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Phenylketonuria (PKU) is an inborn error of amino acid metabolism with an autosomal recessive inheritance caused in most cases by mutations in the phenylalanine hydroxylase (PAH) gene. PKU has wide allelic heterogeneity. Here we report a novel heterozygous substitution (c.1223G>T (p.Arg408Leu)) in the PAH gene in an Iranian PKU family. The patient was 19-yr-old female with diagnosis of moderate PKU referred to Department of Medical Genetics, Tehran University of Medical Sciences, Tehran, Iran for genetic counseling/analysis in April 2015. We used PCR-Sequencing to identify any sequence variations in the PAH gene.

Objective(s)Rapid tests for detection of Streptococcus agalactiae or Group B Streptococci (GBS) at the onset of labor are needed to permit early intrapartum antibiotic prophylaxis. This study aimed to evaluate the PCR assays targeting the 16S ribosomal RNA gene (16S rDNA) for detection of the GBS in comparison with a specific culture method. Materials and MethodsTwo swabs were used to obtain vaginal specimens from the 330 pregnant women attended delivery room at Hedayat hospital, Tehran, Iran. One swab was analyzed by direct plating onto selective GBS agar medium (ISLAM) and the other swab was used for a PCR assay, which amplified the 16S rDNA of S. agalactiae. Comparative study between the selective culture and the PCR assay was done among the 330 tested women.ResultsThe GBS colonization rate based on the culture results was 20.6% (68/330). Both culture and PCR methods were positive for 56 and negative for 253 women. The culture method was positive and PCR was negative in 12 women. The culture was negative and the PCR positive for 9 women. Sensitivity of the PCR assay was 82.3% and specificity was 96.5%. The positive predictive value was 86.15% and negative predictive value was 95.4%. ConclusionISLAM diagnostic procedure and PCR are rapid and reliable analyzing methods, which might be useful for accurate diagnosis of GBS colonization in pregnant women at the time of delivery.

Since Aristotle, it has repeatedly been stressed that for engaging in meaningful discussion or debate, the discussion parties must share, besides a language, also knowledge, information, values and goals. What do we know today about this issue? How can that knowledge be used and advanced? The purpose of this paper is to consolidate our understanding on the many concepts that refer to preconditions for communication and collaboration in construction projects. The underlying research is conceptual by nature, and it is underpinned by a literature review. The findings show that currently there is a wide variety of terms and theoretical approaches that refer to the discussed phenomena. This situation invites for a conceptual synthesis and empirical research for its validation.

Purpose This paper aims to Test the capabilities/accuracies of four deep learning pre trained convolutional neural network (CNN) models to detect and classify types of highway cracks, as well as developing a new CNN model to maximize the accuracy at different learning rates. Design/methodology/approach A sample of 4,663 images of highway cracks were collected and classified into three categories of cracks, namely, “vertical cracks,” “horizontal and vertical cracks” and “diagonal cracks,” subsequently, using “Matlab” to classify the sample to training (70%) and testing (30%) to apply the four deep learning CNN models and compute their accuracies. After that, developing a new deep learning CNN model to maximize the accuracy of detecting and classifying highway cracks and testing the accuracy using three optimization algorithms at different learning rates. Findings The accuracies result of the four deep learning pre-trained models are above the averages between top-1 and top-5 and the accuracy of classifying and detecting the samples exceeded the top-5 accuracy for the pre-trained AlexNet model around 3% and by 0.2% for the GoogleNet model. The accurate model here is the GoogleNet model as the accuracy is 89.08% and it is higher than AlexNet by 1.26%. While the computed accuracy for the new created deep learning CNN model exceeded all pre-trained models by achieving 97.62% at a learning rate of 0.001 using Adam’s optimization algorithm. Practical implications The created deep learning CNN model will enable users (e.g. highway agencies) to scan a long highway and detect types of cracks accurately in a very short time compared to traditional approaches. Originality/value A new deep learning CNN-based highway cracks detection was developed based on testing four pre-trained CNN models and analyze the capabilities of each model to maximize the accuracy of the proposed CNN.

Objective Cystic fibrosis (CF) is characterized by chronic pulmonary disease, insufficient pancreatic and digestive function, and abnormal sweat concentration. There is controversy about predisposing factors of nephrolithiasis and nephrocalcinosis in patients with cystic fibrosis. We assessed the results of metabolic evaluation in patients with cystic fibrosis and its correlation with nephrocalcinosis. Methods Forty five CF patients, mean age 47.1 months, were enrolled in the study. No one had past history of nephrolithiasis and/or nephrocalcinosis. The records were reviewed for clinical characteristics and all patients underwent metabolic evaluation including serum electrolyte measurements and spot urine analysis. Ultrasonography was performed in all patients to detect nephrocalcinosis and urolithiasis. Findings Nephrocalcinosis was found in 5 (11%) patients. No patient had clinical symptoms of nephrolithiasis and/or micro/macroscopic hematuria. Metabolic evaluation of the CF patients versus normal reference values showed decreased serum uric acid in 48.8%, elevated serum phosphate in 24.4%, and urine oxalate excretion in 51%. Metabolic evaluation of the nephrocalcinosis positive patients versus nephrocalcinosis negative group showed no statistical difference in serum electrolytes. The mean value of urine calcium excretion was lower in patients with nephrocalcinosis (P=0.001). Despite lack of any significant correlation, higher numerical hyperoxaluria was observed in patients with severe steatorrhea. There was no statistical correlation between steatorrhea and urine calcium as well as oxalate excretion. Conclusion Hypocalciuria in the nephrocalcinotic CF patients may be seen. It can be hypothesized that hypocalciuria may be due to a primary defect in renal calcium metabolism in CF patients.

The current practice of Tolerance Management (TM) is still very ad hoc and reactive, despite increasing calls for waste reduction and an improved quality of buildings particularly within industrialised construction. This paper aims to identify the root causes of tolerance problems, the reasons why current methods have not been as successful as expected and why the industry still struggles with this issue. Having reviewed and interpreted the existing literature, it is apparent that tolerance problems fall into two categories defined by intrinsic and extrinsic factors. Furthermore, the drawbacks of the existing methods for TM were analysed, and the findings show that none of the existing methods have been considered in a continuous and holistic process and they remain scattered.

Présentation d’une analyse de la synergie relativement récemment mise en évidence entre les processus BIM et Lean et qui est en constante évolution, car vouée à se développer. Une mise en perspective réciproque des contributions du BIM au Lean et du Lean au BIM sont présentées respectivement dans la conception, la construction et la maintenance des ouvrages.