Background. The development of coronary collaterals is variable among patients with coronary artery disease and remains incompletely understood. We aimed to demonstrate the predictors of poorly developed coronary collateral circulation (CCC) in patients with subclinical hypothyroidism suffered from chronic stable angina.Methods. The study was conducted on 226 patients with subclinical hypothyroidism suffered from chronic stable angina, coronary angiography documented total occlusion at any major coronary artery or coronary artery lumen diameter stenosis >90%. Patients were divided into two groups according to grade of CCC, group A: 138 patients with  and group B: 88 patients with . To classify CCC, we used Rentrop’s classification.Results. Multivariate regression analysis was performed and identified the independent predictors of poor coronary collaterals: N/L ratio , and TSH . The ROC analysis provided a cut-off value of >4.6 for N/L ratio, and >9 µIU/mL for TSH to predict poor coronary collaterals.Conclusion. An elevated level of N/L ratio >4.6 and TSH level >9 µIU/mL were the independent predictors of poorly developed CCC in patients with subclinical hypothyroidism suffered from chronic stable angina. The number of collaterals and the extent of their coverage are associated with improved survival in patients with CAD. Although these anastomoses are often incapable of restoring flow to normal levels, but help in alleviating episodes of myocardial ischemia, enhancing residual myocardial contractility, reducing infarct size, preserving left ventricular function, reducing coronary atherosclerotic disease progression and decreasing mortality3,4. In normal individuals, coronary collaterals present between the large coronary arteries, but it cannot be visualized on coronary angiography because there is no significant volume of blood inside5,6.Coronary artery disease (CAD) is one of the most common causes of morbidity and mortality worldwide. The development of coronary collateral circulation (CCC) is considered a physiological adaption of the myocardium to circumvent ischemia, also serves as a natural bypass for blood supplying myocardium jeopardized by ischemia7. The mechanisms for different individual ability to develop CCC are still unclear. It is well known and accepted that their active functioning is directly related to the occurrence of severe and recurrent ischemia, but other factors may also influence their development, e.g., the presence of diabetes mellitus, hypertension, endothelial dysfunction and levels of inflammatory cells were all suggested as potential determinants of collateral development. The inflammatory process plays an important role at all stages of CAD8. Impaired development of CCC may be associated with increase in acute phase reactants, e.g., C-reactive protein (CRP), and indicates the role of inflammation in this process9. It is also well known that leukocyte activation occurs during an inflammatory reaction. White blood cell subtypes, especially the neutrophil-to-lymphocyte ratio (N/L ratio), can be used as an indicator of systemic inflammation and may be associated with poor CCC development.The development of CCC involves the development of capillaries (angiogenesis) and muscular collateral arteries (arteriogenesis). Arteriogenesis is responsible for the development of epicardial CCC10. Vascular smooth muscle cells are an important target of thyroid hormones, and high levels of TSH may lead to an increase in the risk of adverse cardiovascular events, such as micro vascular endothelial dysfunction, inhibition of vascular smooth muscle cell migration11,12, acceleration of atherosclerosis, and an increase in the risk of CAD13. Endothelial dysfunction is characterized by an imbalance between vasodilator and vasoconstrictor activity and may affect microvascular or epicardial vessels14.Subclinical hypothyroidism is a common disease that is characterized by high level of thyroid-stimulating hormone (TSH) with free thyroxin concentrations within the lower normal reference rangeThe underlying physiological and pathological factors influencing the development of CCC remain unclear. In the present study, our objective was to investigate the most important predictors of poor CCC, as evaluated by coronary angiography in patients with subclinical hypothyroidism suffered from chronic stable angina. We must gain further insight into these factors as CCC is considered one of the compensatory mechanisms for amelioration of ischemia, particularly in patients for whom revascularization is not amenable.The present study was conducted on 226 patients with subclinical hypothyroidism suffered from chronic stable angina. Coronary angiography documented total occlusion at any major coronary artery or coronary artery lumen diameter stenosis >90%. All patients were referred to our cardiovascular department for elective coronary angiography during the period January 2016 to August 2018. Informed consent was obtained from all participants in this research. Every patient included in the study had a code number pointed to his name, address and telephone number and the data was saved in a special file. The study was approved by the local ethical committee, and was in accordance with the principles of the declaration of Helsinki II.Patients were divided into two groups according to grade of CCC. Group A:  and Group B: . In the present study we excluded patients with history of myocardial infarction, patients with severe hepatic or renal impairment (serum creatinine level more than 1.5 mg/dL), patients with severe anemia, patients with decompensated heart failure, patients with concomitant inflammatory and neoplastic diseases, patients with coronary artery bypass grafting, patients with clinical thyroid disorders such as overt hyper or hypothyroidism and thyroiditis and patients who used the following medications .15Baseline clinical, demographic data, medical histories and detailed risk profile were recorded from all patients. Full physical examination was performed on all patients. Blood pressure was measured with a standard mercury sphygmomanometer with an appropriately-sized cuff. Resting standard twelve lead ECG was done for all patients. Transthoracic echocardiography was performed in accordance with the recommendations of the American Society of Echocardiography. Left ventricular ejection fraction (LVEF) was calculated from linear dimensions measurements by M-mode, linear internal dimensions of left ventricle were measured from the parasternal long axis view carefully obtained perpendicular to the left ventricle long axis and measured at the level of mitral valve leaflet tips.16Before coronary angiography, all individuals were subjected to a 12 hour overnight fast, followed by collection of venous blood samples. Laboratory investigations included: complete blood count, hematological parameters such as hemoglobin, platelets, white blood cells and their subtypes. N/L ratio was measured by dividing total number of neutrophils by lymphocytes number. Lipid profile, blood urea, serum creatinine and serum uric acid were also measured. Serum thyroid hormones including free triiodothyronine (FT3), free thyroxine (FT4) and TSH levels were evaluated by electro chemiluminescence immunoassay method (ECLIA) using E2010 (elecsys module) immunoassay analyzers . Subclinical hypothyroidism represents a state with increased values of TSH and normal values of FT3 (2.3–4.2 pg/mL) and FT4 (0.8–1.8 ng/dL). The diagnosis was made based on the results of laboratory findings when the level of TSH reaches values above 4.0 µIU/mL.17Coronary angiography had been performed using femoral or radial approach. Coronary angiograms and also collateral grading were examined by interventional cardiologists who were blinded to the clinical characteristics, laboratory results of the patients and study design. Coronary collaterals were graded according to the Cohen-Rentrop method: grade 0, no filling of any collateral vessels; grade 1, filling of side branches of the artery to be perfused by collateral vessels without visualization of epicardial segment; grade 2, partial filling of the epicardial artery by collateral vessels; and grade 3, complete filling of the epicardial artery by a collateral vessel. Patients with grade 0 to 1 were classified as poor coronary collaterals and patients with grade 2 to 3 were classified as good coronary collaterals.Statistical analysis was performed using SPSS 20 . Quantitative data were expressed as mean ± standard deviation (SD). Qualitative data were expressed as frequency and percentage. Student’s t-test was used to test significance between two groups in quantitative data. Chi-square test of significance was used in order to compare proportions between two qualitative parameters. P value <0.05 was considered statistically significant. Multivariate regression analysis was done to detect the independent predictors for poor coronary collateralization. Receiver Operating Characteristic curve (ROC-curve) analysis was performed for detection of sensitivity, specificity, positive and negative predictive values.The study included 226 subclinical hypothyroidism patients with stable coronary artery disease who presented for elective coronary angiography and documented total occlusion at any major coronary artery or coronary artery lumen diameter stenosis >90%. Patients were divided into two groups according to grade of CCC. Group A were patients with grade 3 or grade 2 coronary collaterals were defined as the group with good collaterals which included 138 patients with a mean age of 57.10 ± SD 9.02 years, 84 males (60.9%). Group B patients had grade 1 or grade 0 coronary collaterals and were defined as the group with poor collaterals. They included 88 patients with a mean age of 55.69 ± SD 9.14 years, 47 males (53.4%). Demographic, clinical characteristics, hematological, biochemical parameters and angiographic results of all patients are summarized in With respect to laboratory results, there was also no statistically significant difference between the groups regarding hemoglobin level, platelet count, random blood sugar, blood urea, serum creatinine, lipid profile, FT3 and FT4. However, there was statistically significant difference between the groups with higher N/L ratio in poor collaterals group compared to good collaterals group, . Also serum uric acid was higher in the poor collaterals group compared to the good collaterals group, .For serum TSH levels there was statistically significant difference between both groups, with higher TSH levels in the poor collaterals group . Among 138 patients with good CCC, 57 patients had Rentrop grade 3 and 81 patients had Rentrop grade 2. Among 88 patients with poor CCC, 51 patients had Rentrop grade 1 and 37 patients had had Rentrop grade 0.Multivariate regression analysis was performed and identified the independent predictors of poor coronary collaterals: N/L ratio OR 0.413 , and TSH OR 2.511 . ROC curve analysis was performed to detect cut-off value, sensitivity and specificity as shown in Coronary collateral circulation plays an important role in obstructive CAD as an adaptive response to myocardial ischemia. In the human heart, the major epicedial coronary arteries communicate with one another by means of anastomotic channels. Normally, these small collateral arteries allow very little blood flow. However, if arterial obstruction induces a pressure gradient across such a channel, then with time, the collateral vessel may dilate and serve as an alternative blood conveying circuit to the ischemic myocardium.18. Rentrop et al.19 demonstrated that the prevalence of angiographically demonstrable CCC increased as the lesion of collateral receiving coronary artery increased beyond 70% diameter, narrowing during elective coronary angioplasty.Endothelium activated by shear stress produces and releases adhesion molecules, accumulated monocytes and macrophages produce a variety of angiogenic growth factors and proteases. These bioactive substances contribute to coronary collateral growth20 found that coronary collaterals may help to protect the myocardium in patients with CAD by limiting myocardial ischemia during coronary occlusion. Fukai et al.21 found that well-developed coronary collaterals may minimize the infarct area and predict the presence of viable myocardium in patients with a history of myocardial infarction. Sabia et al.22 demonstrated that the myocardium may remain viable for a prolonged period in patients with a recent myocardial infarction and an occluded infarct related coronary artery in the presence of collaterals. Billinger et al.23 showed that patients with poor CCC experience higher incidence of acute coronary syndrome than those with good collaterals.There are many studies demonstrating the importance of coronary collaterals and their protective effect. Cohen and RentropThe inflammatory process plays an important role at all stages of coronary atherosclerosis from initiation through progression and also in the thrombotic complications of this disease. The N/L ratio is a combination of two independent markers of inflammation, the ratio between the number of neutrophils and the number of lymphocytes provides a simple method for assessment of the inflammatory status and prognosis in patients with CAD. In our study, there was a significant relationship between elevated levels of N/L ratio and poor coronary collaterals, The ROC analysis provided a cut-off value of >4.6 for N/L ratio to predict poor coronary collaterals with sensitivity 82.9% and specificity 86.9%.24 investigated predictors of poor coronary collaterals in a cohort of 248 patients who had high-grade coronary stenosis or occlusion. They used the Rentrop classification to classify CCC. They reported that patients with poorly developed CCC had significantly higher N/L ratio compared with those with well- developed CCC, . They concluded that an elevated level of N/L ratio is independently associated with a significant impairment in coronary collateralization.In agreement with our results, Akın et al.,11,12, acceleration of atherosclerosis, and an increased risk of CAD. In the present study we noticed a significant relationship between high levels of serum TSH and poor coronary collaterals. The ROC analysis provided a cut-off value of >9 µIU/mL for TSH to predict poor coronary collaterals with sensitivity 81.8% and specificity 83.3%. Our results were similar to the results of Balli et al.,25 who studied the relationship between serum thyroid hormones levels and coronary collateral circulation in patients with stable CAD and found that there was statistically significant difference between good collaterals group and poor collaterals group as regard to the level of serum TSH . Yoneda et al.,12 showed that injecting TSH in conduit arteries resulted in significant impairment of endothelial vasodilatation and peripheral vascular resistance increase, which may contribute to poor coronary collaterals development.High levels of TSH may lead to microvascular endothelial dysfunction, inhibition of vascular smooth muscle cell migration26.The grade of coronary collaterals was directly related to the dose of statin therapy. Patients who were on high-intensity statin therapy showed better coronary collateral grade than patients who were on low- or moderate-intensity statin therapy with (p = 0.009). In agreement with our results Dincer et al, who studied the association between the dosage and duration of statin treatment with coronary collateral development among 400 patients, reported that patients who were on statin therapy for more than 3 months had significantly better collateral development (p = 0.002). Statin therapy had no effect on collateral development in patients having <10 mg atorvastatin-equivalent dose (p = 0.13)This study has the following limitations. First, the number of the patients in this study was relatively small. Also this study is a single-center study, which does not reflect the whole population, so other multicenter studies will help to validate the results. Coronary collateralization progresses over many years and we can’t determine the precise time of fully collateral development. In this study, our patients did not undergo invasive hemodynamic measurements as the gold standard for measuring coronary collateralization is intravascular hemodynamic assessment (usually collateral flow index; either pressure or velocity-derived. Also, the Rentrop classification has its limitations, as small microvascular caliber vessels may not be visualized angiographically. Finally, we did not assess oxidative markers, cytokines, and pro-arteriogenic markers, as they might give more information on the role of endothelial dysfunction, angiogenesis and its relation to coronary collateral development.Presence of good coronary collaterals is important for patients with obstructive CAD, particularly in patients for whom coronary revascularization is unsuitable. Our study showed the most important independent predictors of poor coronary collateral development. An elevated level of N/L ratio >4.6 as a marker of inflammation is independently associated with a significant impairment in CCC. TSH level >9 µIU/mL should be considered as a modifiable risk factor for poor coronary collateral development in patients with subclinical hypothyroidism suffered from chronic stable angina. This study will lead to more evaluation and consideration of the effects of subclinical hypothyroidism on cardiac morbidity and coronary artery disease progression, which in turn will be reflected on guidelines for early screening of thyroid disorders in ischemic heart disease. Also it will open the door for further studies to evaluate the efficacy of therapy and the target TSH level that can decrease or prevent CAD progression.The authors declare no conflicts of interest.


A large number of studies support the notion that synthetic images within a certain intermediate fractal-scaling range possess an intrinsic esthetic value. Interestingly, the fractal-scaling properties that define this intermediate range have also been found to characterize a vast collection of representational, abstract, and graphic art. While some have argued that these statistic properties only serve to maximize the visibility of the artworks’ spatial structure, others argue that they are intrinsically tied to the artworks’ esthetic appeal. In this study, we bring together these two threads of research and make a direct comparison between visual preference for varying fractal-scaling characteristics in both synthetic images and artworks. Across two studies, viewers ranked and rated sets of synthetic noise images and artworks that systematically varied in fractal dimension for liking, pleasantness, complexity, and interestingness. We analyzed both average and individual patterns of preference between the two image classes. Average preference peaked for intermediate fractal dimension values for both categories, but individual patterns of preferences for both high and low values also emerged. Correlational analyses indicated that individual preferences between the two image classes remained moderately consistent and were improved when the fractal dimensions between synthetic images and artworks were more closely matched. Overall, these findings further support the role of fractal-scaling statistics both as a key determinant of an object’s esthetic value and as a valuable predictor of individual differences in esthetic preference. Since the introduction of fractal geometry by α amplitude spectrum. Whereas the slope α of the 1/fα amplitude spectrum quantifies the relationship between the amplitude of luminance variations across low to high spatial frequency variations, fractal dimension does this with structural density and the two are inversely related. Importantly, both these measures of fractal-scaling statistics have been adopted as a means for objectively quantifying one of the central elements in empirical esthetics – visual complexity . Fractal dimension relates the amount of spatial structure occurring at different levels of magnification to the overall structure. More specifically, D increases as the overall structure increases in fine spatial detail . As suchmplexity .Since the past century, complexity has been a central pillar in our understanding of beauty and esthetic appreciation. In his seminal study, Subsequent studies have tackled the difficult task of quantifying and manipulating complexity in many unique ways. Generally speaking, methods of selecting specific parameters  and changing them as a direct manipulation of complexity have been the most typical . For exaMeasures of fractal-scaling characteristics, such as fractal dimension and amplitude spectrum slope (α), afforded researchers additional flexibility and objectivity in the types of stimuli through which they could explore esthetic complexity. Overall, these studies have revealed remarkably robust relationships between fractal-scaling statistics, complexity, and esthetic preferences. For example, we previously found that average esthetic preferences consistently peaked at the intermediate fractal dimension values across a wide array of image types ranging from grayscale, two-tone, edge, and terrain variations . Even thIt is likely that this preference intimately links to the pervasive presence of fractal-scaling statistics in both natural scenes and art. That is, intermediate values of fractal dimension also broadly define the range occupied by most natural scenes . NaturalWhile these findings implicate a close relationship between fractal dimension and esthetic preference in art, investigations so far have predominantly been descriptive. In other words, they have been limited to measuring the statistical properties of art itself rather than the subjective responses of individual observers. Paintings falling in the intermediate D range may not necessarily always be the ones observers find the most attractive. Furthermore, while many artists choose to reflect intermediate fractal-scaling properties in their art, individual differences in artistic choices have also emerged. For example, Jackson Pollock’s work is uniquely characterized by its extreme detail and complexity, subsequently having measured D values far greater than the average abstract artwork , 2011. SThis study investigates whether preference patterns for specific fractal-scaling characteristics remain consistent both inter- and intraindividually across both synthetic fractal images and real-world art. Our interest in the stability of individual differences in preference across different types of images was one of the key factors motivating this research. For this reason, we chose to run this study online in order to be able to get access to a more diverse pool of subjects from relatively different backgrounds and demographics compared to the standard pool of university students.Both advantages and reservations about using an online recruitment platform in perception research have been extensively discussed . HoweverWe predict that, on average, most individuals will prefer synthetic fractal images and paintings with fractal dimensions in the intermediate range. However, congruent with previous studies, we also predict that clusters of individuals with preferences for lower and higher fractal dimensions will also emerge . We predThe study adopted a 10 × 3 within-subjects design. The first variable, image type, consisted of 10 categories: three types of synthetic fractal images  and seven types of paintings . Secondly, all image categories varied on three levels of fractal dimension: low, intermediate, and high. Four outcomes were measured: preference, pleasantness, complexity, and interest.1. Due to the online nature of the experiment, all participants were asked to read an online information statement prior to the study and indicate their consent. They were reimbursed with US$4.50 for taking part in the study. All experimental procedures were approved by the UNSW Human Research Ethics Advisory Panel (UNSW HREAP-C).A total of 184 participants were recruited via the online recruitment platform Amazon Mechanical TurkPrior to analysis, data attained from the online experiment were manually filtered for quality and proportion of completion. Specifically, data from subjects who clearly failed to complete the task, neglected instructions, or showed an explicit indifference to task requirements were excluded from the final analysis. As a result, 13 subjects were excluded and of the remaining 171 subjects: 29.2% were female and the mean age was 33.15 years. A total of 65% indicated their country of residence as the United States, 24% as India, and 7% as the United Kingdom.Figure 1. The top to bottom rows depict grayscale, thresholded, and edges image types, respectively. The columns depict low (left), intermediate (middle), and high (right) fractal dimension variants of the three different image types.The examples of three different types of synthetic fractal images used in this study are depicted in α falloff, given by amplitude spectrum slopes (α) of 0.5, 1.2, and 2.3. The mean brightness and the RMS contrast of grayscale images were controlled at 126 and 0.30, respectively. Following these specifications, four different seeds of grayscale images were created, resulting in a total of 12 grayscale fractal images . These images were then converted into their thresholded (black and white) counterparts by binarizing the grayscale image at the mean luminance level, such that pixels below mean luminance were assigned as black and those above as white. Edge-only images were created by extracting the edges from the thresholded black and white images  selected from a Gaussian distribution. A Fourier transform was then performed to create amplitude frequency spectra with three different levels of 1/fages see . AltogetTable 1). These values are in good agreement with the empirically derived conversion values reported previously . Average fractal dimension and luminance values for each of the painting categories are shown in Table 1. An analysis of variance (ANOVA) was performed to ensure that the average D values of each fractal dimension group differed significantly .Separately for each category, paintings were ordered from high to low in both D and α. We partitioned paintings with D > 1.65 into the “High D” group, paintings with 1.3 < D < 1.50 into the “Intermediate D” group, and paintings with D < 1.20 into the “Low D” group. In total, 123 paintings were used for the study , resulting in 45 images per level of D  = 9.451, p < 0.01] and marginally across fractal dimension . However, there were no significant interactions between the two . This meant that painting categories with markedly lower or higher than average luminance  shared these characteristics across all levels of fractal dimension. While the differences in average luminance may not be optimal, we aimed to retain as much of paintings’ original appearance as possible. Because luminance did not significantly differ across fractal dimension within individual painting categories, we regard this as an acceptable compromise to maintain the original values of the artworks.Average luminance was found to vary significantly across painting category . This preference was consistent within each image category  and did not differ significantly between them . However, a marginally significant interaction  indicated that the peak in preference for intermediate D was slightly lower, and preference for low D higher, in synthetic edge images compared to the other two variations.For synthetic images, we found a significantly greater average preference for images with intermediate D (46% of choices) compared to low (29.4%) and high (24.6%) D . Analysis also revealed a significant interaction between fractal dimension and painting category . Intermediate D images were preferred in abstract, building, mountain, river, and seascape paintings but not in flower and forest paintings, in which low D images were more highly preferred.For paintings, intermediate D paintings accounted for 37.8% of choices and this was significantly higher compared to low (30.5%) and high (31.7%) D images  between synthetic fractal and painting cluster membership.For the synthetic fractal images, K-means clustering revealed three distinct patterns of preference: (1) low D preference (30.41%), (2) intermediate D preference (30.99%), and (3) high D preference (38.60%). For the paintings, these preference patterns could also be distinguished: (1) low/intermediate D preferences (27.49%), (2) intermediate D preference (47.95%), and (3) high D preference (24.56%). However, in paintings, the clusters defined by low and intermediate preference patterns were less distinct in comparison to the synthetic fractal images. We conducted a correlational analysis to determine whether an individual’s membership to a particular cluster for synthetic stimuli was predictive of their painting cluster membership. We found a significant correlation of n = 14) or low (n = 16) complexity paintings. Another 57 of participants switched from preferring either high (n = 36) or low (n = 21) synthetic images to preferring intermediate paintings. The preference changes in these majority cases were all toward adjacent image categories. In contrast, only 16 participants changed preferences from one extreme to the other: low D synthetic to high D painting preference (n = 7) and vice versa (n = 9).As an additional metric of internal consistency in preference between synthetic fractal and art images, we also calculated what proportion of subjects remained in the same cluster from synthetic to painting preference. We found that 68 of the 171 participants (39.8%) were consistent in their cluster membership and exhibited the same preference pattern across both stimulus types. For the remaining 103 participants, we examined the nature of the preference change between synthetic and art images. In sum, when the change happened, it almost always involved switching to an adjacent preference category. For example, 30 participants switched from liking intermediate synthetic images to preferring either high  = 93.380, p < 0.01]. The main effect of image type  was not significant . Averaged across image category, intermediate D images were rated as both more pleasant  and more interesting  compared to low D and high D images. Higher D images were also consistently rated as more complex than both low and intermediate D . The interaction effect  revealed that the relationship between ratings and fractal dimension varied depending on image category. Intermediate D edge images were rated as more pleasant than intermediate D grayscale images. High D edge images were also rated as more complex compared to high D grayscale and thresholded images.Average ratings of pleasantness, interestingness, and complexity are depicted in F = 37.765, p < 0.01] and fractal dimension . Both abstract and sea paintings were given significantly lower average pleasantness and interest ratings compared to all other painting categories (p < 0.01), with the former also being rated even lower than the latter (p < 0.01). Similarly, abstract and sea paintings were rated as less complex compared to most other painting categories, and, in addition, the building paintings were rated as more complex compared to all other categories (p < 0.01).For paintings, multivariate analyses indicated significant main effects of image category . Though the nature of these interactions is too numerous to fully outline, we can summarize that this interaction seems driven by both abstract and sea paintings being rated low in pleasantness, but this decreased rating was particularly large when the images had a high fractal dimension. On the other hand, flower paintings had a notably greater pleasantness rating compared to other painting categories, but this effect was most pronounced for low D images. Most painting categories had greater complexity ratings for higher D images; however, this increase was particularly steep for abstract images and followed a more curvilinear function for sea paintings.However, an interaction revealed that these outcomes depended on specific painting categories  and a non-significant effect of image category . Intermediate D images made up 38% of choices, which was significantly higher than the proportion of times low D images  and high D images  were chosen. A significant interaction between image category and fractal dimension indicated that preference for high and low fractal dimensions differed significantly for grayscale images compared to edge and thresholded ones . The proportion of times low D grayscale images were chosen was significantly lower than the thresholded and edge images, and the proportion of times high D grayscale images were chosen was significantly higher.For synthetic images, there was a significant effect of fractal dimension  and painting category . Intermediate D paintings were chosen 37.6% of the time, which was significantly more than both low  and high D . As in Experiment 1, preference patterns across the various painting categories also varied slightly. Almost all categories exhibited a greater preference at the intermediate D level. However, low D preferences were the greatest for flower and sea paintings.Similarly for paintings, there were significant effects of fractal dimension  in which thresholded images were rated as more pleasant and interesting compared to both grayscale and edge variations, and more complex compared to grayscale synthetic fractal images. The main effect of fractal dimension was significant  with higher ratings of pleasantness given to low D images. Interestingly, the reverse trend was found for interest ratings, which was the lowest for low D images and increased significantly for intermediate and high D. Finally, high D images were rated as more complex compared to both intermediate (p < 0.01) and low D images (p < 0.01). Results also indicated a significant interaction effect between image category × fractal dimension for pleasantness  and complexity ratings . Low D grayscale images were given lower pleasantness ratings compared to low D thresholded and edge images. The direction of effect between these image types reversed for intermediate and high D images, but not to significant effect. In regard to complexity, low D edge images were rated as less complex than both grayscale and thresholded variations in the same low D group. However, this is reversed in the high D group where edge images were rated as more complex than the grayscale and thresholded variations.For synthetic images, there was a main effect of image type  and fractal dimension . Ratings for paintings were similar to those found in Experiment 1. Both abstract and sea paintings were given the lowest ratings of pleasantness and interest (p < 0.01) compared to all other painting categories, and building paintings were rated as the most complex. Pleasantness ratings differed significantly across fractal dimension . Averaging across painting categories, results showed that intermediate D paintings were rated as more pleasant than both high D (p < 0.01) and low D paintings (p = 0.01). Similarly, intermediate D paintings were considered significantly more interesting than low D (p < 0.01), though not high D (p = 0.270) paintings. Intermediate D paintings were rated as more complex than low D paintings (p < 0.01), and high D paintings were rated as more complex than intermediate (p < 0.01) and low D (p < 0.01) paintings. A significant interaction  revealed similar outcomes for painting ratings to those found in Experiment 1.For paintings, all ratings varied as a function of painting category , complexity , and interest . However, analyses did reveal a significant fractal dimension × experiment × image type interaction for preference , pleasantness, , complexity , and interest . This interaction essentially indicated that the differences between synthetic images on these measures, between Experiments 1 and 2, were greater than they were for paintings. For example, there were no significant differences in painting preference between Experiments 1 and 2 across any values of fractal dimension. However, the average synthetic image preference in Experiment 2 was significantly lower at intermediate D and significantly higher at high D compared to Experiment 1 .Univariate comparisons of individual measures between experiments, averaged over fractal dimension and stimulus type, indicated no significant differences in ratings of pleasantness [Congruent with our prediction, within-subject agreement in preference patterns between synthetic fractal images and paintings was greater when the range of fractal-scaling variations between them were more closely matched. For example, compared to Experiment 1 in which 39.8% of participants were members of the same preference cluster for synthetic images and paintings, 51.44% were members of the same preference clusters in Experiment 2. Similarly, while 29.2% of subjects shifted between intermediate and high D preferences in Experiment 1, Experiment 2 found fewer subjects (16.8%) shifted between these two preference patterns.r = 0.384 in Experiment 1, r = 0.366 in Experiment 2), the correlation between preference cluster membership was markedly higher in Experiment 2 .Across both experiments, average preference for paintings and synthetic images peaked for intermediate D. These results were congruent with the well-established inverted U-curve preference–complexity relationship first proposed by This was likely because more subjects remained in the same preference cluster across stimulus type in Experiment 2 (51.40%), compared to Experiment 1 (39.77%). In both studies, it was found that preference patterns between synthetic and art images rarely changed from one extreme to another. In other words, subjects who preferred low D synthetic images less likely preferred high D paintings, and generally chose paintings that were congruent with a low-intermediate preference cluster (and vice versa). Taken together, these results reaffirm the same patterns of individual differences found in previous studies  and broaFigure 10), we find that the shape of the function defined by the narrower D range in Experiment 2 overlaps almost exactly with the function defined by the wider range in Experiment 1. This further supports the idea that esthetic judgments were exquisitely sensitive to small variations regarding the underlying fractal-scaling properties of our stimuli . Initially, these outcomes appear to dispute the patterns found in Experiment 1; however, this is not the case. When we map these average ratings as a function of their actual numerical D values along the same axis  due to values in the low D category being higher than the average for low D paintings. This resulted in flowers having the highest average fractal dimension of all paintings. Subsequently, the greater preference for low D images created by this reduced range in D would fit in line with our findings comparing the changes in range between Experiments 1 and 2. In the end, it was not surprising that low D flower paintings, with the average D value of 1.290, were the most preferred as this value falls well within the typical “highly pleasant” D range observed in previous preference studies , 2016. SThese general discrepancies highlight the intrinsic problem to using stimuli like artworks in empirical studies. While fractal dimension offers a highly efficient and objective means of measuring structural complexity, it does not account for all aspects of variation in an image. While the shorter fractal dimension range may explain some of the unexpected outcomes in the average preference for flower paintings, it is certainly not the whole story. Future research into the specific effects of image properties such as luminance and its interaction with fractal dimension is strongly encouraged to provide clarity for our current findings.We are also aware of the possible concerns regarding the online nature of our study, specifically of how this limits our control over subjects’ viewing conditions. Our previous studies have been conducted in controlled laboratory settings where we found the robust effect of fractal-scaling statistics on esthetic preference , 2016. AAnother possible factor that could have influenced our results is that some of the images might have been perceived to be of poorer photographic quality and thus rated low. For example, lowering the quality of photographs of natural scenes has been previously shown to decreased how much they were preferred . Indeed,However, while image blur certainly seems to contribute to the observed low visual preference of grayscale images with low fractal-scaling properties, our synthetic edge and thresholded variants do not have compromised image quality. Yet thresholded and edge images with low fractal-scaling characteristics are still associated with lower visual preference compared to their counterparts with intermediate fractal-scaling characteristics. One can argue that with these images fractal scaling plays a more direct role in influencing perceived complexity and esthetic preference. Nevertheless, it would seem valuable to include a measure of perceived image quality in future studies of esthetic preference.To conclude, this study advances the growing body of research implicating the integral role of fractal-scaling properties in empirical esthetics. Previously, studies elucidating the relationship between fractal scaling and esthetics in art have predominantly remained descriptive – highlighting the remarkably close overlap in statistical properties between artworks and natural scenes . Our stuHuman participants were presented with an information statement prior to beginning the study outlining what the experiment involved and their rights to privacy and confidentiality. A debrief statement containing the aims and hypotheses of the study, the contact information of the investigators and relevant mental health helplines were included at the end of the study. Experimental procedures were approved by the UNSW Human Research Ethics Advisory Panel (HREAP-C).BS and CV designed the study, analyzed, interpreted the data, and wrote the manuscript. CV ran the study, and collected and analyzed the data.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Traditional methods for developing polymorphic microsatellite loci without reference sequences are time-consuming and labor-intensive, and the polymorphisms of simple sequence repeat (SSR) loci developed from expressed sequence tag (EST) databases are generally poor. To address this issue, in this study, we developed a new software (PSSRdt) and established an effective method for directly obtaining polymorphism details of SSR loci by analyzing diverse transcriptome data. The new method includes three steps, raw data processing, PSSRdt application, and loci extraction and verification. To test the practicality of the method, we successfully obtained 1940 potential polymorphic SSRs from the transcript dataset combined with 44 pea aphid transcriptomes. Fifty-two SSR loci obtained by the new method were selected for validating the polymorphic characteristics by genotyping in pea aphid individuals. The results showed that over 92% of SSR loci were polymorphic and 73.1% of loci were highly polymorphic. Our new software and method provide an innovative approach to microsatellite development based on RNA-seq data, and open a new path for the rapid mining of numerous loci with polymorphism to add to the body of research on microsatellites. The increasing progress of next generation sequencing (NGS) has promoted the explosion of transcriptome data, providing large-scale essential data for the application of molecular markers ,2,3. MicSander lucioperca based on its RNA-seq data, Han et al. (2016) found that 18% of SSR loci were polymorphic and only one locus (1%) was highly polymorphic [Casuarina equisetifolia transcriptome [Even though SSR loci obtained from whole genome data are generally polymorphic ,18, the ymorphic . Li et acriptome . The indcriptome ,32,33,34criptome , is not criptome ,36,37, nAcyrthosiphon pisum) individuals by genotyping.In the present study, a new method for simplifying polymorphic SSR site screening using RNA-seq data is proposed. In brief, the concise method primarily consists of three stages: raw data processing, software development and implementation, and the extraction and checking of sequences. A new Perl-based open-source software developed in this study is the key component for the task, which efficiently processes the dataset of multiple transcriptomes. Providing a clear experimental range for polymorphic site mining, the new software can obtain numerous unverified polymorphic SSR loci. Additionally, the software can reflect the polymorphism details of most SSR loci, meaning that the SSR loci with high polymorphism can be further selected and many SSRs with few repeats will not be neglected. However, the method is generally unable to be used for species with transcriptomes less than five. We verify the practicability of the method in pea aphid (https://www.ncbi.nlm.nih.gov/sra/). The RNA-seq data was submitted by nine different institutions including the University of Arizona [The RNA-seq of pea aphids were used in this study. A total of 44 pea aphid transcriptomes were downloaded from National Center for Biotechnology Information (NCBI) SRA database . This program is based on Perl and available in the input files in FASTA format, which is compatible with multiple systems including Linux and Windows. The usage of PSSRdt is consistent with the regular Perl scripts and is also listed in the software manual. The program primarily depends on the data primitiveness of transcripts assembled using the de novo assembly method. PSSRdt first seeks out SSRs from the input file. The criteria of SSR detection refer to microsatellite searching program-MISA (available online at http://pgrc.ipk-gatersleben.de/misa/misa.html) (https://bioperl.org/). Users should first ensure the setup success of Bioperl modules, where the common installation methods of the modules are listed in the software manual.To establish a novel method for screening polymorphic SSR loci using multiple transcriptomes, we first developed one software  . Unlike After running PSSRdt, users will obtain two output files containing the details of total detected SSRs and unverified polymorphic ones respectively  Step 2. A novel method was constructed to efficiently excavate potential polymorphic microsatellite loci using multiple transcriptomes, which was divided into three steps . First, de novo assembly. The ‘cat’ command on Linux was used to realize this step. The dataset was then computed by PSSRdt (parameter: 10) as an input file. Sublime Text 3 was performed to check the SSR details generated by PSSRdt and extract 100–300 bp sequences containing SSRs (each flanking sequence ≥ 50 bp).To intuitively present and test the method, the experiments were conducted with pea aphids as samples. The raw reads of pea aphid transcriptomes were filtered to generate clean reads by removing adapter sequences, low-quality reads , reads with unknown bases ‘N’ and < 30 bp reads. All raw reads were assembled into transcripts using Trinity , the shoPea aphid individuals were used for validation of the SSR polymorphism. These individuals are randomly taken from five populations originated from different locations and were fed by dactylethrae with broad bean plants in phytotron. The cultivation conditions were set to temperature 24 ± 1 °C, relative humidity 60% ± 10%, photoperiod L:D = 16 h:8 h.We tested 52 SSR loci with high polymorphisms in the ‘FindStr.result’ file for validation by genotyping. SSR primers were designed by PRIMER3  and the A. pisum sample using the Easy Pure Genomic DNA kit . Five individuals were randomly selected from each of the five populations for DNA extraction. Because of the small body size, the DNA extracted from a single pea aphid sample can only be used for the verification of 15 pairs of primers. Therefore, after the DNA was used up, we took another five individuals from each of the population for the next 15 primers, amounting to 25 pea aphid individuals from each population were used for validation of the 52 SSR loci selected. The DNA extracted was tested by 1.0% agarose gel electrophoresis to estimate the quality. Each 25 μL PCR included 1.5 μL pea aphid genomic DNA (the concentration of the primers 10–30 ng/μL), 12.5 μL of TransGen Biotech Taq MasterMix, 0.5 μL forward primer (10 μM), 2.0 μL reverse primer (10 μM), M13 primer 2.0 ul (10 μM). The PCR amplification conditions for microsatellite loci were as follows: DNA initial denaturation at 94 °C for 2 min; 30 cycles of 94 °C for 20 s, annealing temperature of specific primer for 20 s, 72 °C for 20 s; 8 cycles of 94 °C for 30 s, 53 °C for 45 s , 72 °C for 45 s, and a final step 72 °C for 10 min.We extracted genomic DNA from individual The PCR products of microsatellite DNA were detected by 2% agarose gel electrophoresis. The products containing target bands were sent to Sangon Biotech for microsatellite genotyping detection. The PCR products with fluorescent labels were taken for fluorescence detection using capillary electrophoresis method in an ABI3730XL DNA automated analyzer . The obtained peak electrophoregrams were converted into the fragment length of amplified products using GENEMAPER v4.0 software . Lastly, the number of alleles (Na), polymorphism information content (PIC) and other analyses for evaluating polymorphism of SSRs were conducted by PowerMarker v3.25 .de novo assembly  merged by 44 RNA-seq assembled data contained 1,089,298 transcripts. PSSRdt were executed on Intel i7-4770 1600 MHz with 4 Gb RAM, running on Windows 7. The transcript set in FASTA format was processed in 14 min and produced two documents, which included a total of 16,384 SSR loci  and 1940Primer sequences are showed in With the rapid increase in popularity of NGS, an increasing number of transcriptomes are being uploaded on public databases ,49, signFor the sake of controlling cost and workload, researchers were previously prone to using SSRs with more repeats because of the probability of SSR loci with fewer repeats showing high polymorphism might be unsatisfactory, resulting in a number of SSRs being ignored . The proPSSRdt can rapidly complete microsatellite detection and produce two different files, thereby helping users manage different issues and simplify workload. However, if part of the flanking sequences at the same locus of SSRs is mutated, the microsatellite loci with mutations will not be matched with others. Thus, missing a few potential polymorphic loci is unavoidable. Besides, the principle of the program regarding the identification of SSRs is based on whether the number of tandem repeats above certain thresholds; therefore, it is unable to distinguish between perfect and imperfect SSRs. Fortunately, the minimum repeat time of the imperfect SSRs is very close to or not lower than the thresholds ,53, therA. pisum, and analyzed the backgrounds of RNA-seq data, including the experimental objectives and methods, the attributes of the samples, and the submitted institutions. In fact, there is no need to download and assemble all transcriptomes of research objects when the amount of data is abundant. Many cDNA libraries for RNA-seq have multiple duplications, and researchers can choose part of the raw data to save time. In addition, many factors can generate a large influence on microsatellite alteration, such as long-term pesticide treatment and extreme temperature. Thus, using more representative data that samples through different types of treatments can improve the possibility of polymorphic loci mining.Although, transcriptome assembly is a complex task and has certain requirements for server hardware. In this study, we downloaded 44 pea aphid transcriptomes submitted in SRA database of Many new polymorphic SSR loci were obtained in pea aphids and the subsequent experiment verified the efficiency of the method. This approach can be used for more species with sufficient transcriptome data. Numerous new SSR loci with polymorphisms in various species will be found and some research concerning microsatellites may be extended with the data support, for instance, the distribution rules and structural features of SSRs with polymorphisms in functional regions of genes, the influences of external environment on SSR mutations, and characteristics of SSR alleles among different species ,55,56. MA. pisum. This concise method includes three stages: raw data processing, program development and application, and loci extraction and verification. The method provides a clear range for polymorphic loci mining and the experiment success rate was high compared with the traditional methods using RNA-seq data. PSSRdt was especially designed for SSR detection, which was better than the indel analysis software for SSR studies. The novel method provides a new path for rapidly screening numerous polymorphic SSR loci and abundant data for further studies of SSRs.In this study, a novel software and method were presented to efficiently excavate polymorphic SSR loci from RNA-seq data and tested on


Feed consumption contributes a large percentage for total production costs in the poultry industry. Detecting genes associated with feeding traits will be of benefit to improve our understanding of the molecular determinants for feed efficiency. The objective of this study was to identify candidate genes associated with feed conversion ratio (FCR) via genome-wide association study (GWAS) using sequence data imputed from single nucleotide polymorphism (SNP) panel in a Chinese indigenous chicken population.A total of 435 Chinese indigenous chickens were phenotyped for FCR and were genotyped using a 600K SNP genotyping array. Twenty-four birds were selected for sequencing, and the 600K SNP panel data were imputed to whole sequence data with the 24 birds as the reference. The GWAS were performed with GEMMA software.After quality control, 8,626,020 SNPs were used for sequence based GWAS, in which ten significant genomic regions were detected to be associated with FCR. Ten candidate genes, ubiquitin specific peptidase 44, leukotriene A4 hydrolase, ETS transcription factor, R-spondin 2, inhibitor of apoptosis protein 3, sosondowah ankyrin repeat domain family member D, calmodulin regulated spectrin associated protein family member 2, zinc finger and BTB domain containing 41, potassium sodium-activated channel subfamily T member 2, and member of RAS oncogene family were annotated. Several of them were within or near the reported FCR quantitative trait loci, and others were newly reported.Results from this study provide valuable prior information on chicken genomic breeding programs, and potentially improve our understanding of the molecular mechanism for feeding traits. Increasing the productivity of livestock species and minimizing their environmental impact are the major goals in the livestock husbandry . ImproviCompared with previous studies, genome-wide association studies (GWAS) can accurately identify genes involved in economically important traits of cattle , pigs 77, and chThe objective of this study was to identify candidate genes associated with FCR via GWAS in a Chinese indigenous chicken population using sequence data imputed from a SNP panel.The study population was obtained from a Chinese indigenous chicken breed that has been maintained for 25 generations by the Wens Nanfang Poultry Breeding Co. Ltd . We obtained 435 male birds from the 25th generation, which were generated by 30 males and 360 females from the 24th generation and were reared with the same recommended nutritional and environmental conditions. The FCR was calculated according to recorded data during the feeding trial. For more details about this population, please refer to Zhang et al .Gallus gallus 4.0 genome using the Burrows-Wheeler Alignment tool [http://sourceforge.net/projects/picard/files/picard20tools/1.119/). GATK software [All of 435 male chickens from the 25th generation and 15 sires of the 24th generation were selected for genotyping. The genomic DNA from the 450 birds was extracted from blood samples using the NRBC Blood DNA Kit  according to the manufacturer’s instructions, and DNA samples were analyzed for genome DNA concentration and integrity. DNA samples of the appropriate quality were genotyped using 600K Affymetrix Axiom HD chicken genotyping array, which contains 580,961 SNPs distributed on 28 autosomes, two linkage groups (LGE64 and LGE22C19W28_E50C23), and two sex chromosomes. Then, twenty-four key individuals were selected for sequencing from the 435 birds and their 15 sires based on a strategy of maximizing the expected genetic relationship, while maximizing the proportion of unique genomes sequenced in the population. In this process, we used G matrix to calculate the genetic relationship between key individuals and the remaining population, details of the selection of key individuals in our study were described by Ye et al ,12. Finaent tool . Duplicasoftware  was usedsoftware  with theMixed linear model was used for the GWAS, and the analysis was performed with GEMMA software (Zhou and Stephens ). The stY is the vector of phenotypic values for all individuals, b is a vector of fixed effects including the batch effect, which has three levels, a is the substitution effect of the SNP under consideration, u is the random additive genetic effect; X and Z are the design matrices for b and u, respectively; S is the design vector for a; e is the vector of random residuals. In this model, u and e were assumed to have the structure G is the additive genomic relationship matrix which was derived from the sequence data, X is the n×p matrix of genotypes of n individuals’ p markers, Xi as its ith column representing genotypes of ith SNP X̄i, as the sample mean and In as a n×1 vector of 1’s, where software  was usedTo visualize the result from the GWAS, Manhattan plots and quantile-quantile plots were drawn by the qqman package  in R forhttp://www.ensembl.org/index.html) and NCBI (https://www.ncbi.nlm.nih.gov/) annotation of the Gallus gallus 4.0 genome version.Candidate genes which were located within or nearby the significant SNPs (within 0.3 Mb) were identified by Ensembl . Based on the physical location, QTLs close to the significant SNPs were extracted, with a maximum distance of 5 Mb between the QTL and the SNP to be compared.SNPs with p-value <1.0×10Descriptive statistics for trait FCR are presented in −8  for a genome-wide significance level, and to 1.58×10−6  for a genome-wide suggestive significance level.The effective number of independent tests was 631,181 as calculated by the simple method. Hence, the threshold p-value was adjusted to 7.92×10ZBTB41) was found to be associated with FCR, and 9 SNPs reached the suggestive significance level (USP44), leukotriene A4 hydrolase (LTA4H), and ETS transcription factor (ELK3), respectively. One SNP was located on chromosome 2 and 60 Kb distance away from its nearest R-spondin 2 (RSPO2) gene. Two SNPs were located on a narrow region of chromosome 4 (from 15.59 Mb to 16.04 Mb), and the nearest genes were inhibitor of apoptosis protein 3 (IAP3) and sosondowah ankyrin repeat domain family member D (SOWAHD). Another two SNPs were located on the positions of 1.39 Mb and 2.71 Mb of chromosome 8 respectively, with the first SNP locating on the 14th intron of calmodulin regulated spectrin associated protein family member 2 (CAMSAP2) and the other locating on the 4th intron of potassium sodium-activated channel subfamily T member 2 (KCNT2). The remaining 1 SNP was located on the position of 3.20 Mb of chromosome 26 and member of RAS oncogene family (RAP1A) was its nearest gene. In the case of using chip data, only one SNP reach a suggestive significance level, and the SNP was also detected by using imputed whole sequence . The hereditability estimates for FCR were higher than the estimate of Aggrey et al . Based oTo our expectation, the imputed sequence data including more SNPs could capture more genetic variation and detect more signals than a SNP array. With the same statistical model and significance level for both datasets, the SNPs detected from imputed whole sequence data were more than that from the chip data. On one hand, this demonstrated the power of imputed whole sequence data. On another hand, the stronger associations with imputed genotypes were partly due to some imputation artefacts driven by allele frequencies in the imputed loci.SOWAHD is a protein coding gene linked to Iroquois genes, suggesting that regulatory constraints underlie the maintenance of the Iroquois-Sowah syntenic block [ZBTB41 is a protein gene, which may be involved in transcriptional regulation [KCNT2 is a human gene that encodes the KNa protein potassium channel activated by internal raises in sodium or chloride ions [In this study, the ten candidate genes detected for FCR are partly functionally related to feeding traits. Functional study showed that USP44 prevents the premature activation of the anaphase-promoting complex and regulating centrosome separation, positioning, and mitotic spindle geometry. LTA4H is an enzyme that would generate leukotriene B4 (LTB4) . LTB4 isic block . CAMSAP2ic block ,29. ZBTBgulation . KCNT2 iide ions . RAP1A eide ions .QTLs that reported to be associated with chicken FCR are distributed across the whole genome. Part of them were reported several times in previous studies and overlapped with significant SNPs detected in this study, which suggested that the candidate regions detected in this study were reliable. Other SNPs that reached the suggestive significant levels were not located in QTL region may be the new candidate loci or statistically false positive signals.The selection of significant level was seriously considered in this study. Though Bonferroni correction is widely used, it is rigorous in the genotype data with high linkage disequilibrium. Especially for such a high density imputed sequence data, the false negative results are not neglectable . Hence, In this study, we conducted a genome wide association study for FCR using both imputed whole sequence data and SNP array in a Chinese indigenous chicken population. A list of significant SNPs and ten candidate genes were identified, and several of these regions were overlapped with QTLs reported in previous studies. Results from this study provided valuable prior information for chicken genomic breeding program and would potentially improve our understanding of the molecular mechanism for feeding traits.


Bovine papillomavirus (BPV) belongs to the Papillomaviridae family and infects epithelial cells of bovines and closely related animals, causing hyperproliferative lesions known as warts or papillomas, which may regress or progress to form benign or malignant tumors. The virus enters the host cell and interacts with it by altering the regulation of genes that are responsible for controlling the cell cycle, thus triggering lesion formation. It is not yet known which host genes are regulated by viral infection. Therefore, the objective of this study was to make use of next-generation RNA sequencing methods to identify differentially expressed genes associated with BPV infection, which might elucidate possible marker genes that could be used to control the disease.Transcriptome analysis revealed that 1343 genes were differentially regulated (FDR < 0.05). A comparison of gene expression in infected and noninfected cows indicated that 655 genes were significantly upregulated, and 688 genes were significantly downregulated. Most differentially expressed genes were associated with BPV infection pathways, which supports the hypothesis that viral infection was the mechanism associated with this regulation.This is the first study that focused on a large-scale evaluation of gene expression associated with BPV infection, which is important to identify possible metabolic pathways regulated by host genes for lesion development. In addition, novel targets could be identified in order to find ligands that interact with BPV, with the aim of interrupting the infection cycle.The online version of this article (10.1186/s12864-018-5361-y) contains supplementary material, which is available to authorized users. With a herd of 218.23 million head of cattle and annual milk production of 33.62 billion liters , 5.The process of viral infection is coupled to replication of the viral genome. This process depends on different host genetic factors. During infection, host genes are inhibited or activated by pathogen proteins . StudiesIn the present study, we have analyzed the differential expression of genes involved in the host-pathogen relationship, thus contributing to the development of new markers associated with viral infection. Next-generation RNA sequencing technology (RNA-seq) allows detailed profiling of gene expression levels. In this type of sequencing, a large amount of RNA sequence information is generated and requires automated analysis. This approach has been used successfully to study bovine mammary tissue –15 and tHistopathological analysis confirmed the presence of papillomatous lesions in three samples. The presence of hyperkeratosis of the epithelial layer, koilocytosis and acanthosis  mapped to the bovine genome  . Differences in gene expression between the case and control groups were statistically significant when both methods were used, RNA-seq and qPCR . In this study, we have identified 120 genes with a role in regulating the inflammatory process and immune response, for example, the S100A8 and S100A9 genes were upregulated in infected animals along with their partners S100A12, IL8 and IL1B genes. This expression profile was also observed in a previous study [Of the genes that participate in the immune response, 61 were found to be downregulated in infected animals, such as the CCL20 gene, which is responsible for chemotaxis in dendritic cells (DC), effector/memory T-cells, and B-cells and plays an important role on skin and mucosal surfaces under homeostatic and inflammatory conditions, as well as in pathogenesis, including that of cancer and autoimmune diseases.Sperling et al.  proposedGenes involved in cell proliferation and growth were also found to be differentially expressed. These genes include members of the SOX family of transcription factors mainly involved in the regulation of the malignant process through their ability to regulate numerous cancer markers, including those of cell proliferation, apoptosis, survival, invasion, migration, and differentiation . StudiesSeveral mechanisms have been found to cause bcl-2 deregulation and cancer, including activation of chromosomal translocation and upregulation upon viral infection, for instance, upregulated bcl-2 expression has been described in different tumors, including premalignant and malignant lesions of the uterine cervix induced by human papillomaviruses . p53 hasThe E5, E6, E7, and E2 proteins of PVs are known to control the expression of host genes. E6 and E7 control the keratinocyte cell cycle, cell differentiation, and apoptosis programs. Thus, the alterations in gene expression that we observed in this study might be attributed to the functions of BPV oncoproteins. These changes are important for the infection cycle of the virus and persistent infection , 10. TheIn conclusion, we report RNA-Seq analysis of changes in the cattle transcriptome caused by BPV infection. Infection caused massive changes in the expression profile of keratinocyte, immune system, cell proliferation, and apoptosis genes. These changes mainly resulted in an expression profile characteristic of viral infection rather than tumor progression. The large dataset we have developed opens up the possibility of a deeper understanding of the cycle of BPV infection in the host. Therefore, this study serves as the basis for the realization of new studies aimed at understanding the functions of genes that could act in specific pathways during viral infection, increasing our understanding regarding the infectious cycle in the animal. In addition, this study provides information that could be used to find molecular targets for possible drugs that interrupt the cycle of infection of BPV, thus preventing papillomatosis.The animals used in this study were owned by the São José farm in the Central Agreste region of the state of Sergipe, northeastern Brazil. Sample collection was performed by qualified veterinary medical professionals and registered in the class council, according to the procedures established by the corresponding ethics committee. Briefly, six female Girolando dairy breeders in the Central Agreste region of the state of Sergipe, northeastern Brazil, were used. Animal care, management, and usage procedures were approved by the ethics committee on production animals of the Federal University of Sergipe (protocol number: 05/14). Three animals with papillomatosis and three healthy animals were used. We collected samples of skin from the neck region of each animal. We used different animals because recent studies have detected the DNA of HPV in various parts of the host, such as peripheral blood mononuclear cells (PBMC), plasma, urine, semen, spermatozoa, trophoblasts, and the bladder. These studies have shown that PVs are not strictly epitheliotropic –28. In aFormalin-fixed tissue samples were processed by standard paraffin wax techniques. Samples were then cut into 3–5-μm sections and stained by the hematoxylin and eosin method (HE). Slides were evaluated microscopically at increasing magnifications .Each sample was individually macerated for total DNA extraction with the aid of the DNeasy Blood and Tissue kit (Qiagen), following the supplier’s instructions. About 100 ng of DNA were used for polymerase chain reaction (PCR) using the general detection primers FAP59/64 . All ampTotal RNA (~ 15 mg/sample) were purified using the Realipred™ RNA Tissue Miniprep System Kit (Promega) according to the manufacturer’s instructions. The concentration of RNA was measured with Nanodrop ND-2000 instrument , and the quality was assessed with an Agilent 2100 Bioanalyzer . Libraries were generated from 1000 ng of total RNA using the TruSeq stranded mRNA Sample Preparation LS Protocol Kit  as per the manufacturer’s recommendations. Libraries were quantified using the Kapa Library Quantification Kits . The average fragment size was determined using the 2100 Bioanalyzer instrument (Agilent Technologies). Cluster formation on the flow cell was performed using the cBot instrument . All libraries were sequenced on a HiSeq 2500 Version 4 (Illumina Inc.) running HCS software v2.2.6.Bos taurus 8 reference genome using Tophat v2.0.11 [After sequencing, the Galaxy Project  interfac v2.0.11  and then v2.0.11  with gen v2.0.11 . Because v2.0.11 .For the functional annotation of differentially expressed genes, the Gene Ontology (GO) and UNIPROT databases were used to obtain functional information. The genes were searched for functions associated with the progression of papillomatous lesions in cattle, and the metabolic pathways in which the products of these genes participate were mapped with the KEGG database .The expression levels of nine differentially expressed genes  were analyzed using real-time quantitative PCR (qPCR) to validate RNA sequencing results. These genes were chosen because they present different functions important for the virus infection cycle. Two reference genes (RPS15 and GAPDH) were used following a published protocol . Reverset method . A StudePrimer validation was performed by PCR using the Master Mix PCR kit (Promega). For each reaction, 12 μL of PCR Master Mix, 1 μL of each primer, 9 μL of ultrapure water, and 2 μL of DNA was used. The PCR cycling conditions were as follows: initial denaturation at 94 °C for 10 min, followed by 35 cycles of 94 °C for 1 min, 60 °C for 1 min, and 72 °C for 1 min, and a final extension at 72 °C for 10 min. PCR products were visualized using 1.5% agarose gel electrophoresis.Additional file 1:Reads and mapping statistics. (XLSX 11 kb)Additional file 2:RPKM density histogram of transcripts from RNA-seq of BPV infected and non-infected groups. The diagram shows the distribution of the density of expressed genes at different RPKM levels. 2a) distribution of the density of expressed genes in infected animals. 2b) distribution of the density of expressed genes in non-infected animals. (PDF 96 kb)Additional file 3:Differentially expressed genes between infected and healthy cows. (XLSX 80 kb)Additional file 4:Functional annotation of differentially expressed genes. (XLSX 293 kb)Additional file 5:Genes and primer sequences used in qPCR validation of RNA-sequencing data. (XLSX 12 kb)


Aedes mcintoshi. We used a zero-inflated negative binomial regression and multimodel averaging approach with georeferenced Ae. mcintoshi mosquito counts and remotely sensed climate and topographic variables to predict where and when abundances would be high in Kenya and western Somalia. The data supported a positive effect on abundance of minimum wetness index values within 500 m of a sampling site, cumulative precipitation values 0 to 14 days prior to sampling, and elevated land surface temperature values ~3 weeks prior to sampling. The probability of structural zero counts of mosquitoes increased as percentage clay in the soil decreased. Weekly retrospective predictions for unsampled locations across the study area between 1 September and 25 January from 2002 to 2016 predicted high abundances prior to RVFV outbreaks in multiple foci during the 2006–2007 epizootic, except for two districts in Kenya. Additionally, model predictions supported the possibility of high Ae. mcintoshi abundances in Somalia, independent of Kenya. Model-predicted abundances were low during the 2015–2016 period when documented outbreaks did not occur, although several surveillance systems issued warnings. Model predictions prior to the 2018 RVFV outbreak indicated elevated abundances in Wajir County, Kenya, along the border with Somalia, but RVFV activity occurred west of the focus of predicted high Ae. mcintoshi abundances.Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic arbovirus with important livestock and human health, and economic consequences across Africa and the Arabian Peninsula. Climate and vegetation monitoring guide RVFV forecasting models and early warning systems; however, these approaches make monthly predictions and a need exists to predict primary vector abundances at finer temporal scales. In Kenya, an important primary RVFV vector is the mosquito  Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic disease of great economic, livestock, and human health importance in Africa and the Arabian Peninsula , 2. TranAedes mosquitoes, which are considered virus reservoirs . Da. Da39]. zi is the dependent variable (Ae. mcintoshi counts at location and sampling day i), πi represents the probability of structural zeroes, μi is the expected count at location and sampling day i if a structural zero does not occur, and α is the overdispersion parameter (α ≥ 0). πi and μi depend on the covariates, here denoted xi and yi, in this way: μi = exp(β0 +β1xi1 + ⋯ + βkxki) and πi = λi/(1+ λi) for λi = exp(γ0 +γ1yi1 + ⋯ + γlyli) for parameters β and γ. Zero-inflated regression modelling is a standard technique used in ecology and entomology [Our count data included a greater number of zeros than may be expected under a Poisson or negative binomial distribution . Ignorintomology –45; implw) were also calculated; these values sum to 1 across all models and indicate the weight of evidence supporting a model. Likewise, BIC weights (BICw) were computed. Best-performing models were considered the so-called 99% confidence set of models [w values and with a cumulative sum of these weights just exceeding 0.99. Importance of predictors was assessed using a standard sum-of-weights approach [w  values for all models in the 99% confidence set that contained a given predictor was computed, this sum being an index of importance of that predictor. Model-averaged coefficients were computed using a weighted average of the coefficients of the models in the 99% confidence set, with weights the AICw  values. We fit zero-inflated negative binomial models to a random sample of 80% of our data set (n = 127), assigning Ae. mcintoshi abundance values as the response variable and the assembled environmental data as predictor variables (with different subsets of variables used in different models). All models were fitted using the zeroinfl function specifying a negative binomial distribution with a logit link in the pscl package in R 3.13 [We evaluated the importance of relationships between mosquito abundances and our various environmental variables using an Akaike’s Information Criterion (AIC) and a Bayesian Information Criterion (BIC) approach –48. Lowef models , i.e., fapproach : the sumn R 3.13 .We calculated a Pearson’s correlation matrix to assess the potential for multicollinearity between environmental variables, and found high correlation values within, but not between, sets of variables . Only onWe investigated model residuals from the lowest-AIC model for evidence of spatial autocorrelation using a spline correlogram in the ncf package in R , 51, andEstimated parameters from best performing models were used to predict mosquito abundances using a random sample of 20% of the abundance data (n = 31) withheld from the regression models, and a root mean square predictive error was calculated to evaluate accuracy. We used the predict function in the pscl package, with type = “response” to incorporate both the structural zero portion and the sampling portion of the zero-inflated negative binomial model when predicting values , 52.w values as weights. Predicted values were rasterized into a 10 x 10 km grid for visualization purposes. Values greater than 50,000 were considered extrapolative and excluded from visualizations, based on the fact that the greatest number of mosquitoes trapped in a single day across all species in our study locations was 47,694. Values > 50,000 constituted a very small percentage (~0.01%) of predicted values in each raster and occurred only at the fringes and across portions of large water bodies, presumably, where extreme compound topographic wetness index values are present. Data and sample R code are available in For retrospective predictions, unsampled locations were generated across the study region at 10 km intervals, predictions were generated from each model in the 99% confidence set, and a weighted average of predictions was computed using the AICAe. mcintoshi development and population ecology. The 99% confidence set of models with respect to AIC consisted of 11 models (w or BICw). The same variables tended to be included as predictors in the best-supported models according to both AIC and BIC, and model-averaged coefficients were similar  for East Africa based upon the NASA/USDA/DoD RVFV risk prediction model (https://ars.usda.gov/saa/cmave/rvf) indicating elevated risk in September of 2016 [Elevated abundances were predicted for 1 November 2006 in Bay, Bakool, Jubbuda Dhexe, and Gedo Provinces, Somalia . Althoug of 2016 .Ae. mcintoshi abundances at any time in this area. Nguku et al. (2010) [Human RVFV cases were first reported retrospectively on 6 December 2006 in Kilifi District, near the eastern coast of Kenya (. (2010)  found thAe. mcintoshi abundances in Baringo District at any time during the study period, suggesting that other species may have played a role in driving the RVF transmission . Additional sampling is required to capture environmental conditions that produce high numbers of Ae. mcintoshi in Baringo District in order to predict abundances accurately.Illness in Baringo District was estimated to begin in late December, 2006, with the first human case reported on 25 January 2007 , 63, but species . Our datAe. mcintoshi abundances on 15 April in Wajir County on the border with Somalia, approximately 6–7 weeks prior to the RVF outbreak, but the area with highest predicted abundances was ~125 km southeast of the outbreak area . The very low number of Ae. mcintoshi collected from this region was likely due to the sampling effort available in the data set used for model calibration, demonstrating the need for additional and regular sampling to capture conditions that contribute to large emergences of Ae. mcintoshi.Although our model can predict high mosquito abundances, our data set does not appear to capture the breadth of environmental conditions suitable for In addition to this limitation, model predictions do not incorporate mosquito density dependence, which may affect the accuracy of predicted values, particularly at higher values. Additionally, we have lower confidence in predicted values greater than ~7,000 because of the potential for model extrapolation. The choice of 7,000 individuals is a user defined threshold and should be interpreted as such. Predicted values greater than the maximum 4,426 individuals in our data set could be interpreted as extrapolation, but we assume that the possibility exists for a greater number of individuals to be collected in a single trap night. This threshold allows for predicted values to exceed the maximum individuals in our data set, while providing a value at which to indicate visually when and where much higher predicted values occur in model outputs. Input data consist of irregular sampling in space and time. This sampling scheme proved challenging to assess the potential for spatial and temporal autocorrelation that may affect parameter estimates, but we did not find evidence of this phenomenon. We predict mosquito abundances across a relatively broad spatial region, which also increases the potential for extrapolation; although, the combination of environments across the prediction area are not unlike those encountered at the sampling sites, and we assume that the effect of these environments on mosquito abundances are similar to the effect of these environments on the sample data, except in the central and western portion of the study area, where we determined insufficient data for accurate model predictions. We also predict across a broad temporal period. The irregular sampling scheme, across a five-year time period helps mitigate temporal extrapolation across the predictive time period.Predicting abundances from mosquito trap data presents challenges because of the potential for bias between individual traps, trap types, and trap bait . AdditioAe. mcintoshi or other medically important vector species. For improved model performance, we recommend conducting mosquito collections at regular and more frequent temporal intervals and that these collections capture greater spatial and ecological diversity throughout RVFV potential epizootic areas.Our model is the first to use predicted vector abundances at an 8-day temporal resolution as a means of predicting primary RVFV mosquito vector emergence in this region. This approach provides a powerful framework to help inform current RVFV monitoring systems. Additionally, our framework has the potential to reveal new information about primary vector activity during inter-epizootic years that may contribute to a better understanding of RVFV ecology. The potential exists to extend our framework to multiple vectors and disease systems. Our approach also allows exploration of the effects of climate change on S1 Fig(TIF)Click here for additional data file.S2 FigThe horizontal black line located at 0.0 on the y-axis represents complete spatial randomness; the red line plots the residual correlation from the model over distance; and the additional black lines show 95% bootstrap confidence intervals around the observed residual correlation.(TIF)Click here for additional data file.S1 Data Code(ZIP)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 Movie File(MP4)Click here for additional data file.