**3. Conclusion**

Various approaches have been developed for competing microbial virulence and resistance. Quorum sensing signals and global regulators play an essential role in controlling the gene expression of virulence factors, and the expression of proteins required for adaptation to environmental and stress condition. Therefore, control of these regulators will stop the microbial pathogenicity. In addition, biofilms act as a defense mechanism against host immunity and antimicrobial therapy. Natural and synthetic compounds have approved activities in eradication of biofilm formation. Besides, phage therapy, which is currently successful in destruction of bacterial pathogens that do not respond to conventional antimicrobials. These methods would open up new perspectives for management the up growing problem of microbial resistance. Further, *in vivo* studies are required for real applications of these trends in eradication of microbial infections.


**3. Parameters with the greatest impact on dose and image quality**

The objective of mammography is to provide the early detection of cancer, and therefore the image quality is a fundamental aspect. The image with a suitable diagnostic-quality has to be acquired with a radiation dose as low as possible. Nowadays, it is widely accepted that mean glandular dose (DG) is the best indicator to estimate the risk associated with breast irradiation in mammography (see definition in SectionV). Both factors, image quality and DG, are depending on breast characteristics (glandularity and thickness), exposure factors (beam quality, exposure time and compression force), detector features and mammography system performance (automatic exposure control) and characteristics (geometry, focal spot size).

#### **3.1 Characteristics of the breast**

Breast composition varies among women due to different proportion of glandular, fibrous and adipose tissue. The composition also changes with the age of the woman such that the proportion of adipose tissue increases with age.

Image Quality Requirements for Digital Mammography in Breast Cancer Screening 121

For both digital and screen-film detection techniques, the energy spectrum of the X-ray beam (including their filtering) is of a great concern when evaluating the performance of the mammographic systems, demanding careful and accurate quality controls in a clinical

Fig. 6. X-ray spectra for 30 kVp operating potential for Mo/Mo (a), Mo/Rh (b), Rh/Rh (c),

The attenuation coefficient of the glandular tissue is similar to that of the tumour tissue which makes difficult the visualization of smaller tumours; low energy X-ray beam is needed to demonstrate the subtle density differences between non-calcified normal and abnormal tissues. X-ray tubes of mammography systems are equipped with special

and W/Rh (d) source/filter assemblies (NCRP 149, 2005).

**3.4 X-ray spectrum**

environment.

Differences among the x-ray attenuation properties of the different breast tissues can be observed at the breast images. Glandular and fibrous tissues are visualised in mammography as radio-opaque whereas the adipose tissues are observed as radio-lucent (dark).

The lesions of interest for diagnosis are microcalcifications, masses, asymmetries and distortions of the breast architecture. Microcalcifications are small (100 µm) and "easily" detected regardless of breast density. The masses tend to have low contrast, making it difficult to detect. Therefore, mammography must have - besides optimal resolution - good contrast, which makes visible those anatomic structures and pathological signs which have very similar densities.

#### **3.2 The compression system**

A proper compression of the breast is fundamental to provide a good quality image. Breast compression brings the structures close to the detector enhancing sharpness, prevents breast movement, reduces the breast thickness penetrated by X-rays and reduces the scattered radiation. All these factors improve the contrast and even reduce the absorbed dose (Karellas, 2004). In addition, the exposure factors in modern mammography systems are automatically selected in base of the compressed breast thickness. Quality control programmes have to include procedures to verify the compression force and the accuracy of the breast thickness determined by the system.

#### **3.3 Automatic exposure control**

In mammography, the automatic exposure control (AEC) (also known as "photo timer") cuts off the exposure when arrives to the AEC radiation sensor, which lies below the antiscatter grid and the image receptor, the necessary dose resulting in optimum optical density or pixel value. The sensor can be placed at several positions (3 in most of the units) between the thorax and the nipple in depending of the breast size.

Flat panel detectors of digital mammography units operate as AEC sensors. In this case, the information can be derived from the whole area of the detector or from predefined regions.

The AEC of most modern mammography units uses the information associated with a preexposure to determine the attenuation of the breast. This information along with the breast thickness automatically detected (compressor) determines all the exposure factors (anode/filter, kV, mAs).

The AEC is committed to provide images with an appropriate optical density, independently from the beam quality and the characteristics of the breast. Thus, there is a guarantee that the information will be registered in the linear region of the characteristic curve of the film.

In digital systems, the main role of the AEC is to assure that signal noise ratio (SNR) and contrast noise ratio (CNR) are adequate throughout the image and that the dose values comply with recommendations.

#### **3.4 X-ray spectrum**

120 Imaging of the Breast – Technical Aspects and Clinical Implication

Differences among the x-ray attenuation properties of the different breast tissues can be observed at the breast images. Glandular and fibrous tissues are visualised in mammography as radio-opaque whereas the adipose tissues are observed as radio-lucent (dark).  Microcalcifications are small (100 µm) and "easily" detected regardless of breast density. The masses tend to have low contrast, making it difficult to detect. Therefore, mammography must have - besides optimal resolution - good contrast, which makes visible those anatomic structures and pathological signs which have

A proper compression of the breast is fundamental to provide a good quality image. Breast compression brings the structures close to the detector enhancing sharpness, prevents breast movement, reduces the breast thickness penetrated by X-rays and reduces the scattered radiation. All these factors improve the contrast and even reduce the absorbed dose (Karellas, 2004). In addition, the exposure factors in modern mammography systems are automatically selected in base of the compressed breast thickness. Quality control programmes have to include procedures to verify the compression force and the accuracy of

In mammography, the automatic exposure control (AEC) (also known as "photo timer") cuts off the exposure when arrives to the AEC radiation sensor, which lies below the antiscatter grid and the image receptor, the necessary dose resulting in optimum optical density or pixel value. The sensor can be placed at several positions (3 in most of the units) between

Flat panel detectors of digital mammography units operate as AEC sensors. In this case, the information can be derived from the whole area of the detector or from predefined

The AEC of most modern mammography units uses the information associated with a preexposure to determine the attenuation of the breast. This information along with the breast thickness automatically detected (compressor) determines all the exposure factors

The AEC is committed to provide images with an appropriate optical density, independently from the beam quality and the characteristics of the breast. Thus, there is a guarantee that the information will be registered in the linear region of the characteristic

In digital systems, the main role of the AEC is to assure that signal noise ratio (SNR) and contrast noise ratio (CNR) are adequate throughout the image and that the dose values

glandular tissues) absorbs a higher amount of radiation than an adipose breast.

very similar densities.

**3.2 The compression system**

**3.3 Automatic exposure control**

regions.

(anode/filter, kV, mAs).

comply with recommendations.

curve of the film.

the breast thickness determined by the system.

the thorax and the nipple in depending of the breast size.

For both digital and screen-film detection techniques, the energy spectrum of the X-ray beam (including their filtering) is of a great concern when evaluating the performance of the mammographic systems, demanding careful and accurate quality controls in a clinical environment.

Fig. 6. X-ray spectra for 30 kVp operating potential for Mo/Mo (a), Mo/Rh (b), Rh/Rh (c), and W/Rh (d) source/filter assemblies (NCRP 149, 2005).

The attenuation coefficient of the glandular tissue is similar to that of the tumour tissue which makes difficult the visualization of smaller tumours; low energy X-ray beam is needed to demonstrate the subtle density differences between non-calcified normal and abnormal tissues. X-ray tubes of mammography systems are equipped with special

Image Quality Requirements for Digital Mammography in Breast Cancer Screening 123

Fig. 7. Modulation transfer function of mammography systems (ICRU, 2009).

(i.e., the sd is related to the square root of the Kerma) (Chevalier, 2010).

The main component of noise in radiographic images is the quantum noise, which is associated with the statistical fluctuations in the photons' fluence on the detector and with the random variations in the absorption. The simplest way to define the noise is through a measurement of the standard deviation (sd) of the number of photons absorbed (N) in a region of the detector. This figure complies with Poisson statistics, and therefore sd = N0.5

The structural noise in the digital detectors emerges mainly from the lack of homogeneity in the sensitivity of the elementary detectors (i.e., from the fixed spatial variation of the image detecting structure), which means that it is also proportional to the dose. Moreover, this noise causes the appearance of a structured background in the image, which is usually removed through the flat field techniques. These techniques include the creation of a corrective mask from a direct and uniform image of the X-ray beams (Chevalier, 2010).

In digital systems, what has to be added to these two types of noise is the electronic one, which emerges from the electronic readout outside the pixels and in the amplification of the signal and which does not depend on the dose. The electronic noise owes essentially to the dark noise in the detectors and decreases if the temperature of the surroundings is lowered or reduced. Therefore, the digital equipment has to operate in temperatures at intervals

The initial operation which usually occurs is a "flat-field", a correction of the uniformity of gain. The non-uniformity of the sensitivity of the detector is corrected through a gain map and is also used to correct all the images acquired. Moreover, if an element of a single (pixel) detector is defective its signal can be replaced with a reasonable combination of adjacent detector signals. This is acceptable if the defective detectors are isolated and only few of them are faulty. Detectors of the CR type presents a lack of uniformity due to the heal effect

**4.2 Noise**

between 20-30 ºC.

that is very depending on the X-ray unit.

**4.3 Uniformity**

anode/filter combinations, such as Mo/Mo or Mo/Rh, operating in the 25-35 kVp range. The spectra of several anode/filter combinations (Fig. 6) show the important proportion of of X-rays characteristics (17.5 and 19.7 keV) from the molybdenum target and the strong suppression of the spectrum at energies >20 keV because of the k-shell absorption edge of the molybdenum filter (Fig. 6a) or else higher than 23 keV because of the Rh filter (Fig. 6b). The characteristics of the new digital detectors make it possible to use other anode/filter combinations such as Rh/Rh (Fig. 6c), W/Rh (Fig. 6d), W/Ag and W/Al which have some advantages for imaging dense or thick breasts. In addition, the breast doses associated with these combinations are lower than those delivered with Mo/Mo or Mo/Rh.

The adequate selection of the spectrum (beam) may reduce the dose values above 20% (Young, 2006; Dance, 2000; Riabi, 2010). The threshold value for breast thickness where the spectrum is changed depends on the AEC calibration which is performed by technical services who install the equipment (which should be done together with those medical staff who use the equipment). The correct selection of the X-ray beam will strongly influence the dose and image quality.


*3.2. RNA-Seq and de Novo Transcriptome Assembly*

To investigate the transcriptome modulation that occurred during exposure to iron nanoparticles, the KG1a and HL60 cells were treated with 50 μg/mL of FeNPs and PBNPs for 72 h, respectively. To obtain reliable global gene expression profiles, RNA-Seq was performed with as many as 12 samples, which consisted of two biological replicates of each treatment. Following the removal of adapters and low-quality reads, total mapped reads, unique mapped reads, and multiple reads were summarized and are presented in Table S2. The statistics of distribution of clean reads in different regions of the genome are shown in Figure S1. After alignment with the reference genome, the statistical results of mRNA peak insert size distribution were shown in Table S3. The correlation analysis of expression levels

among samples and the density map of gene expression level of all samples were further calculated to demonstrate reliability of the experiment and rationality of the sample selection (Figures S2 and S3).


*Article* **Physiological Stress Reactions in Red Deer Induced by Hunting Activities**

**Sofia Vilela 1, António Alves da Silva 1, Rupert Palme 2, Kathreen E. Ruckstuhl 3, José Paulo Sousa 1 and Joana Alves 1,\***

Received: 19 May 2020; Accepted: 5 June 2020; Published: 8 June 2020

**Simple Summary:** Game hunting is an activity largely practiced all over the world. Understanding its consequences on wildlife is crucial for the proper management and development of hunting directives. In this study, we examined stress levels in hunted wild red deer by assessing cortisol levels and its metabolites in multi-temporal biological samples. Overall, we found evidence for an influence on stress levels of red deer caused by repeated exposure to hunting events, which could have important implications on the sustainability and conservation of wild populations. Furthermore, our results highlight the use of hair samples as a useful long-term stress indicator.

**Abstract:** Hunting activity is usually seen as a factor capable of causing an intense stress response in wildlife that may lead to short but also long-term stress. In the Lousã Mountain, Portugal, the population of red deer (*Cervus elaphus*) is the target of intensive seasonal hunting. We collected and measured cortisol (and its metabolites) in three tissues types (blood, feces and hair) from red deer hunted during two hunting seasons to evaluate the stress levels at di fferent time windows. We also assessed the immunological and physical condition of the animals. We predicted that the hunting activity would act as a stressor inducing increased short and long-term stress levels in the population. Results showed an increase in hair cortisol levels during the months of harvesting. Surprisingly, the tendency for plasma cortisol levels was to decrease during the hunting season, which could be interpreted as habituation to hunting activity, or due to the hunting duration. Contrary to our predictions, fecal cortisol metabolites did not show any clear patterns across the months. Overall, our results suggest an influence of hunting activities on the physiological stress in red deer. In addition, hair seems to be useful to measure physiological stress, although more studies are required to fully understand its suitability as an indicator of long-term stress. Methodologically, our approach highlights the importance of simultaneously using di fferent methods to assess short and long-term e ffects in studies on physiological stress reactions.

**Keywords:** *Cervus elaphus*; plasma; feces; hair; glucocorticoids; hunting; stress


*2.3. Surface Thermal Resistances*

#### 2.3.1. Simplified Method

The assumption of the correct value of surface thermal resistances (*Rsi* and *Rse*) on both sides of the tested building enclosure has a noticeable effect on the calculated values of its total thermal resistance (*Rtot*) and thermal transmittance (*U*). The European standard ISO 6946 [22] in its Table 7 suggests that *Rsi* = 0.13 m2K/W and *Rse* = 0.04 m2K/W for an external partition and for horizontal heat flow.

#### 2.3.2. Calculation—Surface Temperatures and Air Movement Velocities Are Known

In real situations, depending on weather conditions, surface resistances *Rsi* and *Rse* can diverge from the above values. In such cases, a more accurate method can be the one described in Appendix A of the ISO 6946 standard [22], according to which one can use the actual wind speed to obtain the value of the convective heat transfer coefficient, *hc*. In the research, it was assumed that the air movement caused by the fans of heating/cooling units in the climate chambers was an equivalent of wind, and because of that, coefficients *hci* and *hce* for both sides of the tested walls will be calculated in that way. The air movement was measured on, respectively, the internal side and the external side at the tested building enclosure by means of anemometers. Then, surface thermal resistance *Rsi* and *Rse* should be determined for both sides for the horizontal heat flux flow, using the formulas:

$$R\_{\rm si} = \frac{1}{h\_i} = \frac{1}{h\_{ci} + h\_{ri}} \tag{10}$$

$$R\_{\mathfrak{s}\varepsilon} = \frac{1}{h\_{\mathfrak{e}}} = \frac{1}{h\_{\mathfrak{e}\varepsilon} + h\_{\mathfrak{e}\varepsilon}}\tag{11}$$

where:

*hci, hce*—the convective heat transfer coefficient on respectively the internal side and the external side, calculated from the formulas:

$$
\hbar\_{ci} = 4 + 4\nu\_i \tag{12}
$$

$$h\_{\varepsilon\varepsilon} = 4 + 4\upsilon\_{\varepsilon} \tag{13}$$

where:

*νi*, *νe*—wind speed adjacent to the surface of respectively the internal and the external side (m/s),

*hri*, *hre*—the radiative heat transfer coefficient on respectively the internal side and the external side, calculated from the formulas:

$$h\_{ri} = \varepsilon h\_{ri,0} = \varepsilon 4\sigma T\_{mi}^3 \tag{14}$$

$$
\hbar\_{rc} = \varepsilon h\_{rc,0} = \varepsilon 4\sigma T\_{mc}^3 \tag{15}
$$

where:

ε—surface emissivity into a half-space, assumed as equal to 0.9 (–), *hri*,0, *hre*,0—a black body radiation heat transfer coefficient (W/m2K), σ—the Stefan–Boltzmann constant equal to 5.67 × 10−8 (W/(m2K4)), *Tmi*, *Tme*—mean thermodynamic temperatures of the surfaces (K).

2.3.3. Calculation—Air Temperatures, Surface Temperatures and Heat Flux Density Are Known

In the case when it is possible to carry out additional surface temperature measurements on both sides of the tested building enclosure and to determine the heat flux, the

surface thermal resistances can be experimentally determined in accordance with the formulas:

$$R\_{si} = \frac{(T\_i - T\_{si})}{q} \tag{16}$$

$$R\_{s\varepsilon} = \frac{(T\_{s\varepsilon} - T\_{\varepsilon})}{q} \tag{17}$$

where:

*Tsi*, *Tse*—the temperature of respectively the internal and external surface of the analyzed element (K),

*Ti*, *Te*—the air temperature on respectively the internal side and the external side (K), *q*—the heat flux flowing through the building enclosure (W/m2).

The above methods were used in the analyses carried out by the authors, but this does not exhaust the subject. There are many methods of estimating radiative heat transfer coefficients, *hr*, and convective heat transfer coefficients, *hc*. A comprehensive analysis of this subject can be found in [63].

#### **3. Materials and Methods**

#### *3.1. Tested Elements*

The research was carried out for a part of the building enclosure, namely the external walls. Three different materials were tested: aerated concrete blocks, ceramic bricks and concrete blocks, as these are some of the most commonly used materials in residential buildings in Poland. The homogeneous tested walls were divided into two groups: uninsulated and insulated. Wall A was made of 240 mm × 240 mm × 590 mm class 600H+H aerated concrete blocks laid in Baumit ThermoMörtel 50 insulating mortar. In the case of the insulated version of wall A, 10 cm thick EPS (expanded polystyrene) boards were glued to it on the cooler side and then covered with fiberglass-reinforced mineral render. Wall B was made of 60 mm × 120 mm × 250 mm solid ceramic bricks laid in cement-lime mortar. In the case of the insulated version of wall B, 10 cm thick EPS boards with the same properties as before were glued to it on the cooler side and then covered with fiberglass-reinforced mineral render. Wall C was made of 120 mm × 250 mm × 380 mm solid concrete blocks laid in cement-lime mortar. In the case of the insulated version of wall C, 10 cm thick EPS boards were also used. After building each wall, a time of 4–6 weeks was used to season the construction and to get rid of construction moisture. During that time, the doors of the climate chambers were left open. The basic material specifications of the walls are presented in Table 1. The given thermal conductivity coefficients are declared values and were taken from producer technical specifications, provided by the construction material warehouse or from tabular data from Polish technical standards [71–73]. These values were not experimentally verified (measured) by the authors.

#### *3.2. Test Setup*

Measurements of the thermal resistance of selected external walls were conducted in a sleeve between two connected climate chambers, as shown in Figure 1. The major specifications of the set are as follows:

**Table 1.** Basic specifications of partitions tested in climate chambers.

\* Taken from the technical data sheet for the material, \*\* provided by construction material warehouse, \*\*\* taken from tabular data from Polish technical standards.

**Figure 1.** Test setup: (**a**) view of two climate chambers connected together, (**b**) locations of tested walls in sleeve connecting two climate chambers.

The tested wall would be placed in a sleeve connecting the two climate chambers, in which conditions simulating the behavior of building enclosures in the heating season were maintained, that is:

All homogenous building enclosures were tested from the instant the temperature settings in the chambers were activated (at the instant of activation the air temperature in the chambers was equal to the temperature in the laboratory room, approximately 20–25 ◦C) until the temperatures in the chambers and the heat fluxes stabilized. Then, the measurements were conducted for at least 7 days. All the enclosures were positioned along the axis of the sleeve connecting the two climate chambers. The erected wall was left for the mortar to set. A schematic diagram of the sensor connection on both sides of the tested building enclosures is shown in Figure 2. Then, sensors were placed on both sides, and as part of the measurements, the following were registered:

**Figure 2.** Schematic showing arrangement of sensors and measuring instrumentation on analyzed wall's warm side (**a**) and on its cold side (**b**). Description of visible elements: 1—black sheet of paper (ambient temperature measurement by IR camera), 2—Hukseflux TRSYS01 system heat flux density sensors, 3—Hukseflux TRSYS01 system sensors measuring building enclosure internal surface temperature, 4—Ahlborn thermal anemometer (measuring internal air temperature and humidity), 5—Ahlborn sensors (thermocouples) measuring building enclosure surface temperature, 6—Ahlborn sensor (0.5 m × 0.5 m) measuring heat flux density, 7—FLIR P65 thermal imaging camera, 8—data loggers, 9—Hukseflux TRSYS01 system sensors for measuring building enclosure external surface temperature, 10—Ahlborn thermal anemometer (measuring external air temperature and humidity), 11—Ahlborn sensors (thermocouples) measuring building enclosure surface temperature.

All the Ahlborn sensors were connected to Ahlborn Almemo data loggers (2690-8, 2890-9 or 5690-2M09). In addition to this, a Hukseflux TRSYS01 system, dedicated to measuring the thermal resistance of building enclosures, with an accuracy of ±3% and the operating range of −30–+70 ◦C, which doubled the surface temperature measurements on both sides of the building enclosure and the density of the heat flux penetrating through the latter, was connected. A photo of one of the measurements carried out is shown in Figure 3.

**Figure 3.** Photo of tested building enclosure (in this case, insulated wall A) with visible arrangement of sensors: (**a**) on warm chamber side, (**b**) on cold chamber side.


**6. Conclusions**

We studied the occurrence of the paradox of enrichment in prey–predator models with Holling types I and II functional and numerical responses. We proved through Theorems 1 and 2 that the paradox of enrichment does not occur with Holling type I in two or three dimensions. However, the paradox of enrichment occurs with Holling type II in two and three dimensions, respectively, as shown through Corollary 1, Theorem 5, and the numerical simulations. The numerical simulations explain the occurrence of the paradox of enrichment in systems (1) and (2) with Holling type II when the carrying capacity of prey increases and a map of changes of the dynamic behaviors is given for stable to periodic, quasi-periodic, or chaos cases. We conclude that the linearity and nonlinearity of functional and numerical responses plays important roles in the occurrence of the enrichment paradox. We introduce a new approach connecting the enrichment paradox phenomenon and persistence and extinction dynamics by deriving the persistence and extinction conditions based on the carrying capacity parameter (*k*). We used different analytical techniques to derive the persistence and extinction conditions. We introduce several theorems and corollaries to present our results. We introduce some biological explanations to support our results.

**Acknowledgments:** This work is funded by the Basic Science Research Unit, Scientific Research Deanship at Majmaah University under the research project No. 31/37. The author is extremely grateful to Majmaah University, Deanship of Scientific Research and Basic Science Research Unit, Majmaah University.

**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.