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Although Clostridioides difficile infection (CDI) incidence is high in the United States, standard-of-care (SOC) stool collection and testing practices might result in incidence overestimation or underestimation. We conducted diarrhea surveillance among inpatients >50 years of age in Louisville, Kentucky, USA, during October 14, 2019–October 13, 2020; concurrent SOC stool collection and CDI testing occurred independently. A study CDI case was nucleic acid amplification test‒/cytotoxicity neutralization assay‒positive or nucleic acid amplification test‒positive stool in a patient with pseudomembranous colitis. Study incidence was adjusted for hospitalization share and specimen collection rate and, in a sensitivity analysis, for diarrhea cases without study testing. SOC hospitalized CDI incidence was 121/100,000 population/year; study incidence was 154/100,000 population/year and, in sensitivity analysis, 202/100,000 population/year. Of 75 SOC CDI cases, 12 (16.0%) were not study diagnosed; of 109 study CDI cases, 44 (40.4%) were not SOC diagnosed. CDI incidence estimates based on SOC CDI testing are probably underestimated.

Microvasculature analysis is an important task in the medical field due to its various applications. It has been used for the diagnosis and threat of diseases in fields such as ophthalmology, dermatology, and neurology by measuring relative blood flow or blood vessel morphological properties. However, light scattering at the periphery of the blood vessel causes a decrease in contrast around the vessel borders and an increase in the noise of the image, making the localization of blood vessels a challenging task. Therefore, this work proposes integrating known information from the experimental setup into a deep learning architecture with multiple inputs to improve the generalization of a computational model for the segmentation of blood vessels and depth estimation in a single inference step. The proposed R-UNET + ET + LA obtained an intersection over union of 0.944 ± 0.065 and 0.812 ± 0.080 in the classification task for validation (in vitro) and test sets (in vivo), respectively, and a root mean squared error of 0.0085 ± 0.0275 μm in the depth estimation. This approach improves the generalization of current solutions by pre-training with in vitro data and adding information from the experimental setup. Additionally, the method can infer the depth of a blood vessel pixel by pixel instead of in regions as the current state of the art does.

The stability issues assessment by the impedance-based method demands the computation of accurate small-signal models. However, obtaining impedance models can be a time-consuming task if analytical models or the perturbation-based method are used. Especially in large-scale, poorly damped, distributed, and frequency-dependent parameter systems. A third approach, which has been less explored, is based on numerical derivative approximations, rather than analytical equations or time-domain simulations. This approach works with large-scale systems, avoiding tedious mathematical expressions and large recursive time-domain simulations. As a step-forward in this approach, this paper proposes a numerical-oriented method, based on the finite-difference method, to compute impedance models of time-delay power-electronics-based power systems. An outstanding feature of the proposal is the capability to incorporate the exact delays into the numerical models of impedance without resorting to approximations nor increasing the size of the system model. We compare the proposed method with the analytical and perturbation methods using a grid-connected microgrid with two power electronic inverters as a test system. The results confirm the correct performance of the proposal.

Abstract Background Influenza is associated with excess morbidity and mortality of individuals each year. Few therapies exist for treatment of influenza infection, and each require initiation as early as possible in the course of infection, making efficacy difficult to estimate in the hospitalized patient with lower respiratory tract infection. Using causal machine learning methods, we re-analyze data from a randomized trial of oseltamivir versus standard of care aimed at reducing clinical failure in hospitalized patients with lower respiratory tract infection during the influenza season. Methods This was a secondary analysis of the Rapid Empiric Treatment with Oseltamivir Study (RETOS). Conditional average treatment effects (CATE) and 95% confidence intervals were computed from causal forest including 85 clinical and demographic variables. RETOS was a multicenter, randomized, unblinded, trial of adult patients hospitalized with lower respiratory tract infections in Kentucky from 2009 through 2012. Adult hospitalized patients with lower respiratory tract infection were randomized to standard of care or standard of care plus oseltamivir as early as possible after hospital admission but within 24 h of enrollment. After randomization, oseltamivir was initiated in the treatment arm per package insert. The primary outcome was clinical failure, a composite measure including failure to reach clinical improvement within 7 days, transfer to intensive care 24 h after admission, or rehospitalization or death within 30 days. Results A total of 691 hospitalized patients with lower respiratory tract infections were included in the study. The only subgroup of patients with a statistically significant CATE was those with laboratory-confirmed influenza infection with a 26% lower risk of clinical failure when treated with oseltamivir (95% CI 3.2–48.0%). Conclusions This study suggests that addition of oseltamivir to standard of care may decrease clinical failure in hospitalized patients with influenza-associated lower respiratory tract infection versus standard of care alone. These results are supportive of current recommendations to initiate antiviral treatment in hospitalized patients with confirmed or suspected influenza as soon as possible after admission. Trial registration Original trial: Clinical Trials.Gov; Rapid Empiric Treatment With Oseltamivir Study (RETOS) (RETOS); ClinicalTrials.gov Identifier: NCT01248715 https://clinicaltrials.gov/ct2/show/NCT01248715

Lower respiratory infections include acute bronchitis, influenza, community-acquired pneumonia, acute exacerbation of COPD and acute exacerbation of bronchiectasis. They are a major cause of death worldwide and often affect the most vulnerable: children, elderly and the impoverished. In this paper, we review the clinical presentation, diagnosis, severity assessment and treatment of adult outpatients with lower respiratory infections. The paper is divided into sections on specific lower respiratory infections, but we also dedicate a section to COVID-19 given the importance of the ongoing pandemic. Lower respiratory infections are heterogeneous entities, carry different risks for adverse events, and require different management strategies. For instance, while patients with acute bronchitis are rarely admitted to hospital and generally do not require antimicrobials, approximately 40% of patients seen for community-acquired pneumonia require admission. Clinicians caring for patients with lower respiratory infections face several challenges, including an increasing population of patients with immunosuppression, potential need for diagnostic tests that may not be readily available, antibiotic resistance and social aspects that place these patients at higher risk. Management principles for patients with lower respiratory infections include knowledge of local surveillance data, strategic use of diagnostic tests according to surveillance data, and judicious use of antimicrobials.

This paper is dedicated to presenting control-tuning methodologies for the rotor-side converter (RSC) and grid-side converter (GSC) of series-compensated DFIG-based wind farms (WF) and to determining the origin of subsynchronous resonance (SSR). Unlike conventional approaches to re-tuning controller gains to achieve desirable performance, these methodologies only consider the mathematical models of the states variables to be controlled. A PI cascade topology is used to control the RSC and GSC. An inner loop is required for current control and an outer loop for voltage control. Special consideration is taken for tuning the RSC because this converter is coupled with the mechanical variables as mechanical rotor speed of the DFIG. Compared with standard tuning, a better system behavior during a resonance condition is achieved with the proposed tuning methodology. Regarding the analysis of the SSR origin, the modal impedance (MI) technique is used as a tool to study SSR issues; its advantages compared to the driving point impedance technique are highlighted. In this context, the small-signal-stability (SSS) analysis is performed to evaluate the overall system. With the combination of MI and SSS, the phenomena of induction generator effect, torque interaction, and torque amplification are analyzed. As a result, the modes involved in the SSR phenomenon are identified and a discrimination procedure is described to determine the origin of the SSR.

SARS-CoV-2 and influenza are primary causes of viral community-acquired pneumonia (CAP). Both pathogens have exhibited high transmissibility and are recognized causes of pandemics. Controversy still exists regarding the clinical outcomes between patients hospitalized with CAP due to these viruses. This secondary analysis identified patients with either influenza or SARS-CoV-2 infections from three cohorts of patients hospitalized for CAP. Clinical outcomes between patients with CAP due to influenza or due to SARS-CoV-2 were evaluated. Primary outcomes included length of stay and in-hospital mortality. To account for population differences between cohorts, each case of influenza CAP was matched to two controls with SARS-CoV-2 CAP. Matching criteria included sex, age, and nursing home residency. Stratified cox-proportional hazards regression or conditional logistic regression were used where appropriate. A total of 259 patients with influenza CAP were matched to two controls with SARS-CoV-2 CAP, totaling to 518 controls. Patients with SARS-CoV-2 CAP were 2.23 times more likely to remain hospitalized at any point in time (95% confidence interval: 1.77–2.80), and had 3.84 times higher odds of dying in-hospital (95% confidence interval: 1.91–7.76) when compared to patients with influenza CAP. After matching and adjusting for confounding variables, patients admitted with SARS-CoV-2 CAP had consistently worse outcomes in comparison to their influenza CAP counterparts. This information can help clinicians decide on the level of care needed for patients with confirmed infections due to these pathogens. Additionally, estimates of disease burden can inform individuals at-risk for poor clinical outcomes, and further highlight the importance of effective preventative strategies.

The Federal Highway Administration (FHWA) mandates biannual bridge inspections to assess the condition of all bridges in the United States. These inspections are recorded in the National Bridge Inventory (NBI) and the respective state’s databases to manage, study, and analyze the data. As FHWA specifications become more complex, inspections require more training and field time. Recently, element-level inspections were added, assigning a condition state to each minor element in the bridge. To address this new requirement, a machine-aided bridge inspection method was developed using artificial intelligence (AI) to assist inspectors. The proposed method focuses on the condition state assessment of cracking in reinforced concrete bridge deck elements. The deep learning-based workflow integrated with image classification and semantic segmentation methods is utilized to extract information from images and evaluate the condition state of cracks according to FHWA specifications. The new workflow uses a deep neural network to extract information required by the bridge inspection manual, enabling the determination of the condition state of cracks in the deck. The results of experimentation demonstrate the effectiveness of this workflow for this application. The method also balances the costs and risks associated with increasing levels of AI involvement, enabling inspectors to better manage their resources. This AI-based method can be implemented by asset owners, such as Departments of Transportation, to better serve communities.

Microvasculature analysis in biomedical images is essential in the medical area to evaluate diseases by extracting properties of blood vessels, such as relative blood flow or morphological measurements such as diameter. Given the advantages of Laser Speckle Contrast Imaging (LSCI), several studies have aimed to reduce inherent noise to distinguish between tissue and blood vessels at higher depths. These studies have shown that computing Contrast Images (CIs) with Analysis Windows (AWs) larger than standard sizes obtains better statistical estimators. The main issue is that larger samples combine pixels of microvasculature with tissue regions, reducing the spatial resolution of the CI. This work proposes using adaptive AWs of variable size and shape to calculate the features required to train a segmentation model that discriminates between blood vessels and tissue in LSCI. The obtained results show that it is possible to improve segmentation rates of blood vessels up to 45% in high depths (≈900 μm) by extracting features adaptively. The main contribution of this work is the experimentation with LSCI images under different depths and exposure times through adaptive processing methods, furthering the understanding the performance of the different approaches under these conditions. Results also suggest that it is possible to train a segmentation model to discriminate between pixels belonging to blood vessels and those belonging to tissue. Therefore, an adaptive feature extraction method may improve the quality of the features and thus increase the classification rates of blood vessels in LSCI.

The obesity paradox postulates that increased body mass index (BMI) is protective in certain patient populations. We aimed to investigate the association of BMI and different weight classes with outcomes in hospitalised patients with community-acquired pneumonia (CAP). This cohort study is a secondary data analysis of the University of Louisville Pneumonia Study database, a prospective study of hospitalised adult patients with CAP from June, 2014, to May, 2016, in Louisville, KY, USA. BMI as a predictor was assessed both as a continuous and categorical variable. Patients were categorised as weight classes based on World Health Organization definitions: BMI of

Background Experimental studies have shown that vaccination can reduce viral replication to attenuate progression of influenza-associated lower respiratory tract illness (LRTI). However, clinical studies are conflicting, possibly due to use of non-specific outcomes reflecting a mix of large and small airway LRTI lacking specificity for acute lung or organ injury. Methods We developed a global ordinal scale to differentiate large and small airway LRTI in hospitalized adults with influenza using physiologic features and interventions (PFIs): vital signs, laboratory and radiographic findings, and clinical interventions. We reviewed the literature to identify common PFIs across 9 existing scales of pneumonia and sepsis severity. To characterize patients using this scale, we applied the scale to an antiviral clinical trial dataset where these PFIs were measured through routine clinical care in adults hospitalized with influenza-associated LRTI during the 2010–2013 seasons. Results We evaluated 12 clinical parameters among 1020 adults; 210 (21%) had laboratory-confirmed influenza, with a median severity score of 4.5 (interquartile range, 2–8). Among influenza cases, median age was 63 years, 20% were hospitalized in the prior 90 days, 50% had chronic obstructive pulmonary disease, and 22% had congestive heart failure. Primary influencers of higher score included pulmonary infiltrates on imaging (48.1%), heart rate ≥110 beats/minute (41.4%), oxygen saturation 24 breaths/minute (21.0%). Key PFIs distinguishing patients with severity < or ≥8 (upper quartile) included infiltrates (27.1% vs 90.0%), temperature ≥ 39.1°C or 24 breaths/minute (7.9% vs 47.1%), heart rate ≥110 beats/minute (29.3% vs 65.7%), oxygen saturation 15,000 (5.0% vs 27.2%), and need for invasive or non-invasive mechanical ventilation (2.1% vs 15.7%). Conclusion We developed a scale in adults hospitalized with influenza-associated LRTI demonstrating a broad distribution of physiologic severity which may be useful for future studies evaluating the disease attenuating effects of influenza vaccination or other therapeutics.

Background Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome-coronavirus-2. Consensus suggestions can standardise care, thereby improving outcomes and facilitating future research. Methods An International Task Force was composed and agreement regarding courses of action was measured using the Convergence of Opinion on Recommendations and Evidence (CORE) process. 70% agreement was necessary to make a consensus suggestion. Results The Task Force made consensus suggestions to treat patients with acute COVID-19 pneumonia with remdesivir and dexamethasone but suggested against hydroxychloroquine except in the context of a clinical trial; these are revisions of prior suggestions resulting from the interim publication of several randomised trials. It also suggested that COVID-19 patients with a venous thromboembolic event be treated with therapeutic anticoagulant therapy for 3 months. The Task Force was unable to reach sufficient agreement to yield consensus suggestions for the post-hospital care of COVID-19 survivors. The Task Force fell one vote shy of suggesting routine screening for depression, anxiety and post-traumatic stress disorder. Conclusions The Task Force addressed questions related to pharmacotherapy in patients with COVID-19 and the post-hospital care of survivors, yielding several consensus suggestions. Management options for which there is insufficient agreement to formulate a suggestion represent research priorities.

Twenty-five species of Quercus were collected in the state of Aguascalientes, 11 members of Quercus sect. Lobatae (red oak) and 14 members of Quercus sect. Quercus (white oak). Ten species were newly recorded. Quercus potosina is the commonest and most widely distributed species in the state. Eight species were found only in a single municipality, Calvillo or San José de Gracia. The species of Quercus are mainly distributed in oak and pine-oak forest in the western part of Aguascalientes. The municipalities with the greatest numbers of species are San José de Gracia, Calvillo and Jesús María, while Aguascalientes, Asientos, Cosío, El Llano, Rincón de Romos, Pabellón de Arteaga, and Tepezalá have the fewest representatives. San Francisco de los Romo is the only municipality without any Quercus records.

La mora es un fruto que se cosecha en los andes colombianos y apetecido por los consumidores por su intenso sabor y por sus propiedades antioxidantes. Este fruto, en su mayoría, se consume procesado debido a su alto grado de perecibilidad. El objetivo de este trabajo fue formular un jugo de mora funcional. Las formulaciones del jugo presentaron pulpa de mora, sucralosa, goma CMC, pectinasa, citrato de sodio, vitamina C y citrato de calcio. La adición de calcio y vitamina C se realizó con base en el 20% de del valor diario recomendado (VDR) propuesto en la Resolución 333 de 2011 del Ministerio de la Protección Social (Colombia). Los resultados para el producto final fueron: calcio 112,46 ± 10,877 mg/100 g y vitamina C 263,38 ± 6,180 mg/L. Se observó un cambio en los parámetros a* y b* al día 18 de su almacenamiento. El pH (4,3) y la viscosidad (590 cP) permanecieron constantes hasta los 18 días de evaluación. El alimento funcional formulado puede ser catalogado como excelente fuente de calcio y vitamina C, a pesar de la degradación de la vitamina C por tratamientos térmicos aplicados.