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Environmental effects on gene expression and offspring development can be mediated by epigenetic modifications. It is well established that maternal diet influences DNA methylation patterns and phenotypes in the offspring; however, the epigenetic effects of paternal diet on developing offspring warrants further investigation. Here, we examined how a prepubertal methionine-enriched paternal diet affected sperm DNA methylation and its subsequent effects on embryo gene expression. Three treatment and three control rams were bred to seven ewes, and blastocysts were flushed for RNA extraction. Semen was collected from all rams and submitted for reduced representation bisulfite sequencing analysis. In total, 166 differentially methylated cytosines were identified in the sperm from treatment versus control rams. Nine genes were found to be differentially expressed in embryos produced from treatment versus control rams, and seven differentially methylated cytosines in the sperm were found to be highly correlated with gene expression in the embryos. Our results demonstrate that sperm methylation differences induced by diet may influence fetal programming.

Transgenerational epigenetic inheritance (TEI) requires transmission of environmentally induced epigenetic changes and associated phenotypes to subsequent generations without continued exposure to the environmental factor that originated the change. TEI is well-established in plants and Caenorhabditis elegans; however, occurrence in mammals is debated and poorly understood. Here, we examined whether paternal diet from weaning to puberty-induced changes in sperm DNA methylation that were transmitted to subsequent generations. Over 100 methylated cytosines, environmentally altered in the F0 generation, were inherited by the F1 and F2 generations. Furthermore, the F0 paternal diet was associated with growth and male fertility phenotypes in subsequent generations. Differentially methylated cytosines were correlated with gene expression. Our results demonstrate that some sperm methylation sites may escape DNA methylation erasure and are transmitted to subsequent generations despite the 2 waves of epigenetic programming: in primordial germ cells and in embryos after fertilization. These results advance our understanding of the complex relationships between nature and nurture.

A set of five vessel loading conditions was developed for exposed-waters towing vessels in support of compliance with applicable stability regulations invoked under the U.S. Code of Federal Regulations (CFR) Title 46, Subchapter M. These loading conditions are envisioned as a starting framework for the naval architect or third-party organization when pursuing a U.S. Coast Guard stability letter with the fewest operational restrictions. These conditions do not represent required operational scenarios. For each loading condition, variable loads based on both tank location and tank contents were specified with the goal of encouraging conservative stability evaluations, while maintaining a level of realism to the resulting vessel attitude at each condition. Use of these developed loading conditions as a replacement for the nearly forty-year-old McGowan and Meyer conditions is anticipated. Using 3D models and General HydroStatics stability software, three vessels representative of modern exposed-waters towing vessels, but designed before the enactment of Subchapter M, were tested against 46 CFR Subchapter S stability criteria at each loading condition. Results of the analysis are presented for each vessel and for each applicable Subchapter S criterion. As expected, vessels not designed for Subchapter M/Subchapter S stability regulations can have trouble passing using the proposed loading conditions. The authors experimented with simple changes to the tank geometry of these pre-Subchapter M vessels, creating compliance with nearly all stability criteria for all loading conditions. Based on relevant literature and the results of this work, it is recommended that, for conservatism, the free-to-trim method be used for stability analysis regardless of the loading conditions applied. It is recommended that if an exposed-waters towing vessel passes the applicable Subchapter S stability criteria using the loading conditions developed in this work, then the vessel should be considered for a stability letter with minimal operational restrictions.

© 2013 IEEE. The rapid advancements in computing, storage, communications, and networking technologies have enabled the creation of Digital Twins (DTs). A DT is a digital representation of a real-world physical component, product, or equipment. A DT can be used for 3-D design, testing, simulation, and prototyping prior to the manufacturing of the physical component. Once a physical component is in operation, a DT can be used for configuration, monitoring, diagnostics, and prognostics. It is expected that DTs will gain significant attention in the foreseeable future, and will play a key role in Industry 4.0. However, today's approaches, systems, and technologies leveraged for the creation of DTs are mostly centralized and fall short of providing trusted data provenance, audit, and traceability. Also, data related to transactions, logs, and history are not secure or tamper-proof. In this paper, we propose a blockchain-based creation process of DTs to guarantee secure and trusted traceability, accessibility, and immutability of transactions, logs, and data provenance. Our proposed approach uses smart contracts to govern and track transactions initiated by participants involved in the creation of DTs. Our approach also employs decentralized storage of interplanetary file systems to store and share DTs data. Moreover, we present details on our system design and architecture, implementation, and algorithms. Furthermore, we provide security and cost analysis, and show how our approach fulfills the requirements of DTs process creation. We make the smart contract code for creating DTs publicly available on Github.

INTRODUCTION: There is no clear unified definition of “county programs” in emergency medicine (EM). Key residency directories are varied in designation, despite it being one of the most important match factors for applicants. The Council of Residency Directors EM County Program Community of Practice consists of residency program leadership from a unified collective of programs that identify as “county.” This paper's framework was spurred from numerous group discussions to better understand unifying themes that define county programs. METHODOLOGY: This institutional review board–exempt work provides qualitative descriptive results via a mixed‐methods inquiry utilizing survey data and quantitative data from programs that self‐designate as county. UNIQUE TREATMENT, ANALYSIS AND CRITIQUE: Most respondents work, identify, and trained at a county program. The majority defined county programs by commitment to care for the underserved, funding from the city or state, low‐resourced, and urban setting. Major qualitative themes included mission, clinical environment, research, training, and applicant recommendations. Comparing the attributes of programs by self‐described type of training environment, county programs are typically larger, older, in central metro areas, and more likely to be 4 years in duration and have higher patient volumes when compared to community or university programs. When comparing hospital‐level attributes of primary training sites county programs are more likely to be owned and operated by local governments or governmental hospital districts and authorities and see more disproportionate‐share hospital patients. IMPLICATIONS FOR EDUCATION AND TRAINING IN EM: To be considered a county program we recommend some or most of the following attributes be present: a shared mission to medically underserved and vulnerable patients, an urban location with city or county funding, an ED with high patient volumes, supportive of resident autonomy, and research expertise focusing on underserved populations.

Background Free Open-Access Medical education (FOAM) use among residents continues to rise. However, it often lacks quality assurance processes and residents receive little guidance on quality assessment. The Academic Life in Emergency Medicine Approved Instructional Resources tool (AAT) was created for FOAM appraisal by and for expert educators and has demonstrated validity in this context. It has yet to be evaluated in other populations. Objectives We assessed the AAT's usability in a diverse population of practicing emergency medicine (EM) physicians, residents, and medical students; solicited feedback; and developed a revised tool. Methods As part of the Medical Education Translational Resources: Impact and Quality (METRIQ) study, we recruited medical students, EM residents, and EM attendings to evaluate five FOAM posts with the AAT and provide quantitative and qualitative feedback via an online survey. Two independent analysts performed a qualitative thematic analysis with discrepancies resolved through discussion and negotiated consensus. This analysis informed development of an initial revised AAT, which was then further refined after pilot testing among the author group. The final tool was reassessed for reliability. Results Of 330 recruited international participants, 309 completed all ratings. The Best Evidence in Emergency Medicine (BEEM) score was the component most frequently reported as difficult to use. Several themes emerged from the qualitative analysis: for ease of use-understandable, logically structured, concise, and aligned with educational value. Limitations include deviation from questionnaire best practices, validity concerns, and challenges assessing evidence-based medicine. Themes supporting its use include evaluative utility and usability. The author group pilot tested the initial revised AAT, revealing a total score average measure intraclass correlation coefficient (ICC) of moderate reliability (ICC = 0.68, 95% confidence interval [CI] = 0 to 0.962). The final AAT's average measure ICC was 0.88 (95% CI = 0.77 to 0.95). Conclusions We developed the final revised AAT from usability feedback. The new score has significantly increased usability, but will need to be reassessed for reliability in a broad population.

Accurate micro-crack detections on the whole surface of civil structures have great significance. Distributed optical fiber sensor based on Brillouin optical time-domain analysis technology exhibits great facility to measure strain distributions along the whole surface of structures with a high spatial resolution, thus providing a potential and competitive solution to the detection problem. However, mainly due to low signal-to-noise ratio in measurements, such sensor system is still limited in crack detection–based structural health monitoring applications. How to extract high-quality micro-crack feature representations from the low signal-to-noise ratio–distributed strain measurements is crucial to solve the problem. It has been demonstrated in field of pattern recognition that deep learning can automatically extract high-quality noise-robust feature representations from mass chaos data. Therefore, a micro-crack detection method is proposed herein based on deep learning to analyze the full-scale strain measurements. Each measurement is normalized and segmented into a set of equal-length subsequences. Autoencoders, a typical kind of building block of deep neural network, are stacked layer-wise into a deep network and then exploited to automatically extract feature representations from the subsequences. Each extracted feature representation is labeled as one of the two categories by a Softmax regression. One category originates in the subsequences acquired from structure sections with crack defects and another from sections without any cracks. The micro-crack detections are achieved by solving such a crack/non-crack binary classification problem. A 15-m-long steel I-beam with artifact crack defects is built up in laboratory to verify the proposed method. Experimental results demonstrate that the minimum size of detectable crack opening width reaches to 23 μm, and besides, the proposed method is significantly better than traditional Fisher linear discriminant analysis method and classical support vector machine on the detection accuracy.

Traffic monitoring, including the estimation of axle and gross vehicle weights of passing trucks over bridges plays an important role in the determination of actual service loads. Knowledge of thes...

A method for distributed detection of damage along the lengths of multi-span continuous Bridges is introduced. It is based on monitoring the distributed strains along the length of the bridge by using Brillouin scattering based optical fiber sensors. Identification of damage beyond the signal to noise limits of the instrument is achieved by normalization of the measured distributed strains during multiple positions of a truck with respect to the theoretical influence line of the bridge. Formulation of the method leads to the introduction of a damage index for identification of microcrack locations along the spans of the Bridge. Effectiveness of the proposed method was evaluated by load testing of a five span continuous precast post-tensioned concrete box girder bridge. Locations of detected microcracks by the distributed sensor system were further verified by visual inspections.

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. We propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL's BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

BACKGROUND: The Association of American Medical Colleges instituted a standardized video interview (SVI) for all applicants to emergency medicine (EM). It is unclear how the SVI affects a faculty reviewer's decision on likelihood to invite an applicant (LTI) for an interview. OBJECTIVES: The objective was to determine whether the SVI affects the LTI. METHODS: Nine Accreditation Council of Graduate Medication Education (ACGME)‐accredited EM residency programs participated in this prospective, observational study. LTI was defined on a 5‐point Likert scale as follows: 1 = definitely not invite, 2 = likely not invite, 3 = might invite, 4 = probably invite, 5 = definitely invite. LTI was recorded at three instances during each review: 1) after typical screening (blinded to the SVI), 2) after unblinding to the SVI score, and 3) after viewing the SVI video. RESULTS: Seventeen reviewers at nine ACGME‐accredited residency programs participated. We reviewed 2,219 applications representing 1,424 unique applicants. After unblinding the SVI score, LTI did not change in 2,065 (93.1%), increased in 85 (3.8%) and decreased in 69 (3.1%; p = 0.22). In subgroup analyses, the effect of the SVI on LTI was unchanged by United States Medical Licensing Examination score. However, when examining subgroups of SVI scores, the percentage of applicants in whom the SVI score changed the LTI was significantly different in those that scored in the lower and upper subgroups (p

Additional file 3: Electronic Supplementary File 3. Forest plots for outcome variables. Black filled squares represent the mean and 95% confidence interval for individual studies. Filled diamond represents mean and 95% confidence interval for all pooled results.

Additional file 2: Electronic Supplementary File 2. Risk of bias table.

Additional file 1: Electronic Supplementary Material File 1. Search strategy.

Knowledge of non-genomic inheritance of traits is currently limited. Although it is well established that maternal diet influences offspring inheritance of traits through DNA methylation, studies on the impact of prepubertal paternal diet on DNA methylation are rare. This study aimed to evaluate the impact of prepubertal diet in Polypay rams on complex traits, DNA methylation, and transmission of traits to offspring. A total of 10 littermate pairs of F0 rams were divided so that one ram was fed a control diet, and the other was fed the control diet with supplemental methionine. Diet was associated with earlier age at puberty in treatment vs. control F0 rams. F0 treatment rams tended to show decreased pubertal weight compared to control rams; however, no differences were detected in overall growth. A total of ten F0 rams were bred, and the entire F1 generation was fed a control diet. Diet of F0 rams had a significant association with scrotal circumference (SC) and weight at puberty of F1 offspring. The paternal diet was not significantly associated with F1 ram growth or age at puberty. The DNA methylation of F0 ram sperm was assessed, and genes related to both sexual development (e.g., DAZAP1, CHD7, TAB1, MTMR2, CELSR1, MGAT1) and body weight (e.g., DUOX2, DUOXA2) were prevalent in the data. These results provide novel information about the mechanisms through which the prepubertal paternal diet may alter body weight at puberty and sexual development.

Building energy code compliance is the crucial link between the actual energy savings and the efficiency prescribed in energy codes. A research project aiming to identify opportunities to reduce energy consumption in new single-family residential construction by increasing compliance with the building energy code was conducted in several states of the United States. The study was comprised of three phases: (1) a baseline study to document typical practice and identify opportunities for improvement based on empirical data gathered from the field; (2) an education and training phase targeting the opportunities identified; and (3) a post-study to assess whether a reduction in average state-wide energy use could be achieved following the education and training phase. We proposed a novel methodology based on large-scale building energy simulation inferred by limited field data to assess the performance of a large population of homes. This paper presents the methodology, findings, and results of this study. The state-wide average energy consumption decreased at Phase III from Phase I for five of the seven states involved in the analysis. The measure-level savings potential analysis shows an overall reduction. Overall, the training and education phase plays a recognizable role in improving compliance with building energy codes.

CSforALL is collaborating with the US Air Force JROTC HQ and additional partners to launch JROTC-CS, a demonstration project to design and test implementation models for the long-term scale up of CS and cybersecurity education programs within the Junior Reserve Officers Training Corps (JROTC). There are approximately 500K Junior Reserve Officer Training Corps (JROTC) cadets at 3,400 partner high schools across the US. The JROTC represents a significant pool of public service oriented talent. The JROTC program is comprised of a highly diverse population, with a majority-minority student population and 40% of the JROTC being female, and JROTC is strongly represented in schools serving economically disadvantaged populations (over 50% Title 1 schools). The JROTC infrastructure is under-leveraged as a technology talent development opportunity, and represents an accessible, high ROI/low cost opportunity to accelerate preparation of the future workforce in critical areas such as computing, cybersecurity, defense technologies, AI and more. The JROTC-CS Demonstration project aims to design and test experiences for the participating districts, schools, teachers, JROTC instructors, and cadets that lead to a comprehensive implementation model for future scale across the JROTC infrastructure. An estimated additional 60K students will benefit from this project, as capacity for CS and cyber security is increased at their schools.

Early detection of defects and anomalies is important for safety and efficient management of structural elements. Brillouin scattering–based optical fiber sensors provide distributed sensing capabilities by monitoring the strain and temperature over large distances in structural elements. Their use has been limited to oil and gas explorations, mainly due to the inherent low signal-to-noise ratio in such systems, preventing detection of microcracks in structural monitoring applications. This study introduces a method based on the visual saliency approach through which the digital images acquired by the distributed strain data are employed for the detection of surface microcracks. When using this method, strain data sequences along the entire length of a structural element are sampled with a Brillouin scattering–based optical fiber sensor and then divided into a set of equal-length subsequences. A similarity measure matrix is composed based on the distributed strain data and then converted into a grayscale image. The saliency maps of the acquired grayscale images are calculated and a center-hollowed square template is defined and exploited for convolution with the binarized saliency map as a filter operator. The pixels retained after the filtering correspond to the locations of microcracks. Verification of the method was accomplished by experiments on a 15-m-long steel beam with fabricated defects.

The work presented in this article describes development of a machine learning (ML)-based platform for condition assessment of building structures in the aftermath of extreme events. The methods employed in the study include support vector machines, neural networks, and Gaussian Naive Bayes techniques for training of the structural health monitoring model. The ML platform relates the change in stiffness, and strains in various structural components of the system to the intensity and location of damage. Evaluation of the proposed method was accomplished by using it for the characterization of damage in a turn-of-the-century, six-story building with timber frames and masonry walls. The building was damaged due to differential settlement of its foundation. Application of the proposed method in the building required load testing of selected structural elements, and use of the strains acquired from the field tests as input to the ML model.

Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in temperature and load biases from using an increasing number of weather stations over the western U.S. Our novel approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to evaluate the ability to capture weather patterns across different seasons. Using 8 stations across the western U.S., one from each IECC climate zone, results in an average absolute summertime temperature bias of ∼4.0 °C with respect to a high-resolution gridded dataset. The mean absolute bias drops to ∼1.5 °C using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.5%. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20–40% bias of peak building loads during both summer and winter, a significant error for capacity expansion planners who may use these types of simulations. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.

In this paper, a modified construction for the traditional ϕ -OTDR fiber sensor sensitive for all of distance is presented, the related numerical simulation and experiment analysis results show that this construction can reduce the gain imbalance for all of the distance along the fiber caused by the Rayleigh scattering loss of the fiber and the gain imbalance of Raman fiber amplifier in this fiber sensor system. In order to improve further the vibration sensitivity of this system, the possible methods to restrain the influences of modulation instability effect, Stimulated Brillouin effect, reduce the amplified spontaneous emission (ASE) noises of Raman laser (RL) and Erbium3+-doped fiber amplifiers (EDFA), double Rayleigh backscattering noise in this system are discussed, which will offer a great reference value for the science research and engineering application in the field of fiber sensor as we believe.

Social media is a prevalent part of today’s society. Its main use is for communication. Social media has become ingrained in the daily lives of digital natives. As educators continue to engage their learners, social media has become a tool for education. This chapter examines social media’s role in daily life and how educators are harnessing the power of social media to build connections, enhance education, and further their career. Additionally, this chapter provides information on how to use a wide variety of social media platforms and provides best practices to avoiding violating HIPAA when posting medical content online. Electronic communication between physicians and patients is also explored.

Author(s): Egan, Daniel J.; Husain, Abbas; Bond, Michael C.; Caputo, William; Cygan, Lukasz; VanDermark, Jeff; Shoenberger, Jan M.; Li, Ida; Krauss, William; Bronner, Jonathan; White, Melissa; Chung, Arlene S.; Shah, Kaushal H.; Taylor, Todd; Silver, Matthew; Ardolic, Brahim; Weizberg, Moshe | Abstract: Introduction: In 2017, the Standardized Video Interview (SVI) was required for applicants to emergency medicine (EM). The SVI contains six questions highlighting professionalism and interpersonal communication skills. The responses were scored (6-30). As it is a new metric, no information is available on correlation between SVI scores and other application data. This study was to determine if a correlation exists between applicants’ United States Medical Licensing Examination (USMLE) and SVI scores. We hypothesized that numeric USMLE Step 1 and Step 2 Clinical Knowledge (CK) scores would not correlate with the SVI score, but that performance on the Step 2 Clinical Skills (CS) portion may correlate with the SVI since both test communication skills. Methods: Nine EM residency sites participated in the study with data exported from an Electronic Residency Application Service (ERAS®) report. All applicants with both SVI and USMLE scores were included. We studied the correlation between SVI scores and USMLE scores. Predetermined subgroup analysis was performed based on applicants’ USMLE Step 1 and Step 2 CK scores as follows: (≥ 200, 201-220, 221-240, 241-260, g260). We used linear regression, the Kruskal-Wallis test and Mann-Whitney U test for statistical analyses. Results: 1,325 applicants had both Step 1 and SVI scores available, with no correlation between the overall scores (p=0.58) and no correlation between the scores across all Step 1 score ranges, (p=0.29). Both Step 2 CK and SVI scores were available for 1,275 applicants, with no correlation between the overall scores (p=0.56) and no correlation across all ranges, (p=0.10). The USMLE Step 2 CS and SVI scores were available for 1,000 applicants. Four applicants failed the CS test without any correlation to the SVI score (p=0.08). Conclusion: We found no correlation between the scores on any portion of the USMLE and the SVI; therefore, the SVI provides new information to application screeners.