Familial hypercholesterolemia (FH) is an autosomal dominant disease, predominantly caused by variants in the low-density lipoprotein (LDL) receptor gene (LDLR). Herein, we describe genetic analysis of severely affected homozygous FH patients who were mostly resistant to statin therapy and were managed on an apheresis program. We identified a recurrent frameshift mutation p.(G676Afs*33) in exon 14 of the LDLR gene in 9 probands and their relatives in an apparently unrelated Saudi families. We also describe a three dimensional homology model of the LDL receptor protein (LDLR) structure and examine the consequence of the frameshift mutation p.(G676Afs*33), as this could affect the LDLR structure in a region involved in dimer formation, and protein stability. This finding of a recurrent mutation causing FH in the Saudi population could serve to develop a rapid genetic screening procedure for FH, and the 3D-structure analysis of the mutant LDLR, may provide tools to develop a mechanistic model of the LDLR function.

Familial hypercholesterolemia (FH) is most commonly caused by mutations in the LDL receptor (LDLR), which is responsible for hepatic clearance of LDL from the blood circulation. We described a severely affected FH proband and their first-degree blood relatives; the proband was resistant to statin therapy and was managed on an LDL apheresis program. In order to find the causative genetic variant in this family, direct exon sequencing of the LDLR, APOB and PCSK9 genes was performed. We identified a compound heterozygous mutation in the proband with missense p.(W577C) and frameshift p.(G676Afs33) variants at exons 12 and 14 of the LDLR gene respectively. DNA sequencing of LDLR gene from the parents demonstrated that the missense variant was inherited from the mother and frameshift variant was inherited from the father. The frameshift variant resulted in a stop signal 33 codons downstream of the deletion, which most likely led to a truncated protein that lacks important functional domains, including the trans-membrane domain and the cytoplasmic tail domain. The missense variant is also predicted to be likely pathogenic and affect EGF-precursor homology domain of the LDLR protein. The segregation pattern of the variants was consistent with the lipid profile, suggesting a more severe FH phenotype when the variants are in the compound heterozygous state. The finding of a compound heterozygous mutation causing severe FH phenotype is important for the genotype-phenotype correlation and also enlarges the spectrum of FH-causative LDLR variants in the Arab population, including the Saudi population.

Familial hypercholesterolemia (FH) is an autosomal dominant disease predominantly caused by a mutation in the low-density lipoprotein receptor (LDLR) gene. Here, we describe two severely affected FH patients who were resistant to statin therapy and were managed on an apheresis program. We identified a novel duplication variant c.1332dup, p.(D445*) at exon 9 and a known silent variant c.1413A>G, p.(=), rs5930, NM_001195798.1 at exon 10 of the LDLR gene in both patients.

ObjectivesFrom the first description by Leo Kanner [1], autism has been an enigmatic neurobehavioral phenomenon. The new genetic/genomic technologies of the past decade have not been as productive as originally anticipated in unveiling the mysteries of autism. The specific etiology of the majority of cases of autism spectrum disorder (ASD) is unknown, although numerous genetic/genomic variants and alterations of diverse cellular functions have been reported. Prompted by this failure, we have investigated whether the metabolomics approach might yield results which could simultaneously lead to a blood-based screening/diagnostic test and to treatment options. Methods Plasma samples from a clinically well-defined cohort of 100 male individuals, ages 2-16+ years, with ASD and 32 age-matched typically developing (TD) controls were subjected to global metabolomic analysis. ResultsWe have identified more than 25 plasma metabolites among the approximately 650 metabolites analyzed, representing over 70 biochemical pathways, that can discriminate children with ASD as young as 2 years from children that are developing typically. The discriminating power was greatest in the 2-10 year age group and weaker in older age groups. The initial findings were validated in a second cohort of 83 children, males and females, ages 2-10 years, with ASD and 76 age and gender-matched TD children. The discriminant metabolites were associated with several key biochemical pathways suggestive of potential contributions of increased oxidative stress, mitochondrial dysfunction, inflammation and immune dysregulation in ASD. Further, targeted quantitative analysis of a subset of discriminating metabolites using tandem mass spectrometry provided a reliable laboratory method to detect children with ASD. Conclusion Metabolic profiling appears to be a robust technique to identify children with ASD ages 2-10 years and provides insights into the altered metabolic pathways in ASD, which could lead to treatment strategies. ObjectivesTo uncover novel traits associated with nicotine and alcohol use genetics, we performed a phenome-wide association study in a large multi-ethnic cohort. Methods We investigated 7,688 African-Americans (AFR), 1,133 Asian-Americans (ASN), 14,081 European-Americans (EUR), and 3,492 Hispanic-Americans (HISP) from the Women's Health Initiative, analyzing risk alleles located in the CHRNA5-CHRNA3 locus (rs8034191, rs1051730, rs12914385, rs2036527, and rs16969968) for nicotine-related traits and ADH1B (rs1229984 and rs2066702) and ALDH2 (rs671) for alcohol-related traits with respect to anthropometric characteristics, dietary habits, social status, psychological circumstances, reproductive history, health conditions, and nicotine-and alcohol-related traits. ResultsThe investigated loci resulted associated with novel traits: rs1229984 were associated with family income (p=4.1*10 −12 ), having a pet (p=6.5*10 −11 ), partner education (p=1.8*10 −10 ), "usually expect the best" (p=2.4*10 −7), "felt calm and peaceful" (p=2.6*10 ), and num...

Managing power plants usually involves monitoring many data and parameters that occur within minutes, hours, or days. Nowadays, a mount of digital data can be exchanged, analyzed and easily accessed through modern technology called the Internet of Things (IoT). In this paper, an IoT (using Wi-Fi development kit called Photon) is used to remote control and monitor the performance of the University of Mosul power plant. This includes monitoring the Power Factor, supplied voltage and total load current of each sub-station within the university area. The system also applies a safety feature to completely close the power plant in the event of a serious condition such as a fire. ThingSpeak is used in this paper as an IoT analytics platform service which lets the programmers collect, visualize and analyze incoming data streams to the cloud. The collected data is sent to the ThingSpeak by the Photon devices, create instant visualization of live data related to the monitored station, and send the required alerts. The engineers responsible for the station can monitor the progress of the work of the station through any connected device - a computer or a smartphone - from anywhere in the world and even reprogram the system during operation, if necessary.

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System reliability is an important issue in designing modern multiprocessor systems. This paper proposes a fault-tolerant, scalable, multiprocessor system architecture that adopts a pipeline scheme. To verify the performance of the proposed system, the SimEvent/Stateflow tool of the MATLAB program was used to simulate the system. The proposed system uses twelve processors (P), connected in a linear array, to build a ten-stage system with two backup processors (BP). However, the system can be expanded by adding more processors to increase pipeline stages and performance, and more backup processors to increase system reliability. The system can automatically reorganize itself in the event of a failure of one or two processors and execution continues without interruption. Each processor communicates with its neighboring processors through input/output (I/O) ports which are used as bypass links between the processors. In the event of a processor failure, the function of the faulty processor is assigned to the next processor that is free from faults. The fast Fourier transform (FFT) algorithm is implemented on the simulated circuit to evaluate the performance of the proposed system. The results showed that the system can continue to execute even if one or two processors fail without a noticeable decrease in performance.

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