Demand flexibility management for buildings-to-grid integration with uncertain generation

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A Case of Atypical McCune-Albright Syndrome with Vaginal Bleeding

Abstract Background: McCune-Albright syndrome (MAS) is a rare non-inherited disorder characterized by the clinical triad of precocious puberty, cafe-au-lait skin lesions, and fibrous dysplasia of bone. Case Presentation: We report a girl with MAS, presenting initially with vaginal bleeding at the age of 17 months. Ultrasonography revealed unilateral ovarian cysts and ureteral and ovarian enlargement. Considering the clinical and paraclinical findings, the patient diagnosed as a case of gonadotropin-independent precocious puberty was treated with medroxy-progestrone acetate (MPA) for three months. During the follow up, recurrent episodes of bleeding, ovarian activation and cyst formation, as well as breast size development were reported. At the age of 5.5 years, fibrous dysplasia was detected, which in coexistence with precocious puberty confirmed the diagnosis of MAS. The patient had no cafe-au-lait skin macles during follow up. Conclusion: Considering that clinical manifestations of MAS appear later in the course of recurrent periods of ovarian activation and cyst formation, a careful clinical observation and follow up of patients is necessary and the diagnosis of MAS must be kept in mind in cases with gonadotropinindependent precocious puberty

Correlational Analysis of Agronomic and Seed Quality Traits in Camelina sativa Doubled Haploid Lines under Rain-Fed Condition

Camelina (Camelina sativa (L.) Crantz) is an emerging industrial crop from the Brassicaceae family, with its seed oil and cake being used for food, feed, and fuel applications. In this study, the relationships between economically important agronomic traits including seed yield (SY), days to maturity (DM), 1000-seed weight (TSW), seed protein content (PC), seed oil content (OC), and fatty acid composition in 136 doubled haploid (DH) camelina lines were investigated under rain-fed conditions in two consecutive years. There was prominent diversity among the studied DH lines for the agronomic traits such as seed yield, erucic acid, omega3, protein content, etc. Based on the Pearson correlation analysis of the data, SY was positively correlated with DM and OC, and negatively correlated with TSW, PC, and linolenic acid (C18:3) content. The positive relationships of the main characteristics, relevant to industrial applications, suggest the feasibility of developing new higher-yielding camelina cultivars with high seed oil content. The high seed yield of some camelina lines (DH044 and DH075) during the two growing seasons showed the potential of the lines. On the other hand, the contrasting genotypes for key traits in this study promised a favorable source to develop the superior breeding lines with higher seed yield and food/nonfood traits. Therefore, it can be concluded that the diversity of camelina DH lines traits is crucial for developing new cultivars. Furthermore, the present study reports some significant correlations among the DH lines, which may be useful for the current and future camelina breeding program

ENIGMA-Sleep:Challenges, opportunities, and the road map

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine

Frequency of blaKHM-1, blaIMP-1,2 and blaSPM-1 genes in clinical isolates of metallo β- lactamase producing Pseudomonas aeruginosa in hospitalized burned patients in Ghotbeddin Shirazi Hospital

Background: Metallo-β-lactamase (MBL) producing Pseudomonas aeruginosa is an important gram negative opportunistic bacterium in hospitals which its increasing number is of clinicians’ concerns. Objective: The aim of this study was to evaluate the frequency of blaIMP-1, blaIMP-2, blaSPM-1 and blaKHM-1 genes in clinical isolates of MBL producing Pseudomonas aeruginosa in hospitalized burned patients in Ghotbeddin Shirazi Center. Methods: This cross-sectional study was conducted in 210 burn wound samples from 2012 to 2013. Sensitivity of confirmed Pseudomonas aeruginosa was examined for standard antimicrobial agents using disk diffusion method. Detection of MBL producing isolates was performed by the double disk synergy test (DDST) and the desired genes were detected by PCR. Data were analyzed using Chi-square test. Findings: By the phenotypic methods, 42 isolates (20%) were identified as Pseudomonas aeruginosa that were resistant to the most studied antibiotics including Carbapenem (100%) and were only sensitive to Colicitin (100%). 26 isolates (61.9%) were identified as MBL producing Pseudomonas aeruginosa. 9 isolates (34.61%) carried the blaIMP-2 and blaKHM-1 genes. The blaIMP-1 and blaSPM-1 genes were not found in any of the isolates. Conclusion: With regards to the results, it is suggested to periodically study the reasons for antibiotic resistance in each center. Keywords: Pseudomonas aeruginosa, Beta-lactamase IMP-1, Burns, Hospital

Determination of Risk Factors Affecting Survival of Patients with Gastric Adenocarcinoma in Hamadan, Iran

Background: Gastric cancer is the second leading cause of cancer death. The aim of this study was to determine the survival rate affected by risk factors in patients with gastric adenocarcinoma. Methods: We performed this retrospective cohort study on patients diagnosed with gastric adenocarcinoma during 2005-2012 in Hamadan, Iran. All patients with pathological diagnosis enrolled in the study. The effects of patients' demographical and pathological data were assessed in terms of survival. The univariate and multivariate Weibull models were used to determine the effects of these factors on survival rate. Data was analyzed by SPSS16 and STATA10 software. Results: A total of 112 gastric adenocarcinoma patients were followed. Patients included 74 ( 66.1) males. During the follow-up, 102 ( 91.1) patients died. Patients' had a mean ( SD) survival of 21.9 ( 1.9) months and a median survival of 15 months. The "one-, three-and five-year survival rates were 62, 16 and 9 respectively. The results showed that metastasis, chemotherapy, tumor site and grade had statistically significant impacts on patient survival. Conclusion: A potentially important role for tumor grade, tumor site, metastasis, and pathologic stage of disease existed in terms of patient survival after surgery. The current research has indicated that neoadjuvant treatment increased survival in patients with gastric adenocarcinoma. It is expected that the prognostic model based on the mentioned factors may assist individual risk stratification and help in the planning of potential forthcoming studies

Predicting falls and injuries in people with multiple sclerosis using machine learning algorithms

Falls in people with Multiple Sclerosis (PwMS) is a serious issue. It can lead to a lot of problems including sustaining injuries, losing consciousness and hospitalization. Having a model that can predict the probability of these falls and the factors correlated with them and can help caregivers and family members to have a clearer understanding of the risks of falling and proactively minimizing them. We used historical data and machine learning algorithms to predict three outcomes: falling, sustaining injuries and injury types caused by falling in PwMS. The training dataset for this study includes 606 examples of monthly readings. The predictive attributes are the following: Expanded Disability Status Scale (EDSS), years passed since the diagnosis of MS, age of participants in the beginning of the experiment, participants� gender, type of MS and season (or month). Two types of algorithms, decision tree and gradient boosted trees (GBT) algorithm, were used to train six models to answer these three outcomes. After the models were trained their accuracy was evaluated using cross-validation. The models had a high accuracy with some exceeding 90. We did not limit model evaluation to one-number assessments and studied the confusion matrices of the models as well. The GBT had a higher class recall and smaller number of underestimations, which make it a more reliable model. The methodology proposed in this study and its findings can help in developing better decision-support tools to assist PwMS. © 2021 Elsevier Lt

A Scalable and Lightweight Grouping Proof Protocol for Internet of Things Applications

The Internet of Things (IoT) is a new technology, which enables objects to exchange data via the internet network. One part of the infrastructure of IoT is Radio Frequency Identification (RFID). One way to fortify the system and prevent it against an unauthorized access is an authentication process. A grouping proof protocol is a protocol by which a reader authenticates two or more tags simultaneously in an authentication process. In this paper, we present a novel scalable grouping proof protocol. Since scalability is a challenge in grouping proof protocol, to solve the scalability problem in the proposed protocol, the reader broadcasts the messages and the tags respond to it independently. In terms of the performance, we use a 64-bit lightweight Pseudo-Random Number Generator (64-PRNG) function, which meets the needs of low-power and low-cost systems. In addition, the security analysis results prove that the proposed protocol is resistant against RFID threats and provides an acceptable security level and low computation cost. © 2017, Springer Science+Business Media, LLC

A Scalable and Lightweight Grouping Proof Protocol for Internet of Things Applications

The Internet of Things (IoT) is a new technology, which enables objects to exchange data via the internet network. One part of the infrastructure of IoT is Radio Frequency Identification (RFID). One way to fortify the system and prevent it against an unauthorized access is an authentication process. A grouping proof protocol is a protocol by which a reader authenticates two or more tags simultaneously in an authentication process. In this paper, we present a novel scalable grouping proof protocol. Since scalability is a challenge in grouping proof protocol, to solve the scalability problem in the proposed protocol, the reader broadcasts the messages and the tags respond to it independently. In terms of the performance, we use a 64-bit lightweight Pseudo-Random Number Generator (64-PRNG) function, which meets the needs of low-power and low-cost systems. In addition, the security analysis results prove that the proposed protocol is resistant against RFID threats and provides an acceptable security level and low computation cost. © 2017, Springer Science+Business Media, LLC