Plasma insulin-like growth factor-I, CA-125, estrogen, and progesterone in women with leiomyomas

OBJECTIVE: To determine plasma levels of insulin-like growth factor-I (IGF-I), CA-125, estrone (E1), E2, and P in women with uterine leiomyomas compared with normal women. DESIGN: Women with leiomyomas were compared with normal women (control). SETTING: University Department of Obstetrics and Gynecology. PATIENTS: Fifty-one premenopausal women with uterine myomas > 14 weeks gestation and 30 normal fertile women (controls) were studied. Peripheral blood samples were obtained before myomectomy or hysterectomy and during the nonmenstruating phase in the controls. MAIN OUTCOME MEASURES: Plasma levels of E1, E2, P, CA-125, and IGF-I were determined by specific and sensitive RIAs and immunoradiometric assays. RESULTS: Plasma IGF-I levels were 2,006 +/- 185 mU/mL (mean +/- SEM, n = 35) and 2,335 +/- 287 mU/mL (n = 16) in women with leiomyomas during the follicular and luteal phases, respectively, whereas the corresponding values for normal women were 1,702 +/- 120 (n = 30) and 1,774 +/- 239 mU/mL (n = 30). Similarly, plasma CA-125 levels were unchanged in women with leiomyomas (myomas: 18.8 +/- 2.4, 21.5 +/- 3.7 U/mL; normal: 15.9 +/- 1.5, 15.8 +/- 1.3 U/mL during follicular and luteal phases, respectively). Women with leiomyomas had plasma E1, E2, and P levels during the follicular phase (91.9 +/- 11.5 pg/mL; conversion factor to SI unit, 3.699; 94.6 +/- 19.0 pg/mL; conversion factor to SI unit, 3.671; and 1.5 +/- 0.4 ng/mL; conversion factor to SI unit, 3.180, respectively) and the luteal phase (105.8 +/- 11.2 pg/mL; conversion factor to SI unit, 3.699; 128.7 +/- 24.8 pg/mL; conversion factor to SI unit, 3.671; and 9.6 +/- 1.6 ng/mL; conversion factor to SI unit, 3.180) similar to normal women. CONCLUSION: Plasma levels of IGF-I, CA-125, E1, E2, and P are normal in women with leiomyomas.     

Sediment flux from Lesser Zab River in Dokan Reservoir: Implications for the sustainability of long‐term water resources in Iraq

Prudent management of Iraqi water resources under climate change conditions requires plans to be based on actual figures of the storage capacity of existing reservoirs. With the absence of sediment flushing measures, the actual storage capacity of Dokan Reservoir (operated since 1959) has been affected by the amount of sediment delivered during its operational life leading to an undetermined reduction in its storage capacity. In consequence, there has not been an update on the dam's operational storage capacity curves. In this research, new operational curves were established for the reservoir based on a recent bathymetric survey undertaken in 2014. The reduction in reservoir capacity during the period between 1959 and 2014 was calculated by the mean of the difference between the designed storage capacity and the storage capacity which was concluded from the 2014 bathymetric survey. Moreover, the rate of sediment transported to the reservoir was calculated based on the overall quantities of accumulated sediment and the water discharge of the Lesser Zab River into the reservoir. The results indicate that the dam capacity is reduced by 25% due to sedimentation of an estimated volume of 367 million cubic metres at water level 480 m.a.s.l. The annual sedimentation rate was about 6.6 million cubic metres, and the sediment yield was estimated to be 701.2 t?km^?3?year.     

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images. Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere. For removing the qualitative aspect, tongue images are quantitatively inspected, proposing a novel disease classification model in an automated way is preferable. This article introduces a novel political optimizer with deep learning enabled tongue color image analysis (PODL-TCIA) technique. The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue. To attain this, the PODL-TCIA model initially performs image pre-processing to enhance medical image quality. Followed by, Inception with ResNet-v2 model is employed for feature extraction. Besides, political optimizer (PO) with twin support vector machine (TSVM) model is exploited for image classification process, shows the novelty of the work. The design of PO algorithm assists in the optimal parameter selection of the TSVM model. For ensuring the enhanced outcomes of the PODL-TCIA model, a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.     

A new method of reinforcing graphene nanoplatelets in glass/epoxy composites

This research aims to develop a method for the amalgamation of graphene nanoplatelets in glass/epoxy composites. The poor interface bonding between the fiber and matrix is critical and hinders the full performance of the composites. Glass fabric and epoxy were used as reinforcement and matrix in the composite, respectively. Graphene nanoplatelets were utilized as an additional nano-materials filler for the composites. Glass/graphene/epoxy and glass/epoxy composites were fabricated via vacuum infusion molding. The new method of applying graphene nanoplatelets as secondary reinforcement in the composite was developed based on proper functionalization in the sonication process. The physical, tensile, flexural, and short beam interlaminar properties of fabricated composites were examined to analyze the method's effectiveness. The results showed that density decreased by around 5 %; however, thickness increased by around 34 % after introducing graphene nanoplatelets into the composites. The tensile strength and modulus of the composites declined by approximately 19 %, on the other hand, flexural strength and modulus increased by around 63.3 % and 8.3 %, respectively, after the addition of graphene nanoplatelets into the composites. Moreover, interlaminar shear strength of the composite was enhanced by approximately 50 %.     

Characterization of methylumbilliferone (mendiaxon r)-induced choleresis in the isolated perfused rat liver

The influence of the choleretic drug methylumbilliferone on bile formation in the isolated perfused rat liver is characterized. The compound induces rapidly an elevation of bile flow, bile acid secretion and soium excretion. The increased production of bile is of canalicular origin. The choleretic effect was defined as "bile acid like" choleresis due to excretion of the drug into the bile. It is discussed that the excretion of methylumbilliferone can influence the transport of bile in form of a positive cooperation on transport mechanism.     

Real-Time Natural Disasters Detection and Monitoring from Smart Earth Observation Satellite

Current remote sensing satellites with multispectral sensors capture high-resolution images and produce vast quantities of data. The size and volume of this information has dramatically increased in the last decade as sensor resolution and capabilities have significantly improved, without a similar improvement on the satellite system capacity to accommodate these changes. Remote sensing satellites currently operate on a “store and forward” paradigm, where data is stored on the satellite until the satellite is in view of the ground station. Low Earth orbit satellites may only see a ground station for a 10–15 min window per pass, in which time all the collected information must be telemetered to the ground. This process requires large and expensive onboard storage resources and places tremendous stress on communication channels. Hence, a complete image may not be successfully telemetered in one pass causing a significant delay between capture and analysis and limiting the benefits of these images. Smart satellites are more technologically advanced, require less ground station support and data storage, and are capable of transmitting required information quickly and easily to ground stations. With onboard reconfigurable data processing, these satellites have faster data product turnaround, less communication requirements, and provide more useful information. The high performance computing (HPC-I) payload on board the Australian satellite FedSat, launched in December 2002, is a demonstration device of the feasibility of reconfigurable computing technology in space. This device is small in size, requires low power, and has the processing capacity to handle large data volumes. Using this device in conjunction with a high-resolution imaging sensor, such as the bispectral infrared detection (BIRD) sensor, smart dedicated satellites become a feasible and cost effective solution to remote sensing needs. This paper elaborates on the system level design of a real-time fire observation system in the context of a smart satellite mission for detecting and monitoring natural disasters. The proposed system is built upon flight tested field programmable gate arrays based HPC-I technology, and would be capable of producing useful information about natural disasters directly broadcasted to interested parties within rapid timeframes. The algorithms for onboard real-time detection of direction, intensity, and location of fires are discussed, and reliable algorithms for detecting and verifying these fires using smoke plume detection are presented. Further work is described including fire-front analysis and the tracking of fire movement.