In the last decades, it has been shown that inflammatory processes play a role in the development of mental and physical problems; although some studies have researched the relationship between inflammation and psychological variables, the inclusion of biochemical variables as possible confounders has been limited. Therefore, the aim of this study was to determine whether psychological variables are associated with the inflammatory marker, highly sensitive CRP (hs-CRP), after controlling for personal and biochemical variables in the Mexican population. The study was performed at the University of Guadalajara facilities, during the second half of 2022. Healthy subjects were invited to participate in the study, which consisted of the measurement of personal, psychological, and biochemical variables. We included 172 participants, 92 (52.9%) of which were women; the median (range) of age of the whole sample was 22 (18–69) years old. In the bivariate analysis, significant positive correlations were observed between hs-CRP and body mass index (BMI) and waist/hip ratio (WHR) in both sexes, together with leukocytes, uric acid, low-density lipoprotein (LDL), triglycerides, and the liver enzymes gamma glutamyl transferase (GGT) and alkaline phosphatase (ALP). In the multivariate regression analysis of the global and men’s samples, anxiety was positively associated with hs-CRP, while depression and positive relations with others were negatively associated with hs-CRP. In conclusion, psychological variables influence inflammation mainly in men, and anxiety was the main contributor; in addition, positive relation with others is a variable that should be further explored as a psychological protector of inflammation in both sexes. It has been shown, during the last decades, that the immune system and inflammatory processes play a role in the development of mental and physical problems, with an important impact in morbi-mortality worldwide, with chronic diseases being the most significant cause of death around the world . In addiFocusing on the relationship between inflammation and psychological variables, we found that many reports have studied the association of genetic, environmental, psychological, and personal factors with inflammatory markers ,5,6,7,8.However, although some studies have included the measurement of biochemical variables as possible confounders in the determination of the association between psychological factors and CRP ,16, no sThe inclusion criteria of the study were: (a) subjects older than 18 years old, (b) without chronic or acute diseases (known by the subject), (c) who were not consuming illegal drugs (including marijuana), (d) who were not consuming hormonal products to increase muscular mass, (e) who were not pregnant, (f) who were not genetically related with another participant of the study , and (g) who preferably did not smoke. These requirements were used in order to diminish confounding factors. The elimination criteria were the absences of the measurements of any variable.The study was conducted from August to November of 2022. A detailed explanation of the procedures was given to all the subjects invited. The invitation was performed via social networks . All the subjects that met the inclusion criteria and accepted to participate were cited (in groups from 11–20 participants) in the facilities of the University of Guadalajara, where they signed an informed consent and filled an electronic questionnaire that included personal and psychological variables; these measurements were performed in a computer room inside the University. After the filling of that questionnaire, the height and weight were obtained by trained students with a Tanita brand scale (model bc-533) and a measuring tape stuck to the wall, in order to calculate the BMI. The hips and waist circumferences were also obtained by trained students, using a measuring tape, in order to calculate the waist/hip ratio (WHR). The blood and urine samples (to perform the laboratory tests) were obtained by qualified personnel  who worked for a certified laboratory; they were attending to one participant at a time. After obtaining the samples, these were transported to a certified biochemical laboratory, where the biochemical analyses were performed by trained personnel.Around 7 students were helping with the questionnaire filling and anthropometric measurements. All the procedure lasted around one hour for each participant. The flowchart of the procedure is exemplified in The sample size was calculated with the formula for correlations described by Díaz-Pértegas , which yThe study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the ethical committee of the Health Sciences University Center, with the registration number 19–21. All the participants signed an informed consent.The personal and sociodemographic variables measured were age, sex, schooling, whether they had a romantic partner, whether they had a job, socioeconomic level, daily free hours, daily hours of physical activity, monthly extra money , and alcohol and smoking consumption frequency . Sleep satisfaction was measured with the first item of the OVIEDO sleep questionnaire, from 1 (very unsatisfied) to 7 (very satisfied); sleep quality was measured with the second item (consisting of 5 items) of the OVIEDO sleep questionnaire, from 1–5  . The quaThe following psychological variables were measured: depression, with the 10-item CES-D scale, from 1–4  ,21; anxiThe laboratory tests measured, most of which included biochemical variables, were: (1) a complete blood count test , whose measurements were performed with impedance and cytometry methods; (2) a complete lipid profile test, including total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides; (3) a liver function test, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma glutamyl transferase (GGT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH) enzymes and direct and indirect bilirubin; (4) blood chemistry, including glucose, creatinine, urea, blood urea nitrogen (BUN) and uric acid; and (5) a general urine test, including leukocyte esterase, proteins, nitrites, erythrocytes and leukocytes, whose measurements were performed with colorimetry and microscopy methods. The high-sensitivity CRP (hs-CRP) was also measured with the ELISA technique. These analyses were performed in a private and certificated laboratory , located in the city of Guadalajara.t-test or Mann–Whitney U test for quantitative ones. In order to compare quantitative variables with hs-CRP, we used the Spearman correlation test, considering the non-parametric distribution of hs-CRP. In addition, multiple linear regression analysis (using the stepwise method) with the hs-CRP as the dependent variable was performed for all the sample and segmented by sex.In order to describe the numerical variables, we used mean and standard deviations when the distribution was parametric and median and ranges for some variables with non-parametric distributions, depending on the variable (on how the variable was better described). In order to detect the distribution of the variable, we used the Kolmogorov–Smirnov test. To compare sociodemographic variables between sexes, we used the chi-squared test for qualitative variables and the Student 2 of the model, as well as the change in R2 observed for each variable included in the model. This change in R2 refers to the contribution to the variability of hs-CRP that each included variable adds to the final model. In addition, we obtained the tolerance values for each variable included in the model. In this case, tolerance values > 0.3 were considered acceptable because it indicates that there is not interrelation (collinearity) between the variables included in the model, assuming in this way, that each contributing variable in the model is independent from each other.For this analysis, we obtained the R and Rp value < 0.05 was considered as significant.Finally, the Cronbach alpha test was obtained for all the psychological instruments  in order to obtain the reliability of each scale applied. All analyses were performed with the software SPSS v.25, and a A total of 172 participants were screened and no one was excluded; of these, 91 (52.91%) were women and the median and range of the age was 22 (18–69) years old. The descriptive data of sociodemographic and psychological variables are described in p = 0.003, as well as the exception of WHR, which was higher in men in comparison with women, with a mean ± SD of 0.84 ± 0.07 vs. 0.77 ± 0.05, p < 0.001.In the comparison of sociodemographic variables between sexes, we observed that both sexes were similar in all variables with the exception of daily free hours, which were higher in men than in women, with a median (range) of 4 (1–14) vs. 4 (0–11), The bivariate significant correlations in the global sample and/or when separated by sex are presented in The multivariate regression analyses for hs-CRP in the global sample and by sex are presented in In the multivariate regression analysis for the global sample, we observed that WHR (b = 0.456) was the most significantly associated variable with hs-CRP; the other positively associated variables in this analysis were the number of monocytes (b = 0.219), alcohol consumption frequency (b = 0.306), uric acid (b = 0.214), anxiety (b = 0.364), emotional regulation (b = 0.231), daily free hours (b = 0.131) and erythrocytes in urine (b = 0.122), while the negatively associated variables found were male sex (b = −0.413), schooling (b = −0.140), positive relations with others (b = −0.172) and depression (b = −0.310). The R of the model was robust: 0.658.In the case of women, we found that the most significantly associated variable for hs-CRP was the number of neutrophils (b = 0.391), followed by uric acid (b = 0.188), BMI (b = 0.284), erythrocytes in urine (b = 0.251), ALP (b = 0.174), alcohol consumption frequency (b = 0.187), and the presence of leukocyte esterase in urine (b = 0.125). In this case, no psychological variables associated were found. The R of the model was 0.793.Finally, in the multivariate regression analysis for hs-CRP in men, we observed that the variables positively associated were WHR (b = 0.628), leukocytes in urine (b = 0.279), emotional regulation (b = 0.273), number of monocytes (b = 0.161), anxiety (b = 0.534) and lymphocytes (b = 0.204), while the negatively associated variables with hs-CRP were sleep satisfaction (b = −0.384), schooling (b = −0.244), depression (b = −0.625), with job (b = −0.255) and triglycerides (b = −0.201). The R of the model was 0.806.The association between psychological and inflammatory markers has been widely studied ,5,6,7,8;In the present study, we mainly focused on a possible causal relationship between psychological variables and inflammation, with the inclusion of many biochemical confounders, which has been performed in very few studies ,16, and p < 0.001), a difference that was not found in BMI between sexes.In this study, we observed that HWR and BMI showed a moderate positive correlation with hs-CRP, with a higher correlation with HWR for men and with BMI for women; these findings coincide with most reports that have measured BMI ,32. HoweThese correlations may explain the positive and significant correlations observed between hs-CRP and serum lipids  and the two liver enzymes , considering that HWR and BMI were positively correlated with these variables; however, elevated levels of liver enzymes have been previously associated with high CPR . The samIn the bivariate correlations, it is also interesting, although expected, to see the positive correlations between hs-CRP and the total number of leukocytes (being higher in men), particularly with the subpopulations monocytes and neutrophils, which showed positive significant correlations in both sexes. As mentioned, this is an expected correlation when considering that inflammatory processes, including infections, involve an increase in leukocytes and their subpopulations. Another interesting finding is the low but significant positive correlation between hs-CRP and uric acid in both sexes, with it being higher in women, results that coincide with a previous report associating hyperuricemia with hs-CRP in the Mexican population . FinallyIn the bivariate analysis, we did not find any significant correlation between psychological variables and hs-CRP in the global sample or segmented by sex; however, in the multivariate regression analysis, after controlling for confounders, we found that anxiety was positively associated with hs-CRP in global and men’s samples. Likewise, emotional regulation showed a positive and significant association with hs-CRP in these samples. On the other hand, depression was negatively associated with hs-CRP in global and men’s samples, while positive relations with others also showed a negative correlation with hs-CRP in the global sample.In the case of anxiety, these findings are similar to those reported by Tyefi et al.  and DuivOn the other hand, it is interesting to note the positive association found between emotional regulation (questions like “I know how to snap out my negative moods”) and hs-CRP in the multivariate regression analysis of the global and men’s samples, which could suggest that people consider themselves able to perform good emotional management of negative emotions because they experience them frequently; in addition, it could be that the repression of negative moods could increase inflammation. However, this association needs to be further explored, considering that no reports that measured this variable were found.Finally, the negative correlation between positive relations with others and hs-CRP in the global sample is a finding not previously reported that could indicate that emotional support provided by others has an anti-inflammatory effect in both sexes. This is an interesting finding considering that this was the only positive psychological variable negatively associated with hs-CRP, even though positive affect  was also studied but did not reach statistical significance in the multivariate regression analysis.In the multivariate regression analysis, we also observed that being a woman was positively associated with hs-CRP, which could be explained by the presence of menstruation, which was associated with hs-CRP (by the measurement of erythrocytes in urine), the more frequent presence of asymptomatic urinary infections found in this study, as well as with the presence of more reported diseases in comparison with men reported in previous studies . In addiConsidering the inclusion of other non-psychological variables in the multivariate regression analysis of the global sample, including the frequency of alcohol consumption, which was positively associated with hs-CRP in the global and women’s samples, is explained by the possible irritant effect that alcohol produces on gastric and/or other organs, leading to inflammation; however, this association was not found in previous studies exploring alcohol consumption and CRP ,16.On the other hand, the negative association of the variable schooling with hs-CRP in the global and men’s samples could be explained by a better socioeconomic position throughout life, which is also related with better familiar conditions that would be associated with less adverse events in life and diminished inflammation; in this regard, we found reports that associate adverse life events with inflammation and the presence of chronic diseases . FinallyIn the case of men, the negative correlations in the multivariate regression analysis between sleep satisfaction and hs-CRP can be explained by considering that sleep promotes a better mood, with less anxiety, and therefore less inflammation; in this case, we did not find studies related exploring this association. Likewise, the negative correlation with having a job could be also related with the better emotional states that an occupation brings, although more studies are needed in order to confirm this explanation. Finally, the negative correlation with triglycerides (opposite to the bivariate correlations) could be a spurious association  or indeed could be a causal association that needs to be corroborated with larger sample sizes.The present results yield much information related with potential clinical applications in preventing programs addressed to diminish inflammation in healthy and/or ill populations; for instance, the inclusion of programs intended to diminish BMI and WHR (mainly through lifestyle modifications), along with the increase in perceived emotional support by improving the positive relations with others, together with psychological trainings for anxiety management, could be useful for this purpose. In addition, the diminishment of inflammation would prevent the appearance of chronic diseases or its progression. Furthermore, the improvement in lifestyle and mental health would also diminish the likelihood of alcohol consumption, with was another related variable with hs-CRP.The main limitation of the study is the small sample size, which, along with the inclusion of several variables, could increase the appearance of spurious correlations that do not correspond to the real associations among the studied variables; this is due to the random error. However, the strengths of the study are in the inclusion of many confounders  and the segmented analysis by sex, which increase the possibility to detect more reliable results and specific for each sex, which should be confirmed with larger sample sizes. The inclusion of a larger sample size would have permitted us to detect potential interactions among variables.In this sense, although the small sample sizes could increase the random error, the adjusting for many confounders could also diminish the confusing bias. Another limitation is the cross-sectional design which does not permit to detect causal relationships between the measured variables. These causal relationships could be only detected with longitudinal studies.In conclusion, we confirmed the moderate positive correlation between hs-CRP and BMI, HWR, uric acid and hepatic enzymes. We additionally detected the positive correlation between leukocytes and their subpopulations (mainly monocytes and neutrophils) with hs-CRP in both sexes. We also detected positive correlations between hs-CRP and the presence of erythrocytes, leukocytes and leukocyte esterase in urine, correlations that have not been previously researched. Finally, we corroborated the association between anxiety symptoms and hs-CRP, in a healthy population, after controlling for many confounders, and showed that some symptoms of depression are negatively correlated with hs-CRP. In addition, the variable “positive relation with others” was the only positive psychological variable negatively associated with hs-CRP in the global sample. All these results lead us to confirm that hs-CRP, a marker of inflammation, is conditioned by many factors, including psychological ones, where anxiety was the most important variable associated with hs-CRP, mainly in men. In addition, positive relations with others is a positive psychological variable that can diminish inflammation in both sexes. However, further longitudinal studies with larger sample sizes that include the same or even more confounding variables will clarify these observations.


Background: The lack of awareness and information about PD may be a barrier to early diagnosis and the delivery of the best care to patients with the condition, given its rising prevalence. In order to determine the variables that are connected to these parameters, this study sought to ascertain the general public's knowledge and awareness of PD in Tabuk City.Methods: In Tabuk City, a cross-sectional demographic survey was carried out. A validated structured questionnaire was used to interview adult respondents by random sampling regarding specific knowledge, attitudes, and awareness related to Parkinson's disease. According to the density of the city, a total of 426 members of the general population were chosen at random and interviewed by skilled interviewers.Results: Age and educational attainment were independently linked to PD awareness. Bachelor's degree subjects and those between the ages of 18 and 45 displayed a greater awareness of PD. Those above 60 and those between the ages of 46 and 60 lacked sufficient knowledge. The majority of participants demonstrated adequate understanding and awareness of PD in their respective occupations.Conclusions: Age, gender, occupation, and level of education were all adequately covered by knowledge and understanding of PD. To increase public knowledge, attitudes, and awareness of PD, however, suitable educational tactics and approaches targeting particular subgroups are required. Parkinson's disease (PD) is an intricate, progressive neurological disorder characterized by tremors, stiffness, and bradykinesia. Some sufferers may develop postural instability as the illness gets worse. Our understanding of PD has progressively expanded. James Parkinson first described it in 1817, and Jean-Martin Charcot later provided further elaboration [The condition has a serious clinical effect on individuals, their caregivers, and their families because of its increasing degenerative consequences on muscular control and mobility. Although the existence of nonmotor symptoms implies neuronal degeneration in nondopaminergic areas, it is thought that PD's motor symptoms are caused by the lack of striatal dopaminergic neurons. As a symptom complex, parkinsonism is a phrase that describes the motor symptoms of PD, such as resting tremor, cogwheeling, and bradykinesia. The primary cause of parkinsonism is PD, although there are a variety of secondary causes as well, including diseases that mirror PD and drug-induced causes .Estimates of the annual incidence of PD vary, with reported rates ranging from five to over 35 new cases per 100,000 individuals. Between the sixth and ninth decades of life, the incidence increases by five to 10 times. The likelihood of PD increases with age. A meta-analysis involving four North American groups found that the prevalence rose from under 1% of men and women aged 45-54 to 4% of men and 2% of women aged 85 and higher. Mortality rates in individuals with PD do not significantly rise during the first ten years following diagnosis compared to unaffected individuals, but they increase thereafter. The prevalence of PD is expected to more than double over the next 20 years as the world's population ages. The societal and financial cost of PD will increase along with it if new therapies, treatments, or prevention measures are not discovered [Dopaminergic neurons are pathologically lost in PD, primarily within the substantia nigra pars compacta (SNpc), a midbrain region that also hosts Lewy bodies-cytoplasmic inclusions containing insoluble alpha-synuclein clumps. However, more severe disease in other regions of the brain, such as non-dopaminergic neurons, distinguishes PD from other forms of the condition. Although non-motor symptoms like smell blindness, functional constipation, major depressive disorder, and REM sleep disorder can manifest years before motor deficiencies, the clinical diagnosis of PD is primarily based on motor features like a slowly progressing resting tremor, cogwheeling, and bradykinesia. Other non-motor symptoms, such as pain, autonomic dysfunction, and cognitive impairment, may also appear in the late stage of the illness ,5.There are very few studies on the understanding of PD among caretakers. These studies revealed that the level of PD knowledge among the caretakers was incredibly low ,7. The mThe PD diagnostic delays and treatment discrepancies that are now unexplained may be greatly influenced by knowledge and attitudes about the disease. An Alzheimer's disease study found that increased help-seeking activity was associated with higher degrees of condition awareness . Few peoLiterature on knowledge and attitude in the assessment of PD in Saudi Arabia is scarce. Hence, the objective of this study is to bridge the knowledge gap and enhance comprehension of this subject.Study design and settingThe cross-sectional study was used to evaluate the level of PD knowledge and awareness among the general population in Tabuk City, KSA. Data were collected at public areas chosen randomly between June 1 and August 30, 2023.Sample size techniqueThe sample size for this study was determined using the following formula:2 * p * (1-p))/E2n = (ZWhere:n = sample sizeZ = Z-score for a 95% confidence level (1.96)p = estimated prevalence of knowledge and attitude towards the effects of alcohol in the population (50%)E = margin of error (5%)Based on the above formula, the minimum sample size required for this study is 384 participants. However, for a 95% confidence level with a 5% margin of error, we aimed to include 426 participants in our study.Eligibility criteriaThis study included all persons (18 years of age and older) living in Tabuk City who were willing to participate in the study. Individuals who are unable to offer informed consent and Individuals who are unable to communicate effectively in Arabic or English were excluded.Data collection tools and techniquesA random sampling technique was employed in this study. Participants were selected randomly from the general public of Tabuk City. Those who met the inclusion criteria were invited to participate in the study. The study variables included sociodemographic variables such as age, gender, education level, and nationality, as well as knowledge-related variables such as familiarity with PD, signs and symptoms of PD, risk factors, and treatment options.The structured interview questionnaire used in this study was developed based on a review of prior research and expert opinions, as detailed in the questionnaire provided in the appendix. The questionnaire was pre-tested on 30 participants of the target population that were not included into study to ensure its clarity, relevance, and validity (piloting study). The result of Cronbach's alpha was 0.76 which was an acceptable level.Data analysis planThe data were analyzed using SPSS version 28 . Descriptive statistics such as frequencies and percentages were used to summarize the sociodemographic and knowledge-related variables. Inferential statistics such as the Chi-square test and logistic regression analysis were used to identify any associations between sociodemographic variables and knowledge level. If the p-value was less than 0.05, all statistical test findings were deemed significant.Ethical considerationThis study obtained ethical approval from the Institutional Review Board (IRB) of King Salman Armed Forces Hospital, as indicated by approval number KSAFH-REC-2023-514. Before participating in the trial, all study participants provided oral informed consent. The questionnaire was collected from respondents and treated with confidentiality while keeping optimal privacy.A total of 426 responses were received. Table As illustrated in Table From Table In Table We can see from Table As illustrated in Table Globally, PD is becoming more prevalent; hence, it is crucial to gauge how well-informed and aware the general public is of the condition. The disease affects older people more frequently, and its prevalence rises with age. Although the precise origin of PD is uncertain, a mix of hereditary and environmental factors is thought to be responsible . We usedThe majority of the participants heard about PD with a percentage of 83.6% (n=356), and 93.66% (n=399) of the participants showed that the common symptom of PD is tremors, our findings were supported by Park et al. , educatiFrom our results, we noticed that the answer about whether PD is a result of poor lifestyle choices was not clear; 31.7% (n=135) agreed, while 36.4% (n=155) disagreed. Furthermore, 71.1% (n=303) of the participants saw that individuals with PD should be supported and helped by the community. The same findings were stated by Soilemezi et al. . Most ofYounger ages, females, single participants, students, and occupations showed a higher level of good knowledge and awareness about PD; our findings were on the same lines as those of Pan et al.  and AlyaLimitations of the studySome critical gaps in knowledge were found which highlighted the need for awareness campaigns about PD. This was due to the limited population that participated in this study, more priorities are needed to increase awareness of PD, develop better infrastructure for research and management of PD, foster healthcare policy discussions for PD, and provide educational opportunities within the Kingdom, overall, we want to underline that the information presented here should only be viewed as preliminary research.The cause of PD is still unknown; however, it is likely to be a mix of biological and environmental determinants, most notably age and gender. Factors related to higher mortality in Parkinsonism may include the severity, the rate of progression, and a poor response to therapy. Some of these characteristics may contribute to the risk of a Parkinson-plus condition being misdiagnosed as idiopathic PD. Acknowledging this problem in the differential diagnosis is critical.In general, our participants showed good knowledge about awareness of PD; however, educational efforts may be necessary to increase people's understanding of PD symptoms, treatments, and public awareness of the disease. According to the results of our study, educational programs should inform the population that PD is typically sporadic rather than familial, that non-motor symptoms are frequent in PD, and that resting tremor is a characteristic but not essential PD diagnosis.


Recently, the number of vehicles on the road, especially in urban centres, has increased dramatically due to the increasing trend of individuals towards urbanisation. As a result, manual detection and recognition of vehicles  become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based deep learning (DL) techniques to identify Jordanian vehicles automatically. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish license plate detection, character recognition, and vehicle logo detection. In contrast, the VGG16  model was re-trained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7035 Jordanian vehicle images, the second dataset consists of 7176 Jordanian license plates, and the third dataset consists of 8271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection. In comparison, the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, the mean average precision (mAP) measure was used to assess the YOLOv3 model of the detection tasks . For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of the license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, and F-measure were 98%, 98%, and 98%, respectively. The performance of the vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively. The automatic license plate and vehicle logo detection and recognition (ALP and VLDR) is an advanced computer vision-based task to identify vehicles without human intervention . MoreoveLPs Variations: There are many variations in LPs in terms of size, orientation, location, font, style, colour, language, etc.VLs Variations: There are many variations in VLs in terms of size, orientation, location, style, colour, shape, etc.Environmental Condition Variations: The vehicles are captured with different environmental conditions such as illumination (lighting), variance (contrast), shadows, etc.Automatic license plate detection and recognition is sometimes known by various other terms such as Automatic number plate recognition or automatic license plate recognition. Moreover, the automatic vehicle logo detection and recognition task is known by another term, automatic vehicle manufacture detection and recognition. Recently, ALP and VLDR has become a vital topic and has attracted the attention of many researchers. However, they are still considered to be challenging tasks due to several reasons ,11 such In addition, many factors have made ALP and VLDR a challenging research topic, such as the dirt and oblique LPs and VLs, the quality of the camera that captures the vehicles, and the distance between the camera and the vehicles.The structure of the LPs is determined by the government of the country to which the LP belongs. In Jordan, the LP consists of a maximum of seven digits where the first number or the first two numbers determine the use of the vehicle (code number(s)) while the remaining numbers determine the vehicle ID. Also, it contains the name of the kingdom in English and Arabic, which are “الاردن” and “JORDAN”, respectively. The numbers and letters of LP appear in black, and the background is in white. Further, the Jordanian LP consists of four colour codes: red, yellow, white, and green. In addition, two types of Jordanian LPs, American and European, differ in shape. American LP takes a square shape with  vehicle .The UK Police Scientific Development Branch first invented the ALPDR tasks in 1976 , and theThis stage is responsible for recognising LPs from the input images as it passes through two other sub-stages to perform this task as follows:The methods that have been implemented to achieve this sub-stage can be divided into four main categories: traditional methods , hybrid image processing methods, machine learning (ML) methods, and deep learning (DL) methods.In traditional license plate detection (LPD) methods, image processing techniques extract the most desirable features from the vehicle image. These features are used to detect LP as a region of interest (ROI), which is divided into two categories: handcraft features (inherent attributes) and general feature descriptors .Handcrafted Features—methods based on these features identify interesting features from the image as key points, called Feature Detector. Several features have been listed under this type, including boundary/edge  and coloGeneral Feature Descriptors—methods based on this type discover interesting features and describe the surrounding pixels called Feature Detector–Descriptor. These methods provide more information about the pixel area surrounding the key points, improving the performance of LPD. For example, the histogram of oriented gradients (HOG) .In other cases, traditional methods have been hybridised to improve LPD accuracy. For example, a hybrid of colour and texture features .Another trend is to use ML techniques along with image processing techniques, where ML techniques are used after extracting traditional features as classifiers. Once the traditional elements are extracted, an ML technique is trained on these features. After that, true LPs are extracted using ML techniques such as support vector machine (SVM) .Recently, DL using the convolutional neural networks (CNNs) model has achieved remarkable results in accomplishing computer vision (CV) tasks, which include object detection and recognition . FurtherFor CR of LPs sub-stage, many methods have been used. These methods are classified into two categories: ML methods and DL methods.ML methods include SVM .DL methods include VGG-net  and SSD This stage is responsible for recognising VLs from the input images as it passes through two other sub-stages to perform this task as follows:The methods that have been implemented to achieve the VLR stage can be divided into three main categories: traditional methods, ML methods, and DL methods.Image processing methods: As mentioned earlier, the methods that fall under this category can be categorised into two types: traditional methods, such as perspective transformation  and geneML methods include the AdaBoost classifier .DL methods include ResNet-50  and SSD The methods that have been implemented to achieve the VLR stage can be divided into four main categories: traditional methods, ML methods, and DL methods.Traditional methods include HOG .ML methods such as SVM .DL methods such as LeNet-5 .To our knowledge, the current works that have been applied for automatic Jordanian LP detection and recognition depend on traditional techniques (image processing techniques) ,35,36. HThe rest of the paper is organised as follows: Over the last few decades, ALP and VLDR have become vital research topics and attracted the attention of many researchers. A lot of techniques can be found in the literature that have been used to achieve the stages of the ALP and VLDR system. In this section, we discuss the approaches that have been proposed for each stage in the ALP and VLDR system.This stage is responsible for recognising LPs from the input images as it passes through two other sub-stages to perform this task: LPD and CR. Several methods have been proposed for these sub-stages as follows:This sub-stage is responsible for detecting the LP’s place from the vehicle. Several methods have been used to achieve this stage successfully.Generally, most LPs take a rectangular shape with a fixed aspect ratio, as image processing techniques can be used to detect all possible edges/borders (rectangles) of a vehicle image. Ref.  applied Ref.  used canEdge-based image processing techniques are usually easy to implement and have low time complexity but require clear, continuous edges to work efficiently. Morphological operations (MOs) such as opening, closing, dilation, erosion, etc., have been used to filter the image by removing undesirable edges. These operations greatly improved the detection process of LPs. In ,42,43,44Recently, DL technique-based CNN models have been introduced. These models achieved remarkable results in most CV tasks. Moreover, unlike ML techniques, DL techniques do not require a feature extraction step, as these features are automatically extracted without introducing hand-coded rules or human domain knowledge.So Ref. , a slidiDespite the fact that regional algorithms are accurate in detecting objects, they are very slow and have high computational costs. For these reasons, proposal-free algorithms have been proposed in the literature, directly detecting objects without generating any proposed regions. SSD has been used by , which iThe last sub-stage of LP recognition is CR, which gives the result by recognising the characters of the LPs that have been segmented and extracted from the previous stage. Several methods have been proposed in the literature for this purpose.Recently, DL-based CNN models have been used in most CV tasks, where they achieved state-of-the-art accuracy. Ref.  modifiedThis stage recognises VLs from the input images as it passes through two other sub-stages to perform this task: VLD and VLR. Several methods have been proposed for these sub-stages as follows:This sub-stage is responsible for detecting the VL’s place from the vehicle. Several methods have been used to achieve this task successfully.Ref.  applied As mentioned, all image processing techniques can achieve acceptable results under specific conditions. So, ML techniques have been used along with image processing techniques for detecting VLs. In , Haar-liDespite the robustness of ML techniques and their ability in object detection, it has a high computational cost due to the feature extraction step required to train an ML technique to apply the classification step based on these features. So, DL techniques have been used for detecting VLs. Ref. , applied90.5%.Even though regional algorithms are accurate in detecting objects, they are very slow and have high computational costs. For these reasons, proposal-free algorithms have been proposed in the literature, directly detecting objects without generating any proposed regions. In , the VGGThis sub-stage is responsible for recognising the detected VL from the previous sub-stage. Several methods have been used to achieve this task successfully.Ref.  applied Despite the robustness of ML techniques and their ability in object detection, it has a high computational cost due to the feature extraction step required to train an ML technique to apply the classification step based on these features. So, DL techniques have been for recognising VLs. Ref.  applied As previously mentioned, vehicle control in autonomous vehicles and CAVs is one of the main applications of ITS. Therefore, it has recently attracted the attention of researchers. Ref.  proposedAccording to the above literature review, and up to our knowledge, the works that have been applied for automatic Jordanian license plate detection and recognition depend on traditional techniques (image processing techniques). Furthermore, VLD and recognition methods do not cover all VLs included in our research. For these reasons and to benefit from the methods mentioned earlier advantages, we develop an AJLP and VLDR system based on DL-based CNNs using transfer learning (TL). The proposed system uses image processing techniques for image enhancement to increase our system’s accuracy in detecting and recognising LPs and VLs as an initial stage. Moreover, the DL-based CNN model will be re-trained using TL for LPs and VLs detection and recognition.In this research, we propose a system for automatically detecting and recognising Jordanian LPs and VLs using DL-based CNN models.YOLOv3 stands for You Only Look Once, version 3, which was developed by Joseph Redmon and Ali Farhadi in 2018 from the University of Washington. As the name suggests, YOLOv3 passes the entire image only once  through the network to make the prediction using detector .YOLOv3 uses a fully deep convolutional neural network as a backbone to extract appropriate features from the input images, namely Darknet-53. This network consists of 53 convolutional layers, each of which is followed by a batch normalisation layer and Leaky ReLU activation . The norIn addition, in Darknet-53, a residual skip connection  was introduced as ResNet to solve the vanishing gradient and degradation problems caused by deep neural networks . The rescut path ,67.Furthermore, Darknet-53 has no pooling layer; instead, a convolutional layer with stride two is used to downsample the feature maps in half. Therefore, Darknet-53 is referred to as a fully convolutional neural network. This helps prevent the loss of low-level features that are excluded by the pooling layer and improves the ability to detect small objects .YOLOv3 uses another 53 layers stacked on the Darknet-53 convolutional layers for the detection task, resulting in 106 fully convolutional architecture layers for yolov3 .Typically, other CNN models use the softmax function to determine the class of objects in the input image. Softmax assumes that classes are mutually exclusive; in other words, softmax assigns an object to one class with a high degree, whereas if an object belongs to one class, it cannot belong to another class . Howeverding box .On the other hand, the input images may also include objects of different sizes. Other CNN models fail to detect small objects. Therefore, YOLOv3 uses a multi-scale feature pyramid network (MSFPN) to process different object sizes in the input image. MSFPN is placed in the YOLOv3 network as heads for feature extraction . MSFPN iAt first, YOLOv3 divides the input image into  objects ,70. The  classes ,72.In addition, three scales were detected at three different places in the YOLOv3 network. In the first 81 layers, the image is downsampled by a stride of size 32, where the first detection is made by the 82 layers using the stride 8 .In YOLOv3, each grid in each scale can predict three bounding boxes using predefined anchors (priors). In total, nine anchor boxes are used, which are arranged in descending order, where the three largest anchors are assigned to the first scale, the next three to the second scale, and the last three to the third scale. Every predicted bounding box consists of width , fully connected layers, and output layer (softmax layer). Fully connected layers and output layers are also called dense layers .In the VGG16 model, convolutional layers have h object ,76.Basically, the VGG16 model starts with two repetitions of two convolutional layers followed by a max pooling layer. These layers are then followed by three repetitions of three convolutional layers, each followed by a max pooling layer. Finally, it consists of two fully connected layers followed by the classifier (softmax layer). The VGG16 model has thirteen convolutional layers, five max pooling layers, and two fully connected layers, followed by a softmax layer, giving us 21 layers. But VGG16 only has 16 layers with learnable weights, which are 13 convolutional layers and dense layers, so it was named VGG16 .By default, the input of the VGG16 model is a fixed size of e 0 to 1 .The proposed approach consists of three phases, as shown in 1.1.1.CNN models perform Image resizing automatically, where the image size of YOLOv3 and VGG16 is 1.2.Improve the brightness of the images using gamma correction.Image Pre-processing:2.2.1.2.1.1.Use data augmentation techniques to increase the dataset size .2.1.2.For the LPR phase:LPD:The input is the Jordanian vehicle images with their corresponding annotation files.Re-training the YOLOv3 model using TL.Character detection and recognition:The input is the Jordanian LP images with their corresponding annotation files.Re-training the YOLOv3 model using TL.Character detection and recognition:For the VLR phase:The input is the Jordanian vehicle images with their corresponding annotation files.Re-training the YOLOv3 model using TL.VLD:The input is the vehicle logo images.Re-training VGG16 model using TL.VLR:Annotating images of the YOLOv3 model using the LabelImg tool.Dataset preparation:Training phase steps for CNN models :3.3.1.The input is the Jordanian vehicle image.3.2.Improving the input image as mentioned in step 1.3.3.The outputs are the LP characters and VL of the input image.Testing phase steps of the proposed system :According to the above Figure, the proposed AJLP and VLDR system passes through several phases as follows:The accuracy of the other stages of the AJLP and VLDR system depends mainly on the quality of the input images. Images are affected by several factors and conditions, the most important of which are environmental conditions such as illumination, variance, weather, etc. Several image processing techniques were used to improve the input images and clarify their details.CNN models are affected by large-size images, which require too much memory and take a lot of computational time to process, train and test (output the result) ,77. TherThe input images’ brightness may affect the proposed system’s performance. Images may be captured in an unrestricted environment, and capture devices may not capture lighting/luminance properly. Moreover, the devices that display the input images may not display the original brightness correctly. Typically, the brightness/luminance of each pixel is a value between 0 and 1, where 0 indicates the darkness/black image, and 1 indicates the brightness/white image. The higher the gamma value, the higher the image brightness (the image tends to be white) and vice versa. The best value of gamma is  results . FurtherTwo methods were used to prepare the datasets for the CNN model training process as follows:Normalises images by Random rotation in range 0 and 45.Random zoom with a range equal to 0.2.Width and height shift with a range equal to 0.2.Random shear with a range equal to 0.2.Average blurring using a Random translation with a range equal to 0.5.Random horizontal flip with a range equal to 0.5.Random crop with a range equal to 0.5.Data augmentation (DA) is a technique used to improve the performance of the model being trained ,79. As mThese transformation methods have been applied to our datasets interchangeably. The first six transformations were applied to our VL dataset as they were all applied together for the first time. After that, these six methods were divided into groups, and each group was applied separately. Further, the last three transformations were applied by the YOLOv3 model automatically during the training process.As is known, object detection models require annotation files for training. Since YOLOv3 is considered an object detection model, text annotation files are needed. Typically, the annotation file contains information about the bounding box (ground truth) surrounding the object. LabelImg is a graphical annotation, free and open-source tool for manually annotating and labelling objects in images. This annotation tool is written in Python and uses Qt for developing the graphical interface. Moreover, it provides several annotation formats that many ML models support. These formats are VOC PASCAL for XML format and text files for YOLO ,84.Moreover, the name of each annotation file must be the same as the corresponding image name. In addition, annotation information about the bounding boxes of each object is saved in a text file for YOLO, each on a separate line. Each line has the following format :<object = class> <x-centre> <y-centre> <width> <height><object = class>: It is a number ranging from 0 to (number of classes –1), which represents the class (label) of the object.<x-centre> and <y-centre>: x and y range from 0.0 to 1.0, representing the bounding box’s centre.<width> and <height>: width and height range from 0.0 to 1.0, representing the width and height of the bounding box.Object detection is a CV technique comprising two tasks: object detection  and object classification (recognition/identification). Object localisation is responsible for identifying objects’ location in the input image by drawing a bounding box around them, whereas object classification is responsible for classifying the localised object into a specific class ,84,85. IDetecting the location of the LP from the input vehicle image is one of the most important stages because the accuracy of the CR stage depends on it. The YOLOv3 model is one of the most accurate and fast CNN models for object detection. This model was trained on a large-scale object detection dataset, Common Objects in Context, consisting of 330 K images belonging to 80 different classes (labels). The model achieved a mAP of 57.9% in 51 ms for object detection ,66,86.As it is known, TL is one of the most effective ways to improve training of DL CNN models on a small dataset since training such models from scratch requires a huge dataset . TherefoConsequently, only trainable layers  were re-trained on the labelled Jordanian LPs from our Jordanian vehicle dataset to fit the task of Jordanian LPD, while all other layers were frozen. The output of the fitted YOLOv3 model is the Jordanian LP location (the bounding box containing the LP) with the “License_Plate” label and its confidence score. This sub-stage is responsible for giving the result by recognising the characters from the previously detected Jordanian LP. Since the YOLOv3 model is very effective in detecting and recognising small objects, it has been re-trained to achieve this task using TL. In general, YOLOv3 has only three trainable layers , constrained by the number of classes . Since JIn this stage, the vehicle manufacturer  is recognised from the input vehicle images. To achieve this task, this stage goes through two other sub-stages: VLD and VLR.This sub-stage is responsible for detecting the logo from the input vehicle images. As mentioned earlier, the YOLOv3 model outperforms other object detection models in detecting small objects . So, it VLR  is the last stage of our developed system, which is responsible for recognising (classifying) the previously detected logo. VGG16 model is considered an off-the-shelf classification model that achieved 92.7% accuracy in ImageNet dataset, consisting of fourteen million images belonging to a thousand different classes . Since tAt first, we instantiated a base model and then loaded the weights of the pre-trained VGG16 base layers (backbone/feature extractor) onto it.Dense layers have been cropped, leaving only the base layers  by setting include_top = false.Layers in the instantiated base model have been frozen by setting trainable = false to avoid destroying any features (weights) during training rounds.New trainable layers (dense layers) have been added on top of the frozen layers and have been re-trained on our VL dataset to fit the VLR task.Dropout layers have been added after the activation function (fully connected layers) to reduce the overfitting.The process of re-training the VGG16 model using TL to fit the task of recognising the VL (classification) is as follows:A total of 7102 single vehicles.A total of 1169 multiple vehicles.A total of 2290 front vehicles.A total of 7645 rear vehicles.A total of 7265 vehicles were captured in sunny weather.A total of 1006 vehicles were captured in cloudy weather.A total of 8271 vehicles were captured during the day.A total of 3853 vehicles with American LPs.A total of 6082 vehicles with European LPs.A total of 9838 vehicles with white colour codes.A total of 81 vehicles with green colour codes.A total of 8 vehicles with yellow colour codes.A total of 8 vehicles with red colour codes.LPs colour codes:In this work, we collected four different datasets to accomplish the AJLP and VLDR system . To our knowledge, there is no publicly available dataset for Jordanian vehicles. So, we collected our own dataset by taking photos of Jordanian vehicles from different places: garages, public streets, malls, universities, and hospitals. The vehicle images were captured from front and rear at different angles from −35° to 35° at various distances from 2 m to 8 m using a Canon digital camera. Moreover, these images were captured in different illumination conditions and environments . The total number of captured images is 8271 with As mentioned earlier, the LPR stage consists of two other sub-stages: LPD and CR. Therefore, two different datasets were used to accomplish this stage. For the first sub-stage , we used a dataset consisting of 7035 Jordanian vehicle images taken from the images we captured, whereas, for the second sub-stage , we manually cropped Jordanian LPs from the vehicle images used in the first sub-stage. In total, the number of cropped Jordanian LPs was 7176. As a result, these cropped LPs formed a new dataset.We cropped some of these images from the Jordanian vehicles images we captured, which equals 11,147 images.We collected some of them from different websites, which equals 36,620 images.We generated some of them by applying DA techniques, which equals 104,805 images.Furthermore, two other different datasets were used to accomplish our developed system’s second stage . This stage consists of two other sub-stages: VLD and VLR. For the first sub-stage , we used a dataset of all 8271 Jordanian vehicle images we captured, whereas, for the second sub-stage , we collected 152,572 VL images. These images were collected as follows:In addition, we used some images from the VL dataset created by Kuba Siekierzynski after filtering, which equals 5658 images belonging to 28 VLs . EventuaIn this section, we present and discuss the experimental results which were carried out based on the proposed methodology.The previously mentioned datasets used to accomplish the developed AJLP and VLDR system were divided into three partitions  with specific ratios, as shown in ®, 2.79 GHs CPU, 32 GB RAM, and 64-bit Windows 10 operating system with NVIDIA Tesla k40 GPU using Python programming language with TensorFlow and Keras libraries. Moreover, all the CNN models’ input sizes and hyper-parameters were configured, as shown in In this sub-section, we conducted experiments to validate the performance of our developed AJLP and VLDR system. The implementation (training) and experiments were carried out on an Intel (R) Xeon The performance of the CNN models used to achieve the developed system was monitored and examined during the training process using learning curves. Learning curves belong to two types: optimisation learning curves and performance learning curves. Optimisation learning curves are charts that are calculated using optimisation matrices, by which model parameters are optimised, such as loss. Performance learning curves are also charts that are calculated using performance metrics, such as accuracy, by which the model is evaluated and selected. Furthermore, these curves are calculated for both the training and validation datasets during the training process. Thus, two curves are generated: the train learning curve and the validation learning curve. The training learning curve is calculated on the training dataset, giving an idea of how well the model has learned, whereas the validation learning curve is calculated on the validation dataset, which gives an idea of how good the model is to generalise . In our During the YOLOv3 training process, the optimisation learning curve is calculated using the loss function for both the training and validation datasets in each batch. As a result, two charts are generated: training loss and validation loss. The training loss chart shows how well the model fits the training dataset, while the validation loss chart gives an idea of how well the model fits the unseen dataset (new dataset). The lower the loss, the better the model’s performance . In our While training the YOLOv3 model on the Jordanian LPD task, its performance was monitored and validated using the loss function. According to the above figure, both training loss and validation loss gradually decrease, which tend to coincide after epoch 40. The training loss was 0.24, and the validation loss was 0.60. Moreover, the training loss indicates that the model fits the training dataset, converges rapidly after epoch 40 and remains stable. Also, the validation loss indicates that the model fits the new dataset .The performance of the YOLOv3 model for the character detection and recognition task was monitored and validated during the training process using the loss function. According to In this stage, the performance was monitored and validated using both learning curves mentioned earlier. The optimisation learning curve was used for the YOLOv3 model dedicated to VLD tasks, which is the loss, whereas, for the VGG16 model dedicated to VLR (classification), the performance learning curve was used, which is the accuracy.The performance of the YOLOv3 model for the VLD tasks was monitored and validated during the training process using the loss function. According to the above figure, both training loss and validation loss gradually decrease, which tend to coincide after epoch 50. The training loss was 0.726, and the validation loss was 1.002. Moreover, the training loss indicates that the model fits the training dataset, converges rapidly after epoch 50 and remains stable. Also, the validation loss indicates that the model fits the new dataset . Initially, we set the training epochs to 100, but the model stopped improving  after epoch 68. So, we stopped the training process in this epoch.The performance of the VGG16 model for the VLR (classification) task was monitored and validated during the training process using an accuracy matrix. The training and validation accuracy were visualised using the matplotlib library provided by the Python programming language, as depicted in According to the above figure, the training and validation accuracy gradually increase and tend to coincide. The training accuracy was 0.9991, and the validation accuracy was 0.99. Moreover, the training loss and validation loss were 0.0041 and 0.0604, respectively. The training loss indicates that the model fits the training dataset, converges rapidly after epoch 3 and remains stable. Also, the validation loss indicates that the model fits the new dataset .Data augmentation.Normalisation.Dropout regularisation.Moreover, the training accuracy is higher than the validation accuracy due to overfitting, despite the methods that have been applied to reduce it, as we mentioned earlier in the methodology section, which is summarised as follows:Several metrics have been conducted to evaluate the performance of the proposed approach. These matrices are precision, recall, and F-measure, which were applied to all test datasets. Moreover, the mAP measure was used to evaluate the YOLOv3 model of the detection tasks . In other words, each stage in the developed system was evaluated by calculating the test metrics for its dataset. As described in the methodology chapter, the LPR stage consists of two other sub-stages: LPD and CR. Therefore, the effectiveness and robustness of this stage in Jordanian license plate recognition were evaluated by evaluating each sub-stage separately. The overall performance is then assessed by evaluating the two sub-stages together as a sequence.The performance of the YOLOv3 model that was trained to fit the Jordanian LPD task was evaluated using the previously mentioned text measures (metrics), which were defined for this task as follows :(1)PreciThe performance of the YOLOv3 model that was trained to fit the Jordanian LP character detection and recognition task was evaluated using the previously mentioned text measures (metrics) defined for this task. Moreover, the recognition result is valid when all LP characters are recognised correctly.For this task, The overall performance of the developed system for Jordanian license plate recognition was evaluated by evaluating the two sub-stages  together as a sequence. The test set from the first dataset was used for evaluation. For this task, ehicles) . The preAs described in the methodology chapter, the VLR stage consists of two other sub-stages: VLD and VLR (classification). Therefore, the effectiveness and robustness of this stage in vehicle recognition were evaluated by evaluating each sub-stage separately. The overall performance is then evaluated by evaluating the two sub-stages together as a sequence.The performance of the YOLOv3 model that was trained to fit the vehicle logos detection task was evaluated using the previously mentioned text measures. For this task, The performance of the VGG16 model that was trained to fit the vehicle logos recognition (classification) task was evaluated using the previously mentioned text measures. At first, these metrics were calculated for each class (VL), which were defined as follows: follows ,92:(6) M follows ,92:(8)MiThe micro-average precision and micro-average recall were 98% and 98%, respectively. Meanwhile, the micro F-measure reached 98%.The overall performance of the developed system for VLR was evaluated by evaluating the two sub-stages ) together as a sequence. The test set from the first dataset was used for evaluation. The proposed AJLP and VLDR system achieved high performance in text measures  and mAP measures for detection tasks, as shown in Moreover, the ALP and VLDR system can generally be applied for still images and video streams. Also, it can be applied to images in PNG or JPG formats. Further, in object detection tasks, the true negative from the confusion matrix represents all predicted bounding boxes that do not include objects . This case is not useful for object detection because there are a lot of potential true negatives in the image that should not be detected . Our resIn this research, an efficient ALP and VLDR system for Jordanian vehicles has been developed. As mentioned earlier, the AJLP and VLDR system mainly consists of two stages: LPR and VLR. Moreover, these two main stages also consist of two other sub-stages. The first stage is LPD and character detection and recognition, while the second stage is VLD and VLR (classification). DL-based CNN using TL was used to accomplish the proposed system. The YOLOv3 model was re-trained using TL to accomplish the LPD, character detection and recognition, and VLD tasks. In contrast, the VGG16 model was also re-trained to accomplish the VLR (classification) task. Since these sub-stages have different tasks, four datasets were collected for training and testing. The first dataset consists of 7035 Jordanian vehicle images used to train and test the YOLOv3 model for the Jordanian LPD task. The second dataset consists of 7176 Jordanian LPs used to train and test the YOLOv3 model for CR of Jordanian LPs. The third dataset consists of 8271 Jordanian vehicle images used to train and test the YOLOv3 model for the VLD task. The fourth dataset consists of 158,230 VL images used to train and test the VGG16 model for the VLR task. The implemented experimental results proved a high performance of our proposed system in terms of precision, recall, F-measure, and mAP. Moreover, the experiment showed that the training accuracy is higher than the validation accuracy of the VGG16 model due to overfitting.As a future work, we will develop a system to detect and recognise Jordanian LPs and VLs from a real-time video stream. Moreover, we will expand the VL dataset to include new VLs. Also, this research can be extended to compare with other SOTA methods and to apply other techniques to reduce overfitting.


Background: The treatment of triple-negative breast cancer (TNBC) is one of the main focuses and key difficulties because of its heterogeneity, and the source of this heterogeneity is unclear.Methods: Single-cell RNA (scRNA) and transcriptomics data of TNBC and normal breast samples were retrieved from Gene Expression Omnibus (GEO) database and TCGA-BRCA database. These cells were clustered using the t-SNE and UMAP method, and the marker genes for each cluster were found. We annotated the clusters using the published literature, CellMarker database and “SingleR” R package.Results: A total of 1535 cells and 21785 genes from 6 TNBC patients and 2068 cells and 15868 genes from 3 normal breast tissues were used for downstream analyses. The scRNA data were divided into 14 clusters labeled into 8 cell types, including epithelial cells, immunocytes, CAFs/fibroblasts and etc. In the TNBC samples, CAFs were divided into three clusters and labelled as prCAFs, myCAFs and emCAFs, and the marker genes were DCN, FAP and RGS5, respectively. The prCAF subgroup is functionally characterized by promoting proliferation and multi drug resistance; myCAF subgroup is involved in constituting the extracellular matrix and collagen production, matrix composition and collagen production, and the emCAF functionally characterized by energy metabolism.Conclusions: TNBC has inter- and intra-tumor heterogeneity, and CAF is one of the sources of this heterogeneity. CD74, SASH3, CD2, TAGAP and CCR7 served as significant marker genes with prognostic and therapeutic value. Female breast cancer has surpassed lung cancer as the most prevalent cancer, with an estimated 2.3 million new cases 11.7%), and triple-negative breast cancer (TNBC) accounts for 11%-20% of all breast cancers 1.7%, and. TherapeThe tumor microenvironment, which includes the surrounding cells and molecules that interact with cancer cells, plays a critical role in the development of tumors, including the occurrence, progression, and immune suppression of the tumors , 15, canIn the present study, to explore the heterogeneity of CAFs in TNBC and the prognostic value of CAFs, we identified the biomarkers and functions of three subpopulations of CAFs and molecular fractionation of TNBC through scRNA-seq and bulk RNA-seq, described the tumor heterogeneity in TNBC and developed a prognostic model based on CAF-related genes.https://www.ncbi.nlm.nih.gov/geo/). A total of 21785 genes and 1534 cells were obtained in 6 TNBC patients and 21921 genes and 4840 cells in 3 normal breast tissues. Transcriptomics data from GSE19615 and GSE21653 in the GEO database and 107 TNBC patients were screened by clinical information. Transcriptomics data of 142 TNBC patients were screened by corresponding clinical information from TCGA-BRCA in the TCGA database . RNA-seq data were normalized, and batch effects were removed by the R packages “limma” and “sva”.scRNA-seq was retrieved from GSE75688, GSE118389 and GSE226391 in the Gene Expression Omnibus (GEO) database (http://xteam.xbio.top/CellMarker/index.jsp) and referring to the annotation results of the “SingleR” R package. The marker genes for each cluster were considered to be the most significantly different genes in PCs. Tumor cells and normal cells were identified by the R package “copykat”. The distribution of cell types for each sample was counted and visualized. Finally, cell trajectory analysis was performed using the “monocle” R package to derive the trajectory for each type of cell in terms of state, clustering and pseudotime distribution to find the differential genes on each branch. The results of each of the above steps were visualized.The extracted data need to be quality controlled. Data quality control and analysis were applied using the “seurat” R package. First, the percentage of mitochondrial genes was tested, and a percentage less than 5% was considered satisfactory for cell quality control . Second,To identify the function of each CAF subgroup, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the genes and expressions of each subgroup. GO and KEGG enrichment analyses using cluster marker genes were performed with R with the aid of the packages clusterProfiler, enrichplot, and ggplot2. Terms with FDR values of < 0.05 were considered significantly enriched. GO analyses included GO biological process, GO molecular function and GO cellular component. The top 10 terms of all of the above analyses were visualized by barplot.We intended to cluster the sample with the inner feature in tumor samples and extracted biological correlation coefficients, and the R package “ConsensusClusterPlus” was used to take the above action. We selected an optimum k value of 2 to 9 in consideration of stability and clustering performance. A suitable k value was chosen based on consensus CDF and delta area. Kaplan-Meier analysis was conducted between clusters via the “survival” R package. We also examined the differences in clinical characteristics in clusters and visualized them.We used ESTIMATE to assess the tumor microenvironment by the R package “estimate” and obtained ESTIMATE scores, immune scores, stromal scores and tumor purity. By using the CIBERSORT algorithm, we calculated the absolute abundance of 22 types of immune cells. By contrasting the scores between clusters, it was possible to identify the infiltrating cells. The “ggpubr” package was used to visualize the results above. Differences in common immune checkpoints between clusters were analyzed by the R package “limma”. Kaplan-Meier analysis was conducted between clusters via the “survival” R package.CAF signature genes were found in the pseudotime analysis, which were used to build prognostic prediction model. To screen out CAF-related genes that may be related to prognosis, we performed Cox regression with the R packages “survival” and “survminer” to obtain candidate genes. The Lasso algorithm was executed to construct a prognostic model by the “glmnet” R package. We determined the risk score using the formula:The mathematical meaning of the representation of this formula is to calculate the expression of each candidate gene multiplied by the regression coefficient of the multivariate Cox and then to sum all the values. We divided all cases into a high-risk group and a low-risk group based on the median risk score. The training set was composed of GEO data, and the testing set was composed of TCGA data. Receiver operating characteristic (ROC) curve analysis was used to confirm that the prognostic model was stable. To establish the risk score as a significant prognostic factor, univariate and multivariate Cox regression was conducted.The R program “rms” was used to finish the establishment, and “regplot” was used to complete the visualization. The model was built taking into account nomogram risk scores and clinical features. We offer a measurable tool to forecast overall survival at 1, 3, and 5 years. Calibration curves were created to evaluate the effectiveness of the nomogram.https://cn.string-db.org/) based on CAF-related genes, followed by reconstruction with Cytoscape version 3.6.1. Nodes with confidence of interactive relationship larger than 0.95 were used for building the network. The number of adjacent nodes of each gene was counted by R.Hub genes, which are high-degree genes, have high connectivity in the protein-protein interaction network. The PPI network was constructed by the STRING database . In comparisons of two classes of data, we used the Mann-Whitney U test  or Student’s t test . Spearman’s correlation test  or Pearson’s correlation test  was applied to evaluate the correlation between two groups of data. We used Fisher’s exact or chi-square tests to assess the association between two categorical variables. We utilized the FDR, evaluated by the Benjamini-Hochberg method, to adjust for multiple tests.All data generated or analysed during this study are included in this published article.The flow of analyses in the presented study is shown in To better specify the types of these cells, we used the t-SNE and UMAP method to classify them into 14 clusters in TNBC, as shown in In TNBC, clusters 7, 11 and 13 were labeled CAFs, and the marker genes for these clusters were DCN, FAP and RGS5, respectively, which are widely reported markers of CAFs. We performed a separate t-SNE analysis of CAFs and found that they were clearly divided into three subgroups, as shown in To identify the function of each subgroup, we performed GO and KEGG analyses to identify their functional differences, as shown in The distribution of clusters in the trajectory diagram is shown in There were 450 characteristic genes in the first state, and these genes were further analyzed. The 107 patients from GSM19615 and GSM21653 were divided into 3 clusters based on the expression of these genes. The clinical characteristics of patients were shown as To investigate the correlation between cluster and TIME, ESTIMATE and CIBERSORT analyses were performed. The results showed that there were differences in the ESTIMATE score, immune score, stromal score and tumor purity between C1 and C2 and between C2 and C3 (P<0.001), as shown in A checkpoint distribution among the three clusters was performed to predict the response to immunotherapy. There were differences among clusters in the distribution of genes encoding the immune checkpoints WEE1, SHH, PVRIG, PRMT5, PIK3CA, PDCD1, EGFR, DLL4, DDR2, CTLA4, CD274 and BRCA . In KaplThe 33 genes associated with prognosis were screened using univariate regression analysis and then compressed to 16 genes using LASSO regression, shown as Breast cancer is a highly heterogeneous disease and this heterogeneity leads to the development of drug resistance and progression , in ordeIn the scRNA-seq data, we found that the sample consisted mainly of epithelial cells, and cancer cells were almost concentrated in the epithelial cells. This result is consistent with the fact that breast cancer originates from the epithelial cells of the breast. Other cells within the sample were cancer-associated fibroblasts, tissue stem cells, smooth muscle cells, embryonic stem cell-like cells, endothelial cells, macrophages and T cells. Cancer-associated fibroblasts were clearly divided into three clusters, and their marker genes were DCN, FAP and RGS5. DCN is considered a marker for one of these subgroups in CAFs of colorectal cancer and high-grade serous ovarian cancer samples , 28. FAPThe CAFs marker FAP mentioned in the presented study has been the subject of several preclinical studies and is considered a promising target. Treatment of xenograft models of lung, pancreatic and head and neck cancers with a novel antibody-metanicinol conjugate, FAP5-DM1, resulted in long-term inhibition of tumor growth and complete regression with no signs of intolerance . Depletiin vitro increases motility, invasiveness, and proliferation [Interestingly, we found a cluster of embryonic stem cell-like cells in these triple-negative breast cancer samples. Poorly differentiated basal-like breast cancer with similarities in gene expression to embryonic and induced pluripotent stem cells . The extferation . Similarferation . The epiferation , 55. BasWe identified 5 hub genes, including CD74, SASH3, CD2, TAGAP and CCR7. CD74 is a type II transmembrane protein that is primarily expressed on antigen-presenting cells and functions intracellularly as a major histocompatibility complex class II chaperone , 57. ItsIn this study, three CAF subgroups and marker genes were identified, their functions were annotated, and a prognostic model based on CAF-related genes was constructed. However, some limitations in our study should be acknowledged. Clinical information corresponding to single-cell data was not available for this study, resulting in failure to analyze the relationship between CAF percentage and survival in individual samples. The functions of the CAF subgroup proposed in this study need to be verified by biological tests. In the future, we will perform flow cytometry to classify CAFs and investigate their effects on the proliferation, migration and apoptosis of TNBC cells.In summary, we identified three subgroups: prCAFs (marked by DCN), myCAFs (marked by FAP) and emCAFs (marked by RGS5). Their functions are promoting angiogenesis, constructing matrix composition and collagen production, having myofibroblast characteristics and inducing endocrine resistance. CAFs are a source of inter- and intra-tumor heterogeneity in TNBC tumors. A prognostic model based on CAF-related genes was constructed and verified by a testing set. Five hub genes, CD74, SASH3, CD2, TAGAP and CCR7, served as significant marker genes with prognostic value.Supplementary Figures


Delivery is not easily predictable in horses and the consequences of dystocia can be serious for both the mare and foal. An induction protocol with low doses of oxytocin has been reported as a safe procedure. This study investigates the effect of induced delivery on at-term mares’ sympathetic-vagal balance. Fourteen mares were included and divided into two groups, one subjected to spontaneous delivery (SD), and one to induced delivery (ID). In both groups, an ECG was recorded using an elastic belt with integrated smart textile electrodes. The recording started before the delivery , continued close to delivery (Pre-delivery) and during delivery (Delivery), and ended after parturition . From the ECGs, Heart Rate Variability (HRV) parameters relating to time and frequency domains and non-linear analysis were extrapolated. The HRV analysis was performed both within the same group (IntraGA) and between the two groups (InterGA). In the present study, spontaneous and induced delivery did not appear to differ in autonomic nervous system functioning. In IntraGA analysis, both for SD and ID mares, delivery and placental expulsion periods were parasympathetic dominated since vagal-related HRV parameters increased. Moreover, no differences were found in InterGA comparison between SD and ID mares, except for the pre-delivery period of ID mares, during which both branches of the autonomic nervous system were activated. These results are in line with the literature on parasympathetic dominance during parturition and no change in Heart Rate Variability following exogenous oxytocin administration in parturient mares. In equine species, foaling is considered a critical event because the length of gestation is variable , and theIn mares, induced labour is mainly performed in the presence of stud farm personnel and, in the case of high-risk pregnancies, in the presence of veterinary assistance. Most foaling happens during the night  when theOxytocin (OX) is usually administered to induce labour in mares, using different dosages . HoweverOxytocin is physiologically released during delivery through a reflex mechanism due to the dilatation of the cervix and vagina caused by the foetus . In rodeThe HRV index represents the short fluctuations of HR and is used to estimate the influence of the sympathetic or vagal branch on the sinus node. HRV analysis is performed by identifying R-R peaks on an ECG recording , and HRVIt is thus very important to have a good quality ECG recording. This can be achieved using smart textile electrodes, which are comfortable and easy to use , provideThe present study aimed to compare the HRV parameters assessed in mares during spontaneous and induced parturition with a low dose of oxytocin. The hypothesis was that the induction of foaling might influence the sympathetic-vagal balance.The study was conducted on 14 trotter mares, aged between 7–18 years (median age: 11.5 years) from a stud farm associated with the Veterinary Teaching Hospital of the Department of Veterinary Sciences , during one foaling season. All the mares enrolled in this study were housed in the stud farm because the owners requested attended parturition.The inclusion criteria for the mares were: 1) healthy based on a general physical examination performed during the last week of gestation; 2) foetal anterior presentation confirmed by ultrasound examination at admission; 3) multiparous pregnancies; 4) similar management.ad libitum and flaked grain. They were checked daily, turned out in the paddock during the day, and moved to a foaling box during the night for continuous monitoring with video cameras as soon as udder enlargement was detected and/or when day 320 of gestation was reached. A commercial foaling alert system  was attached to the vulva, one week before the estimated date of foaling, and connected to the breeder’s phone. The calcium concentration in mammary secretion was evaluated at 5:00 pm each day, starting when the mammary secretion changed from watery to sticky, using a Foal Watch® Test Kit .Mares were divided into two groups: spontaneous delivery (SD) and induced delivery (ID). Each group was composed of seven mares, all bedded on straw and fed with hay ®, Fatro, Italy) IV at 18:15 when calcium concentrations reached ≥ 250 ppm [In the ID group, the delivery was induced as previously described  with a sThe expulsion of the foetus was assisted, in both spontaneous and induced foaling, with gentle traction by an operator on the foetus’ front limbs.Information on parity, gestational length, length of stage II of parturition  and placental expulsion were recorded. Placenta retention was also assessed, defined as no expulsion of the foetal membrane within three hours of foaling , 20.Foal viability was evaluated using a 5-parameter Apgar score  five minElectrocardiograms (ECGs) were recorded using an elastic belt with integrated textile electrodes . The belt was placed behind the wither using two straps, one at the sternum, and the other at the caudal abdomen. The belt had a small pocket in which the recording device  was placed. The textile electrodes (13x5 cm) were positioned in a modified base-apex configuration. One electrode was placed behind the shoulder blades in the dorsal half-height, while the other was positioned about 20 cm behind and 10 cm above the elbow.When fitting the belt, the straps were coupled and attached with sellotape . The eleThe signal was sent via Bluetooth to a portable tablet and checked in real-time every five minutes.The ECGs were recorded using the following protocols:SD group: recording started at 8:00 pm and stopped at 6:00 am if the delivery did not occur, or at the moment of placenta expulsion in the case of foaling.ID group: recording started at 6:00 pm and stopped at the moment of placenta expulsion, after foaling. The oxytocin was injected 15 min after the beginning of the recording.Four different periods  were recBasal: the period started with the beginning of the ECG recording for both groups (SD and ID) and ended 1h before the rupture of the chorioallantoic membrane (lower limit for the onset of stage I of parturition) for SD mares and at the time of oxytocin injection for the ID mares. During this period the mare was left alone in the box.Pre-delivery: the period started 1h before the rupture of the chorioallantoic membrane for the SD mares or at the time of oxytocin injection for the ID mares and ended with the rupture of the chorioallantoic membrane for both groups. This represents the time just before delivery (stage I of parturition). During this period the mare was left alone in the box.Delivery: the period started with the rupture of the chorioallantoic membrane and ended with the expulsion of the foal (stage II of parturition) in both groups. During this period one operator assisted the foetus’s expulsion by gentle traction of the foetus’s forelimbs.Placental expulsion: the period started after the completion of foetal expulsion and ended with the placental expulsion (stage III of parturition). During this period, both the mare and foal were left alone in the box.Within these four periods, 4 windows were selected (one for each period), all lasting 5 minutes (both for SD and ID groups). From each of the four windows, HRV parameters were extracted. The four windows were collected, respectively:at the farthest end from the delivery event, in the basal period.in the middle of the pre-delivery period.at the beginning of the delivery period, including the actual delivery event.at the end of the placental expulsion period, including the actual placental expulsion event.Data concerning gestational and stage II labour were reported as median, minimum, and maximum values. ECG signals were processed by Matlab software  to extract the HRV time series. Since the computation of some ECG extracted parameters is dependent on the time-window length, the ECG signal was analysed within time windows of equal length for all the animals to obtain comparable results in each period. All ECGs were also pre-processed using a bandpass filter to remove noise and movement artefacts. From QRS complexes, R-peaks were detected and then used to obtain the InterBeat Interval (IBI), that is the time interval between individual beats, whose variations over time are known as Heart Rate Variability.The HRVs were analysed in the time domain, frequency domain, and by a non-linear method. In the time domain, from heart rate (HR) and HRV, we extracted meanRR, SDNN and RMSSD parameters. In the frequency domain, the power spectrum was computed in three specific frequency bandwidths, namely LF, HF and LF/HF. As a non-linear HRV analysis, Poincaré plot analysis was performed; in particular, the SD1 and SD2, (the two well-known Poincaré plot parameters) were computed . QuantitThe HRV normality distribution was assessed through the Shapiro-Wilk test in each window within the four periods . Since the distribution of the feature samples was not Gaussian, nonparametric statistical tests were adopted.Two statistical analyses were performed: an intra-group analysis (IntraGA)  and an inter-group analysis (InterGA) (comparing the two groups for each protocol window within the four periods).For the IntraGA, the Friedman test and the Bonferroni-corrected Wilcoxon signed-rank test as post-hoc were applied on HRV parameters to verify significant differences in each pairwise comparison among the four different periods analysed .For the InterGA, a Bonferroni-corrected Mann-Whitney test for unpaired data was used to assess statistical differences between ID and SD groups for each period analysed .The significant value was set for p<0.05. The statistical analysis was performed using commercial software .The research protocol was approved by the Institutional Animal Care and Use Committee of the University of Pisa (33476/2016). Owner-informed consent was obtained for each horse.The median gestational length was within the reference interval (320–360 days)  with a mNo dystocia occurred in the SD Group, while one premature separation of the placenta (red bag) was recorded in the ID group; the chorioallantoic membrane was immediately broken and the foetus was safely delivered. Foetal membranes were retained over 3 hours and in one mare in both groups, spontaneously expelled within 12 hours.Foals of both groups were born alive and showed no signs of prematurity/dysmaturity. Four out of 7 fillies and 3/7 colts were born after spontaneous delivery and 5/7 fillies and 2/7 colts after induced delivery. All the foals were clinically healthy based on a daily physical examination performed up to 7 days of age. In the SD group, the Apgar score was 9–10 in all the foals; in the ID group, the Apgar score was 9–10 in 6/7 foals, while 1/7 showed an Apgar score of 8. No statistical differences were found in viability between the two groups (p = 0.192).In the SD group, more time was needed to acquire the sternal recumbency and the standing position in 2/7 and 1/7 foals, respectively, while no foals showed a prolonged time until the first suckling. In the ID group, a prolonged time for sternal recumbency and acquiring a standing position was found in 3/7 foals, respectively, while in 1/7 foals, the time until the first suckling was prolonged. In the SD group all the foals showed HR, RR and BT values within the reference ranges, while in the ID group, 3/7 foals showed a lower RR. Statistical differences were not found for time to sternal recumbency (p = 0.2552), time to acquire standing position (p = 0.0775) and to suckle (p = 0.2576), for either HR (p = 0.8322) or RR (p = 0.1498). Statistical differences were found in BT values  (p = 0.0006).Regarding HRV analysis, the extracted HRV parameters were expressed as median values and absolute deviation from the median (MAD), both for SD and ID groups, for each period analysed and reported in The IntraGA analysis showed statistical differences in SD group for basal and pre-delivery periods vs. delivery and placental expulsion periods. In particular, SDNN, RMSSD, LF, HF, SD1, and SD2 values increased in the delivery vs. the pre-delivery and the basal period. Moreover, meanRR, LF, HF, and SD2 values increased in the placental expulsion vs. basal period, while meanRR, SDNN, and SD2 values raised in the placental expulsion vs. the pre-delivery period. All the statistical significance had a p-value of 0.008.For the ID group, the IntraGA analysis showed a statistically significant reduction in meanRR value in pre-delivery period, an increase in SDNN and SD2 values in delivery period, and an increase in meanRR value in placental expulsion period, all compared to basal. In addition, an increase in SDNN, RMSSD, LF, HF, SD1, and SD2 values for delivery vs. pre-delivery, and an increase in meanRR, RMSSD, and SD1 values for placental expulsion vs. pre-delivery was found. Lastly, meanRR value increased in the placental expulsion period vs. the delivery period. All the statistical significance had a p-value of 0.008.The InterGA statistical analysis showed a significant decrease in SDNN (p = 0.009), LF (p = 0.004) and SD2 (p = 0.004) values in pre-delivery period of the ID vs. SD group.Oxytocin is the drug of choice for induced parturition in mares and is widely considered as safe and easy-to-use when employed at a dosage of 2.5/3.5 UI , 29.In horses, induced parturition, by PGF2 alpha or oxytocin at high or repeated doses until foaling can be related to complications, such as dystocia, premature placental separation, retained placental membranes, foetal hypoxia, or failure of passive transfer , 30–33. In horses, the main problem related to induced delivery is the increased risk of foal prematurity . In our In this study, we hypothesized that the treatment with exogenous oxytocin to induce foaling in at-term mares might influence their sympathetic-vagal balance. To the best of our knowledge, no studies evaluated the HRV after the administration of oxytocin to induce parturition in mares.Overall, in the SD group IntraGA comparison, cardiac parasympathetic activity seems to be more represented in delivery and placental expulsion periods compared to basal and pre-delivery ones see . This isMoreover, in SD group the influence of ANS on cardiac function seems to be the same in basal vs pre-delivery periods. This is not surprising, as parasympathetic activity starts increasing 30 minutes before delivery and then peaks 15 minutes after . The lacIn IntraGA comparison, for ID group, the HRV increased from basal and pre-delivery to delivery and placental expulsion periods see . This agWhen the two groups were compared (IntraGa comparison), no differences were found between periods, except for the pre-delivery one. In particular, in ID mares HRV tended to decrease during the pre-delivery period in those parameters representative of both sympathetic and parasympathetic activity see . CoactivIn our study, spontaneous and oxytocin-induced delivery did not appear to differ in terms of autonomic nervous system functioning and did not alter the physiological course of the events of delivery. This is in line with what has been found in other studies , 40. TheOur study certainly has limitations, which consist of the low number of subjects monitored and the electrocardiographic monitoring performed so close to delivery. Undoubtedly, increasing the number of subjects involved and the hours of electrocardiographic recording both before and after delivery may be useful implementations to be applied in further studies on the evolution of autonomic nervous system functioning during spontaneous and induced delivery in the equine species.We confirmed that induced parturition with low doses of oxytocin is safe both for mares and foals, in line with several other reports , 35, 39.The hypothesis that the treatment with exogenous oxytocin to induce foaling in at-term mares might influence their sympathetic-vagal balance was not fully confirmed. In our study, we found no differences in mares’ autonomic nervous system function during spontaneous or induced delivery. The only difference found was a decrease in HRV in the pre-delivery of mares whose foaling was induced, and this could be attributable to the manipulations the mares were subjected to during drug administration. Parturition and placental expulsion appear to be dominated by parasympathetic functioning in both induced and spontaneous delivery mares. In conclusion, our findings suggest that the physiology of the parturition was preserved by applying a delivery induction protocol with a low dose of oxytocin.S1 File(XLSX)Click here for additional data file.