by pyrolysis of residues from orchard pruning were presented. Three types of biomass waste were selected as raw materials and were obtained from the most common fruit trees in Poland: apple (AP), pear (PR), and plum (PL) tree prunings. Two heating rates and three final pyrolysis temperatures were applied. For the slow (SP) and fast pyrolysis (FP) processes, the heating rates were 15 °C/min and 100 °C/min, respectively. The samples were heated from 25 °C up to 400, 500, and 600 °C. Chemical analyses of the raw materials were conducted, and the pyrolysis product yields were determined. A significant rise of higher heating value (HHV) was observed for the solid pyrolysis products, from approximately 23.45 MJ/kg for raw materials up to approximately 29.52 MJ/kg for pyrolysis products at 400 °C, and 30.53 MJ/kg for pyrolysis products at 600 °C. Higher carbon content was observed for materials obtained by fast pyrolysis conducted at higher temperatures.

COVID-19 infections pose a serious global health concern so it is crucial to identify the biomarkers for the susceptibility to and resistance against this disease that could help in a rapid risk assessment and reliable decisions being made on patients’ treatment and their potential hospitalisation. Several studies investigated the factors associated with severe COVID-19 outcomes that can be either environmental, population based, or genetic. It was demonstrated that the genetics of the host plays an important role in the various immune responses and, therefore, there are different clinical presentations of COVID-19 infection. In this study, we aimed to use variant descriptive statistics from GWAS (Genome-Wide Association Study) and variant genomic annotations to identify metabolic pathways that are associated with a severe COVID-19 infection as well as pathways related to resistance to COVID-19. For this purpose, we applied a custom-designed mixed linear model implemented into custom-written software. Our analysis of more than 12.5 million SNPs did not indicate any pathway that was significant for a severe COVID-19 infection. However, the Allograft rejection pathway (hsa05330) was significant (p = 0.01087) for resistance to the infection. The majority of the 27 SNP marking genes constituting the Allograft rejection pathway were located on chromosome 6 (19 SNPs) and the remainder were mapped to chromosomes 2, 3, 10, 12, 20, and X. This pathway comprises several immune system components crucial for the self versus non-self recognition, but also the components of antiviral immunity. Our study demonstrated that not only single variants are important for resistance to COVID-19, but also the cumulative impact of several SNPs within the same pathway matters.

Luminescence methods for non-contact temperature monitoring have evolved through improvements of hardware and sensor materials. Future advances in this field rely on the development of multimodal sensing capabilities of temperature probes and extend the temperature range across which they operate. The family of Cr-doped oxides appears particularly promising and we review their luminescence characteristics in light of their application in non-contact measurements of temperature over the 5&ndash, 300 K range. Multimodal sensing utilizes the intensity ratio of emission lines, their wavelength shift, and the scintillation decay time constant. We carried out systematic studies of the temperature-induced changes in the luminescence of the Cr3+-doped oxides Al2O3, Ga2O3, Y3Al5O12, and YAlO3. The mechanism responsible for the temperature-dependent luminescence characteristic is discussed in terms of relevant models. It is shown that the thermally-induced processes of particle exchange, governing the dynamics of Cr3+ ion excited state populations, require low activation energy. This then translates into tangible changes of a luminescence parameter with temperature. We compare different schemes of temperature sensing and demonstrate that Ga2O3-Cr is a promising material for non-contact measurements at cryogenic temperatures. A temperature resolution better than &plusmn, 1 K can be achieved by monitoring the luminescence intensity ratio (40&ndash, 140 K) and decay time constant (80&ndash, 300 K range).

Terrestrial laser scanner (TLS) measurements can be used to assess the technical condition of buildings and structures; in particular, high-resolution TLS measurements should be taken in order to detect defects in building walls. This consequently results in the creation of a huge amount of data in a very short time. Despite high-resolution measurements typically being needed in certain areas of interest, e.g., to detect cracks, reducing redundant information on regions of low interest is of fundamental importance in order to enable computationally efficient and effective analysis of the dataset. In this work, data reduction is made by using the Optimum Dataset (OptD) method, which allows to significantly reduce the amount of data while preserving the geometrical information of the region of interest. As a result, more points are retained on areas corresponding to cracks and cavities than on flat and homogeneous surfaces. This approach allows for a thorough analysis of the surface discontinuity in building walls. In this investigation, the TLS dataset was acquired by means of the time-of-flight scanners Riegl VZ-400i and Leica ScanStation C10. The results obtained by reducing the TLS dataset by means of OptD show that this method is a viable solution for data reduction in building and structure diagnostics, thus enabling the implementation of computationally more efficient diagnostic strategies.

In the next decade, due to the desire for significant reduction in the carbon footprint left by the aviation sector and the development of a sustainable alternatives to petroleum, fuel from renewable sources will play an increasing role as a propellant for turbine aircraft engines. Currently, apart from five types of jet fuel containing synthesized hydrocarbons that are certified by the ASTM D7566 standard, there is yet another synthetic blending component that is at the stage of testing and certification. Hydroprocessed esters and fatty acids enable the production of a synthetic component for jet fuel from any form of native fat or oil. Used feedstock affects the final synthetic blending component composition and consequently the properties of the blend for jet fuel and, as a result, the operation of turbine engines. A specialized laboratory test rig with a miniature turbojet engine was used for research, which is an interesting alternative to complex and expensive tests with full scale turbine engines. The results of this study revealed the differences in the parameters of engine performance and emission characteristics between tested fuels with synthetic blending components and neat jet fuel. The synthetic blending component was obtained from two different feedstock. Noticeable changes were obtained for fuel consumption, CO and NOx emissions. With the addition of the hydroprocessed esters and fatty acids (HEFA) component, the fuel consumption and CO emissions decrease. The opposite trend was observed for NOx emission. The tests presented in this article are a continuation of the authors’ research area related to alternative fuels for aviation.

Historic buildings, due to their architectural, cultural, and historical value, are the subject of preservation and conservatory works. Such operations are preceded by an inventory of the object. One of the tools that can be applied for such purposes is Light Detection and Ranging (LiDAR). This technology provides information about the position, reflection, and intensity values of individual points, thus, it allows for the creation of a realistic visualization of the entire scanned object. Due to the fact that LiDAR allows one to 'see' and extract information about the structure of an object without the need for external lighting or daylight, it can be a reliable and very convenient tool for data analysis for improving safety and avoiding disasters. The main goal of this paper is to present an approach of automatic wall defect detection in unlit sites by means of a modified Optimum Dataset (OptD) method. In this study, the results of Terrestrial Laser Scanning (TLS) measurements conducted in two historic buildings in rooms without daylight are presented. One location was in the basement of the ruins of a medieval tower located in Dobre Miasto, Poland, and the second was in the basement of a century-old building located at the University of Warmia and Mazury in Olsztyn, Poland. The measurements were performed by means of a Leica C-10 scanner. The acquired dataset of x, y, z, and intensity was processed by the OptD method. The OptD operates in such a way that within the area of interest where surfaces are imperfect (e.g., due to cracks and cavities), more points are preserved, while at homogeneous surfaces (areas of low interest), more points are removed (redundant information). The OptD algorithm was additionally modified by introducing options to detect and segment defects on a scale from 0 to 3 (0&mdash, harmless, 1&mdash, to the inventory, 2&mdash, requiring repair, 3&mdash, dangerous). The survey results obtained proved the high effectiveness of the modified OptD method in the detection and segmentation of the wall defects. The values of area of changes were calculated. The obtained information about the size of the change can be used to estimate the costs of repair, renovation, and reconstruction.

Terrestrial laser scanning (TLS) is a non-destructive testing method for the technical assessment of existing structures. TLS has been successfully harnessed for monitoring technical surface conditions and morphological characteristics of historical buildings (e.g., the detection of cracks and cavities). TLS measurements with very high resolution should be taken to detect minor defects on the walls of buildings. High-resolution measurements are mostly needed in certain areas of interest, e.g., cracks and cavities. Therefore, reducing redundant information on flat areas without cracks and cavities is very important. In this case, automatic down-sampling of datasets according to the aforementioned criterion is required. This paper presents the use of the Optimum Dataset (OptD) method to optimize TLS dataset. A Leica ScanStation C10 time-of-flight scanner and a Z+F IMAGER 5016 phase-shift scanner were used during the research. The research was conducted on a specially prepared concrete sample and real object, i.e., a brick citadel located on the Kościuszko Mound in Cracow. The reduction of dataset by the OptD method and random method from TLS measurements were compared and discussed. The results prove that the large datasets from TLS diagnostic measurements of buildings and structures can be successfully optimized using the OptD method.