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A hospital water system colonized with Legionella bacteria (three of four buildings, with > 50% of positive samples) was able to reduce detections to <1% positivity in the long term only after ClO2 was iteratively added first to the cold-water and then hot-water systems followed by pipe replacements (n = 6835 total samples).
In functional magnetic resonance imaging (fMRI), the blood oxygenation level dependent (BOLD) signal is often interpreted as a measure of neural activity. However, because the BOLD signal reflects the complex interplay of neural, vascular, and metabolic processes, such an interpretation is not always valid. There is growing evidence that changes in the baseline neurovascular state can result in significant modulations of the BOLD signal that are independent of changes in neural activity. This paper introduces some of the normalization and calibration methods that have been proposed for making the BOLD signal a more accurate reflection of underlying brain activity for human fMRI studies.
Functional magnetic resonance imaging (fMRI) is a noninvasive method for measuring brain function by correlating temporal changes in local cerebral blood oxygenation with behavioral measures. fMRI is used to study individuals at single time points, across multiple time points (with or without intervention), as well as to examine the variation of brain function across normal and ill populations. fMRI may be collected at multiple sites and then pooled into a single analysis. This paper describes how fMRI data is analyzed at each of these levels and describes the noise sources introduced at each level.
With global wind energy capacity ramping up, accurately predicting damage equivalent loads (DELs) and fatigue across wind turbine populations is critical, not only for ensuring the longevity of existing wind farms but also for the design of new farms. However, the estimation of such quantities of interests is hampered by the inherent complexity in modeling critical underlying processes, such as the aerodynamic wake interactions between turbines that increase mechanical stress and reduce useful lifetime. While high-fidelity computational fluid dynamics and aeroelastic models can capture these effects, their computational requirements limits real-world usage. Recently, fast machine learning-based surrogates which emulate more complex simulations have emerged as a promising solution. Yet, most surrogates are task-specific and lack flexibility for varying turbine layouts and types. This study explores the use of graph neural networks (GNNs) to create a robust, generalizable flow and DEL prediction platform. By conceptualizing wind turbine populations as graphs, GNNs effectively capture farm layout-dependent relational data, allowing extrapolation to novel configurations. We train a GNN surrogate on a large database of PyWake simulations of random wind farm layouts to learn basic wake physics, then fine-tune the model on limited data for a specific unseen layout simulated in HAWC2Farm for accurate adapted predictions. This transfer learning approach circumvents data scarcity limitations and leverages fundamental physics knowledge from the source low-resolution data. The proposed platform aims to match simulator accuracy, while enabling efficient adaptation to new higher-fidelity domains, providing a flexible blueprint for wake load forecasting across varying farm configurations.
Patients with stroke while hospitalized experience important delays in symptom recognition. This study aims to describe the overall management of an in-hospital stroke population and how it compares with an out-of-hospital community-onset stroke population.
Methods:
In this retrospective observational study, we included consecutive patients with in-hospital and out-of-hospital strokes (both ischemic and hemorrhagic) over a period of one year treated at a comprehensive stroke center. Demographic and clinical data were extracted, and patient groups were compared with regard to stroke treatment time metrics.
Results:
A total of 362 patients diagnosed with acute stroke were included, of whom 38 (10.5%) had in-hospital and 324 (89.5%) had out-of-hospital strokes. The median delay to stroke recognition (time between the last time seen well and first time seen symptomatic) was significantly longer in in-hospital compared to out-of-hospital strokes (77.5 [0–334.8] vs. 0 [0–138.5] min, p = 0.04). The median time interval from stroke code activation to the arrival of the stroke team at the bedside was significantly shorter in in-hospital versus out-of-hospital cases (10 [6–15] vs. 15 [8–24.8] min, p = 0.01). In-hospital strokes were less likely to receive thrombolysis (12.8% vs. 45.4%, p < 0.01) with significantly higher mortality (18.2% versus 2.6%, p < 0.01) and longer overall median hospital stay (3 [1–7] vs. 12 days [7–23], p < 0.01) compared to out-of-hospital strokes.
Conclusion:
This study showed significant delays in stroke symptom recognition and stroke code activation for in-hospital stroke patients despite comparable overall stroke time metrics. Development of in-hospital stroke protocols and systematic staff training on stroke symptom recognition should be implemented to improve care for hospitalized patients.
We aim to analyze the efficacy and safety of TMS on cognition in mild cognitive impairment (MCI), Alzheimer’s disease (AD), AD-related dementias, and nondementia conditions with comorbid cognitive impairment.
Design:
Systematic review, Meta-Analysis
Setting:
We searched MEDLINE, Embase, Cochrane database, APA PsycINFO, Web of Science, and Scopus from January 1, 2000, to February 9, 2023.
Participants and interventions:
RCTs, open-label, and case series studies reporting cognitive outcomes following TMS intervention were included.
Measurement:
Cognitive and safety outcomes were measured. Cochrane Risk of Bias for RCTs and MINORS (Methodological Index for Non-Randomized Studies) criteria were used to evaluate study quality. This study was registered with PROSPERO (CRD42022326423).
Results:
The systematic review included 143 studies (n = 5,800 participants) worldwide, encompassing 94 RCTs, 43 open-label prospective, 3 open-label retrospective, and 3 case series. The meta-analysis included 25 RCTs in MCI and AD. Collectively, these studies provide evidence of improved global and specific cognitive measures with TMS across diagnostic groups. Only 2 studies (among 143) reported 4 adverse events of seizures: 3 were deemed TMS unrelated and another resolved with coil repositioning. Meta-analysis showed large effect sizes on global cognition (Mini-Mental State Examination (SMD = 0.80 [0.26, 1.33], p = 0.003), Montreal Cognitive Assessment (SMD = 0.85 [0.26, 1.44], p = 0.005), Alzheimer’s Disease Assessment Scale–Cognitive Subscale (SMD = −0.96 [−1.32, −0.60], p < 0.001)) in MCI and AD, although with significant heterogeneity.
Conclusion:
The reviewed studies provide favorable evidence of improved cognition with TMS across all groups with cognitive impairment. TMS was safe and well tolerated with infrequent serious adverse events.
Nightclubs are entertainment and hospitality venues historically vulnerable to terrorist attacks. This study identified and characterized terrorist attacks targeting nightclubs and discotheques documented in the Global Terrorism Database (GTD) over a 50-y period.
Methods:
A search of the Global Terrorism Database (GTD) was conducted from 1970 to 2019. Precoded variables for target type “business” and target subtype “entertainment/cultural/stadium/casino” were used to identify attacks potentially involving nightclubs. Nightclub venues were specifically identified using the search terms “club,” “nightclub,” and “discotheque.” Two authors manually reviewed each entry to confirm the appropriateness for inclusion. Descriptive statistics were performed using R (3.6.1).
Results:
A total of 114 terrorist attacks targeting nightclub venues were identified from January 1, 1970, through December 31, 2019. Seventy-four (64.9%) attacks involved nightclubs, while forty (35.1%) attacks involved discotheques. A bombing or explosion was involved in 84 (73.7%) attacks, followed by armed assault in 14 (12.3%) attacks. The highest number of attacks occurred in Western Europe and Sub-Saharan Africa. In total, 284 persons died, and 1175 persons were wounded in attacks against nightclub venues.
Conclusions:
While terrorist attacks against nightclub venues are infrequent, the risk for mass casualties and injuries can be significant, mainly when explosives and armed assaults are used.
Most students in MD-PhD programs take a leave of absence from medical school to complete PhD training, which promotes a natural loss of clinical skills and knowledge and could negatively impact a student’s long-term clinical knowledge. To address this concern, clinical refresher courses in the final year of PhD training have traditionally been used; however, effectiveness of such courses versus a longitudinal clinical course spanning all PhD training years is unclear.
Methods:
The University of Alabama at Birmingham MD-PhD Program implemented a comprehensive continuing clinical education (CCE) course spanning PhD training years that features three course components: (1) clinical skills; (2) clinical knowledge; and (3) specialty exposure activities. To evaluate course effectiveness, data from an anonymous student survey completed at the end of each semester were analyzed.
Results:
Five hundred and ninety-seven surveys were completed by MD-PhD students from fall 2014 to 2022. Survey responses indicated that the majority of students found the course helpful to: maintain clinical skills and knowledge (544/597, 91% and 559/597, 94%; respectively), gain exposure to clinical specialties (568/597, 95%), and prepare them for responsibilities during clinical clerkships. During semesters following lockdowns from the COVID-19 pandemic, there were significant drops in students’ perceived preparedness.
Conclusions:
Positive student survey feedback and improved preparedness to return to clinic after development of the course suggests the CCE course is a useful approach to maintain clinical knowledge during research training.
The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.
This chapter models the use of digital humanities methodologies to study semantic history. Corpus analysis and geographical information systems techniques are applied to trace the use of the word ‘sublime’ in a large collection of digitized literary works from the final decade of the nineteenth century. This collection, which comprises nearly 10,000 texts from the 1890s, was extracted from the British Library’s Nineteenth-Century Books Corpus. The chapter explains the steps involved in extracting and analyzing this portion of the corpus. It then presents a case study focused on the contexts, meanings, and locations associated with the word ’sublime’ in literary works from the 1890s. This case study tests a hypothesis derived by consulting the Oxford English Dictionary, which suggests that by the end of the nineteenth century, ‘sublime’ was often used unsystematically as an intensifier, as a word for labeling any experience or phenomena that defied description.
The field of mathematical psychology began in the 1950s and includes both psychological theorizing, in which mathematics plays a key role, and applied mathematics motivated by substantive problems in psychology. Central to its success was the publication of the first Handbook of Mathematical Psychology in the 1960s. The psychological sciences have since expanded to include new areas of research, and significant advances have been made both in traditional psychological domains and in the applications of the computational sciences to psychology. Upholding the rigor of the original Handbook, the New Handbook of Mathematical Psychology reflects the current state of the field by exploring the mathematical and computational foundations of new developments over the last half-century. The third volume provides up-to-date, foundational chapters on early vision, psychophysics and scaling, multisensory integration, learning and memory, cognitive control, approximate Bayesian computation, and encoding models in neuroimaging.
Sports venues foster community and support local economies. Due to their capacity to host hundreds to thousands of spectators, sports venues are vulnerable to becoming targets of terrorism. Types of venues targeted, regional trends, and methods of attack employed world-wide have not been well-described.
Methods:
A search of the Global Terrorism Database (GTD) was conducted from 1970 through the end of 2019. Pre-coded variables for target type “business” and target subtype “entertainment/cultural/stadium/casino” were used to identify attacks involving venues where sports events might be viewed by spectators as part of an audience. Sports venues were specifically identified using the search terms “sport,” “stadium,” ”arena,” and “ring,” as well as mention of any specific sport. Two authors then manually reviewed each entry for specific information to confirm appropriateness for inclusion, selecting preferentially for attacks against venues where watching a sports event was the primary focus for the majority of the attendees. Descriptive statistics were performed using R (3.6.1).
Results:
Seventy-four (74) terrorist attacks targeting sports venues were identified from January 1, 1970 through December 31, 2019. Thirty-three (33) attacks, or 44.6% of attacks, involved soccer stadiums or soccer venues, while 33.8% of attacks (25 attacks) involved unspecified sports venues. A bombing or explosion was the most frequent method of attack employed, comprising 87.8% of attacks. The highest number of attacks occurred in the Middle East & North Africa. In total, 213 persons died and 699 more were wounded in attacks against sports venues.
Conclusion:
Although terrorist attacks against sports venues are uncommon, they carry the risk of mass casualties, especially when explosives are used. A greater understanding of the threat posed by terrorist attacks against sports venues can aid emergency preparedness planning and future medical responses.
The severity of respiratory distress occurring prior to loss of posture during exposure to: 20, 30, 40, 50, 60, 70, 80 or 90 per cent carbon dioxide in air; 2 or 5 per cent residual oxygen in argon; 30 per cent carbon dioxide in argon with either 2 or 5 per cent residual oxygen; or 40 per cent carbon dioxide in argon with either 2 or 5 per cent residual oxygen, was subjectively determined in pigs from their behaviour. The results indicated that exposure to 2 per cent oxygen in argon (anoxia) induced minimal respiratory distress, 30 per cent carbon dioxide in argon with 2 per cent residual oxygen induced a moderate distress and exposure to all the concentrations of carbon dioxide in air induced severe respiratory distress in the pigs. From the animal welfare point of view, using 2 per cent oxygen in argon (anoxia) appears to be the optimum choice for gas stunning pigs. Secondly, a mixture of 30 per cent carbon dioxide in argon with 2 per cent residual oxygen is preferred to 90 per cent carbon dioxide in air.
The aversive effects of 90 per cent argon in air, 30 per cent carbon dioxide in air or 90 per cent carbon dioxide in air were investigated in slaughter weight pigs. Aversion was assessed from their reluctance to enter the three gaseous atmospheres to obtain a reward (apples). The pigs did not show any aversion to the inhalation of 90 per cent argon in air. The majority of the pigs did not show aversion to the presence of 30 per cent carbon dioxide in air. By contrast, the inhalation of 90 per cent carbon dioxide was aversive to the majority of the pigs. Fasting them for up to 24h prior to testing did not overcome the pigs ‘ reluctance to enter an atmosphere containing 90 per cent carbon dioxide.