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There is a lot of interest in the policymakers’ plans to have grids run entirely on renewable energy in the upcoming years. As a result of the high renewable penetration, future large interconnected grids (LIGs) become more volatile and difficult to operate. To understand future grids better, the planning studies of future grids must consider a wide range of operating conditions and contingencies. In this paper, we propose a novel future grid planning framework that can simulate a wide range of operating conditions and contingencies while considering future policy and topology changes, production cost models, dynamics, steady state AC power flow, and cascade constraints. Using this proposed framework, we propose an approach to identify voltage stability critical locations in the future grid without assumptions on switching devices and load change trajectories during system collapse, while also considering the cascading/time-series behavior of future LIGs. The proposed future grid planning framework is used to generate 1.485 terabytes of dynamic simulation data for 2028 WECC system containing 4455 scenarios. The proposed voltage stability approach is demonstrated on the 2028 representation of the WECC system, and IEEE 30, 300-bus systems.

The 100% renewable energy targets from the policymakers for the future grids have drawn a significant amount of interest. The high renewable penetration made future large interconnected grids (LIGs) more volatile and harder to understand using historical observations. This led to the need to study future LIGs using a wide range of future-year operating conditions and contingencies. However, existing planning tools are not sufficient for the dynamic security assessment (DSA) of these future LIGs due to a lack of detailed modeling capabilities and computational limitations when processing a wide range of scenarios. This paper addresses these two challenges by proposing; 1) an efficient modeling framework that can generate large grid’s dynamic data with cascade behaviors for a wide range of scenarios. This data is generated by respecting the constraints of production cost models and their respective AC power flow dynamic simulation models at an hourly resolution; 2) an unsupervised machine learning(ML)-based approach for fast scanning the DSA data. The proposed approach uses feature engineering techniques and fast Fourier transforms to transform the time series signals into visually distinguishable frequency domain signals for DSA. The proposed simulation framework is used to generate 1.485 terabytes of dynamic simulation data for the 2028 WECC system containing 4455 scenarios. The proposed ML framework used the 2028 WECC system to demonstrate its effectiveness and speed in identifying critical scenarios.

ImportanceMortality prediction among critically ill patients in resource limited settings is difficult. Identifying the best mortality prediction tool is important for counseling patients and families, benchmarking quality improvement efforts, and defining severity of illness for clinical research studies.ObjectiveCompare predictive capacity of the Modified Early Warning Score (MEWS), Universal Vital Assessment (UVA), Tropical Intensive Care Score (TropICS), Rwanda Mortality Probability Model (R-MPM), and quick Sequential Organ Failure Assessment (qSOFA) for hospital mortality among adults admitted to a medical-surgical intensive care unit (ICU) in rural Kenya. We performed a pre-planned subgroup analysis among ICU patients with suspected infection.Design, setting, and participantsProspective single-center cohort study at a tertiary care, academic hospital in Kenya. All adults 18 years and older admitted to the ICU January 2018–June 2019 were included.Main outcomes and measuresThe primary outcome was association of clinical prediction tool score with hospital mortality, as defined by area under the receiver operating characteristic curve (AUROC). Demographic, physiologic, laboratory, therapeutic, and mortality data were collected. 338 patients were included, none were excluded. Median age was 42 years (IQR 33–62) and 61% (n = 207) were male. Fifty-nine percent (n = 199) required mechanical ventilation and 35% (n = 118) received vasopressors upon ICU admission. Overall hospital mortality was 31% (n = 104). 323 patients had all component variables recorded for R-MPM, 261 for MEWS, and 253 for UVA. The AUROC was highest for MEWS (0.76), followed by R-MPM (0.75), qSOFA (0.70), and UVA (0.69) (p < 0.001). Predictive capacity was similar among patients with suspected infection.Conclusion and relevanceAll tools had acceptable predictive capacity for hospital mortality, with variable observed availability of the component data. R-MPM and MEWS had high rates of variable availability as well as good AUROC, suggesting these tools may prove useful in low resource ICUs.

Although resting-state functional magnetic resonance imaging (rsfMRI) has the potential to offer insights into changes in functional connectivity networks after traumatic brain injury (TBI), there are few studies that examine the effects of moderate TBI for monitoring functional recovery in experimental TBI, and thus the neural correlates of brain recovery from moderate TBI remain incompletely understood. Non-invasive rsfMRI was used to longitudinally investigate changes in interhemispheric functional connectivity (IFC) after a moderate TBI to the unilateral sensorimotor cortex in rats (n?=?9) up to 14 days. Independent component analysis of the rsfMRI data was performed. Correlations of rsfMRI sensorimotor networks were made with changes in behavioral scores, lesion volume, and T2- and diffusion-weighted images across time. TBI animals showed less localized rsfMRI patterns in the sensorimotor network compared to sham (n?=?6) and normal (n?=?5) animals. rsfMRI clusters in the sensorimotor network showed less bilateral symmetry compared to sham and normal animals, indicative of IFC disruption. With time after injury, many of the rsfMRI patterns in the sensorimotor network showed more bilateral symmetry, indicative of IFC recovery. The disrupted IFC in the sensorimotor and subsequent partial recovery showed a positive correlation with changes in behavioral scores. Overall, rsfMRI detected widespread disruption and subsequent recovery of IFC within the sensorimotor networks post-TBI, which correlated with behavioral changes. Therefore, rsfMRI offers the means to probe functional brain reorganization and thus has the potential to serve as an imaging marker to longitudinally stage TBI and monitor for novel treatments.

IntroductionThis study aimed to quantify the total cost of violent firearm-related offenses in British Columbia in 2016 Canadian dollars over a five-year period, 2012 to 2016. The purposes of this study were to estimate the direct costs to the health care system and indirect costs to society for violent firearm injuries and deaths; and to estimate criminal justice system costs pertaining to firearm incidents.MethodsHuman and economic costs to the health care system and productivity losses were calculated using health administrative datasets such as B.C. Vital Statistics and Discharge Abstract Database. Criminal justice system costs pertaining to firearm incidents were estimated by applying weighted average costs to aggregate expenditures using methodology consistent with that used by Statistics Canada.ResultsThere was a total of 108 deaths and 245 hospitalizations resulting from violent firearm injuries. The total estimated cost of all violent firearm crime averaged $294,378,985 per year; human costs averaged $188,416,841 per year, where health care costs averaged $3,910,317 per year, productivity losses from workforce and household averaged $17,299,054 and $4,559,470 per year, respectively, and loss of life averaged $162,648,000; and $105,021,145 in criminal justice system costs, and $941,000 in programming costs.ConclusionThis study clearly demonstrates the significant cost of violent firearm injury in British Columbia and the impacts on the health care system, criminal justice system, and to society at large, particularly within the criminal justice system where the costs were significantly higher than health care.

Abstract Purpose This study used machine learning classification of texture features from MRI of breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular subtypes to predict eventual pathological complete response (PCR) to neoadjuvant chemotherapy. Materials and method This study employed a subset of patients (N = 166) with PCR data from the I-SPY-1 TRIAL (2002–2006). This cohort consisted of patients with stage 2 or 3 breast cancer that underwent anthracycline–cyclophosphamide and taxane treatment. Magnetic resonance imaging (MRI) was acquired pre-neoadjuvant chemotherapy, early, and mid-treatment. Texture features were extracted from post-contrast-enhanced MRI, pre- and post-contrast subtraction images, and with morphological dilation to include peri-tumoral tissue. Molecular subtypes and Ki67 were also included in the prediction model. Performance of classification models used the receiver operating characteristics curve analysis including area under the curve (AUC). Statistical analysis was done using unpaired two-tailed t-tests. Results Molecular subtypes alone yielded moderate prediction performance of PCR (AUC = 0.82, p = 0.07). Pre-, early, and mid-treatment data alone yielded moderate performance (AUC = 0.88, 0.72, and 0.78, p = 0.03, 0.13, 0.44, respectively). The combined pre- and early treatment data markedly improved performance (AUC = 0.96, p = 0.0003). Addition of molecular subtypes improved performance slightly for individual time points but substantially for the combined pre- and early treatment (AUC = 0.98, p = 0.0003). The optimal morphological dilation was 3–5 pixels. Subtraction of post- and pre-contrast MRI further improved performance (AUC = 0.98, p = 0.00003). Finally, among the machine-learning algorithms evaluated, the RUSBoosted Tree machine-learning method yielded the highest performance. Conclusion AI-classification of texture features from MRI of breast tumor at multiple treatment time points accurately predicts eventual PCR. Longitudinal changes in texture features and peri-tumoral features further improve PCR prediction performance. Accurate assessment of treatment efficacy early on could minimize unnecessary toxic chemotherapy and enable mid-treatment modification for patients to achieve better clinical outcomes.

Recent advancement in power systems induces complexity in large-scale interconnected systems and poses challenges in performing security assessment studies at various operating conditions. Traditional model-based methods are computationally intensive and may not meet the requirements for real-time applications. This paper presents a novel data-driven framework for accelerating the process of obtaining multiple AC power flow (ACPF) solutions for large systems using deep convolutional neural networks (DCNN). DCNN models are designed and trained using various representative power flow cases from a system, whose outputs can be used to perform steady-state security assessment studies. Distributed training with multiple Graphical processing units (GPU)s is implemented using TensorFlow to reduce computation time. The proposed framework is implemented to identify critical buses and recognize the ACPF cases expected to cause steady-state bus voltage violations. The efficacy and feasibility of the proposed framework are evaluated on the Western Electricity Coordinating Council (WECC) 2028 system. Results demonstrate that the proposed framework is highly accurate and possess good interpretability in performing various transmission planning and operation assessment studies for large scale power networks.

BackgroundCardiovascular diseases (CVDs) are the leading cause of death worldwide and are increasingly affecting younger populations, particularly African Americans in the southern United States. Access to preventive and therapeutic services, biological factors, and social determinants of health (ie, structural racism, resource limitation, residential segregation, and discriminatory practices) all combine to exacerbate health inequities and their resultant disparities in morbidity and mortality. These factors manifest early in life and have been shown to impact health trajectories into adulthood. Early detection of and intervention in emerging risk offers the best hope for preventing race-based differences in adult diseases. However, young-adult populations are notoriously difficult to recruit and retain, often because of a lack of knowledge of personal risk and a low level of concern for long-term health outcomes. ObjectiveThis study aims to develop a system design for the MOYO mobile platform. Further, we seek to addresses the challenge of primordial prevention in a young, at-risk population (ie, Southern-urban African Americans). MethodsUrban African Americans, aged 18 to 29 years (n=505), participated in a series of co-design sessions to develop MOYO prototypes (ie, HealthTech Events). During the sessions, participants were orientated to the issues of CVD risk health disparities and then tasked with wireframing prototype screens depicting app features that they considered desirable. All 297 prototype screens were subsequently analyzed using NVivo 12 (QSR International), a qualitative analysis software. Using the grounded theory approach, an open-coding method was applied to a subset of data, approximately 20% (5/25), or 5 complete prototypes, to identify the dominant themes among the prototypes. To ensure intercoder reliability, 2 research team members analyzed the same subset of data. ResultsOverall, 9 dominant design requirements emerged from the qualitative analysis: customization, incentive motivation, social engagement, awareness, education, or recommendations, behavior tracking, location services, access to health professionals, data user agreements, and health assessment. This led to the development of a cross-platform app through an agile design process to collect standardized health surveys, narratives, geolocated pollution, weather, food desert exposure data, physical activity, social networks, and physiology through point-of-care devices. A Health Insurance Portability and Accountability Act–compliant cloud infrastructure was developed to collect, process, and review data, as well as generate alerts to allow automated signal processing and machine learning on the data to produce critical alerts. Integration with wearables and electronic health records via fast health care interoperability resources was implemented. ConclusionsThe MOYO mobile platform provides a comprehensive health and exposure monitoring system that allows for a broad range of compliance, from passive background monitoring to active self-reporting. These study findings support the notion that African Americans should be meaningfully involved in designing technologies that are developed to improve CVD outcomes in African American communities.

Doxorubicin (DOX) is an extremely effective and wide-spectrum anticancer drug, but its long-term use can lead to heart failure, which presents a serious problem to millions of cancer survivors who have been treated with DOX. Thus, identifying agents that can reduce DOX cardiotoxicity and concurrently enhance its antitumor efficacy would be of great clinical value. In this respect, the classical antidiabetic drug metformin (MET) has stood out, appearing to have both antitumor and cardioprotective properties. MET is proposed to achieve these beneficial effects through the activation of AMP-activated protein kinase (AMPK), an essential regulator of mitochondrial homeostasis and energy metabolism. AMPK itself has been shown to protect the heart and modulate tumor growth under certain conditions. However, the role and mechanism of the hypothesized MET-AMPK axis in DOX cardiotoxicity and antitumor efficacy remain to be firmly established by in vivo studies using tumor-bearing animal models and large-scale prospective clinical trials. This review summarizes currently available literature for or against a role of AMPK in MET-mediated protection against DOX cardiotoxicity. It also highlights the emerging evidence suggesting distinct roles of the AMPK subunit isoforms in mediating the functions of unique AMPK holoenzymes composed of different combinations of isoforms. Moreover, the review provides a perspective regarding future studies that may help fully elucidate the relationship between MET, AMPK and DOX cardiotoxicity.

Emerging evidence identifies a potent role for aerobic exercise to modulate activity of neurons involved in regulating appetite; however, these studies produce conflicting results. These discrepancies may be, in part, due to methodological differences, including differences in exercise intensity and pre-exercise energy status. Consequently, the current study utilized a translational, well-controlled, within-subject, treadmill exercise protocol to investigate the differential effects of energy status and exercise intensity on post-exercise feeding behavior and appetite-controlling neurons in the hypothalamus. Mature, untrained male mice were exposed to acute sedentary, low (10m/min), moderate (14m/min), and high (18m/min) intensity treadmill exercise in a randomized crossover design. Fed and 10-hour-fasted mice were used, and food intake was monitored 48h. post-exercise. Immunohistochemical detection of cFOS was performed 1-hour post-exercise to determine changes in hypothalamic NPY/AgRP, POMC, tyrosine hydroxylase, and SIM1-expressing neuron activity concurrent with changes in food intake. Additionally, stains for pSTAT3tyr705 and pERKthr202/tyr204 were performed to detect exercise-mediated changes in intracellular signaling. Results demonstrated that fasted high intensity exercise suppressed food intake compared to sedentary trials, which was concurrent with increased anorexigenic POMC neuron activity. Conversely, fed mice experienced augmented post-exercise food intake, with no effects on POMC neuron activity. Regardless of pre-exercise energy status, tyrosine hydroxylase and SIM1 neuron activity in the paraventricular nucleus was elevated, as well as NPY/AgRP neuron activity in the arcuate nucleus. Notably, these neuronal changes were independent from changes in pSTAT3tyr705 and pERKthr202/tyr204 signaling. Overall, these results suggest fasted high intensity exercise may be beneficial for suppressing food intake, possibly due to hypothalamic POMC neuron excitation. Furthermore, this study identifies a novel role for pre-exercise energy status to differentially modify post-exercise feeding behavior and hypothalamic neuron activity, which may explain the inconsistent results from studies investigating exercise as a weight loss intervention.

Background Globally most neonatal deaths occur within the first week of life and in low-income and middle-income countries. Strengthening health system linkages for frontline providers—such as lay midwives providing home-based obstetrical care—may improve neonatal outcomes in these settings. Here, we conducted a quality improvement study to increase the detection of neonatal complications by lay midwives in rural Guatemala, thereby increasing referrals to a higher level of care.Methods A quality improvement team in Guatemala reviewed drivers of neonatal health services provided by lay midwives. Improvement interventions included training on neonatal warning signs, optimised mobile health technology to standardise assessments and financial incentives for providers. The primary quality outcome was the rate of neonatal referral to a higher level of care.Results From September 2017 to September 2018, participating midwives attended 869 home deliveries and referred 80 neonates to a higher level of care. A proportion control chart, using the preintervention period from January to September 2017 as the baseline, showed an increase in the referral rate of all births from 1.5% to 9.9%. Special cause was obtained in January 2018 and sustained except for May 2018. The proportion of neonates receiving assessments by midwives in the first week of life increased to >90%. A trend toward an increasing number of days between neonatal deaths did not attain special cause.Conclusions Structured improvement interventions, including mobile health decision support and financial incentives, significantly increased the detection of neonatal complications and referral of neonates to higher levels of care by lay midwives operating in rural home-based settings in Guatemala. The results show the value of improving the integration of lay midwives and other first responders into neonatal systems of care in low-resource settings.

Introduction. This is a case report of an isolated vastus lateralis rupture identified by MRI and treated successfully with surgical repair. Case Presentation. A 50-year-old male recreational weightlifter who sustained an isolated vastus lateralis rupture while dead lifting and underwent surgical repair using a suture anchor fixation. Conclusion. An isolated vastus lateralis rupture is a rare injury that may be successfully treated with surgical repair allowing return to preinjury activities.

In recent years, librarians in various sectors have been moving forward a conversation on the distinct information needs and information-seeking behavior of our lesbian, gay, bisexual, trans, and queer (LGBTQ) patrons and how well the profession recognizes and meets those needs. Health sciences librarianship has been slower than other areas of the profession in creating an evidence base covering the needs of its LGBTQ patrons, with, until recently, only very limited literature on this subject. LGBTQ health sciences librarianship is now starting to attract new interest, with librarians working together to bring this emerging specialization to the attention of the broader professional community. In this paper, the authors report on a dedicated panel discussion that took place at the 2016 joint annual meeting of the Medical Library Association and Canadian Health Libraries Association/Association des bibliothèques de la santé du Canada in Toronto, Ontario, Canada; discuss subsequent reflections; and highlight the emerging role for health sciences librarians in providing culturally competent services to the LGBTQ population. Recommendations are also provided for establishing a tool kit for LGBTQ health sciences librarianship from which librarians can draw. We conclude by highlighting the importance of critically reflective practice in health sciences librarianship in the context of LGBTQ health information.

Genetically encodable sensors have been widely used in the detection of intracellular molecules ranging from metal ions and metabolites to nucleic acids and proteins. These biosensors are capable of monitoring in real-time the cellular levels, locations, and cell-to-cell variations of the target compounds in living systems. Traditionally, the majority of these sensors have been developed based on fluorescent proteins. As an exciting alternative, genetically encoded RNA-based molecular sensors (GERMS) have emerged over the past few years for the intracellular imaging and detection of various biological targets. In view of their ability for the general detection of a wide range of target analytes, and the modular and simple design principle, GERMS are becoming a popular choice for intracellular analysis. In this review, we summarize different design principles of GERMS based on various RNA recognition modules, transducer modules, and reporting systems. Some recent advances in the application of GERMS for intracellular imaging are also discussed. With further improvement in biostability, sensitivity, and robustness, GERMS can potentially be widely used in cell biology and biotechnology.